Introduction

In data analysis, especially when dealing with multiple samples or distributions, ensuring comparability and removing biases is crucial. One powerful technique for achieving this is quantile normalization. This method aligns the distributions of values across different samples, making them more similar in terms of their statistical properties.

What is Quantile Normalization?

Quantile normalization is a statistical method used to adjust the distributions of values in different datasets so that they have similar quantiles. This technique is particularly valuable when working with high-dimensional data, such as gene expression data or other omics datasets, where ensuring comparability across samples is essential.

Introducing quantile_normalize() in TidyDensity

The quantile_normalize() function is a new addition to the TidyDensity package, designed to simplify the process of quantile normalization within R. Let’s delve into how this function works and how you can integrate it into your data analysis pipeline.

Function Usage

The quantile_normalize() function takes a numeric matrix as input, where each column represents a sample. Here’s a breakdown of its usage:

quantile_normalize(.data, .return_tibble = FALSE)

• .data: A numeric matrix where each column corresponds to a sample that requires quantile normalization.
• .return_tibble: A logical value (default: FALSE) indicating whether the output should be returned as a tibble.

Understanding the Output

When you apply quantile_normalize() to your data, you receive a list object containing the following components:

1. Quantile-Normalized Matrix: A numeric matrix where each column has been quantile-normalized.
2. Row Means: The means of each row across the quantile-normalized matrix.
3. Sorted Data: The sorted values used during the quantile normalization process.
4. Ranked Indices: The indices of the sorted values.

How Quantile Normalization Works

The quantile_normalize() function performs quantile normalization through the following steps:

1. Sorting: Each column of the input matrix is sorted.
2. Row Mean Calculation: The mean of each row across the sorted columns is computed.
3. Normalization: Each column’s sorted values are replaced with the corresponding row means.
4. Unsorting: The columns are restored to their original order, ensuring that the quantile-normalized matrix maintains the same structure as the input.

Examples

Let’s demonstrate the usage of quantile_normalize() with a simple example:

# Load TidyDensity
library(TidyDensity)

# Create a sample matrix
set.seed(123)
data <- matrix(rnorm(50), ncol = 4)
head(data, 5)

            [,1]       [,2]       [,3]       [,4]
[1,] -0.56047565  0.1106827  0.8377870 -0.3804710
[2,] -0.23017749 -0.5558411  0.1533731 -0.6947070
[3,]  1.55870831  1.7869131 -1.1381369 -0.2079173
[4,]  0.07050839  0.4978505  1.2538149 -1.2653964
[5,]  0.12928774 -1.9666172  0.4264642  2.1689560

# Apply quantile normalization
result <- quantile_normalize(data)

# Access the quantile-normalized matrix
normalized_matrix <- result[["normalized_data"]]

# View the normalized matrix
head(normalized_matrix, 5)

            [,1]       [,2]        [,3]       [,4]
[1,] -0.65451945 -0.3180877  0.84500772 -0.6545195
[2,] -0.06327669  0.8450077  1.09078797 -0.9506544
[3,] -1.40880292 -0.5235134  0.33150422  0.0863713
[4,]  0.84500772  1.0907880  0.08637130  0.1991151
[5,] -0.31808774 -0.6545195 -0.06327669  0.3315042

Let’s now look at the rest of the output components:

head(result[["row_means"]], 5)

[1] -1.4088029 -0.9506544 -0.6545195 -0.5235134 -0.3180877

head(result[["duplicated_ranks"]], 5)

     [,1] [,2] [,3] [,4]
[1,]    9   13   13    7
[2,]   10   10   12   12
[3,]    2   11    2    9
[4,]   13    9    9    3
[5,]    7    1    1   11

head(result[["duplicated_rank_row_indicies"]], 5)

NULL

head(result[["duplicated_rank_data"]], 5)

            [,1]       [,2]      [,3]       [,4]
[1,] -0.23017749 -0.5558411 0.1533731 -0.6947070
[2,]  0.07050839  0.4978505 1.2538149 -1.2653964
[3,]  0.12928774 -1.9666172 0.4264642  2.1689560
[4,] -0.68685285 -0.2179749 0.8215811 -0.4666554
[5,] -0.44566197 -1.0260044 0.6886403  0.7799651

Now, lets take a look at the before and after quantile normalization summary:

as.data.frame(data) |>
  sapply(function(x) quantile(x, probs = seq(0, 1, 1/4)))

             V1         V2          V3          V4
0%   -1.2650612 -1.9666172 -1.13813694 -1.26539635
25%  -0.4456620 -1.0260044 -0.06191171 -0.56047565
50%   0.1292877 -0.5558411  0.55391765 -0.38047100
75%   0.4609162  0.1106827  0.83778704 -0.08336907
100%  1.7150650  1.7869131  1.25381492  2.16895597

as.data.frame(normalized_matrix) |>
  sapply(function(x) quantile(x, probs = seq(0, 1, 1/4)))

              V1          V2          V3          V4
0%   -1.40880292 -1.40880292 -1.40880292 -1.40880292
25%  -0.52351344 -0.52351344 -0.52351344 -0.52351344
50%  -0.06327669 -0.06327669 -0.06327669 -0.06327669
75%   0.33150422  0.33150422  0.33150422  0.33150422
100%  1.73118725  1.73118725  1.73118725  1.73118725

Now let’s use the .return_tibble argument to return the output as a tibble:

quantile_normalize(data, .return_tibble = TRUE)

$normalized_data
# A tibble: 13 × 4
        V1      V2      V3      V4
     <dbl>   <dbl>   <dbl>   <dbl>
 1 -0.655  -0.318   0.845  -0.655 
 2 -0.0633  0.845   1.09   -0.951 
 3 -1.41   -0.524   0.332   0.0864
 4  0.845   1.09    0.0864  0.199 
 5 -0.318  -0.655  -0.0633  0.332 
 6  1.73   -0.0633 -0.133  -0.133 
 7 -0.524  -0.133  -0.524  -0.524 
 8 -0.133   1.73    1.73    1.73  
 9  0.332   0.0864  0.199   1.09  
10  1.09   -0.951  -0.655  -0.318 
11 -0.951  -1.41   -0.318  -1.41  
12  0.199   0.199  -1.41    0.845 
13  0.0864  0.332  -0.951  -0.0633

$row_means
# A tibble: 13 × 1
     value
     <dbl>
 1 -1.41  
 2 -0.951 
 3 -0.655 
 4 -0.524 
 5 -0.318 
 6 -0.133 
 7 -0.0633
 8  0.0864
 9  0.199 
10  0.332 
11  0.845 
12  1.09  
13  1.73  

$duplicated_ranks
# A tibble: 6 × 4
     V1    V2    V3    V4
  <int> <int> <int> <int>
1     9    13    13     7
2    10    10    12    12
3     2    11     2     9
4    13     9     9     3
5     7     1     1    11
6     3     6     7     6

$duplicated_rank_row_indices
# A tibble: 6 × 1
  row_index
      <int>
1         2
2         4
3         5
4         9
5        10
6        12

$duplicated_rank_data
# A tibble: 6 × 4
       V1     V2      V3     V4
    <dbl>  <dbl>   <dbl>  <dbl>
1 -0.230  -0.556  0.153  -0.695
2  0.0705  0.498  1.25   -1.27 
3  0.129  -1.97   0.426   2.17 
4 -0.687  -0.218  0.822  -0.467
5 -0.446  -1.03   0.689   0.780
6  0.360  -0.625 -0.0619 -0.560

Conclusion

In summary, the quantile_normalize() function from the TidyDensity package offers a convenient and efficient way to perform quantile normalization on numeric matrices in R. By leveraging this function, you can enhance the comparability and statistical integrity of your data across multiple samples or distributions. Incorporate quantile_normalize() into your data preprocessing workflow to unlock deeper insights and more robust analyses.

To explore more functionalities of TidyDensity and leverage its capabilities for advanced data analysis tasks, check out the package documentation and experiment with different parameters and options provided by the quantile_normalize() function.