Last updated: 2023-10-12

Checks: 7 0

Knit directory: muse/

This reproducible R Markdown analysis was created with workflowr (version 1.7.0). The Checks tab describes the reproducibility checks that were applied when the results were created. The Past versions tab lists the development history.

R Markdown file: up-to-date

Great! Since the R Markdown file has been committed to the Git repository, you know the exact version of the code that produced these results.

Environment: empty

Great job! The global environment was empty. Objects defined in the global environment can affect the analysis in your R Markdown file in unknown ways. For reproduciblity it’s best to always run the code in an empty environment.

Seed: set.seed(20200712)

The command set.seed(20200712) was run prior to running the code in the R Markdown file. Setting a seed ensures that any results that rely on randomness, e.g. subsampling or permutations, are reproducible.

Session information: recorded

Great job! Recording the operating system, R version, and package versions is critical for reproducibility.

Cache: none

Nice! There were no cached chunks for this analysis, so you can be confident that you successfully produced the results during this run.

File paths: relative

Great job! Using relative paths to the files within your workflowr project makes it easier to run your code on other machines.

Repository version: 1ede478

Great! You are using Git for version control. Tracking code development and connecting the code version to the results is critical for reproducibility.

The results in this page were generated with repository version 1ede478. See the Past versions tab to see a history of the changes made to the R Markdown and HTML files.

Note that you need to be careful to ensure that all relevant files for the analysis have been committed to Git prior to generating the results (you can use wflow_publish or wflow_git_commit). workflowr only checks the R Markdown file, but you know if there are other scripts or data files that it depends on. Below is the status of the Git repository when the results were generated: 


Ignored files:
    Ignored:    .Rhistory
    Ignored:    .Rproj.user/
    Ignored:    r_packages_4.3.0/
    Ignored:    r_packages_4.3.1/

Untracked files:
    Untracked:  analysis/cell_ranger.Rmd
    Untracked:  analysis/complex_heatmap.Rmd
    Untracked:  analysis/sleuth.Rmd
    Untracked:  analysis/tss_xgboost.Rmd
    Untracked:  code/multiz100way/
    Untracked:  data/HG00702_SH089_CHSTrio.chr1.vcf.gz
    Untracked:  data/HG00702_SH089_CHSTrio.chr1.vcf.gz.tbi
    Untracked:  data/ncrna_NONCODE[v3.0].fasta.tar.gz
    Untracked:  data/ncrna_noncode_v3.fa
    Untracked:  data/netmhciipan.out.gz
    Untracked:  data/test
    Untracked:  export/davetang039sblog.WordPress.2023-06-30.xml
    Untracked:  export/output/
    Untracked:  women.json

Unstaged changes:
    Modified:   analysis/graph.Rmd
    Modified:   script/run_rstudio.sh

Note that any generated files, e.g. HTML, png, CSS, etc., are not included in this status report because it is ok for generated content to have uncommitted changes.

These are the previous versions of the repository in which changes were made to the R Markdown (analysis/qn.Rmd) and HTML (docs/qn.html) files. If you’ve configured a remote Git repository (see ?wflow_git_remote), click on the hyperlinks in the table below to view the files as they were in that past version.

File Version Author Date Message
Rmd 1ede478 Dave Tang 2023-10-12 Quantile normalisation

From Wikipedia:

In statistics, quantile normalization is a technique for making two distributions identical in statistical properties. To quantile normalize two or more distributions to each other, without a reference distribution, sort as before, then set to the average (usually, arithmetical mean) of the distributions. So the highest value in all cases becomes the mean of the highest values, the second highest value becomes the mean of the second highest values, and so on.

Wikipedia example

In this section, we will follow the Wikipedia example. First we will create the data set; we will use a list to store everything. The raw data contains four genes (A to D) and three samples (x to z).

eg1 <- list()

eg1$raw <- data.frame(
  x=c(5,2,3,4),
  y=c(4,1,4,2),
  z=c(3,4,6,8)
)

rownames(eg1$raw) <- toupper(letters[1:4])

eg1$raw
  x y z
A 5 4 3
B 2 1 4
C 3 4 6
D 4 2 8

The first step is to determine the gene ranks per sample; the lowest expressed gene will be the smallest number and the highest expressed gene will be largest number. This could be from 1 to 4 but if two genes have the same expression, i.e., tied, then the largest number won’t be 4.

eg1$rank <- apply(eg1$raw, 2, rank, ties.method="min")
eg1$rank
  x y z
A 4 3 1
B 1 1 2
C 2 3 3
D 3 2 4

Next, we sort the genes by their expression per sample. The lowest expressed gene will be on the top and the highest expressed gene will be on the bottom.

eg1$sorted <- data.frame(apply(eg1$raw, 2, sort))
eg1$sorted
  x y z
1 2 1 3
2 3 2 4
3 4 4 6
4 5 4 8

We will use the sorted gene expression matrix to calculate row means.

eg1$mean <- rowMeans(eg1$sorted)
eg1$mean
[1] 2.000000 3.000000 4.666667 5.666667

Finally, we will use the row means as the normalised values for each gene. Since every sample now uses the same set of means for the expression values, every sample will have the same statistical properties.

We will use the raw ranking to determine which mean value to use. For example, if gene A was the most highly expressed gene in sample x, then it will get the highest mean value. The code above simply uses the gene ranks as the index to the means.

eg1$norm <- apply(eg1$rank, 2, function(x) eg1$mean[x])
rownames(eg1$norm) <- toupper(letters[1:4])
eg1$norm
         x        y        z
A 5.666667 4.666667 2.000000
B 2.000000 2.000000 3.000000
C 3.000000 4.666667 4.666667
D 4.666667 3.000000 5.666667

We can combine all the code above (with some additional code for normalising with an average rank method) and save it into a quantile_normalisation function.

quantile_normalisation <- function(x, ties = "min"){
  my_list <- list()
  my_list$raw <- x
  my_list$rank <- apply(my_list$raw, 2, rank, ties.method=ties)
  my_list$sorted <- data.frame(apply(my_list$raw, 2, sort))
  my_list$mean <- rowMeans(my_list$sorted)
  if(ties == "average"){
    my_list$norm <- apply(my_list$rank, 2, function(i){
      sapply(i, function(j){
        if(j %% 1 == 0){
          my_list$mean[j]
        } else {
          (my_list$mean[floor(j)] + my_list$mean[ceiling(j)]) / 2
        }
      })
    })
  } else{
    my_list$norm <- apply(my_list$rank, 2, function(i) my_list$mean[i])
  }
  rownames(my_list$norm) <- rownames(my_list$raw)
  return(my_list)
}

my_df <- data.frame(
  one=c(5,2,3,4),
  two=c(4,1,4,2),
  three=c(3,4,6,8)
)
rownames(my_df) <- toupper(letters[1:4])

quantile_normalisation(my_df)$norm
       one      two    three
A 5.666667 4.666667 2.000000
B 2.000000 2.000000 3.000000
C 3.000000 4.666667 4.666667
D 4.666667 3.000000 5.666667

PH525x example

The data used for this example was from edX course called PH525x but it does not seem to be available anymore; it is probably called something else now. I’ll use this example because the process of quantile normalisation is nicely summarised in the figure below (the figure was from the bird app but I don’t want to link to it):

eg2 <- list()
eg2$raw <- matrix(
  data = c(2,4,4,5,5,14,4,7,4,8,6,9,3,8,5,8,3,9,3,5),
  nrow = 5,
  byrow = TRUE,
  dimnames = list(toupper(LETTERS[1:5]), letters[23:26])
)

eg2$raw
  w  x y z
A 2  4 4 5
B 5 14 4 7
C 4  8 6 9
D 3  8 5 8
E 3  9 3 5

Order values within each sample (or column).

eg2$rank <- apply(eg2$raw, 2, rank, ties.method="min")
eg2$sorted <- apply(eg2$raw, 2, sort)
eg2$sorted
     w  x y z
[1,] 2  4 3 5
[2,] 3  8 4 5
[3,] 3  8 4 7
[4,] 4  9 5 8
[5,] 5 14 6 9

Average across rows.

eg2$mean <- rowMeans(eg2$sorted)
eg2$mean
[1] 3.5 5.0 5.5 6.5 8.5

Re-order averaged values in original order.

eg2$norm <- apply(eg2$rank, 2, function(i) eg2$mean[i])
eg2$norm
       w   x   y   z
[1,] 3.5 3.5 5.0 3.5
[2,] 8.5 8.5 5.0 5.5
[3,] 6.5 5.0 8.5 8.5
[4,] 5.0 5.0 6.5 6.5
[5,] 5.0 6.5 3.5 3.5

Notice that my values are slightly different from those in the figure; this is because I used the minimum value for rank() when there is a tie.

In the rank() function, there are several methods for dealing with ties:

  1. “average”
  2. “first”
  3. “last”
  4. “random”
  5. “max”
  6. “min”

Here’s the documentation for the different methods.

If all components are different (and no NAs), the ranks are well defined, with values in seq_along(x). With some values equal (called “ties”), the argument ties.method determines the result at the corresponding indices. The “first” method results in a permutation with increasing values at each index set of ties, and analogously “last” with decreasing values. The “random” method puts these in random order whereas the default, “average”, replaces them by their mean, and “max” and “min” replaces them by their maximum and minimum respectively, the latter being the typical sports ranking.

The values in the PH525x example were calculated using a “first” approach. We can specify this in our quantile_normalisation function, and now we have the same normalised values.

quantile_normalisation(eg2$raw, ties = "first")$norm
    w   x   y   z
A 3.5 3.5 5.0 3.5
B 8.5 8.5 5.5 5.5
C 6.5 5.0 8.5 8.5
D 5.0 5.5 6.5 6.5
E 5.5 6.5 3.5 5.0

But those with a keen eye will notice that this is also different from the figure. This is to do with the ordering for the expression values that are tied and this is not consistent in the figure. If we go with a “first” approach then the ordering should be from gene A to E. If gene A and E are tied, then gene A will be ranked ahead of E because it comes first. If gene C and D are tied, then gene C is ranked ahead of D. This is what is performed with the rank function.

eg2$raw
  w  x y z
A 2  4 4 5
B 5 14 4 7
C 4  8 6 9
D 3  8 5 8
E 3  9 3 5
quantile_normalisation(eg2$raw, ties = "first")$rank
  w x y z
A 1 1 2 1
B 5 5 3 3
C 4 2 5 5
D 2 3 4 4
E 3 4 1 2

In the figure, gene D and E in sample w are ranked in a first manner and get the mean values in this order; so is gene C and D in sample x, and gene B and C in sample y. But this is not the case for gene A and E in sample z; gene A should get the 3.5 value and gene E should get the 5.0 value, if we want to be consistent.

Bioconductor

The preprocessCore package on Bioconductor already has a function for quantile normalisation called normalize.quantiles. Install the package, if necessary. Note that if I try to install the package the usual way, I get the following error:

Error in normalize.quantiles(mat) : 
  ERROR; return code from pthread_create() is 22

This is a known issue and I can use the function if I install preprocessCore as per these instructions.

if (!require("BiocManager", quietly = TRUE))
    install.packages("BiocManager")

BiocManager::install("preprocessCore", configure.args = c(preprocessCore = "--disable-threading"), force= TRUE, update=TRUE, type = "source")

The normalize.quantiles function takes a matrix as input.

library(preprocessCore)

normalize.quantiles(eg2$raw)
     [,1] [,2] [,3] [,4]
[1,] 3.50 3.50 5.25 4.25
[2,] 8.50 8.50 5.25 5.50
[3,] 6.50 5.25 8.50 8.50
[4,] 5.25 5.25 6.50 6.50
[5,] 5.25 6.50 3.50 4.25

The normalize.quantiles function uses the average method for ties, which we can reproduce with our function.

quantile_normalisation(eg2$raw, ties = "average")$norm
     w    x    y    z
A 3.50 3.50 5.25 4.25
B 8.50 8.50 5.25 5.50
C 6.50 5.25 8.50 8.50
D 5.25 5.25 6.50 6.50
E 5.25 6.50 3.50 4.25

Summary

Quantile normalisation uses the ranked expression values, so it’s robust to outliers. The ranked expression values are used to calculate a set of expression values (the means) that can be used in every sample, which are then substituted back based on the ranking, thus making every sample have the same statistical properties.

The normalisation method is easy to implement (without optimisation). However, there are many different implementations. The Wikipedia example uses a minimum approach; the example by Rafael Irizarry uses a first approach; and the preprocessCore package uses an average approach.

I don’t think changing the manner that ties are treated will significantly impact the results, especially when there are over 60,000 genes. To me, the average approach seems like the best way to deal with ties.

If you want to perform quantile normalisation, I suggest that you use the normalize.quantiles function from the preprocessCore package. If you use it for your work, you should cite:

A comparison of normalization methods for high density oligonucleotide array data based on variance and bias.

Do not use my quantile_normalisation function except for learning how the method works.


sessionInfo()
R version 4.3.1 (2023-06-16)
Platform: x86_64-pc-linux-gnu (64-bit)
Running under: Ubuntu 22.04.3 LTS

Matrix products: default
BLAS:   /usr/lib/x86_64-linux-gnu/openblas-pthread/libblas.so.3 
LAPACK: /usr/lib/x86_64-linux-gnu/openblas-pthread/libopenblasp-r0.3.20.so;  LAPACK version 3.10.0

locale:
 [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C              
 [3] LC_TIME=en_US.UTF-8        LC_COLLATE=en_US.UTF-8    
 [5] LC_MONETARY=en_US.UTF-8    LC_MESSAGES=en_US.UTF-8   
 [7] LC_PAPER=en_US.UTF-8       LC_NAME=C                 
 [9] LC_ADDRESS=C               LC_TELEPHONE=C            
[11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C       

time zone: Etc/UTC
tzcode source: system (glibc)

attached base packages:
[1] stats     graphics  grDevices utils     datasets  methods   base     

other attached packages:
[1] preprocessCore_1.62.1 workflowr_1.7.0      

loaded via a namespace (and not attached):
 [1] jsonlite_1.8.7    compiler_4.3.1    promises_1.2.1    Rcpp_1.0.11      
 [5] stringr_1.5.0     git2r_0.32.0      callr_3.7.3       later_1.3.1      
 [9] jquerylib_0.1.4   yaml_2.3.7        fastmap_1.1.1     R6_2.5.1         
[13] knitr_1.43        tibble_3.2.1      rprojroot_2.0.3   bslib_0.5.0      
[17] pillar_1.9.0      rlang_1.1.1       utf8_1.2.3        cachem_1.0.8     
[21] stringi_1.7.12    httpuv_1.6.11     xfun_0.40         getPass_0.2-2    
[25] fs_1.6.3          sass_0.4.7        cli_3.6.1         magrittr_2.0.3   
[29] ps_1.7.5          digest_0.6.33     processx_3.8.2    rstudioapi_0.15.0
[33] lifecycle_1.0.3   vctrs_0.6.3       evaluate_0.21     glue_1.6.2       
[37] whisker_0.4.1     fansi_1.0.4       rmarkdown_2.23    httr_1.4.6       
[41] tools_4.3.1       pkgconfig_2.0.3   htmltools_0.5.6