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C if...else Statement

C if statement.

The syntax of the if statement in C programming is:

How if statement works?

The if statement evaluates the test expression inside the parenthesis () .

• If the test expression is evaluated to true, statements inside the body of if are executed.
• If the test expression is evaluated to false, statements inside the body of if are not executed.

To learn more about when test expression is evaluated to true (non-zero value) and false (0), check relational and logical operators .

Example 1: if statement

When the user enters -2, the test expression number<0 is evaluated to true. Hence, You entered -2 is displayed on the screen.

When the user enters 5, the test expression number<0 is evaluated to false and the statement inside the body of if is not executed

The if statement may have an optional else block. The syntax of the if..else statement is:

How if...else statement works?

If the test expression is evaluated to true,

• statements inside the body of if are executed.
• statements inside the body of else are skipped from execution.

If the test expression is evaluated to false,

• statements inside the body of else are executed
• statements inside the body of if are skipped from execution.

Example 2: if...else statement

When the user enters 7, the test expression number%2==0 is evaluated to false. Hence, the statement inside the body of else is executed.

C if...else Ladder

The if...else statement executes two different codes depending upon whether the test expression is true or false. Sometimes, a choice has to be made from more than 2 possibilities.

The if...else ladder allows you to check between multiple test expressions and execute different statements.

Syntax of if...else Ladder

Example 3: c if...else ladder.

• Nested if...else

It is possible to include an if...else statement inside the body of another if...else statement.

Example 4: Nested if...else

This program given below relates two integers using either < , > and = similar to the if...else ladder's example. However, we will use a nested if...else statement to solve this problem.

If the body of an if...else statement has only one statement, you do not need to use brackets {} .

For example, this code

is equivalent to

Table of Contents

• if Statement
• if...else Statement
• if...else Ladder

Video: C if else Statement

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C while and do...while Loop

If...Else Statement in C Explained

Conditional code flow is the ability to change the way a piece of code behaves based on certain conditions. In such situations you can use if statements.

The if statement is also known as a decision making statement, as it makes a decision on the basis of a given condition or expression. The block of code inside the if statement is executed is the condition evaluates to true. However, the code inside the curly braces is skipped if the condition evaluates to false, and the code after the if statement is executed.

Syntax of an if statement

A simple example.

Let’s look at an example of this in action:

If the code inside parenthesis of the if statement is true, everything within the curly braces is executed. In this case, true evaluates to true, so the code runs the printf function.

if..else statements

In an if...else statement, if the code in the parenthesis of the if statement is true, the code inside its brackets is executed. But if the statement inside the parenthesis is false, all the code within the else statement's brackets is executed instead.

Of course, the example above isn't very useful in this case because true always evaluates to true. Here's another that's a bit more practical:

There are a few important differences here. First, stdbool.h hasn’t been included. That's okay because true and false aren't being used like in the first example. In C, like in other programming languages, you can use statements that evaluate to true or false rather than using the boolean values true or false directly.

Also notice the condition in the parenthesis of the if statement: n == 3 . This condition compares n and the number 3. == is the comparison operator, and is one of several comparison operations in C.

Nested if...else

The if...else statement allows a choice to be made between two possibilities. But sometimes you need to choose between three or more possibilities.

For example the sign function in mathematics returns -1 if the argument is less than zero, +1 if the argument is greater than zero, and returns zero if the argument is zero.

The following code implements this function:

As you can see, a second if...else statement is nested within else statement of the first if..else .

If x is less than 0, then sign is set to -1. However, if x is not less than 0, the second if...else statement is executed. There, if x is equal to 0, sign is also set to 0. But if x is greater than 0, sign is instead set to 1.

Rather than a nested if...else statement, beginners often use a string of if statements:

While this works, it's not recommended since it's unclear that only one of the assignment statements ( sign = ... ) is meant to be executed depending on the value of x . It's also inefficient – every time the code runs, all three conditions are tested, even if one or two don't have to be.

else...if statements

if...else statements are an alternative to a string of if statements. Consider the following:

If the condition for the if statement evaluates to false, the condition for the else...if statement is checked. If that condition evaluates to true, the code inside the else...if statement's curly braces is run.

Comparison Operators

Logical operators.

We might want a bit of code to run if something is not true, or if two things are true. For that we have logical operators:

For example:

An important note about C comparisons

While we mentioned earlier that each comparison is checking if something is true or false, but that's only half true. C is very light and close to the hardware it's running on. With hardware it's easy to check if something is 0 or false, but anything else is much more difficult.

Instead it's much more accurate to say that the comparisons are really checking if something is 0 / false, or if it is any other value.

For example, his if statement is true and valid:

By design, 0 is false, and by convention, 1 is true. In fact, here’s a look at the stdbool.h library:

While there's a bit more to it, this is the core of how booleans work and how the library operates. These two lines instruct the compiler to replace the word false with 0, and true with 1.

If this article was helpful, share it .

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Next: Execution Control Expressions , Previous: Arithmetic , Up: Top   [ Contents ][ Index ]

7 Assignment Expressions

As a general concept in programming, an assignment is a construct that stores a new value into a place where values can be stored—for instance, in a variable. Such places are called lvalues (see Lvalues ) because they are locations that hold a value.

An assignment in C is an expression because it has a value; we call it an assignment expression . A simple assignment looks like

We say it assigns the value of the expression value-to-store to the location lvalue , or that it stores value-to-store there. You can think of the “l” in “lvalue” as standing for “left,” since that’s what you put on the left side of the assignment operator.

However, that’s not the only way to use an lvalue, and not all lvalues can be assigned to. To use the lvalue in the left side of an assignment, it has to be modifiable . In C, that means it was not declared with the type qualifier const (see const ).

The value of the assignment expression is that of lvalue after the new value is stored in it. This means you can use an assignment inside other expressions. Assignment operators are right-associative so that

is equivalent to

This is the only useful way for them to associate; the other way,

would be invalid since an assignment expression such as x = y is not valid as an lvalue.

Warning: Write parentheses around an assignment if you nest it inside another expression, unless that is a conditional expression, or comma-separated series, or another assignment.

C Conditional Statement: IF, IF Else and Nested IF Else with Example

What is a Conditional Statement in C?

Conditional Statements in C programming are used to make decisions based on the conditions. Conditional statements execute sequentially when there is no condition around the statements. If you put some condition for a block of statements, the execution flow may change based on the result evaluated by the condition. This process is called decision making in ‘C.’

In ‘C’ programming conditional statements are possible with the help of the following two constructs:

1. If statement

2. If-else statement

It is also called as branching as a program decides which statement to execute based on the result of the evaluated condition.

If statement

The condition evaluates to either true or false. True is always a non-zero value, and false is a value that contains zero. Instructions can be a single instruction or a code block enclosed by curly braces { }.

Following program illustrates the use of if construct in ‘C’ programming:

The above program illustrates the use of if construct to check equality of two numbers.

• In the above program, we have initialized two variables with num1, num2 with value as 1, 2 respectively.
• Then, we have used if with a test-expression to check which number is the smallest and which number is the largest. We have used a relational expression in if construct. Since the value of num1 is smaller than num2, the condition will evaluate to true.
• Thus it will print the statement inside the block of If. After that, the control will go outside of the block and program will be terminated with a successful result.

Relational Operators

C has six relational operators that can be used to formulate a Boolean expression for making a decision and testing conditions, which returns true or false :

< less than

<= less than or equal to

> greater than

>= greater than or equal to

== equal to

!= not equal to

Notice that the equal test (==) is different from the assignment operator (=) because it is one of the most common problems that a programmer faces by mixing them up.

For example:

Keep in mind that a condition that evaluates to a non-zero value is considered as true.

The If-Else statement

The if-else is statement is an extended version of If. The general form of if-else is as follows:

n this type of a construct, if the value of test-expression is true, then the true block of statements will be executed. If the value of test-expression if false, then the false block of statements will be executed. In any case, after the execution, the control will be automatically transferred to the statements appearing outside the block of If.

Let’s start.

• We have initialized a variable with value 19. We have to find out whether the number is bigger or smaller than 10 using a ‘C’ program. To do this, we have used the if-else construct.
• Here we have provided a condition num<10 because we have to compare our value with 10.
• As you can see the first block is always a true block which means, if the value of test-expression is true then the first block which is If, will be executed.
• The second block is an else block. This block contains the statements which will be executed if the value of the test-expression becomes false. In our program, the value of num is greater than ten hence the test-condition becomes false and else block is executed. Thus, our output will be from an else block which is “The value is greater than 10”. After the if-else, the program will terminate with a successful result.

In ‘C’ programming we can use multiple if-else constructs within each other which are referred to as nesting of if-else statements.

Conditional Expressions

There is another way to express an if-else statement is by introducing the ?: operator. In a conditional expression the ?: operator has only one statement associated with the if and the else.

Nested If-else Statements

When a series of decision is required, nested if-else is used. Nesting means using one if-else construct within another one.

Let’s write a program to illustrate the use of nested if-else.

The above program checks if a number is less or greater than 10 and prints the result using nested if-else construct.

• Firstly, we have declared a variable num with value as 1. Then we have used if-else construct.
• In the outer if-else, the condition provided checks if a number is less than 10. If the condition is true then and only then it will execute the inner loop . In this case, the condition is true hence the inner block is processed.
• In the inner block, we again have a condition that checks if our variable contains the value 1 or not. When a condition is true, then it will process the If block otherwise it will process an else block. In this case, the condition is true hence the If a block is executed and the value is printed on the output screen.
• The above program will print the value of a variable and exit with success.

Try changing the value of variable see how the program behaves.

NOTE: In nested if-else, we have to be careful with the indentation because multiple if-else constructs are involved in this process, so it becomes difficult to figure out individual constructs. Proper indentation makes it easy to read the program.

Nested Else-if statements

Nested else-if is used when multipath decisions are required.

The general syntax of how else-if ladders are constructed in ‘C’ programming is as follows:

This type of structure is known as the else-if ladder. This chain generally looks like a ladder hence it is also called as an else-if ladder. The test-expressions are evaluated from top to bottom. Whenever a true test-expression if found, statement associated with it is executed. When all the n test-expressions becomes false, then the default else statement is executed.

Let us see the actual working with the help of a program.

The above program prints the grade as per the marks scored in a test. We have used the else-if ladder construct in the above program.

• We have initialized a variable with marks. In the else-if ladder structure, we have provided various conditions.
• The value from the variable marks will be compared with the first condition since it is true the statement associated with it will be printed on the output screen.
• If the first test condition turns out false, then it is compared with the second condition.
• This process will go on until the all expression is evaluated otherwise control will go out of the else-if ladder, and default statement will be printed.

Try modifying the value and notice the change in the output.

• Decision making or branching statements are used to select one path based on the result of the evaluated expression.
• It is also called as control statements because it controls the flow of execution of a program.
• ‘C’ provides if, if-else constructs for decision-making statements.
• We can also nest if-else within one another when multiple paths have to be tested.
• The else-if ladder is used when we have to check various ways based upon the result of the expression.
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Assignment Operators in C

In C, the assignment operator stores a certain value in an already declared variable. A variable in C can be assigned the value in the form of a literal, another variable or an expression. The value to be assigned forms the right hand operand, whereas the variable to be assigned should be the operand to the left of = symbol, which is defined as a simple assignment operator in C. In addition, C has several augmented assignment operators.

The following table lists the assignment operators supported by the C language −

Simple assignment operator (=)

The = operator is the most frequently used operator in C. As per ANSI C standard, all the variables must be declared in the beginning. Variable declaration after the first processing statement is not allowed. You can declare a variable to be assigned a value later in the code, or you can initialize it at the time of declaration.

You can use a literal, another variable or an expression in the assignment statement.

Once a variable of a certain type is declared, it cannot be assigned a value of any other type. In such a case the C compiler reports a type mismatch error.

In C, the expressions that refer to a memory location are called "lvalue" expressions. A lvalue may appear as either the left-hand or right-hand side of an assignment.

On the other hand, the term rvalue refers to a data value that is stored at some address in memory. A rvalue is an expression that cannot have a value assigned to it which means an rvalue may appear on the right-hand side but not on the left-hand side of an assignment.

Variables are lvalues and so they may appear on the left-hand side of an assignment. Numeric literals are rvalues and so they may not be assigned and cannot appear on the left-hand side. Take a look at the following valid and invalid statements −

Augmented assignment operators

In addition to the = operator, C allows you to combine arithmetic and bitwise operators with the = symbol to form augmented or compound assignment operator. The augmented operators offer a convenient shortcut for combining arithmetic or bitwise operation with assignment.

For example, the expression a+=b has the same effect of performing a+b first and then assigning the result back to the variable a.

Similarly, the expression a<<=b has the same effect of performing a<<b first and then assigning the result back to the variable a.

Here is a C program that demonstrates the use of assignment operators in C:

When you compile and execute the above program, it produces the following result −

C Functions

C structures, c short hand if else, short hand if...else (ternary operator).

There is also a short-hand if else, which is known as the ternary operator because it consists of three operands. It can be used to replace multiple lines of code with a single line. It is often used to replace simple if else statements:

Instead of writing:

You can simply write:

It is completely up to you if you want to use the traditional if...else statement or the ternary operator.

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If statement.

Conditionally executes another statement.

Used where code needs to be executed based on a run-time or compile-time (since C++17) condition , or whether the if statement is evaluated in a manifestly constant-evaluated context (since C++23) .

[ edit ] Syntax

• expression which is contextually convertible to bool
• declaration of a single non-array variable with a brace-or-equals initializer .
• is evaluated in a manifestly constant-evaluated context , if ! is not preceding consteval
• is not evaluated in a manifestly constant-evaluated context, if ! is preceding consteval
• is not evaluated in a manifestly constant-evaluated context , if ! is not preceding consteval
• is evaluated in a manifestly constant-evaluated context, if ! is preceding consteval

[ edit ] Explanation

If the condition yields true after conversion to bool , statement-true is executed.

If the else part of the if statement is present and condition yields false after conversion to bool , statement-false is executed.

In the second form of if statement (the one including else), if statement-true is also an if statement then that inner if statement must contain an else part as well (in other words, in nested if-statements, the else is associated with the closest if that doesn't have an else).

except that names declared by the init-statement (if init-statement is a declaration) and names declared by condition (if condition is a declaration) are in the same scope, which is also the scope of both statement s.

both compound-statement and statement (if any) must be compound statements.

If statement is not a compound statement, it will still be treated as a part of the consteval if statement (and thus results in a compilation error):

If a consteval if statement is evaluated in a manifestly constant-evaluated context , compound-statement is executed. Otherwise, statement is executed if it is present.

A case or default label appearing within a consteval if statement shall be associated with a switch statement within the same if statement. A label declared in a substatement of a consteval if statement shall only be referred to by a statement in the same substatement.

If the statement begins with if !consteval , the compound-statement and statement (if any) must both be compound statements. Such statements are not considered consteval if statements, but are equivalent to consteval if statements:

• if ! consteval { /*stmt*/ } is equivalent to if consteval { } else { /*stmt*/ } .
• if ! consteval { /*stmt-1*/ } else { /*stmt-2*/ } is equivalent to if consteval { /*stmt-2*/ } else { /*stmt-1*/ } .

compound-statement in a consteval if statement (or statement in the negative form) is in an immediate function context , in which a call to an immediate function needs not to be a constant expression.

[ edit ] Notes

If statement-true or statement-false is not a compound statement, it is treated as if it were:

is the same as

The scope of the name introduced by condition , if it is a declaration, is the combined scope of both statements' bodies:

If statement-true is entered by goto or longjmp , condition is not evaluated and statement-false is not executed.

[ edit ] Keywords

if , else , constexpr , consteval

[ edit ] Defect reports

The following behavior-changing defect reports were applied retroactively to previously published C++ standards.

[ edit ] See also

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Assignment operator in c.

Last Updated on June 23, 2023 by Prepbytes

This type of operator is employed for transforming and assigning values to variables within an operation. In an assignment operation, the right side represents a value, while the left side corresponds to a variable. It is essential that the value on the right side has the same data type as the variable on the left side. If this requirement is not fulfilled, the compiler will issue an error.

What is Assignment Operator in C language?

In C, the assignment operator serves the purpose of assigning a value to a variable. It is denoted by the equals sign (=) and plays a vital role in storing data within variables for further utilization in code. When using the assignment operator, the value present on the right-hand side is assigned to the variable on the left-hand side. This fundamental operation allows developers to store and manipulate data effectively throughout their programs.

Example of Assignment Operator in C

For example, consider the following line of code:

Types of Assignment Operators in C

Here is a list of the assignment operators that you can find in the C language:

Simple assignment operator (=): This is the basic assignment operator, which assigns the value on the right-hand side to the variable on the left-hand side.

Addition assignment operator (+=): This operator adds the value on the right-hand side to the variable on the left-hand side and assigns the result back to the variable.

x += 3; // Equivalent to x = x + 3; (adds 3 to the current value of "x" and assigns the result back to "x")

Subtraction assignment operator (-=): This operator subtracts the value on the right-hand side from the variable on the left-hand side and assigns the result back to the variable.

x -= 4; // Equivalent to x = x – 4; (subtracts 4 from the current value of "x" and assigns the result back to "x")

* Multiplication assignment operator ( =):** This operator multiplies the value on the right-hand side with the variable on the left-hand side and assigns the result back to the variable.

x = 2; // Equivalent to x = x 2; (multiplies the current value of "x" by 2 and assigns the result back to "x")

Division assignment operator (/=): This operator divides the variable on the left-hand side by the value on the right-hand side and assigns the result back to the variable.

x /= 2; // Equivalent to x = x / 2; (divides the current value of "x" by 2 and assigns the result back to "x")

Bitwise AND assignment (&=): The bitwise AND assignment operator "&=" performs a bitwise AND operation between the value on the left-hand side and the value on the right-hand side. It then assigns the result back to the left-hand side variable.

x &= 3; // Binary: 0011 // After bitwise AND assignment: x = 1 (Binary: 0001)

Bitwise OR assignment (|=): The bitwise OR assignment operator "|=" performs a bitwise OR operation between the value on the left-hand side and the value on the right-hand side. It then assigns the result back to the left-hand side variable.

x |= 3; // Binary: 0011 // After bitwise OR assignment: x = 7 (Binary: 0111)

Bitwise XOR assignment (^=): The bitwise XOR assignment operator "^=" performs a bitwise XOR operation between the value on the left-hand side and the value on the right-hand side. It then assigns the result back to the left-hand side variable.

x ^= 3; // Binary: 0011 // After bitwise XOR assignment: x = 6 (Binary: 0110)

Left shift assignment (<<=): The left shift assignment operator "<<=" shifts the bits of the value on the left-hand side to the left by the number of positions specified by the value on the right-hand side. It then assigns the result back to the left-hand side variable.

x <<= 2; // Binary: 010100 (Shifted left by 2 positions) // After left shift assignment: x = 20 (Binary: 10100)

Right shift assignment (>>=): The right shift assignment operator ">>=" shifts the bits of the value on the left-hand side to the right by the number of positions specified by the value on the right-hand side. It then assigns the result back to the left-hand side variable.

x >>= 2; // Binary: 101 (Shifted right by 2 positions) // After right shift assignment: x = 5 (Binary: 101)

Conclusion The assignment operator in C, denoted by the equals sign (=), is used to assign a value to a variable. It is a fundamental operation that allows programmers to store data in variables for further use in their code. In addition to the simple assignment operator, C provides compound assignment operators that combine arithmetic or bitwise operations with assignment, allowing for concise and efficient code.

FAQs related to Assignment Operator in C

Q1. Can I assign a value of one data type to a variable of another data type? In most cases, assigning a value of one data type to a variable of another data type will result in a warning or error from the compiler. It is generally recommended to assign values of compatible data types to variables.

Q2. What is the difference between the assignment operator (=) and the comparison operator (==)? The assignment operator (=) is used to assign a value to a variable, while the comparison operator (==) is used to check if two values are equal. It is important not to confuse these two operators.

Q3. Can I use multiple assignment operators in a single statement? No, it is not possible to use multiple assignment operators in a single statement. Each assignment operator should be used separately for assigning values to different variables.

Q4. Are there any limitations on the right-hand side value of the assignment operator? The right-hand side value of the assignment operator should be compatible with the data type of the left-hand side variable. If the data types are not compatible, it may lead to unexpected behavior or compiler errors.

Q5. Can I assign the result of an expression to a variable using the assignment operator? Yes, it is possible to assign the result of an expression to a variable using the assignment operator. For example, x = y + z; assigns the sum of y and z to the variable x.

Q6. What happens if I assign a value to an uninitialized variable? Assigning a value to an uninitialized variable will initialize it with the assigned value. However, it is considered good practice to explicitly initialize variables before using them to avoid potential bugs or unintended behavior.

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Mastering The Art Of Assignment: Exploring C Assignment Operators

Dive into the world of C Assignment Operators in our extensive guide. Understand the syntax, deep-dive into variables, and explore complex techniques and practical applications.

💡 KEY INSIGHTS

• Assignment operators in C are not just for basic value assignment; they enable simultaneous arithmetic operations, enhancing code efficiency and readability.
• The article emphasizes the importance of understanding operator precedence in C, as misinterpretation can lead to unexpected results, especially with compound assignment operators.
• Common mistakes like confusing assignment with equality ('=' vs '==') are highlighted, offering practical advice for avoiding such pitfalls in C programming.
• The guide provides real-world analogies for each assignment operator, making complex concepts more relatable and easier to grasp for programmers.

Welcome, bold programmers and coding enthusiasts! Let's set the stage: you're at your desk, fingers hovering over the keyboard, ready to embark on a journey deep into the belly of C programming. You might be wondering, why do I need to know about these 'assignment operators'?

Well, imagine trying to build a house with a toolbox that only has a hammer. You could probably make something that vaguely resembles a house, but without a screwdriver, wrench, or saw, it's going to be a bit...wobbly. This, my friends, is the importance of understanding operators in C. They're like the indispensable tools in your coding toolbox. And today, we're honing in on the assignment operators .

Now, our mission, should we choose to accept it, is to delve into the world of assignment operators in C. Like secret agents discovering the inner workings of a villain's lair, we're going to uncover the secrets that these '=' or '+=' symbols hold.

To all the night owls out there, I see you, and I raise you an operator. Just like how a cup of coffee (or three) helps us conquer that midnight oil, mastering operators in C can transform your coding journey from a groggy stumble to a smooth sprint.

But don't just take my word for it. Let's take a real-world example. Imagine you're coding a video game. You need your character to jump higher each time they collect a power-up. Without assignment operators, you'd be stuck adding numbers line by line. But with the '+=' operator, you can simply write 'jumpHeight += powerUpBoost,' and your code becomes a thing of elegance. It's like going from riding a tricycle to a high-speed motorbike.

In this article, we're going to unpack, examine, and get intimately acquainted with these assignment operators. We'll reveal their secrets, understand their behaviors, and learn how to use them effectively to power our C programming skills to new heights. Let's strap in, buckle up, and get ready for takeoff into the cosmic realms of C assignment operators!

The Basics Of C Operators

Deep dive into assignment operators in c, detailed exploration of each assignment operator, common use cases of assignment operators, common mistakes and how to avoid them, practice exercises, references and further reading.

Alright, get ready to pack your mental suitcase as we prepare to embark on the grand tour of C operators. We'll be stopping by the various categories, getting to know the locals (the operators, that is), and understanding how they contribute to the vibrant community that is a C program.

What Are Operators In C?

Operators in C are like the spicy condiments of coding. Without them, you'd be left with a bland dish—or rather, a simple list of variables. But splash on some operators, and suddenly you've got yourself an extravagant, dynamic, computational feast. In technical terms, operators are special symbols that perform specific operations on one, two, or three operands, and then return a result . They're the magic sauce that allows us to perform calculations, manipulate bits, and compare data.

Categories Of Operators In C

Now, just as you wouldn't use hot sauce on your ice cream (unless that's your thing), different operators serve different purposes. C language has been generous enough to provide us with a variety of operator categories, each with its distinct charm and role.

Let's break it down:

Imagine you're running a pizza shop. The arithmetic operators are like your basic ingredients: cheese, sauce, dough. They form the foundation of your pizza (program). But then you want to offer different pizza sizes. That's where your relational operators come in, comparing the diameter of small, medium, and large pizzas.

You're going well, but then you decide to offer deals. Buy two pizzas, get one free. Enter the logical operators , evaluating whether the conditions for the deal have been met. And finally, you want to spice things up with some exotic ingredients. That's your bitwise operators , working behind the scenes, adding that unique flavor that makes your customers keep coming back.

However, today, we're going to focus on a particular subset of the arithmetic operators: the assignment operators . These are the operators that don't just make the pizza but ensure it reaches the customer's plate (or in this case, the right variable).

Next up: We explore these unsung heroes of the programming world, toasting their accomplishments and discovering their capabilities. So, hold onto your hats and glasses, folks. This here's the wildest ride in the coding wilderness!

Prepare your diving gear and adjust your oxygen masks, friends, as we're about to plunge deep into the ocean of C programming. Hidden in the coral reef of code, you'll find the bright and beautiful creatures known as assignment operators.

What Are Assignment Operators?

In the broad ocean of C operators, the assignment operators are the dolphins - intelligent, adaptable, and extremely useful. On the surface, they may appear simple, but don't be fooled; these creatures are powerful. They have the capability to not only assign values to variables but also perform arithmetic operations concurrently.

The basic assignment operator in C is the '=' symbol. It's like the water of the ocean, essential to life (in the world of C programming). But alongside this staple, we have a whole family of compound assignment operators including '+=', '-=', '*=', '/=', and '%='. These are the playful dolphins leaping out of the water, each adding their unique twist to the task of assignment.

Syntax And Usage Of Assignment Operators

Remember, even dolphins have their ways of communicating, and so do assignment operators. They communicate through their syntax. The syntax for assignment operators in C follows a simple pattern:

In this dance, the operator and the '=' symbol perform a duet, holding onto each other without a space in between. They're the dancing pair that adds life to the party (aka your program).

Let's say you've won the lottery (congratulations, by the way!) and you want to divide your winnings between your three children. You could write out the arithmetic long-hand, or you could use the '/=' operator to streamline your process:

Just like that, your winnings are divided evenly, no calculator required.

List Of Assignment Operators In C

As promised, let's get to know the whole family of assignment operators residing in the C ocean:

Alright, we've taken the plunge and gotten our feet wet (or fins, in the case of our dolphin friends). But the dive is far from over. Next up, we're going to swim alongside each of these assignment operators, exploring their unique behaviors and abilities in the wild, vibrant world of C programming. So, keep your scuba gear on and get ready for more underwater adventure!

Welcome back, dear diver! Now that we've acquainted ourselves with the beautiful pod of dolphins, aka assignment operators, it's time to learn about each dolphin individually. We're about to uncover their quirks, appreciate their styles, and recognize their talents.

The Simple Assignment Operator '='

Let's start with the leader of the pack: the '=' operator. This unassuming symbol is like the diligent mail carrier, ensuring the right packages (values) get to the correct houses (variables).

Take a look at this:

In this code snippet, '=' ensures that the value '5' gets assigned to the variable 'chocolate'. Simple as that. No muss, no fuss, just a straightforward delivery of value.

The Addition Assignment Operator '+='

Next, we have the '+=' operator. This operator is a bit like a friendly baker. He takes what he has, adds more ingredients, and gives you the result - a delicious cake! Or, in this case, a new value.

Consider this:

We started with 12 doughnuts. But oh look, a friend dropped by with 3 more! So we add those to our box, and now we have 15. The '+=' operator made that addition quick and easy.

The Subtraction Assignment Operator '-='

Following the '+=' operator, we have its twin but with a different personality - the '-=' operator. If '+=' is the friendly baker, then '-=' is the weight-conscious friend who always removes extra toppings from their pizza. They take away rather than add.

For instance:

You've consumed 2000 calories today, but then you went for a run and burned 500. The '-=' operator is there to quickly update your calorie count.

The Multiplication Assignment Operator '*='

Say hello to the '*=' operator. This one is like the enthusiastic party planner who multiplies the fun! They take your initial value and multiply it with another, bringing more to the table.

Check this out:

You're at a level 7 excitement about your upcoming birthday, but then you learn that your best friend is flying in to celebrate with you. Your excitement level just doubled, and '*=' is here to make that calculation easy.

The Division Assignment Operator '/='

Here's the '/=' operator, the calm and composed yoga teacher of the group. They're all about division and balance. They take your original value and divide it by another, bringing harmony to your code.

You're pretty anxious about your job interview - let's say a level 10 anxiety. But then you do some deep breathing exercises, which helps you halve your anxiety level. The '/=' operator helps you reflect that change in your code.

The Modulus Assignment Operator '%='

Finally, we meet the quirky '%=' operator, the mystery novelist of the group. They're not about the whole story but the remainder, the leftovers, the little details others might overlook.

Look at this:

You have 10 books to distribute equally among your 3 friends. Everyone gets 3, and you're left with 1 book. The '%=' operator is there to quickly calculate that remainder for you.

That's the end of our detailed exploration. I hope this underwater journey has provided you with a greater appreciation and understanding of these remarkable creatures. Remember, each operator, like a dolphin, has its unique abilities, and knowing how to utilize them effectively can greatly enhance your programming prowess.

Now, let's swerve away from the theoretical and deep-dive into the practical. After all, C assignment operators aren't just sparkling little seashells you collect and admire. They're more like versatile tools in your programming Swiss Army knife. So, let's carve out some real-world use cases for our cherished assignment operators.

Variable Initialization And Value Change

Assignment operators aren't just for show; they've got some moves. Take our plain and humble '='. It's the bread-and-butter operator used in variable initialization and value changes, helping your code be as versatile as a chameleon.

In this scenario, our friend '=' is doing double duty—initializing 'a' with the value 10 and then changing it to 20. Not flashy, but oh-so-vital.

Calculation Updates In Real-Time Applications

Assignment operators are like those awesome, multitasking waitstaff you see in busy restaurants, juggling multiple tables and orders while still managing to serve everyone with a smile. They are brilliant when you want to perform real-time updates to your data.

In this scenario, '+=' and '-=' are the maitre d' of our code-restaurant, updating the user's balance with each buy or sell order.

Running Totals And Averages

Assignment operators are great runners - they don't tire and always keep the tally running.

Here, the '+=' and '-=' operators keep a running tally of points, allowing the system to adjust to the ebbs and flows of the school year like a seasoned marathon runner pacing themselves.

Iterations In Loop Constructs

The '*=' and '/=' operators often lurk within loop constructs, handling iterations with the grace of a prima ballerina. They're the choreographers of your loops, making sure each iteration flows seamlessly into the next.

In this case, '/=' is the elegant dancer gracefully halving 'i' with each twirl across the dance floor (iteration).

Working With Remainders

And let's not forget our mysterious '%=', the detective of the bunch, always searching for the remainder, the evidence left behind.

Here, '%=' is the sleuth, determining whether a number is even or odd by examining the remainder when divided by 2.

So, these are just a few examples of how assignment operators flex their muscles in the real world. They're like superheroes, each with their unique powers, ready to assist you in writing clean, efficient, and understandable code. Use them wisely, and your code will be as smooth as a well-choreographed ballet.

Let's face it, even the best of us trip over our own feet sometimes. And when it comes to assignment operators in C, there are some pitfalls that could make you stumble. But don't worry! We've all been there. Let's shed some light on these common mistakes so we can step over them with the grace of a ballet dancer leaping over a pit of snapping alligators.

Confusing Assignment With Equality

A surprisingly common misstep is confusing the assignment operator '=' with the equality operator '=='. It's like mixing up salt with sugar while baking. Sure, they might look similar, but one will definitely not sweeten your cake.

In this snippet, instead of checking if 'a' equals 10, we've assigned 'a' the value 10. The compiler will happily let this pass and might even give you a standing ovation for your comedy of errors. The correct approach?

Overlooking Operator Precedence

C operators are a bit like the characters in "Game of Thrones." They've got a complex hierarchy and they respect the rule of precedence. Sometimes, this can lead to unexpected results. For instance, check out this bit of misdirection:

Here, '/=' doesn't immediately divide 'a' by 2. It waits for the multiplication to happen (due to operator precedence), and then performs the operation. So it's actually doing a /= (2*5), not (a/=2)*5. It's like arriving late to a party and finding out all the pizza is gone. To ensure you get your slice, use parentheses:

Misusing Modulo With Floats

Ah, the modulo operator, always looking for the remainder. But when you ask it to work with floats, it gets as confused as a penguin in a desert. It simply can't compute.

Modulo and floats go together like oil and water. The solution? Stick to integers when dealing with '%='.

So there you have it. Some common missteps while dancing with assignment operators and the quick moves to avoid them. Just remember, every great coder has tripped before. The key is to keep your chin up, learn from your stumbles, and soon you'll be waltzing with assignment operators like a seasoned pro.

Alright, amigos! It's time to put your newfound knowledge to the test. After all, becoming a master in the art of C assignment operators is not a walk in the park, it's a marathon run on a stony path with occasional dance-offs. So brace yourselves and let's get those brain cells pumping.

Exercise 1: The Shy Variable

Your task here is to write a C program that initializes an integer variable to 10. Then, using only assignment operators, make that variable as shy as a teenager at their first dance. I mean, reduce it to zero without directly assigning it to zero. You might want to remember the '/=' operator here. He's like the high school wallflower who can suddenly breakdance like a champ when the music starts playing.

Exercise 2: Sneaky Increment

The '+=' operator is like the mischievous friend who always pushes you into the spotlight when you least expect it. Create a program that initializes an integer to 0. Then, using a loop and our sneaky '+=' friend, increment that variable until it's equal to 100. Here's the catch: You can't use '+=' with anything greater than 1. It's a slow and steady race to the finish line!

Exercise 3: Modulo Madness

Remember the modulo operator? It's like the friend who always knows how much pizza is left over after a party. Create a program that counts from 1 to 100. But here's the twist: for every number that's divisible by 3, print "Fizz", and for every number divisible by 5, print "Buzz". If a number is divisible by both 3 and 5, print "FizzBuzz". For all other numbers, just print the number. This will help you get better acquainted with our friend '%='.

Exercise 4: Swapping Values

Create a program that swaps the values of two variables without using a third temporary variable. Remember, your only allies here are the assignment operators. This is like trying to switch places on the dance floor without stepping on anyone's toes.

Exercise 5: Converting Fahrenheit To Celsius

Let's play with the ' =' operator. Write a program that converts a temperature in Fahrenheit to Celsius. The formula to convert Fahrenheit to Celsius is (Fahrenheit - 32) * 5 / 9 . As a challenge, try doing the conversion in a single line using the '-=', ' =' and '/=' operators. It's like preparing a complicated dinner recipe using only a few simple steps.

Remember, practice makes perfect, especially when it comes to mastering C assignment operators. Don't be disheartened if you stumble, just dust yourself off and try again. Because as the saying goes, "The master has failed more times than the beginner has even tried". So, good luck, and happy coding!

References and Further Reading

So, you've reached the end of this riveting journey through the meadows of C assignment operators. It's been quite a ride, hasn't it? We've shared laughs, shed tears, and hopefully, we've learned a thing or two. But remember, the end of one journey marks the beginning of another. It's like eating at a buffet – you might be done with the pasta, but there's still the sushi to try! So, here are some materials to sink your teeth into for the next course of your coding feast.

1. The C Programming Language by Brian W. Kernighan and Dennis M. Ritchie

This book, also known as 'K&R' after its authors, is the definitive guide to C programming. It's like the "Godfather" of programming books – deep, powerful, and a little intimidating at times. But hey, we all know that the best lessons come from challenging ourselves.

2. Expert C Programming by Peter van der Linden

Consider this book as the "Star Wars" to the "Godfather" of 'K&R'. It has a bit more adventure and a lot of real-world applications to keep you engaged. Not to mention some rather amusing footnotes.

3. C Programming Absolute Beginner's Guide by Greg Perry and Dean Miller

This one's for you if you're still feeling a bit wobbly on your C programming legs. Think of it as a warm hug from a friend who's been there and done that. It's simple, straightforward, and gently walks you through the concepts.

4. The Pragmatic Programmer by Andrew Hunt and David Thomas

Even though it's not about C specifically, this book is a must-read for any serious programmer. It's like a mentor who shares all their best tips and tricks for mastering the craft. It's filled with practical advice and real-life examples to help you on your programming journey.

This is a great online resource for interactive C tutorials. It's like your favorite video game, but it's actually helping you become a better programmer.

6. Cprogramming.com

This website has a vast collection of articles, tutorials, and quizzes on C programming. It's like an all-you-can-eat buffet for your hungry, coding mind.

Remember, every master was once a beginner, and every beginner can become a master. So, keep reading, keep practicing, and keep coding. And most importantly, don't forget to have fun while you're at it. After all, as Douglas Adams said, "I may not have gone where I intended to go, but I think I have ended up where I needed to be." Here's to ending up where you need to be in your coding journey!

As our immersive journey into C Assignment Operators culminates, we've unraveled the nuanced details of these powerful tools. From fundamental syntax to intricate applications, C Assignment Operators have showcased their indispensability in coding. Equipped with this newfound understanding, it's time for you to embark on your coding adventures, mastering the digital realm with the prowess of C Assignment Operators!

Which C assignment operator adds a value to a variable?

Please submit an answer to see if you're correct!

Continue Learning With These C Guides

• C Syntax Explained: From Variables To Functions
• C Programming Basics And Its Applications
• Basic C Programming Examples For Beginners
• C Data Types And Their Usage
• C Variables And Their Usage

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Mozilla Foundation Security Advisory 2024-18

Security vulnerabilities fixed in firefox 125.

• Firefox 125

# CVE-2024-3852: GetBoundName in the JIT returned the wrong object

Description.

GetBoundName could return the wrong version of an object when JIT optimizations were applied.

• Bug 1883542

# CVE-2024-3853: Use-after-free if garbage collection runs during realm initialization

A use-after-free could result if a JavaScript realm was in the process of being initialized when a garbage collection started.

• Bug 1884427

# CVE-2024-3854: Out-of-bounds-read after mis-optimized switch statement

In some code patterns the JIT incorrectly optimized switch statements and generated code with out-of-bounds-reads.

• Bug 1884552

# CVE-2024-3855: Incorrect JIT optimization of MSubstr leads to out-of-bounds reads

In certain cases the JIT incorrectly optimized MSubstr operations, which led to out-of-bounds reads.

• Bug 1885828

# CVE-2024-3856: Use-after-free in WASM garbage collection

A use-after-free could occur during WASM execution if garbage collection ran during the creation of an array.

• Bug 1885829

# CVE-2024-3857: Incorrect JITting of arguments led to use-after-free during garbage collection

The JIT created incorrect code for arguments in certain cases. This led to potential use-after-free crashes during garbage collection.

• Bug 1886683

# CVE-2024-3858: Corrupt pointer dereference in js::CheckTracedThing<js::Shape>

It was possible to mutate a JavaScript object so that the JIT could crash while tracing it.

• Bug 1888892

# CVE-2024-3859: Integer-overflow led to out-of-bounds-read in the OpenType sanitizer

On 32-bit versions there were integer-overflows that led to an out-of-bounds-read that potentially could be triggered by a malformed OpenType font.

• Bug 1874489

# CVE-2024-3860: Crash when tracing empty shape lists

An out-of-memory condition during object initialization could result in an empty shape list. If the JIT subsequently traced the object it would crash.

• Bug 1881417

# CVE-2024-3861: Potential use-after-free due to AlignedBuffer self-move

If an AlignedBuffer were assigned to itself, the subsequent self-move could result in an incorrect reference count and later use-after-free.

• Bug 1883158

# CVE-2024-3862: Potential use of uninitialized memory in MarkStack assignment operator on self-assignment

The MarkStack assignment operator, part of the JavaScript engine, could access uninitialized memory if it were used in a self-assignment.

• Bug 1884457

# CVE-2024-3863: Download Protections were bypassed by .xrm-ms files on Windows

The executable file warning was not presented when downloading .xrm-ms files. Note: This issue only affected Windows operating systems. Other operating systems are unaffected.

• Bug 1885855

# CVE-2024-3302: Denial of Service using HTTP/2 CONTINUATION frames

There was no limit to the number of HTTP/2 CONTINUATION frames that would be processed. A server could abuse this to create an Out of Memory condition in the browser.

• Bug 1881183
• VU#421644 - HTTP/2 CONTINUATION frames can be utilized for DoS attacks

# CVE-2024-3864: Memory safety bug fixed in Firefox 125, Firefox ESR 115.10, and Thunderbird 115.10

Memory safety bug present in Firefox 124, Firefox ESR 115.9, and Thunderbird 115.9. This bug showed evidence of memory corruption and we presume that with enough effort this could have been exploited to run arbitrary code.

• Memory safety bug fixed in Firefox 125, Firefox ESR 115.10, and Thunderbird 115.10

# CVE-2024-3865: Memory safety bugs fixed in Firefox 125

Memory safety bugs present in Firefox 124. Some of these bugs showed evidence of memory corruption and we presume that with enough effort some of these could have been exploited to run arbitrary code.

• Memory safety bugs fixed in Firefox 125

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Computer Science > Computer Vision and Pattern Recognition

Title: bridging vision and language spaces with assignment prediction.

Abstract: This paper introduces VLAP, a novel approach that bridges pretrained vision models and large language models (LLMs) to make frozen LLMs understand the visual world. VLAP transforms the embedding space of pretrained vision models into the LLMs' word embedding space using a single linear layer for efficient and general-purpose visual and language understanding. Specifically, we harness well-established word embeddings to bridge two modality embedding spaces. The visual and text representations are simultaneously assigned to a set of word embeddings within pretrained LLMs by formulating the assigning procedure as an optimal transport problem. We predict the assignment of one modality from the representation of another modality data, enforcing consistent assignments for paired multimodal data. This allows vision and language representations to contain the same information, grounding the frozen LLMs' word embedding space in visual data. Moreover, a robust semantic taxonomy of LLMs can be preserved with visual data since the LLMs interpret and reason linguistic information from correlations between word embeddings. Experimental results show that VLAP achieves substantial improvements over the previous linear transformation-based approaches across a range of vision-language tasks, including image captioning, visual question answering, and cross-modal retrieval. We also demonstrate the learned visual representations hold a semantic taxonomy of LLMs, making visual semantic arithmetic possible.

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• Using Project Costing

HCM Assignment Status Validations When Importing Person-Related Costs

By default, Project Costing doesn't allow you to import a person-related project cost transaction if the person's HCM assignment status on the date of the project cost is  Inactive or Suspended .

If you want to import project costs associated with persons whose work relationships have recently been terminated, use the Allow Cost Transactions Until Certain Days After Person Termination (ORA_PJC_ALLOW_COSTS_UNTIL_DAYS_AFTER_PERS_TERM) administrator profile option to specify the number of days after the termination date during which Project Costing will accept project cost transactions associated with terminated persons.

However, if you want to skip HCM assignment status validations altogether when you import person-related project cost transactions from specific transaction source documents, select the Allow transactions for inactive or suspended person assignments  option associated with that document.

For example, a person's HR assignment can be suspended, but the person can still be eligible for payroll processing. If you're using Labor Distribution to distribute payroll costs to projects, then the payroll costs incurred for the person's suspended HR assignment will be rejected during processing, as the assignment is not active as of the project cost dates. But if you want to allow these transactions, enable the Allow transactions for inactive or suspended person assignments in the Labor Distribution project transaction source document.

• Time and Labor doesn't transfer hours to Project Costing if they are reported on days after a person's termination date even if you allow transactions for inactive or suspended person assignments for the Oracle Fusion Time and Labor Time Card document.
• The  Allow transactions for inactive or suspended person assignments  document option takes precedence over the Allow Cost Transactions Until Certain Days After Person Termination (ORA_PJC_ALLOW_COSTS_UNTIL_DAYS_AFTER_PERS_TERM) administrator profile option, if you have enabled both.
• When you enable the Allow transactions for inactive or suspended person assignments  document option, the status of a person's assignment is not validated. However Project Costing still requires an HR assignment as of the transaction date for a person-related project cost.

Rays outfielder Josh Lowe set to start rehab assignment Thursday

• Marc Topkin Times staff

ST. PETERSBURG — Outfielder Josh Lowe’s return to the lineup is starting to come into view for the Rays.

Manager Kevin Cash said Lowe, the team’s most productive left-handed hitter last season, is slated to begin a rehab assignment Thursday with Triple-A Durham.

And he might not need too much time there.

“It’s kind of all just how he feels,” Cash said. “(Sunday), he said he feels really good. It’s going to take him a minute to get his timing going. But if he feels good, that’s most important.”

Lowe initially was sidelined in late February, after playing in two spring games, due to left hip inflammation. He was working toward a March 17 return to the lineup with he strained his right oblique.

He started playing in extended spring games last week. Assuming he gets through a Tuesday game with no issues, he will join Durham Wednesday in Worcester, Massachusetts, and play on Thursday. A return to the Rays before the end of April seems possible and would add much-needed left-handed power, plus speed, to the lineup.

Another lefty hitter expected to play a key role, Jonathan Aranda, also is progressing toward a return.

Aranda had two pins in his fractured right ring finger removed Monday and is expected to start taking swings in the next few days.

“He felt like he was going to be pretty much full-go to start ramping up,” Cash said. “He’s been throwing. He felt like he would be able to swing pretty pain-free. So if that’s the case, we’ll start working to get him built up.”

Cash said Aranda, who was injured March 19, will go through the standard progression of batting cage drills, starting with hitting balls off a tee, and work toward taking batting practice on the field before starting a rehab assignment.

“So, still a ways away,” Cash said, “but (moving) in the right direction.”

Hey, it’s you guys again

Starters have different ways of dealing with facing the same team in consecutive outings. Expect Rays right-hander Aaron Civale to take a cerebral approach in making adjustments Tuesday against the Angels.

“He’s a unique mind in terms of how he approaches his starts,” pitching coach Kyle Snyder said Monday. “This is a unique situation, (playing) out of division and you’re facing a team twice in a row.

“But he is very good at kind of reverse-engineering things that have happened. And being able to act on those, whether it’s the second time through (the order in a game) or the second time he faces a team in a week. And it’s pretty impressive the lengths that he goes through in terms of preparing himself and his ability to realize how to keep guys in between (and off-balance). He’s going to lean on some of what he was successful in doing that last time, and then probably just try to apply that.”

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Angels on Assignment, a new support program for Maui cancer patients, takes flight

“Angels on Assignment” is a new, volunteer-based program launched by Maui Cancer Resources to support Maui cancer patients as they get through chemotherapy or radiation treatment.

“Although Angels has been in existence a little over a month, we are finding ways to help and support those with cancer,” said Dr. Bridget Bongaard, medical director.

The program is funded through a generous donation from 100 Women Who Care Maui.

On March 28, Angel volunteers made 20 yarn lei that were delivered to the Maui Health Oncology Department to present to cancer patients when they complete their chemotherapy or radiation treatment. Each lei carried a special uplifting card to let patients know they are supported and loved. Angels has committed to provide 20 lei each month.

The Angels group is also creating Cancer Care Bags for patients in treatment. In each bag, a cancer patient will find a blanket, socks, lotion, mints, a journal, colored pencils with a sharpener and mandalas coloring pages plus crossword puzzles.

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• Open access
• Published: 15 April 2024

Demuxafy : improvement in droplet assignment by integrating multiple single-cell demultiplexing and doublet detection methods

• Drew Neavin 1 ,
• Anne Senabouth 1 ,
• Himanshi Arora 1 , 2 ,
• Jimmy Tsz Hang Lee 3 ,
• Aida Ripoll-Cladellas 4 ,
• sc-eQTLGen Consortium ,
• Lude Franke 5 ,
• Shyam Prabhakar 6 , 7 , 8 ,
• Chun Jimmie Ye 9 , 10 , 11 , 12 ,
• Davis J. McCarthy 13 , 14 ,
• Marta Melé 4 ,
• Martin Hemberg 15 &
• Joseph E. Powell   ORCID: orcid.org/0000-0002-5070-4124 1 , 16  

Genome Biology volume  25 , Article number:  94 ( 2024 ) Cite this article

Metrics details

Recent innovations in single-cell RNA-sequencing (scRNA-seq) provide the technology to investigate biological questions at cellular resolution. Pooling cells from multiple individuals has become a common strategy, and droplets can subsequently be assigned to a specific individual by leveraging their inherent genetic differences. An implicit challenge with scRNA-seq is the occurrence of doublets—droplets containing two or more cells. We develop Demuxafy, a framework to enhance donor assignment and doublet removal through the consensus intersection of multiple demultiplexing and doublet detecting methods. Demuxafy significantly improves droplet assignment by separating singlets from doublets and classifying the correct individual.

Droplet-based single-cell RNA sequencing (scRNA-seq) technologies have provided the tools to profile tens of thousands of single-cell transcriptomes simultaneously [ 1 ]. With these technological advances, combining cells from multiple samples in a single capture is common, increasing the sample size while simultaneously reducing batch effects, cost, and time. In addition, following cell capture and sequencing, the droplets can be demultiplexed—each droplet accurately assigned to each individual in the pool [ 2 , 3 , 4 , 5 , 6 , 7 ].

Many scRNA-seq experiments now capture upwards of 20,000 droplets, resulting in ~16% (3,200) doublets [ 8 ]. Current demultiplexing methods can also identify doublets—droplets containing two or more cells—from different individuals (heterogenic doublets). These doublets can significantly alter scientific conclusions if they are not effectively removed. Therefore, it is essential to remove doublets from droplet-based single-cell captures.

However, demultiplexing methods cannot identify droplets containing multiple cells from the same individual (homogenic doublets) and, therefore, cannot identify all doublets in a single capture. If left in the dataset, those doublets could appear as transitional cells between two distinct cell types or a completely new cell type. Accordingly, additional methods have been developed to identify heterotypic doublets (droplets that contain two cells from different cell types) by comparing the transcriptional profile of each droplet to doublets simulated from the dataset [ 9 , 10 , 11 , 12 , 13 , 14 , 15 ]. It is important to recognise that demultiplexing methods achieve two functions—segregation of cells from different donors and separation of singlets from doublets—while doublet detecting methods solely classify singlets versus doublets.

Therefore, demultiplexing and transcription-based doublet detecting methods provide complementary information to improve doublet detection, providing a cleaner dataset and more robust scientific results. There are currently five genetic-based demultiplexing [ 2 , 3 , 4 , 5 , 6 , 7 , 16 ] and seven transcription-based doublet-detecting methods implemented in various languages [ 9 , 10 , 11 , 12 , 13 , 14 , 15 ]. Under different scenarios, each method is subject to varying performance and, in some instances, biases in their ability to accurately assign cells or detect doublets from certain conditions. The best combination of methods is currently unclear but will undoubtedly depend on the dataset and research question.

Therefore, we set out to identify the best combination of genetic-based demultiplexing and transcription-based doublet-detecting methods to remove doublets and partition singlets from different donors correctly. In addition, we have developed a software platform ( Demuxafy ) that performs these intersectional methods and provides additional commands to simplify the execution and interpretation of results for each method (Fig. 1 a).

Study design and qualitative method classifications. a  Demuxafy is a platform to perform demultiplexing and doublet detecting with consistent documentation. Demuxafy also provides wrapper scripts to quickly summarize the results from each method and assign clusters to each individual with reference genotypes when a reference-free demultiplexing method is used. Finally, Demuxafy provides a script to easily combine the results from multiple different methods into a single data frame and it provides a final assignment for each droplet based on the combination of multiple methods. In addition, Demuxafy provides summaries of the number of droplets classified as singlets or doublets by each method and a summary of the number of droplets assigned to each individual by each of the demultiplexing methods. b  Two datasets are included in this analysis - a PBMC dataset and a fibroblast dataset. The PBMC dataset contains 74 pools that captured approximately 20,000 droplets each with 12-16 donor cells multiplexed per pool. The fibroblast dataset contains 11 pools of roughly 7,000 droplets per pool with sizes ranging from six to eight donors per pool. All pools were processed by all demultiplexing and doublet detecting methods and the droplet and donor classifications were compared between the methods and between the PBMCs and fibroblasts. Then the PBMC droplets that were classified as singlets by all methods were taken as ‘true singlets’ and used to generate new pools in silico. Those pools were then processed by each of the demultiplexing and doublet detecting methods and intersectional combinations of demultiplexing and doublet detecting methods were tested for different experimental designs

To compare the demultiplexing and doublet detecting methods, we utilised two large, multiplexed datasets—one that contained ~1.4 million peripheral blood mononuclear cells (PBMCs) from 1,034 donors [ 17 ] and one with ~94,000 fibroblasts from 81 donors [ 18 ]. We used the true singlets from the PBMC dataset to generate new in silico pools to assess the performance of each method and the multi-method intersectional combinations (Fig. 1 b).

Here, we compare 14 demultiplexing and doublet detecting methods with different methodological approaches, capabilities, and intersectional combinations. Seven of those are demultiplexing methods ( Demuxalot [ 6 ], Demuxlet [ 3 ], Dropulation [ 5 ], Freemuxlet [ 16 ], ScSplit [ 7 ], Souporcell [ 4 ], and Vireo [ 2 ]) which leverage the common genetic variation between individuals to identify cells that came from each individual and to identify heterogenic doublets. The seven remaining methods ( DoubletDecon [ 9 ], DoubletDetection [ 14 ], DoubletFinder [ 10 ], ScDblFinder [ 11 ], Scds [ 12 ], Scrublet [ 13 ], and Solo [ 15 ]) identify doublets based on their similarity to simulated doublets generated by adding the transcriptional profiles of two randomly selected droplets in the dataset. These methods assume that the proportion of real doublets in the dataset is low, so combining any two droplets will likely represent the combination of two singlets.

We identify critical differences in the performance of demultiplexing and doublet detecting methods to classify droplets correctly. In the case of the demultiplexing techniques, their performance depends on their ability to identify singlets from doublets and assign a singlet to the correct individual. For doublet detecting methods, the performance is based solely on their ability to differentiate a singlet from a doublet. We identify limitations in identifying specific doublet types and cell types by some methods. In addition, we compare the intersectional combinations of these methods for multiple experimental designs and demonstrate that intersectional approaches significantly outperform all individual techniques. Thus, the intersectional methods provide enhanced singlet classification and doublet removal—a critical but often under-valued step of droplet-based scRNA-seq processing. Our results demonstrate that intersectional combinations of demultiplexing and doublet detecting software provide significant advantages in droplet-based scRNA-seq preprocessing that can alter results and conclusions drawn from the data. Finally, to provide easy implementation of our intersectional approach, we provide Demuxafy ( https://demultiplexing-doublet-detecting-docs.readthedocs.io/en/latest/index.html ) a complete platform to perform demultiplexing and doublet detecting intersectional methods (Fig. 1 a).

Study design

To evaluate demultiplexing and doublet detecting methods, we developed an experimental design that applies the different techniques to empirical pools and pools generated in silico from the combination of true singlets—droplets identified as singlets by every method (Fig. 1 a). For the first phase of this study, we used two empirical multiplexed datasets—the peripheral blood mononuclear cell (PBMC) dataset containing ~1.4 million cells from 1034 donors and a fibroblast dataset of ~94,000 cells from 81 individuals (Additional file 1 : Table S1). We chose these two cell systems to assess the methods in heterogeneous (PBMC) and homogeneous (fibroblast) cell types.

Demultiplexing and doublet detecting methods perform similarly for heterogeneous and homogeneous cell types

We applied the demultiplexing methods ( Demuxalot , Demuxlet , Dropulation , Freemuxlet , ScSplit , Souporcell , and Vireo ) and doublet detecting methods ( DoubletDecon , DoubletDetection , DoubletFinder , ScDblFinder , Scds , Scrublet , and Solo ) to the two datasets and assessed the results from each method. We first compared the droplet assignments by identifying the number of singlets and doublets identified by a given method that were consistently annotated by all methods (Fig. 2 a–d). We also identified the percentage of droplets that were annotated consistently between pairs of methods (Additional file 2 : Fig S1). In the cases where two demultiplexing methods were compared to one another, both the droplet type (singlet or doublet) and the assignment of the droplet to an individual had to match to be considered in agreement. In all other comparisons (i.e. demultiplexing versus doublet detecting and doublet detecting versus doublet detecting), only the droplet type (singlet or doublet) was considered for agreement since doublet detecting methods cannot annotate donor assignment. We found that the two method types were more similar to other methods of the same type (i.e., demultiplexing versus demultiplexing and doublet detecting versus doublet detecting) than they were to methods from a different type (demultiplexing methods versus doublet detecting methods; Supplementary Fig 1). We found that the similarity of the demultiplexing and doublet detecting methods was consistent in the PBMC and fibroblast datasets (Pearson correlation R = 0.78, P -value < 2×10 −16 ; Fig S1a-c). In addition, demultiplexing methods were more similar than doublet detecting methods for both the PBMC and fibroblast datasets (Wilcoxon rank-sum test: P < 0.01; Fig. 2 a–b and Additional file 2 : Fig S1).

Demultiplexing and Doublet Detecting Method Performance Comparison. a  The proportion of droplets classified as singlets and doublets by each method in the PBMCs. b  The number of other methods that classified the singlets and doublets identified by each method in the PBMCs. c  The proportion of droplets classified as singlets and doublets by each method in the fibroblasts. d The number of other methods that classified the singlets and doublets identified by each method in the fibroblasts. e - f The performance of each method when the majority classification of each droplet is considered the correct annotation in the PBMCs ( e ) and fibroblasts ( f ). g - h  The number of droplets classified as singlets (box plots) and doublets (bar plots) by all methods in the PBMC ( g ) and fibroblast ( h ) pools. i - j  The number of donors that were not identified by each method in each pool for PBMCs ( i ) and fibroblasts ( j ). PBMC: peripheral blood mononuclear cell. MCC: Matthew’s correlationcoefficient

The number of unique molecular identifiers (UMIs) and genes decreased in droplets that were classified as singlets by a larger number of methods while the mitochondrial percentage increased in both PBMCs and fibroblasts (Additional file 2 : Fig S2).

We next interrogated the performance of each method using the Matthew’s correlation coefficient (MCC) to calculate the consistency between Demuxify and true droplet classification. We identified consistent trends in the MCC scores for each method between the PBMCs (Fig. 2 e) and fibroblasts (Fig. 2 f). These data indicate that the methods behave similarly, relative to one another, for heterogeneous and homogeneous datasets.

Next, we sought to identify the droplets concordantly classified by all demultiplexing and doublet detecting methods in the PBMC and fibroblast datasets. On average, 732 singlets were identified for each individual by all the methods in the PBMC dataset. Likewise, 494 droplets were identified as singlets for each individual by all the methods in the fibroblast pools. However, the concordance of doublets identified by all methods was very low for both datasets (Fig. 2 e–f). Notably, the consistency of classifying a droplet as a doublet by all methods was relatively low (Fig. 2 b,d,g, and h). This suggests that doublet identification is not consistent between all the methods. Therefore, further investigation is required to identify the reasons for these inconsistencies between methods. It also suggests that combining multiple methods for doublet classification may be necessary for more complete doublet removal. Further, some methods could not identify all the individuals in each pool (Fig. 2 i–j). The non-concordance between different methods demonstrates the need to effectively test each method on a dataset where the droplet types are known.

Computational resources vary for demultiplexing and doublet detecting methods

We recorded each method’s computational resources for the PBMC pools, with ~20,000 cells captured per pool (Additional file 1 : Table S1). Of the demultiplexing methods, ScSplit took the most time (multiple days) and required the most steps, but Demuxalot , Demuxlet , and Freemuxlet used the most memory. Solo took the longest time (median 13 h) and most memory to run for the doublet detecting methods but is the only method built to be run directly from the command line, making it easy to implement (Additional file 2 : Fig S3).

Generate pools with known singlets and doublets

However, there is no gold standard to identify which droplets are singlets or doublets. Therefore, in the second phase of our experimental design (Fig. 1 b), we used the PBMC droplets classified as singlets by all methods to generate new pools in silico. We chose to use the PBMC dataset since our first analyses indicated that method performance is similar for homogeneous (fibroblast) and heterogeneous (PBMC) cell types (Fig. 2 and Additional file 2 : Fig S1) and because we had many more individuals available to generate in silico pools from the PBMC dataset (Additional file 1 : Table S1).

We generated 70 pools—10 each of pools that included 2, 4, 8, 16, 32, 64, or 128 individuals (Additional file 1 : Table S2). We assume a maximum 20% doublet rate as it is unlikely researchers would use a technology that has a higher doublet rate (Fig. 3 a).

In silico Pool Doublet Annotation and Method Performance. a  The percent of singlets and doublets in the in -silico pools - separated by the number of multiplexed individuals per pool. b  The percentage and number of doublets that are heterogenic (detectable by demultiplexing methods), heterotypic (detectable by doublet detecting methods), both (detectable by either method category) and neither (not detectable with current methods) for each multiplexed pool size. c  Percent of droplets that each of the demultiplexing and doublet detecting methods classified correctly for singlets and doublet subtypes for different multiplexed pool sizes. d  Matthew’s Correlation Coefficient (MCC) for each of the methods for each of the multiplexed pool sizes. e  Balanced accuracy for each of the methods for each of the multiplexed pool sizes

We used azimuth to classify the PBMC cell types for each droplet used to generate the in silico pools [ 19 ] (Additional file 2 : Fig S4). As these pools have been generated in silico using empirical singlets that have been well annotated, we next identified the proportion of doublets in each pool that were heterogenic, heterotypic, both, and neither. This approach demonstrates that a significant percentage of doublets are only detectable by doublet detecting methods (homogenic and heterotypic) for pools with 16 or fewer donors multiplexed (Fig. 3 b).

While the total number of doublets that would be missed if only using demultiplexing methods appears small for fewer multiplexed individuals (Fig. 3 b), it is important to recognise that this is partly a function of the ~732 singlet cells per individual used to generate these pools. Hence, the in silico pools with fewer individuals also have fewer cells. Therefore, to obtain numbers of doublets that are directly comparable to one another, we calculated the number of each doublet type that would be expected to be captured with 20,000 cells when 2, 4, 8, 16, or 32 individuals were multiplexed (Additional file 2 : Fig S5). These results demonstrate that many doublets would be falsely classified as singlets since they are homogenic when just using demultiplexing methods for a pool of 20,000 cells captured with a 16% doublet rate (Additional file 2 : Fig S5). However, as more individuals are multiplexed, the number of droplets that would not be detectable by demultiplexing methods (homogenic) decreases. This suggests that typical workflows that use only one demultiplexing method to remove doublets from pools that capture 20,000 droplets with 16 or fewer multiplexed individuals fail to adequately remove between 173 (16 multiplexed individuals) and 1,325 (2 multiplexed individuals) doublets that are homogenic and heterotypic which could be detected by doublet detecting methods (Additional file 2 : Fig S5). Therefore, a technique that uses both demultiplexing and doublet detecting methods in parallel will complement more complete doublet removal methods. Consequently, we next set out to identify the demultiplexing and doublet detecting methods that perform the best on their own and in concert with other methods.

Doublet and singlet droplet classification effectiveness varies for demultiplexing and doublet detecting methods

Demultiplexing methods fail to classify homogenic doublets.

We next investigated the percentage of the droplets that were correctly classified by each demultiplexing and doublet detecting method. In addition to the seven demultiplexing methods, we also included Demuxalot with the additional steps to refine the genotypes that can then be used for demultiplexing— Demuxalot (refined). Demultiplexing methods correctly classify a large portion of the singlets and heterogenic doublets (Fig. 3 c). This pattern is highly consistent across different cell types, with the notable exceptions being decreased correct classifications for erythrocytes and platelets when greater than 16 individuals are multiplexed (Additional file 2 : Fig S6).

However, Demuxalot consistently demonstrates the highest correct heterogenic doublet classification. Further, the percentage of the heterogenic doublets classified correctly by Souporcell decreases when large numbers of donors are multiplexed. ScSplit is not as effective as the other demultiplexing methods at classifying heterogenic doublets, partly due to the unique doublet classification method, which assumes that the doublets will generate a single cluster separate from the donors (Table 1 ). Importantly, the demultiplexing methods identify almost none of the homogenic doublets for any multiplexed pool size—demonstrating the need to include doublet detecting methods to supplement the demultiplexing method doublet detection.

Doublet detecting method classification performances vary greatly

In addition to assessing each of the methods with default settings, we also evaluated ScDblFinder with ‘known doublets’ provided. This method can take already known doublets and use them when detecting doublets. For these cases, we used the droplets that were classified as doublets by all the demultiplexing methods as ‘known doublets’.

Most of the methods classified a similarly high percentage of singlets correctly, with the exceptions of DoubletDecon and DoubletFinder for all pool sizes (Fig. 3 c). However, unlike the demultiplexing methods, there are explicit cell-type-specific biases for many of the doublet detecting methods (Additional file 2 : Fig S7). These differences are most notable for cell types with fewer cells (i.e. ASDC and cDC2) and proliferating cells (i.e. CD4 Proliferating, CD8 Proliferating, and NK Proliferating). Further, all of the softwares demonstrate high correct percentages for some cell types including CD4 Naïve and CD8 Naïve (Additional file 2 : Fig S7).

As expected, all doublet detecting methods identified heterotypic doublets more effectively than homotypic doublets (Fig. 3 c). However, ScDblFinder and Scrublet classified the most doublets correctly across all doublet types for pools containing 16 individuals or fewer. Solo was more effective at identifying doublets than Scds for pools containing more than 16 individuals. It is also important to note that it was not feasible to run DoubletDecon for the largest pools containing 128 multiplexed individuals and an average of 115,802 droplets (range: 113,594–119,126 droplets). ScDblFinder performed similarly when executed with and without known doublets (Pearson correlation P = 2.5 × 10 -40 ). This suggests that providing known doublets to ScDblFinder does not offer an added benefit.

Performances vary between demultiplexing and doublet detecting method and across the number of multiplexed individuals

We assessed the overall performance of each method with two metrics: the balanced accuracy and the MCC. We chose to use balanced accuracy since, with unbalanced group sizes, it is a better measure of performance than accuracy itself. Further, the MCC has been demonstrated as a more reliable statistical measure of performance since it considers all possible categories—true singlets (true positives), false singlets (false positives), true doublets (true negatives), and false doublets (false negatives). Therefore, a high score on the MCC scale indicates high performance in each metric. However, we provide additional performance metrics for each method (Additional file 1 : Table S3). For demultiplexing methods, both the droplet type (singlet or doublet) and the individual assignment were required to be considered a ‘true singlet’. In contrast, only the droplet type (singlet or doublet) was needed for doublet detection methods.

The MCC and balanced accuracy metrics are similar (Spearman’s ⍴ = 0.87; P < 2.2 × 10 -308 ). Further, the performance of Souporcell decreases for pools with more than 32 individuals multiplexed for both metrics (Student’s t -test for MCC: P < 1.1 × 10 -9 and balanced accuracy: P < 8.1 × 10 -11 ). Scds , ScDblFinder , and Scrublet are among the top-performing doublet detecting methods Fig. 3 d–e).

Overall, between 0.4 and 78.8% of droplets were incorrectly classified by the demultiplexing or doublet detecting methods depending on the technique and the multiplexed pool size (Additional file 2 : Fig S8). Demuxalot (refined) and DoubletDetection demonstrated the lowest percentage of incorrect droplets with about 1% wrong in the smaller pools (two multiplexed individuals) and about 3% incorrect in pools with at least 16 multiplexed individuals. Since some transitional states and cell types are present in low percentages in total cell populations (i.e. ASDCs at 0.02%), incorrect classification of droplets could alter scientific interpretations of the data, and it is, therefore, ideal for decreasing the number of erroneous assignments as much as possible.

False singlets and doublets demonstrate different metrics than correctly classified droplets

We next asked whether specific cell metrics might contribute to false singlet and doublet classifications for different methods. Therefore, we compared the number of genes, number of UMIs, mitochondrial percentage and ribosomal percentage of the false singlets and doublets to equal numbers of correctly classified cells for each demultiplexing and doublet detecting method.

The number of UMIs (Additional file 2 : Fig S9 and Additional file 1 : Table S4) and genes (Additional file 2 : Fig S10 and Additional file 1 : Table S5) demonstrated very similar distributions for all comparisons and all methods (Spearman ⍴ = 0.99, P < 2.2 × 10 -308 ). The number of UMIs and genes were consistently higher in false singlets and lower in false doublets for most demultiplexing methods except some smaller pool sizes (Additional file 2 : Fig S9a and Additional file 2 : Fig S10a; Additional file 1 : Table S4 and Additional file 1 : Table S5). The number of UMIs and genes was consistently higher in droplets falsely classified as singlets by the doublet detecting methods than the correctly identified droplets (Additional file 2 : Fig S9b and Additional file 2 : Fig S10b; Additional file 1 : Table S4 and Additional file 1 : Table S5). However, there was less consistency in the number of UMIs and genes detected in false singlets than correctly classified droplets between the different doublet detecting methods (Additional file 2 : Fig S9b and Additional file 2 : Fig S10b; Additional file 1 : Table S4 and Additional file 1 : Table S5).

The ribosomal percentage of the droplets falsely classified as singlets or doublets is similar to the correctly classified droplets for most methods—although they are statistically different for larger pool sizes (Additional file 2 : Fig S11a and Additional file 1 : Table S6). However, the false doublets classified by some demultiplexing methods ( Demuxalot , Demuxalot (refined), Demuxlet , ScSplit , Souporcell , and Vireo ) demonstrated higher ribosomal percentages. Some doublet detecting methods ( ScDblFinder , ScDblFinder with known doublets and Solo) demonstrated higher ribosomal percentages for the false doublets while other demonstrated lower ribosomal percentages ( DoubletDecon , DoubletDetection , and DoubletFinder ; Additional file 2 : Fig S11b and Additional file 1 : Table S6).

Like the ribosomal percentage, the mitochondrial percentage in false singlets is also relatively similar to correctly classified droplets for both demultiplexing (Additional file 2 : Fig S12a and Additional file 1 : Table S7) and doublet detecting methods (Additional file 2 : Fig S12b). The mitochondrial percentage for false doublets is statistically lower than the correctly classified droplets for a few larger pools for Freemuxlet , ScSplit , and Souporcell . The doublet detecting method Solo also demonstrates a small but significant decrease in mitochondrial percentage in the false doublets compared to the correctly annotated droplets. However, other doublet detecting methods including DoubletFinder and the larger pools of most other methods demonstrated a significant increase in mitochondrial percent in the false doublets compared to the correctly annotated droplets (Additional file 2 : Fig S12b).

Overall, these results demonstrate a strong relationship between the number of genes and UMIs and limited influence of ribosomal or mitochondrial percentage in a droplet and false classification, suggesting that the number of genes and UMIs can significantly bias singlet and doublet classification by demultiplexing and doublet detecting methods.

Ambient RNA, number of reads per cell, and uneven pooling impact method performance

To further quantify the variables that impact the performance of each method, we simulated four conditions that could occur with single-cell RNA-seq experiments: (1) decreased number of reads (reduced 50%), (2) increased ambient RNA (10%, 20% and 50%), (3) increased mitochondrial RNA (5%, 10% and 25%) and 4) uneven donor pooling from single donor spiking (0.5 or 0.75 proportion of pool from one donor). We chose these scenarios because they are common technical effects that can occur.

We observed a consistent decrease in the demultiplexing method performance when the number of reads were decreased by 50% but the degree of the effect varied for each method and was larger in pools containing more multiplexed donors (Additional file 2 : Fig S13a and Additional file 1 : Table S8). Decreasing the number of reads did not have a detectable impact on the performance of the doublet detecting methods.

Simulating additional ambient RNA (10%, 20%, or 50%) decreased the performance of all the demultiplexing methods (Additional file 2 : Fig S13b and Additional file 1 : Table S9) but some were unimpacted in pools that had 16 or fewer individuals multiplexed ( Souporcell and Vireo ). The performance of some of the doublet detecting methods were impacted by the ambient RNA but the performance of most methods did not decrease. Scrublet and ScDblFinder were the doublet detecting methods most impacted by ambient RNA but only in pools with at least 32 multiplexed donors (Additional file 2 : Fig S13b and Additional file 1 : Table S9).

Increased mitochondrial percent did not impact the performance of demultiplexing or doublet detecting methods (Additional file 2 : Fig S13c and Additional file 1 : Table S10).

We also tested whether experimental designs that pooling uneven proportions of donors would alter performance. We tested scenarios where either half the pool was composed of a single donor (0.5 spiked donor proportion) or where three quarters of the pool was composed of a single donor. This experimental design significantly reduced the demultiplexing method performance (Additional file 2 : Fig S13d and Additional file 1 : Table S11) with the smallest influence on Freemuxlet . The performance of most of the doublet detecting methods were unimpacted except for DoubletDetection that demonstrated significant decreases in performance in pools where at least 16 donors were multiplexed. Intriguingly, the performance of Solo increased with the spiked donor pools when the pools consisted of 16 donors or less.

Our results demonstrate significant differences in overall performance between different demultiplexing and doublet detecting methods. We further noticed some differences in the use of the methods. Therefore, we have accumulated these results and each method’s unique characteristics and benefits in a heatmap for visual interpretation (Fig. 4 ).

Assessment of each of the demultiplexing and doublet detecting methods. Assessments of a variety of metrics for each of the demultiplexing (top) and doublet detecting (bottom) methods

Framework for improving singlet classifications via method combinations

After identifying the demultiplexing and doublet detecting methods that performed well individually, we next sought to test whether using intersectional combinations of multiple methods would enhance droplet classifications and provide a software platform— Demuxafy —capable of supporting the execution of these intersectional combinations.

We recognise that different experimental designs will be required for each project. As such, we considered this when testing combinations of methods. We considered multiple experiment designs and two different intersectional methods: (1) more than half had to classify a droplet as a singlet to be called a singlet and (2) at least half of the methods had to classify a droplet as a singlet to be called a singlet. Significantly, these two intersectional methods only differ when an even number of methods are being considered. For combinations that include demultiplexing methods, the individual called by the majority of the methods is the individual used for that droplet. When ties occur, the individual is considered ‘unassigned’.

Combining multiple doublet detecting methods improve doublet removal for non-multiplexed experimental designs

For the non-multiplexed experimental design, we considered all possible method combinations (Additional file 1 : Table S12). We identified important differences depending on the number of droplets captured and have provided recommendations accordingly. We identified that DoubletFinder , Scrublet , ScDblFinder and Scds is the ideal combination for balanced droplet calling when less than 2,000 droplets are captured. Scds and ScDblFinder or Scrublet , Scds and ScDblFinder is the best combination when 2,000–10,000 droplets are captured. Scds , Scrublet, ScDblFinder and DoubletDetection is the best combination when 10,000–20,000 droplets are captured and Scrublet , Scds , DoubletDetection and ScDblFinder . It is important to note that even a slight increase in the MCC significantly impacts the number of true singlets and true doublets classified with the degree of benefit highly dependent on the original method performance. The combined method increases the MCC compared to individual doublet detecting methods on average by 0.11 and up to 0.33—a significant improvement in the MCC ( t -test FDR < 0.05 for 95% of comparisons). For all combinations, the intersectional droplet method requires more than half of the methods to consider the droplet a singlet to classify it as a singlet (Fig. 5 ).

Recommended Method Combinations Dependent on Experimental Design. Method combinations are provided for different experimental designs, including those that are not multiplexed (left) and multiplexed (right), including experiments that have reference SNP genotypes available vs those that do not and finally, multiplexed experiments with different numbers of individuals multiplexed. The each bar represents either a single method (shown with the coloured icon above the bar) or a combination of methods (shown with the addition of the methods and an arrow indicating the bar). The proportion of true singlets, true doublets, false singlets and false doublets for each method or combination of methods is shown with the filled barplot and the MCC is shown with the black points overlaid on the barplot. MCC: Matthew’s Correlation Coefficient

Demuxafy performs better than Chord

Chord is an ensemble machine learning doublet detecting method that uses Scds and DoubletFinder to identify doublets. We compared Demuxafy using Scds and DoubletFinder to Chord and identified that Demuxafy outperformed Chord in pools that contained at least eight donors and was equivalent in pools that contained less than eight donors (Additional file 2 : Fig S14). This is because Chord classifies more droplets as false singlets and false doublets than Demuxafy . In addition, Chord failed to complete for two of the pools that contained 128 multiplexed donors.

Combining multiple demultiplexing and doublet detecting methods improve doublet removal for multiplexed experimental designs

For experiments where 16 or fewer individuals are multiplexed with reference SNP genotypes available, we considered all possible combinations between the demultiplexing and doublet detecting methods except ScDblFinder with known doublets due to its highly similar performance to ScDblFinder (Fig 3 ; Additional file 1 : Table S13). The best combinations are DoubletFinder , Scds , ScDblFinder , Vireo and Demuxalot (refined) (<~5 donors) and Scrublet , ScDblFinder , DoubletDetection , Dropulation and Demuxalot (refined) (Fig. 5 ). These intersectional methods increase the MCC compared to the individual methods ( t -test FDR < 0.05), generally resulting in increased true singlets and doublets compared to the individual methods. The improvement in MCC depends on every single method’s performance but, on average, increases by 0.22 and up to 0.71. For experiments where the reference SNP genotypes are unknown, the individuals multiplexed in the pool with 16 or fewer individuals multiplexed, DoubletFinder , ScDblFinder, Souporcell and Vireo (<~5 donors) and Scds , ScDblFinder , DoubletDetection , Souporcell and Vireo are the ideal methods (Fig. 5 ). These intersectional methods again significantly increase the MCC up to 0.87 compared to any of the individual techniques that could be used for this experimental design ( t -test FDR < 0.05 for 94.2% of comparisons). In both cases, singlets should only be called if more than half of the methods in the combination classify the droplet as a singlet.

Combining multiple demultiplexing methods improves doublet removal for large multiplexed experimental designs

For experiments that multiplex more than 16 individuals, we considered the combinations between all demultiplexing methods (Additional file 1 : Table S14) since only a small proportion of the doublets would be undetectable by demultiplexing methods (droplets that are homogenic; Fig 3 b). To balance doublet removal and maintain true singlets, we recommend the combination of Demuxalot (refined) and Dropulation . These method combinations significantly increase the MCC by, on average, 0.09 compared to all the individual methods ( t -test FDR < 0.05). This substantially increases true singlets and true doublets relative to the individual methods. If reference SNP genotypes are not available for the individuals multiplexed in the pools, Vireo performs the best (≥ 16 multiplexed individuals; Fig. 5 ). This is the only scenario in which executing a single method is advantageous to a combination of methods. This is likely due to the fact that most of the methods perform poorly for larger pool sizes (Fig. 3 c).

These results collectively demonstrate that, regardless of the experimental design, demultiplexing and doublet detecting approaches that intersect multiple methods significantly enhance droplet classification. This is consistent across different pool sizes and will improve singlet annotation.

Demuxafy improves doublet removal and improves usability

To make our intersectional approaches accessible to other researchers, we have developed Demuxafy ( https://demultiplexing-doublet-detecting-docs.readthedocs.io/en/latest/index.html ) - an easy-to-use software platform powered by Singularity. This platform provides the requirements and instructions to execute each demultiplexing and doublet detecting methods. In addition, Demuxafy provides wrapper scripts that simplify method execution and effectively summarise results. We also offer tools that help estimate expected numbers of doublets and provide method combination recommendations based on scRNA-seq pool characteristics. Demuxafy also combines the results from multiple different methods, provides classification combination summaries, and provides final integrated combination classifications based on the intersectional techniques selected by the user. The significant advantages of Demuxafy include a centralised location to execute each of these methods, simplified ways to combine methods with an intersectional approach, and summary tables and figures that enable practical interpretation of multiplexed datasets (Fig. 1 a).

Demultiplexing and doublet detecting methods have made large-scale scRNA-seq experiments achievable. However, many demultiplexing and doublet detecting methods have been developed in the recent past, and it is unclear how their performances compare. Further, the demultiplexing techniques best detect heterogenic doublets while doublet detecting methods identify heterotypic doublets. Therefore, we hypothesised that demultiplexing and doublet detecting methods would be complementary and be more effective at removing doublets than demultiplexing methods alone.

Indeed, we demonstrated the benefit of utilising a combination of demultiplexing and doublet detecting methods. The optimal intersectional combination of methods depends on the experimental design and capture characteristics. Our results suggest super loaded captures—where a high percentage of doublets is expected—will benefit from multiplexing. Further, when many donors are multiplexed (>16), doublet detecting is not required as there are few doublets that are homogenic and heterotypic.

We have provided different method combination recommendations based on the experimental design. This decision is highly dependent on the research question.

Conclusions

Overall, our results provide researchers with important demultiplexing and doublet detecting performance assessments and combinatorial recommendations. Our software platform, Demuxafy ( https://demultiplexing-doublet-detecting-docs.readthedocs.io/en/latest/index.html ), provides a simple implementation of our methods in any research lab around the world, providing cleaner scRNA-seq datasets and enhancing interpretation of results.

PBMC scRNA-seq data

Blood samples were collected and processed as described previously [ 17 ]. Briefly, mononuclear cells were isolated from whole blood samples and stored in liquid nitrogen until thawed for scRNA-seq capture. Equal numbers of cells from 12 to 16 samples were multiplexed per pool and single-cell suspensions were super loaded on a Chromium Single Cell Chip A (10x Genomics) to capture 20,000 droplets per pool. Single-cell libraries were processed per manufacturer instructions and the 10× Genomics Cell Ranger Single Cell Software Suite (v 2.2.0) was used to process the data and map it to GRCh38. Cellbender v0.1.0 was used to identify empty droplets. Almost all droplets reported by Cell Ranger were identified to contain cells by Cellbender (mean: 99.97%). The quality control metrics of each pool are demonstrated in Additional file 2 : Fig S15.

PBMC DNA SNP genotyping

SNP genotype data were prepared as described previously [ 17 ]. Briefly, DNA was extracted from blood with the QIAamp Blood Mini kit and genotyped on the Illumina Infinium Global Screening Array. SNP genotypes were processed with Plink and GCTA before imputing on the Michigan Imputation Server using Eagle v2.3 for phasing and Minimac3 for imputation based on the Haplotype Reference Consortium panel (HRCr1.1). SNP genotypes were then lifted to hg38 and filtered for > 1% minor allele frequency (MAF) and an R 2 > 0.3.

Fibroblast scRNA-seq data

The fibroblast scRNA-seq data has been described previously [ 18 ]. Briefly, human skin punch biopsies from donors over the age of 18 were cultured in DMEM high glucose supplemented with 10% fetal bovine serum (FBS), L-glutamine, 100 U/mL penicillin and 100 μg/mL (Thermo Fisher Scientific, USA).

For scRNA-seq, viable cells were flow sorted and single cell suspensions were loaded onto a 10× Genomics Single Cell 3’ Chip and were processed per 10× instructions and the Cell Ranger Single Cell Software Suite from 10× Genomics was used to process the sequencing data into transcript count tables as previously described [ 18 ]. Cellbender v0.1.0 was used to identify empty droplets. Almost all droplets reported by Cell Ranger were identified to contain cells by Cellbender (mean: 99.65%). The quality control metrics of each pool are demonstrated in Additional file 2 : Fig S16.

Fibroblast DNA SNP genotyping

The DNA SNP genotyping for fibroblast samples has been described previously [ 18 ]. Briefly, DNA from each donor was genotyped on an Infinium HumanCore-24 v1.1 BeadChip (Illumina). GenomeStudioTM V2.0 (Illumina), Plink and GenomeStudio were used to process the SNP genotypes. Eagle V2.3.5 was used to phase the SNPs and it was imputed with the Michigan Imputation server using minimac3 and the 1000 genome phase 3 reference panel as described previously [ 18 ].

Demultiplexing methods

All the demultiplexing methods were built and run from a singularity image.

Demuxalot [ 6 ] is a genotype reference-based single cell demultiplexing method. Demualot v0.2.0 was used in python v3.8.5 to annotate droplets. The likelihoods, posterior probabilities and most likely donor for each droplet were estimated using the Demuxalot Demultiplexer.predict_posteriors function. We also used Demuxalot Demultiplexer.learn_genotypes function to refine the genotypes before estimating the likelihoods, posterior probabilities and likely donor of each droplet with the refined genotypes as well.

The Popscle v0.1-beta suite [ 16 ] for population genomics in single cell data was used for Demuxlet and Freemuxlet demultiplexing methods. The popscle dsc-pileup function was used to create a pileup of variant calls at known genomic locations from aligned sequence reads in each droplet with default arguments.

Demuxlet [ 3 ] is a SNP genotype reference-based single cell demultiplexing method. Demuxlet was run with a genotype error coefficient of 1 and genotype error offset rate of 0.05 and the other default parameters using the popscle demuxlet command from Popscle (v0.1-beta).

Freemuxlet [ 16 ] is a SNP genotype reference-free single cell demultiplexing method. Freemuxlet was run with default parameters including the number of samples included in the pool using the popscle freemuxlet command from Popscle (v0.1-beta).

Dropulation

Dropulation [ 5 ] is a SNP genotype reference-based single cell demultiplexing method that is part of the Drop-seq software. Dropulation from Drop-seq v2.5.1 was implemented for this manuscript. In addition, the method for calling singlets and doublets was provided by the Dropulation developer and implemented in a custom R script available on Github and Zenodo (see “Availability of data and materials”).

ScSplit v1.0.7 [ 7 ] was downloaded from the ScSplit github and the recommended steps for data filtering quality control prior to running ScSplit were followed. Briefly, reads that had read quality lower than 10, were unmapped, were secondary alignments, did not pass filters, were optical PCR duplicates or were duplicate reads were removed. The resulting bam file was then sorted and indexed followed by freebayes to identify single nucleotide variants (SNVs) in the dataset. The resulting SNVs were filtered for quality scores greater than 30 and for variants present in the reference SNP genotype vcf. The resulting filtered bam and vcf files were used as input for the s cSplit count command with default settings to count the number of reference and alternative alleles in each droplet. Next the allele matrices were used to demultiplex the pool and assign cells to different clusters using the scSplit run command including the number of individuals ( -n ) option and all other options set to default. Finally, the individual genotypes were predicted for each cluster using the scSplit genotype command with default parameters.

Souporcell [ 4 ] is a SNP genotype reference-free single cell demultiplexing method. The Souporcell v1.0 singularity image was downloaded via instructions from the gihtub page. The Souporcell pipeline was run using the souporcell_pipeline.py script with default options and the option to include known variant locations ( --common_variants ).

Vireo [ 2 ] is a single cell demultiplexing method that can be used with reference SNP genotypes or without them. For this assessment, Vireo was used with reference SNP genotypes. Per Vireo recommendations, we used model 1 of the cellSNP [ 20 ] version 0.3.2 to make a pileup of SNPs for each droplet with the recommended options using the genotyped reference genotype file as the list of common known SNP and filtered with SNP locations that were covered by at least 20 UMIs and had at least 10% minor allele frequency across all droplets. Vireo version 0.4.2 was then used to demultiplex using reference SNP genotypes and indicating the number of individuals in the pools.

Doublet detecting methods

All doublet detecting methods were built and run from a singularity image.

DoubletDecon

DoubletDecon [ 9 ] is a transcription-based deconvolution method for identifying doublets. DoubletDecon version 1.1.6 analysis was run in R version 3.6.3. SCTransform [ 21 ] from Seurat [ 22 ] version 3.2.2 was used to preprocess the scRNA-seq data and then the Improved_Seurat_Pre_Process function was used to process the SCTransformed scRNA-seq data. Clusters were identified using Seurat function FindClusters with resolution 0.2 and 30 principal components (PCs). Then the Main_Doublet_Decon function was used to deconvolute doublets from singlets for six different rhops—0.6, 0.7, 0.8, 0.9, 1.0 and 1.1. We used a range of rhop values since the doublet annotation by DoubletDecon is dependent on the rhop parameter which is selected by the user. The rhop that resulted in the closest number of doublets to the expected number of doublets was selected on a per-pool basis and used for all subsequent analysis. Expected number of doublets were estimated with the following equation:

where N is the number of droplets captured and D is the number of expected doublets.

DoubletDetection

DoubletDetection [ 14 ] is a transcription-based method for identifying doublets. DoubletDetection version 2.5.2 analysis was run in python version 3.6.8. Droplets without any UMIs were removed before analysis with DoubletDetection . Then the doubletdetection.BoostClassifier function was run with 50 iterations with use_phenograph set to False and standard_scaling set to True. The predicted number of doublets per iteration was visualised across all iterations and any pool that did not converge after 50 iterations, it was run again with increasing numbers of iterations until they reached convergence.

DoubletFinder

DoubletFinder [ 10 ] is a transcription-based doublet detecting method. DoubletFinder version 2.0.3 was implemented in R version 3.6.3. First, droplets that were more than 3 median absolute deviations (mad) away from the median for mitochondrial per cent, ribosomal per cent, number of UMIs or number of genes were removed per developer recommendations. Then the data was normalised with SCTransform followed by cluster identification using FindClusters with resolution 0.3 and 30 principal components (PCs). Then, pKs were selected by the pK that resulted in the largest BC MVN as recommended by DoubletFinder. The pK vs BC MVN relationship was visually inspected for each pool to ensure an effective BC MVN was selected for each pool. Finally, the homotypic doublet proportions were calculated and the number of expected doublets with the highest doublet proportion were classified as doublets per the following equation:

ScDblFinder

ScDblFinder [ 11 ] is a transcription-based method for detecting doublets from scRNA-seq data. ScDblFinder 1.3.25 was implemented in R version 4.0.3. ScDblFinder was implemented with two sets of options. The first included implementation with the expected doublet rate as calculated by:

where N is the number of droplets captured and R is the expected doublet rate. The second condition included the same expected number of doublets and included the doublets that had already been identified by all the demultiplexing methods.

Scds [ 12 ] is a transcription-based doublet detecting method. Scds version 1.1.2 analysis was completed in R version 3.6.3. Scds was implemented with the cxds function and bcds functions with default options followed by the cxds_bcds_hybrid with estNdbl set to TRUE so that doublets will be estimated based on the values from the cxds and bcds functions.

Scrublet [ 13 ] is a transcription-based doublet detecting method for single-cell RNA-seq data. Scrublet was implemented in python version 3.6.3. Scrublet was implemented per developer recommendations with at least 3 counts per droplet, 3 cells expressing a given gene, 30 PCs and a doublet rate based on the following equation:

where N is the number of droplets captured and R is the expected doublet rate. Four different minimum number of variable gene percentiles: 80, 85, 90 and 95. Then, the best variable gene percentile was selected based on the distribution of the simulated doublet scores and the location of the doublet threshold selection. In the case that the selected threshold does not fall between a bimodal distribution, those pools were run again with a manual threshold set.

Solo [ 15 ] is a transcription-based method for detecting doublets in scRNA-seq data. Solo was implemented with default parameters and an expected number of doublets based on the following equation:

where N is the number of droplets captured and D is the number of expected doublets. Solo was additionally implemented in a second run for each pool with the doublets that were identified by all the demultiplexing methods as known doublets to initialize the model.

In silico pool generation

Cells that were identified as singlets by all methods were used to simulate pools. Ten pools containing 2, 4, 8, 16, 32, 64 and 128 individuals were simulated assuming a maximum 20% doublet rate as it is unlikely researchers would use a technology that has a higher doublet rate. The donors for each simulated pool were randomly selected using a custom R script which is available on Github and Zenodo (see ‘Availability of data and materials’). A separate bam for the cell barcodes for each donor was generated using the filterbarcodes function from the sinto package (v0.8.4). Then, the GenerateSyntheticDoublets function provided by the Drop-seq [ 5 ] package was used to simulate new pools containing droplets with known singlets and doublets.

Twenty-one total pools—three pools from each of the different simulated pool sizes (2, 4, 8, 16, 32, 64 and 128 individuals) —were used to simulate different experimental scenarios that may be more challenging for demultiplexing and doublet detecting methods. These include simulating higher ambient RNA, higher mitochondrial percent, decreased read coverage and imbalanced donor proportions as described subsequently.

High ambient RNA simulations

Ambient RNA was simulated by changing the barcodes and UMIs on a random selection of reads for 10, 20 or 50% of the total UMIs. This was executed with a custom R script that is available in Github and Zenodo (see ‘Availability of data and materials’).

High mitochondrial percent simulations

High mitochondrial percent simulations were produced by replacing reads in 5, 10 or 25% of the randomly selected cells with mitochondrial reads. The number of reads to replace was derived from a normal distribution with an average of 30 and a standard deviation of 3. This was executed with a custom R script available in Github and Zenodo (see ‘Availability of data and materials’).

Imbalanced donor simulations

We simulated pools that contained uneven proportions of the donors in the pools to identify if some methods are better at demultiplexing pools containing uneven proportions of each donor in the pool. We simulated pools where 50, 75 or 95% of the pool contained cells from a single donor and the remainder of the pool was even proportions of the remaining donors in the pool. This was executed with a custom R script available in Github and Zenodo (see ‘Availability of data and materials’).

Decrease read coverage simulations

Decreased read coverage of pools was simulated by down-sampling the reads by two-thirds of the original coverage.

Classification annotation

Demultiplexing methods classifications were considered correct if the droplet annotation (singlet or doublet) and the individual annotation was correct. If the droplet type was correct but the individual annotation was incorrect (i.e. classified as a singlet but annotated as the wrong individual), then the droplet was incorrectly classified.

Doublet detecting methods were considered to have correct classifications if the droplet annotation matched the known droplet type.

All downstream analyses were completed in R version 4.0.2.

Availability of data and materials

All data used in this manuscript is publicly available. The PBMC data is available on GEO (Accession: GSE196830) [ 23 ] as originally described in [ 17 ]. The fibroblast data is available on ArrayExpress (Accession Number: E-MTAB-10060) [ 24 ] and as originally described in [ 18 ]. The code used for the analyses in this manuscript are provided on Github ( https://github.com/powellgenomicslab/Demuxafy_manuscript/tree/v4 ) and Zenodo ( https://zenodo.org/records/10813452 ) under an MIT Open Source License [ 25 , 26 ]. Demuxafy is provided as a package with source code available on Github ( https://github.com/drneavin/Demultiplexing_Doublet_Detecting_Docs ) and instructions on ReadTheDocs ( https://demultiplexing-doublet-detecting-docs.readthedocs.io/en/latest/ ) under an MIT Open Source License [ 27 ]. Demuxafy is also available on Zenodo with the link https://zenodo.org/records/10870989 [ 28 ].

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Authors’ Twitter handles

Twitter handles: @drneavin (Drew Neavin), @thjimmylee (Jimmy Tsz Hang Lee), @marta_mele_m (Marta Melé)

Peer review information

Wenjing She was the primary editor of this article and managed its editorial process and peer review in collaboration with the rest of the editorial team.

Review history

The review history is available as Additional file 3 .

This work was funded by the National Health and Medical Research Council (NHMRC) Investigator grant (1175781), and funding from the Goodridge foundation. J.E.P is also supported by a fellowship from the Fok Foundation.

Author information

Authors and affiliations.

Garvan-Weizmann Centre for Cellular Genomics, Garvan Institute for Medical Research, Darlinghurst, NSW, Australia

Drew Neavin, Anne Senabouth, Himanshi Arora & Joseph E. Powell

Present address: Statewide Genomics at NSW Health Pathology, Sydney, NSW, Australia

Himanshi Arora

Wellcome Sanger Institute, Wellcome Genome Campus, Hinxton, UK

Jimmy Tsz Hang Lee

Life Sciences Department, Barcelona Supercomputing Center, Barcelona, Catalonia, Spain

Aida Ripoll-Cladellas & Marta Melé

Department of Genetics, University of Groningen, University Medical Center Groningen, Groningen, The Netherlands

Lude Franke

Spatial and Single Cell Systems Domain, Genome Institute of Singapore (GIS), Agency for Science, Technology and Research (A*STAR), Singapore, Republic of Singapore

Shyam Prabhakar

Population and Global Health, Lee Kong Chian School of Medicine, Nanyang Technological University, Singapore, Republic of Singapore

Cancer Science Institute of Singapore, National University of Singapore, Singapore, Republic of Singapore

Bakar Institute for Computational Health Sciences, University of California, San Francisco, CA, USA

Chun Jimmie Ye

Institute for Human Genetics, University of California, San Francisco, San Francisco, CA, USA

Division of Rheumatology, Department of Medicine, University of California, San Francisco, San Francisco, CA, USA

Chan Zuckerberg Biohub, San Francisco, CA, USA

Bioinformatics and Cellular Genomics, St Vincent’s Institute of Medical Research, Fitzroy, Australia

Davis J. McCarthy

Melbourne Integrative Genomics, School of BioSciences–School of Mathematics & Statistics, Faculty of Science, University of Melbourne, Melbourne, Australia

Present address: The Gene Lay Institute of Immunology and Inflammation, Brigham and Women’s Hospital and Harvard Medical School, Boston, MA, USA

Martin Hemberg

UNSW Cellular Genomics Futures Institute, University of New South Wales, Kensington, NSW, Australia

Joseph E. Powell

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Contributions.

DRN and JEP conceived the project idea and study design. JTHL, AR, LF, SP, CJY, DJM, MM and MH provided feedback on experimental design. DRN carried out analyses with support on coding from AS. JTHL and AR tested Demuxafy and provided feedback. DRN and JEP wrote the manuscript. All authors reviewed and provided feedback on the manuscript.

Corresponding authors

Correspondence to Drew Neavin or Joseph E. Powell .

Ethics declarations

Ethics approval and consent to participate.

Briefly, all work was approved by the Royal Hobart Hospital, the Hobart Eye Surgeons Clinic, Human Research Ethics Committees of the Royal Victorian Eye and Ear Hospital (11/1031), University of Melbourne (1545394) and University of Tasmania (H0014124) in accordance with the requirements of the National Health & Medical Research Council of Australia (NHMRC) and conformed with the Declaration of Helsinki [ 29 ].

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Competing interests

C.J.Y. is founder for and holds equity in DropPrint Genomics (now ImmunAI) and Survey Genomics, a Scientific Advisory Board member for and holds equity in Related Sciences and ImmunAI, a consultant for and holds equity in Maze Therapeutics, and a consultant for TReX Bio, HiBio, ImYoo, and Santa Ana. Additionally, C.J.Y is also newly an Innovation Investigator for the Arc Institute. C.J.Y. has received research support from Chan Zuckerberg Initiative, Chan Zuckerberg Biohub, Genentech, BioLegend, ScaleBio and Illumina.

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Neavin, D., Senabouth, A., Arora, H. et al. Demuxafy : improvement in droplet assignment by integrating multiple single-cell demultiplexing and doublet detection methods. Genome Biol 25 , 94 (2024). https://doi.org/10.1186/s13059-024-03224-8

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Assignment operators are used for assigning value to a variable. The left side operand of the assignment operator is a variable and right side operand of the assignment operator is a value. The value on the right side must be of the same data-type of the variable on the left side otherwise the compiler will raise an error.

Different types of assignment operators are shown below:

1. “=”: This is the simplest assignment operator. This operator is used to assign the value on the right to the variable on the left. Example:

2. “+=” : This operator is combination of ‘+’ and ‘=’ operators. This operator first adds the current value of the variable on left to the value on the right and then assigns the result to the variable on the left. Example:

If initially value stored in a is 5. Then (a += 6) = 11.

3. “-=” This operator is combination of ‘-‘ and ‘=’ operators. This operator first subtracts the value on the right from the current value of the variable on left and then assigns the result to the variable on the left. Example:

If initially value stored in a is 8. Then (a -= 6) = 2.

4. “*=” This operator is combination of ‘*’ and ‘=’ operators. This operator first multiplies the current value of the variable on left to the value on the right and then assigns the result to the variable on the left. Example:

If initially value stored in a is 5. Then (a *= 6) = 30.

5. “/=” This operator is combination of ‘/’ and ‘=’ operators. This operator first divides the current value of the variable on left by the value on the right and then assigns the result to the variable on the left. Example:

If initially value stored in a is 6. Then (a /= 2) = 3.

Below example illustrates the various Assignment Operators:

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Pirates 3B Ke'Bryan Hayes scratched with lower back tightness. Grandal starts rehab assignment

NEW YORK — Gold Glove third baseman Ke’Bryan Hayes was scratched from the Pittsburgh Pirates’ lineup Tuesday night against the New York Mets because of tightness in his lower back.

“It just kind of locked up on him a little bit trying to work out. He couldn’t get it really to release, so just out of the abundance of caution just going to make sure that we keep him down,” manager Derek Shelton said before his team's 3-1 loss.

Jared Triolo shifted from second base to third and moved from sixth in the batting order to Hayes’ spot at No. 3.

Oneil Cruz, who had been scheduled for a night off against left-hander Jose Quintana, was inserted at shortstop. Alika Williams moved from shortstop to second base.

Hayes is batting .277 with no homers and seven RBIs.

In other injury-related news, Yasmani Grandal (left foot plantar fasciitis) began a rehab assignment Tuesday night with Triple-A Indianapolis. The 35-year-old catcher went 0 for 2 with a walk and an RBI. He was expected to play four or five innings, Shelton said, but there’s still no timetable for his return.

“As we kind of build up, we’ll have to go from there,” Shelton said. “He really has had no spring training, so we’ve got to make sure we get a spring training aspect there.”

Grandal, a two-time All-Star, is waiting to make his Pirates debut. He signed a $2.5 million, one-year contract in mid-February after spending the previous four seasons with the Chicago White Sox.

“We’ve been able to do everything with him. He’s been catching. He’s been getting at-bats off the machine. But he hasn’t played in a game,” Shelton said. “So we just need to get the game in. We need to get running in. We need to get him on the bases as much as possible.”

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MLB Trade Rumors

Blue Jays Designate Mitch White For Assignment

By Darragh McDonald | April 16, 2024 at 3:20pm CDT

The Blue Jays announced today that right-handers Jordan Romano and Erik Swanson have been activated off the injured list. In corresponding moves, they optioned righty Nate Pearson to Triple-A and designated righty Mitch White for assignment.

Romano and Swanson were key pieces of the Toronto bullpen last year but they haven’t been able to contribute to the club thus far in 2024. During the spring, Romano had some inflammation in his elbow while Swanson had some tightness in his forearm, leading both to start the season on the injured list.

With those two unavailable, the club has turned to Yimi García and Chad Green for most of the high-leverage work, with those two filling in admirably. García has a 1.17 earned run average through seven appearances, with 11 strikeouts to go with just one walk. Green, meanwhile, has a 2.35 ERA in his seven appearances, with seven punchies and two walks.

Despite that strong work from those two, the Jays are surely glad to get Romano and Swanson back. Romano has emerged as the club’s closer over the past three years, which included saving 36 games in each of the past two campaigns while keeping his ERA under 3.00 in each. Swanson had 29 holds and four saves last year while posting a 2.97 ERA on the season. Those two, along with García and Green, give the Jays a strong four-headed bullpen mix to finish out games.

The health of that group has nudged White off the roster. Now 29, White was a second-round pick of the Dodgers back in 2016 and was considered by Baseball America to be the #69 prospect in the league in 2018. The Jays acquired him in a 2022 deadline deal alongside Alex De Jesus , with prospects Nick Frasso and Moises Brito going the other way.

At the time of that deal, White had thrown 105 2/3 big league innings with a 3.58 ERA, 22% strikeout rate and 8.3% walk rate. Unfortunately, the jersey swap corresponded with an immediate downturn in his results. White tossed 43 innings for the Jays in 2022 with a 7.74 ERA and 15.3% strikeout rate. There was a bit of bad luck in there, as his .368 batting average on balls in play and 54.3% strand rate were both on the unfortunate side, which is why his FIP was 3.76 in that time and his SIERA 4.70.

Luck or not, the poor results meant the Jays couldn’t guarantee a rotation spot to White going into 2023. At that time, four rotation spots were taken by Alek Manoah , Kevin Gausman , José Berríos and Chris Bassitt . White went into Spring Training battling Yusei Kikuchi for the final spot but dealt with some shoulder and elbow injuries and had to start the season on the IL. By the time he got back, there was no rotation spot for him and he worked a long relief role in the bullpen.

He didn’t take to that move, posting a 7.11 ERA in 10 outings before being designated for assignment. The 29 other clubs passed on the chance to grab him off waivers and he was sent outright to Triple-A. He got stretched out in Buffalo and finished the season in good form, with a 1.89 ERA over his last 33 1/3 innings, pairing a 31.4% strikeout rate in that time with a 10.2% walk rate.

The Jays added him back to the 40-man in November to prevent him from reaching minor league free agency, which put him in a similar spot to where he was a year prior, coming into spring out of options and battling for a spot. The Jays had to put Manoah on the IL this spring, which opened a rotation spot, but Bowden Francis beat White for that gig. Now that Yariel Rodríguez has also been stretched out and has seemingly bumped Francis from the rotation, White has been moved even further back. He has only made four long relief appearances this year but his uninspiring 5.40 ERA in those surely didn’t help him.

White has now been bumped off the roster yet again and the Jays will have one week to work out a trade or pass him through waivers. Since he cleared waivers last year, doing so again would give him the right to elect free agency. It’s possible he may garner interest based on his past results and strong finish at Triple-A last year. The fact that he’s out of options means that he needs an active roster spot somewhere, but he has less than three years of service time, meaning he can be controlled for three more seasons beyond this one.

A number of teams around the league are dealing with significant pitching injuries and it was less than a week ago that the Jays managed to flip Wes Parsons to the Guardians for international bonus pool space. Parsons is optionable but is more than two years older than White and doesn’t have the same past prospect pedigree.

33 Comments

12 hours ago

I guess maybe a team like the A’s or Marlins could put in a claim for white, but I assume he’ll remain in the jays system

11 hours ago

Name correlates well to the story about Mitch White.

10 hours ago

White is another overrated prospect from LAD, even the A’s should take a pass on him.

I got to hand it to LAD they surely make out when trading these overhyped prospects and getting good return so far Frasso may have higher upside in the end.

Although they have lost out with Busch but they had no where to place him.

5 hours ago

Like they did with Yordan Alvarez

4 hours ago

White was a lot better with LA than Toronto. I wonder why…

Why the hell would they option Pearson. He’s been nothing short of outstanding. The guy is finally putting it together and they ship him back to AAA

It’s called options. He’ll be back as soon as another injury happens

All other right handers in their pen have looked good, only the two lefties look horrible but they probably don’t want to go with just one lefty in the pen

6.1 innings of awesome after years of not so awesome. Who would you have lost out of the bullpen instead?

8 hours ago

Two Jays starters aren’t fully stretched out. They need a long man in the bullpen. Pearson is not it.

Damn the curse of having options for Pearson.

I recommended the Jays release Mitch White in Aug 2022 when he was acquired because with the BlueJays he is not a MLB level pitcher. What a waste of time and money. Let Mitch White go for good.

The Mitch White deal reminds me of trading star in the making Moreno for Varsho, which was also called a bad deal at that time too.

Pearson belongs in the bigs. He’ll be back. If they use him as a one inning guy Pearson can be a very good pitcher. Pearson needs a one inning per use managed and reduced workload to maintain performance excellence.

Atkins is bad, no one denies it.

Get creative and trade excess 2b to AA , for something we need like a good bat for the of Got clement ikf Biggio Schneider… atl could use a bat at 2b w Albies out for a while

Finally ,another atkins mistake dfa’d . Shakin’ My Head …..

Hasn’t been all bad with Atkins on the pitching side. Brought in guys like Matz Robbie ray stripling Kikuchi etc and got the most out of them. They let em go and they stink or hurt. Good call to go to Gausman instead of ray for the dough . Got Romano Jimmy Garcia for nothing as afterthoughts Built good pens last few years. It’s the offence he’s bad , both drafting and at the mlb level.

And where has that got us ??

They were in on Ray as well. He wanted too much though

3 hours ago

Im not a shatkins guy and doo want them both gon but you can deny that have made great decisions alomost with all there signing and every trade but 2. Even the trad for Grichuk for example sure he never lived up to want they hoped but thay nothing for him. Drafting bad or very inconveniently draft good player is this jays groups down fall

@Murphy NFLD

The Grichuks trade wasn’t all that good. The 5 year, 50M deal was nothing short of awful.

This is a premature move. With Gausman not Gausman’ing, Rodriguez having less than 5 IP of pro ball (no innings pitched last year at any league level) and Kikuchi pitching more than 5 innings only 10 times since the start of ’23, the Jays needed someone to give them innings. Even comparing Francis to White, the numbers are similar, but Francis has an option.

To put this in perspective, if the FO options Francis instead, then a week later a starter hits the IL, Francis can come back up to fill the spot. With this decision, Francis fills the spot and who becomes the long man?

Just poor roster management.

Perhaps they felt like it would not be beneficial for Bowden Francis to go down despite his numbers right now. Mitch White hasn’t had a good track record in the majors for quite some time.

I think they should trade Mitch White (if they can) for more international international signing bonus money…

7 hours ago

The need is to chuck in a guy to cover Rodriguez, Gausman, and maybe Kikuchi. Francis can come in for one of them for multiple innings. Richards can take one of the remaining 2. After that, you’re looking at leveraged arms coming in non leveraged spots. DFA’ing White was a move that was coming but it shouldn’t have been this early. In tennis terms, this is an unforced error.

2 hours ago

I have a feeling that they’ll be able to nab a similar arm off waivers in short order.

Too bad about Pearson but this made some sense. Neither LH bullpen arm is going anywhere, they need Bowden to eat innings, and the rest firmly have a place in the bullpen.

9 hours ago

They could have gotten creative by sending Francis down and putting White in his spot in the rotation. Would he have been worse? I’d argue that, based on Francis’ early season numbers, White may have been slightly better. But probably not enough to warrant such a move. Also, I don’t know if Francis has options left. If not, the Blue Jays probably value him much more than they do White.

@Baseball77

Francis has one option left.

White is better than nobody. His presence has been unnecessary since the horrendous trade happened.

What specific factors led to Mitch White’s designation for assignment, considering his past prospect pedigree and recent strong finish at Triple-A, and how does this decision align with the Blue Jays’ long-term pitching strategy?

I suspect the decision was made by throwing darts at the wall or playing beer pong. Applying logic, reason, or common sense fails.

6 hours ago

Management basically looked at the roster of pitchers and said to themselves ״who’s the worst “ came up with Mitch.

The white flag has been dfa’d

At this point, White is a quadruple-A player. Good enough to be fill in for an injury, but not good enough to stick. Out of options means he’ll be on the dfa carousel and shuttling among the many mlb franchises if he’s lucky. Otherwise, it’s Asia, Mexico, or some other line of work….

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Royals' Michael Massey: Continuing rehab assignment

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Royals manager Matt Quatraro said Monday that Massey (back) will travel with Triple-A Omaha this week as part of his rehab assignment, Anne Rogers of MLB.com reports.

Massey started the season on the 10-day IL after experiencing lower-back tightness late in spring training. Quatraro noted that he wants Massey to get in a few more games and at-bats to ensure the 26-year-old is 100 percent healthy before returning to the Royals. Massey has appeared in five games with Triple-A Omaha, slashing .409/.458/.636 with five doubles and six RBI over 24 plate appearances.

Royals' Michael Massey: Beginning assignment Friday

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