EB3I n1 2025 scRNAseq
-
PRE-PROCESSING (I)
-
Load a count matrix, empty droplets & ambient RNA filtering

1 PREAMBLE

1.1 Purpose of this session

This file describes the different steps to perform the first part of the single cell RNAseq data analysis training course for the EB3I n1 2025, covering these steps :

  • Load a single cell raw counts matrix

  • Remove barcodes from empty droplets

  • Estimate and remove ambient RNA contamination (“soup”)

1.2 An important notice

  • These early (but mandatory) steps of the analysis are not covered by the renowned, higher-level analysis frameworks like Seurat nor scran/scater.

  • Thus, we will write/use a certain amount of lines of code, some of which of a higher complexity for R beginners.

  • Newcomer, do not fear !

    • While you are welcome to try writing some by yourself, depending on your R knowledge and self-confidence in it …

    • … you do not have to know how these code work …

    • … thus you should not feel ashamed copy-pasting the pre-written code hereafter …

    • … but in both cases, please try your best understanding what these code do, and even more, why !

    • In this purpose, you may use whatever help you can by searching.

1.3 What to run ?

  • This training presentation, written in Rmarkdown, contains many chunks ( = code blocks)

  • You do not have to run them all !

  • Chunks follow a color code :

1.3.1 Chunks you ARE expected to run :

# chunk_run

## Example of chunk to run
getwd()
Show output 
[1] "/shared/projects/2538_eb3i_n1_2025/atelier_scrnaseq/RMD/Preproc.1"


1.3.2 Chunks you ARE NOT expected to run

Hopefully, we will demo them. You may run them by yourself, young padawan, but if you get late or worst, break something, you’ll be the one to blame !

# chunk_demo

## Example of chunk to NOT run
utils::sessionInfo()
Show output 
R version 4.4.1 (2024-06-14)
Platform: x86_64-conda-linux-gnu
Running under: Ubuntu 22.04.5 LTS

Matrix products: default
BLAS/LAPACK: /shared/ifbstor1/software/miniconda/envs/r-4.4.1/lib/libopenblasp-r0.3.29.so;  LAPACK version 3.12.0

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: Europe/Paris
tzcode source: system (glibc)

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

loaded via a namespace (and not attached):
 [1] digest_0.6.37     R6_2.6.1          bookdown_0.39     fastmap_1.2.0    
 [5] xfun_0.52         cachem_1.1.0      knitr_1.50        htmltools_0.5.8.1
 [9] rmarkdown_2.29    lifecycle_1.0.4   cli_3.6.5         sass_0.4.10      
[13] jquerylib_0.1.4   compiler_4.4.1    rstudioapi_0.17.1 tools_4.4.1      
[17] evaluate_1.0.3    bslib_0.9.0       rmdformats_1.0.4  yaml_2.3.10      
[21] jsonlite_2.0.0    rlang_1.1.6


1.3.3 Questions

Some questions we would like you to answer during the training course. Please be fair and do not take a look at the answer before we tell you so :)

# chunk_question

## What's the Answer to Life ? The Universe ? Everything ??


1.3.4 Answers

They usually follow questions…

# chunk_answer

cat("The Answer to the Great Question... is ... 
Forty-two. Six by nine : forty-two. That's it. 
That's all there is. (I always thought something was 
fundamentally wrong with the universe.)")
Show output 
The Answer to the Great Question... is ... 
Forty-two. Six by nine : forty-two. That's it. 
That's all there is. (I always thought something was 
fundamentally wrong with the universe.)


1.3.5 Additional exercises / questions

These are beyond the scope of the training course, but you may play it if you feel bored

# chunk_beyond

## Example of chunk to play with but beyond the scope
getwd()
Show output 
[1] "/shared/projects/2538_eb3i_n1_2025/atelier_scrnaseq/RMD/Preproc.1"


2 Start Rstudio

3 Warm-up

  • We now set common parameters as new variables, once and for all for this session :
# setparam


## Set your project name
# WARNING : Do not just copy-paste this ! It's MY project name ! Put YOURS !!
project_name <- "ebaii_sc_teachers"


## Control if the project_name exists on the cluster
cat('PATH CHECK : ', dir.exists(paste0('/shared/projects/', project_name)))
Show output 
PATH CHECK :  TRUE
## Seed for the RNG
my_seed <- 1337L

## Empty droplets max p-value
max_p <- 1E-03

4 Prepare the data structure

  • During the current and remaining training sessions, we will work on different data objects, some retrieved from external resources, some we will create.

  • In order to reuse these for further steps, or other sessions, we need to store them, thus create a directory structure on the cluster.

  • We will create this structure in your project folder (the one you registered at the IFB Core, in which you stored your own data for the tutoring).

  • To ensure that you will always use the same structure for your work, the code hereafter has been writen so that you just have to define a single variable that corresponds to your project name.

    • In MY case, as a SC training teacher, it is ebaii_sc_teachers.

4.1 Main directory

  • Create the training course directory
# maindir

## Prepare the path in a "TD_dir" variable
TD_dir <- paste0("/shared/projects/", project_name, "/SC_TD")

## Create the root directory
dir.create(path = TD_dir, recursive = TRUE)

## Print the root directory on-screen
print(TD_dir)
[1] "/shared/projects/ebaii_sc_teachers/SC_TD"

4.2 Current session

# sessiondir

## Create the session (Preproc.1) directory
session_dir <- paste0(TD_dir, "/01_Preproc.1")
dir.create(path = session_dir, recursive = TRUE)

## Print the session directory on-screen
print(session_dir)
[1] "/shared/projects/ebaii_sc_teachers/SC_TD/01_Preproc.1"

4.3 Input data directory

# indir

## Create the INPUT data directory
input_dir <- paste0(session_dir, "/DATA")
dir.create(path = input_dir, recursive = TRUE)

## Print the input directory on-screen
print(input_dir)
[1] "/shared/projects/ebaii_sc_teachers/SC_TD/01_Preproc.1/DATA"

4.4 Output results directory

# outdir

## Create the OUTPUT data directory
output_dir <- paste0(session_dir, "/RESULTS")
dir.create(path = output_dir, recursive = TRUE)

## Print the output directory on-screen
print(output_dir)
[1] "/shared/projects/ebaii_sc_teachers/SC_TD/01_Preproc.1/RESULTS"



Note : Actually, for this session we won’t export any result, so creating this RESULTS folder was just for practice purpose.


Now, the we have everything needed to :

  • load data (input_dir)

  • save the results we will generate (output_dir).

5 Additional resources

However, some upcoming steps require a bit complicated methods that are not fun to write, nor copy-paste multiple times. So, we will now prepare code functions that will ease these steps. After executing these functions code, we may call (invoke) them when needed.

5.1 Functions

5.1.1 SoupX helper function

This function was written to ease the use of SoupX on a count matrix It was tested on SoupX=1.6.2 with igraph=1.5.1, Seurat=4.4.0,5.1.0

Parameters :

  • scmat_filt : (sparse)matrix corresponding to an empty droplets -filtered count matrix

  • scmat_raw : (sparse)matrix corresponding to an NON- empty droplets -filtered count matrix

  • soupQuantile, contaminationRange : see ?SoupX::autoEstCont

  • contaminationRange : range of the expected soup proportion

  • soupRange : estimate soup fraction from features with total reads comprised in this range of counts (only used when scmat_raw != NULL)

  • return_object : if TRUE, return the “unsouped” count matrix ; if FALSE, only perform soup estimation and return the estimated proportion value (rho)

  • doPlot : Perform the SoupX estimation plot (rho distribution)

# soupx_func

SoupX_auto <- function(scmat_filt = NULL, scmat_raw = NULL, soupQuantile = 0.9,
                       contaminationRange = c(.01, .8), soupRange = c(0,100),
                       return_object = FALSE, doPlot = FALSE) {
  
  ## Checks
  if(is.null(scmat_filt)) stop('A filtered count matrix is required !')
  
  if(is.null(scmat_raw)) message('No unfiltered raw counts matrix provided. Estimation will be based on filtered matrix only.')
  
  ## If no raw matrix
  if (is.null(scmat_raw)) {
    spChanRaw <- SoupX::SoupChannel(
      tod = scmat_filt, 
      toc = scmat_filt, 
      calcSoupProfile = FALSE)
    sc_rowsum <- sparseMatrixStats::rowSums2(scmat_filt)
    spProf <- data.frame(
      row.names = rownames(scmat_filt), 
      est = sc_rowsum/sum(scmat_filt), 
      counts = sc_rowsum)
    spChan <- SoupX::setSoupProfile(spChanRaw, spProf)
  } else {
    spChan <- SoupX::SoupChannel(
      tod = scmat_raw, 
      toc = scmat_filt, 
      calcSoupProfile = FALSE)
    if (min(spChan$nDropUMIs) > max(soupRange)) stop(
      'Minimum found counts per barcode is : ', 
      min(spChan$nDropUMIs), 
      ', which is smaller than the upper bound of soupRange ! Please increase soupRange max !')
    spChan <- SoupX::estimateSoup(sc = spChan, soupRange = soupRange)
  }
  ## Display Top 20 contributing genes
  if (!return_object) {
    cat('\nSoup-contributing features (Top 20) :\n')
    print(knitr::kable(head(
      spChan$soupProfile[order(spChan$soupProfile$est, decreasing = TRUE), ], 
      n = 20)))
  }
  ## Quick clustering needed
  spClust <- scran::quickCluster(scmat_filt, method = "igraph")
  ## Adding clusters to the SoupChannel object
  spChan <- SoupX::setClusters(sc = spChan, clusters = spClust)
  ## Estimating soup
  sX <- SoupX::autoEstCont(sc = spChan, doPlot = doPlot, tfidfMin = 1, 
                           soupQuantile = soupQuantile, maxMarkers = 100, 
                           contaminationRange = contaminationRange, 
                           rhoMaxFDR = .2, priorRho = .05, priorRhoStdDev = .1, 
                           forceAccept = FALSE)
  
  ## Removing soup (adjusting counts)
  if(return_object) {
    cat('Counts BEFORE SoupX : ', sum(scmat_filt), '\n')
    scmat_soupx <- SoupX::adjustCounts(
      sX, method = 'subtraction', roundToInt = TRUE, 
      tol = .001, pCut = .01)
    cat('Counts AFTER SoupX : ', sum(scmat_soupx), '\n')
    rm(scmat_filt)
    return(scmat_soupx)
  } else return(sX$fit$rhoEst)
}


6 Load a 10X Cell Ranger raw count matrix

6.1 The dataset

  • The data we will work on is hosted on Zenodo. To ease its retrieval, we can define its ID as a variable

    # zid

## This is actually to help me updating this training without hassle
zen_id <- '14034221'

## This is the path to the current EB3I backup
sessionid <- '2538_eb3i_n1_2025'

  • Here will we train ourselves to load into R a single cell RNAseq data produced by 10X Genomics’ software Cell Ranger.

  • We will work with a public dataset provided by the manufacturer, that consists in ~ 10,000 PBMC (peripheral bone marrow cells) from a human donor (downsampled to 1/10th, for smaller computation time).

  • The experiment was performed with the 3’ capture kit v3.

  • The analysis was performed with Cell Ranger v3, mapping on the GRCh38-2020-A manufacturer reference.

  • The data consists into the typical 10x 3-files structure, hosted in a Zenodo respository (Id : 14034221)

    • barcodes.tsv.gz : the list of putative droplet barcodes
    • features.tsv.gz : the list of features (genes)
    • matrix.mtx.gz : the feature x barcode expression count matrix

6.2 Download data

  • The relatively complex code chunk hereafter has been written to :

    • let you download the data files from :

      • Zenodo by default

      • if backup = TRUE, a local backup instead (in case we loose the internet connection)

    • if the requested data are already available locally :

      • load this from the local source

      • if force = TRUE a new download is forced

# dlzen10x

### Named files (will be used later on !)
mtx_file <- "matrix.mtx.gz"
features_file <- "features.tsv.gz"
barcodes_file <- "barcodes.tsv.gz"
summary_file <- "pbmc_10k_v3_summary.html"

## Filename(s) to retrieve
toget_files <- c(mtx_file,
                 features_file,
                 barcodes_file,
                 summary_file)

## Folder to store retrieved files
local_folder <- input_dir

## Use local backup ?
backup <- FALSE
if(backup) message("Using local backup !")

## Force download ?
force <- FALSE
if(force) message("Forcing (re)download !")

### Define remote folder
remote_folder <- if (backup) paste("/shared/projects/", sessionid, "/atelier_scrnaseq/TD/BACKUP/10X/") else paste0("https://zenodo.org/records/", zen_id, "/files/")

### Reconstruct the input paths
remote_path <- paste0(remote_folder, "/", toget_files)

### Reconstruct the output paths
local_path <- paste0(local_folder, "/", toget_files)

## Retrieve files (if they don't exist), in loop
for (tg in seq_along(toget_files)) {
  ## If the file does not locally exist
  if (!file.exists(local_path[tg]) | force) {
    ## Retrieve data
    if(backup) {
      file.copy(from = remote_path[tg],
                to = local_path[tg])
    } else {
      download.file(url = remote_path[tg], 
                    destfile = local_path[tg])
    }
    ## Check if downloaded files exist locally
    if(file.exists(local_path[tg])) message("\tOK")
  } else message(paste0(toget_files[tg], " already downloaded !"))
}
matrix.mtx.gz already downloaded !
features.tsv.gz already downloaded !
barcodes.tsv.gz already downloaded !
pbmc_10k_v3_summary.html already downloaded !

6.3 Load into R

  • Load the 10X data files into R :

    # q_r10X

How ?
    • A good starter

    Show output

     
    https://lmddgtfy.net/?q=load%2010x%20data%20in%20R
    • Back to your local help ressource
    # a_r10X2

## Reading the function help page
?Seurat::Read10X



    We can now apply it to our data :

    # load10x

## Loading unfiltered 10X matrix
scmat <- Seurat::Read10X(data.dir = input_dir)


  • However, we don’t know what the loaded object actually looks like in R…

  • Questions :

    • Do you know a way to …
    # q_whatis

... know what IS the type of object that was created ?


    # a_whatis

## To know the type of an R object : methods::is()
?methods::is
    # gois

## Get the type
methods::is(scmat)

    Show output

     
    [1] "dgCMatrix"     "CsparseMatrix" "dsparseMatrix" "generalMatrix"
[5] "AnyMatrix"     "V3Matrix"      "dMatrix"       "sparseMatrix" 
[9] "Matrix"       



    # q_struc

... observe the STRucture of the resulting object ?


    # a_struc

## To get a basic structure description : utils::str()
?utils::str
    # go_str

## Get the basic structure
utils::str(scmat)

    Show output

     
    Formal class 'dgCMatrix' [package "Matrix"] with 6 slots
  ..@ i       : int [1:1718404] 7922 18295 23126 24791 31764 32866 13690 17058 2511 33306 ...
  ..@ p       : int [1:147457] 0 0 0 0 0 0 6 7 8 8 ...
  ..@ Dim     : int [1:2] 33694 147456
  ..@ Dimnames:List of 2
  .. ..$ : chr [1:33694] "RP11-34P13.3" "FAM138A" "OR4F5" "RP11-34P13.7" ...
  .. ..$ : chr [1:147456] "TCCCGATAGCCTTGAT-1" "AGATCTGAGACTGTAA-1" "GCTTGAAGTGCGAAAC-1" "ATCATGGGTATTCGTG-1" ...
  ..@ x       : num [1:1718404] 1 1 1 1 1 1 1 1 1 1 ...
  ..@ factors : list()


    Not that meaningful, though… At least we know we have a matrix, so one can get its dimensions !

    # scmat_dim

## Dimensions of a >1D object
dim(scmat)

    Show output

     
    [1]  33694 147456


6.4 Features cleanup

Seurat does not like at all underscores (_) in feature names …

We will have to :

  • control if our dataset contains some ? Here, we will use the base::grep() function that looks in character strings x if they contain a provided set of characters as a pattern, and returns their position (when value = FALSE, default) or value itself (value = TRUE)

    # us_check1

## Check feature names with underscore(s)
base::grep(pattern = '_', 
     x = rownames(scmat), 
     value = TRUE)

    Show output

     
     [1] "RP11-442N24__B.1" "RP11-99J16__A.2"  "RP11-59D5__B.2"   "RP11-445L13__B.3"
 [5] "RP11-544L8__B.4"  "XXyac-YX65C7_A.2" "XXyac-YX65C7_A.3" "RP11-524D16__A.3"
 [9] "XX-DJ76P10__A.2"  "RP11-1157N2__B.2" "RP1-213J1P__B.1"  "RP1-213J1P__B.2" 
[13] "RP4-633O19__A.1"  "RP4-754E20__A.5"  "CTA-280A3__B.2"


  • if so, clean it ! Here we will use the base::gsub() function that looks for a pattern in a character string x and substitutes it with a replacement character string.

    # us_clean

## Clean features : replacing underscores by dashes
rownames(scmat) <- base::gsub(
  pattern = "_", 
  replacement = "-", 
  x = rownames(scmat))

  • then, control the efficiency of our cleaning (with a new use of base::grep)

    # us_check2

## Post-cleanup control
base::grep(pattern = "_", 
           x = rownames(scmat), 
           value = TRUE)

    Show output

     
    character(0)


7 Empty droplets

  • While a large part of our barcodes contain no count at all, barcodes with at least one total count may not mandatorily correspond to true cells, due to ambient RNA.

  • Here we want to identify empty droplets, then remove them from the matrix.

7.1 Detection

  • To detect the margins of “true” cells versus empty droplets, we will rely on the so-called double-kneeplot : a plot of UMI counts per barcode, in function of their increasingly ordered rank.


  • To draw such plot, we need to rank the barcodes thanks to their total counts.

7.1.1 Ranking barcodes

  • We will use the DropletUtils package

    # h_barcodeRanks

## Read the function help page
?DropletUtils::barcodeRanks()
    # bcranks

## Generate the rank statistics
bc_rank <- DropletUtils::barcodeRanks(scmat)

  • Check the ranking of barcodes, according to their counts

    # bcrank_top

## Show a summary of the ranks (ordered for display convenience)
print(bc_rank[order(bc_rank$rank),])
    DataFrame with 147456 rows and 3 columns
                        rank     total    fitted
                   <numeric> <integer> <numeric>
ATCATGGCAGCCAATT-1         1     21195        NA
GGGCATCGTAGCTTGT-1         2     18193        NA
AGTGGGATCTTAACCT-1         3     16966        NA
GTCGGGTGTGCAGTAG-1         4     16383        NA
TTAGGCAAGCCGCCTA-1         5     15406        NA
...                      ...       ...       ...
TTGAACGTCAGAGGTG-1    100998         0        NA
ACGATGTAGTTGTCGT-1    100998         0        NA
GTCAAGTTCAGTTGAC-1    100998         0        NA
TCGCGAGGTTGCGCAC-1    100998         0        NA
TATCTCAGTTACCAGT-1    100998         0        NA


7.1.2 Kneeplot

Now, we can draw our kneeplot :

# kneeplot1

## Plot
graphics::plot(x = bc_rank$rank, 
               y = bc_rank$total + 1, 
               log = "xy", 
               xlab = "Barcode rank", 
               ylab = "Total UMIs", 
               col = "black", 
               pch = ".", 
               cex = 5, 
               main = "Kneeplot")


  • Question :

    # q_knee

Looking at the kneeplot, can you roughly predict how many barcodes will be 
kept as cells (ie, as non-empty droplets) ?


    # a_knee

## . The "cliff" in the kneeplot is at a 
##  rank value a bit below ~ 1,000.

Then, we can use these ranks to identify “true” cells :

# h_emptyDrops

## Read the function help page
?DropletUtils::emptyDrops

The function we will use relies on random number generation, so we will fix the RNG seed, for reproducibility

# emptydrops1

## Set the RNG seed
set.seed(my_seed)

## Identify empty droplets
bc_rank2 <- DropletUtils::emptyDrops(scmat)

## Whats is the structure of the returned output ?
str(bc_rank2)
Show output 
Formal class 'DFrame' [package "S4Vectors"] with 6 slots
  ..@ rownames       : chr [1:147456] "TCCCGATAGCCTTGAT-1" "AGATCTGAGACTGTAA-1" "GCTTGAAGTGCGAAAC-1" "ATCATGGGTATTCGTG-1" ...
  ..@ nrows          : int 147456
  ..@ elementType    : chr "ANY"
  ..@ elementMetadata: NULL
  ..@ metadata       :List of 5
  .. ..$ lower  : num 100
  .. ..$ niters : num 10000
  .. ..$ ambient: num [1:16738, 1] 8.43e-07 3.38e-05 9.91e-06 8.43e-07 8.43e-07 ...
  .. .. ..- attr(*, "dimnames")=List of 2
  .. .. .. ..$ : chr [1:16738] "RP11-34P13.7" "FO538757.2" "AP006222.2" "RP4-669L17.10" ...
  .. .. .. ..$ : NULL
  .. ..$ alpha  : num 4171
  .. ..$ retain : num 2611
  ..@ listData       :List of 5
  .. ..$ Total  : int [1:147456] 0 0 0 0 0 6 1 1 0 0 ...
  .. ..$ LogProb: num [1:147456] NA NA NA NA NA NA NA NA NA NA ...
  .. ..$ PValue : num [1:147456] NA NA NA NA NA NA NA NA NA NA ...
  .. ..$ Limited: logi [1:147456] NA NA NA NA NA NA ...
  .. ..$ FDR    : num [1:147456] NA NA NA NA NA NA NA NA NA NA ...

We can show a summary of the ordered barcodes :

# emptysmry

## Show a summary of the qualified ranks (ordered for display convenience)
print(bc_rank2[order(bc_rank2$Total, decreasing = TRUE),])
Show output 
DataFrame with 147456 rows and 5 columns
                       Total   LogProb    PValue   Limited       FDR
                   <integer> <numeric> <numeric> <logical> <numeric>
ATCATGGCAGCCAATT-1     21195  -13101.3 9.999e-05      TRUE         0
GGGCATCGTAGCTTGT-1     18193  -12055.5 9.999e-05      TRUE         0
AGTGGGATCTTAACCT-1     16966  -11594.8 9.999e-05      TRUE         0
GTCGGGTGTGCAGTAG-1     16383  -11994.6 9.999e-05      TRUE         0
TTAGGCAAGCCGCCTA-1     15406  -10687.9 9.999e-05      TRUE         0
...                      ...       ...       ...       ...       ...
TTGAACGTCAGAGGTG-1         0        NA        NA        NA        NA
ACGATGTAGTTGTCGT-1         0        NA        NA        NA        NA
GTCAAGTTCAGTTGAC-1         0        NA        NA        NA        NA
TCGCGAGGTTGCGCAC-1         0        NA        NA        NA        NA
TATCTCAGTTACCAGT-1         0        NA        NA        NA        NA


7.1.3 Selection

Barcodes considered as empty droplets have no p-value (FDR = NA)

# bcr2_na

## Creating a validation column (empty droplets have NA as p-value)
bc_rank2$VALID <- !is.na(bc_rank2$FDR) 

## Quantify "real cells"
table(bc_rank2$VALID)
Show output 

 FALSE   TRUE 
146397   1059


We can control the stringency by setting a FDR-adjusted p-value threshold

# valid_fdr

## Recall the max p-value we set as a variable early on
max_p
Show output 
[1] 0.001
## We've set the max p-value to 1E-03
bc_rank2$VALID[bc_rank2$FDR >= max_p] <- FALSE

## Control
table(bc_rank2$VALID)
Show output 

 FALSE   TRUE 
146581    875


Now, we can display our true cells on the kneeplot :

# kneeplot2

## Plot with highlight of the selected barcodes (red)
graphics::plot(x = bc_rank$rank, 
               y = bc_rank$total+1, 
               log = "xy", 
               xlab = "Barcode rank", 
               ylab = "Total UMIs", 
               col = ifelse(bc_rank2$VALID, "red", "black"), 
               pch = ".", 
               cex = ifelse(bc_rank2$VALID, 7, 3), 
               main = paste0("Kneeplot (", 
                             length(which(bc_rank2$VALID)), 
                             " barcodes kept)"))
Show plot


  • Question :

    # q_kneered

Is there something unexpected for the retained "TRUE" cells (red) in this kneeplot ?


    # a_kneered

## . You may observe that the selection of "true" cells (red)
##   does not strictly correspond to a "cut" in the kneeplot
##   descending curve.
##
## . This is because emptyDrops does not only "follow" the curve
##   to determine a threshold corresponding to a "minimal count" 
##   value to consider a barcode as a cell, but also performs a 
##   statistical analysis for each barcode, considering how much 
##   its expression profile ressembles the one of other cells, 
##   even with a very different (lower) global level of expression.



7.2 Removal

We just need to restrict our count matrix to “true” cells.

  • How many barcodes do we have at the moment ?

    # scdim3

dim(scmat)

    Show output

     
    [1]  33694 147456


  • Filter empty droplets out :

    # edfilter

## Restrict to valid barcodes
scmat_cells <- scmat[, bc_rank2$VALID]

## Control
dim(scmat_cells)

    Show output

     
    [1] 33694   875


8 Ambient RNA

  • The counts measured in this matrix of (now considered “true”) single cells do not reflect the measurement of these cells expression only

  • Due to other over-lysed / dying cells in the emulsion, we also measured fraction of ambient RNA that adds up to the “real” expression

  • We may estimate it by observing counts existing at a minimal level across cells that greatly differ in their expression profile

  • Just in case, due to the fun tool name we will use, SoupX, I’ll write down “soup” as an alias for “ambient RNA”, but both terms have the same meaning.


8.1 Estimate the “soup”

SoupX can estimate the rate and composition of ambient RNA, in several ways :

  • Manual mode : providing a list of features expected to reflect the soup (genes expressed at high levels in a majority of the expected cell types)

  • Automatic modes, using the empty droplets-filtered raw count matrix …

    • … along with the unfiltered matrix (ie, containing all quantified droplets). This is the most efficient mode.

    • … solely : this is less efficient, but useful when the unfiltered matrix is not available.

# h_SoupX_auto

## Reading the SoupX package main help page
?SoupX::SoupX

Let’s run SoupX, just asking for an estimation of the “soup” proportion :

# soupxrhoE

## TIP : In case of error :
# install.packages("irlba", type="source", force=TRUE)

## SoupX for ambient fraction estimation
soup_frac <- SoupX_auto(
  scmat_filt = scmat_cells, 
  scmat_raw = scmat)
Show output 

Soup-contributing features (Top 20) :


|       |       est| counts|
|:------|---------:|------:|
|MALAT1 | 0.0321703|  17473|
|B2M    | 0.0194002|  10537|
|TMSB4X | 0.0165850|   9008|
|EEF1A1 | 0.0129267|   7021|
|RPL21  | 0.0103251|   5608|
|RPS27  | 0.0100269|   5446|
|RPL13  | 0.0091542|   4972|
|RPL13A | 0.0087252|   4739|
|RPL10  | 0.0082852|   4500|
|RPLP1  | 0.0079225|   4303|
|RPL34  | 0.0078801|   4280|
|RPS12  | 0.0076481|   4154|
|RPS6   | 0.0075211|   4085|
|RPS18  | 0.0074530|   4048|
|RPS27A | 0.0073093|   3970|
|RPS2   | 0.0072965|   3963|
|RPS3A  | 0.0072909|   3960|
|RPL32  | 0.0072744|   3951|
|RPL41  | 0.0067865|   3686|
|RPL11  | 0.0063446|   3446|


NOTE : We can observe MALAT1 as the top first gene (as well as multiple riboprotein-coding genes).


What’s the estimated fraction of soup in our counts ?

# soupxfrac

cat("Soup fraction : ", soup_frac)
Show output 
Soup fraction :  0.054


  • Question :

    # q_souprate

Should we try to remove such an amount of ambient RNA ?


    # a_souprate

The best way to know is to try it out =D


8.2 Remove the “soup”

SoupX can remove the soup (subtract counts for identified soup genes to all barcodes/cells) using different methods :

  • Automatically (safer)

  • Specifying a fraction of counts to remove.

    • Pros :

      • By removing X % of reads :

        • if soup was effectively present (at a rate <= X), this will mostly affect soup genes first.

        • If not, it will decrease counts globally, thus have negligible effect in differences between cells / cell types.

    • Cons :

      • Counts are already rare in droplet-based single cell technologies… Removing them “by default” makes them even more rare…

      • If actual soup rate is higher than the defined rate X to remove, this mode will remain uneffective…

# soupxrm

## Run SoupX in "removal" mode
scmat_unsoup <- SoupX_auto(
  scmat_filt = scmat_cells, 
  scmat_raw = scmat, 
  return_object = TRUE)
Show output 
Counts BEFORE SoupX :  3766216
Show output 
Counts AFTER SoupX :  3563040


8.3 Effect of the “soup” removal

  • Now, we want to characterize the effect of the removal of the ambient RNA, by comparing the pre- and post- soupX matrices.

  • An easy way is to describe, then visualize such an effect.



8.3.1 Describe and visualize

8.3.1.1 Before SoupX (DEMO)

We can describe our count matrix thanks to some useful metrics :

  • Total counts
# descBS_totcount

## All counts
base::sum(scmat_cells)
Show output 
[1] 3766216
  • Sparsity (fraction of 0s)
# descBS_sparsity

## Compute sparsity (number of matrix values at 0 / cross-product of matrix dim)
base::sum(sparseMatrixStats::colCounts(
  x = scmat_cells, value = 0)
  ) / base::prod(dim(scmat_cells))
Show output 
[1] 0.9614275
  • Distribution of counts in cells
# descBS_nCount

## Sums up counts for each cell
base::summary(sparseMatrixStats::colSums2(x = scmat_cells, na.rm = TRUE))
Show output 
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
    116    3069    3957    4304    5014   21195
  • Distribution of expressed genes in cells
# descBS_nFeature

## All genes minus 0-count genes
base::summary(nrow(scmat_cells) - sparseMatrixStats::colCounts(
  x = scmat_cells, 
  value = 0, 
  na.rm = TRUE))
Show output 
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
     89    1052    1242    1300    1484    3752

8.3.1.2 After SoupX

We can describe our count matrix thanks to some useful metrics :

  • Total counts
# descAS_totcount

## All counts
base::sum(scmat_unsoup)
Show output 
[1] 3563040
  • Sparsity (fraction of 0s)
# descAS_sparsity

## Compute sparsity (number of matrix values at 0 / cross-product of matrix dim)
base::sum(sparseMatrixStats::colCounts(
  x = scmat_unsoup, value = 0)
  ) / base::prod(dim(scmat_unsoup))
Show output 
[1] 0.9635223
  • Distribution of counts in cells
# descAS_nCount

## Sums up counts for each cell
base::summary(sparseMatrixStats::colSums2(x = scmat_unsoup, na.rm = TRUE))
Show output 
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
    113    2912    3735    4072    4754   19797
  • Distribution of expressed genes in cells
# descAS_nFeature

## Compute sparsity (number of matrix values at 0 / cross-product of matrix dim)
base::summary(nrow(scmat_cells) - sparseMatrixStats::colCounts(
  x = scmat_unsoup, 
  value = 0, 
  na.rm = TRUE))
Show output 
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
     88    1002    1175    1229    1400    3435



  • Questions :

    # q_descdiff

Can you describe the difference (especially for counts and sparsity) ?

Was it expected ?


    # a_descdiff

## . Total counts, max value, counts per cell and number of
##   expressed features per cell, all have decreased.
##
## . The sparsity level has slightly increased (removing the soup
##   obliterated all counts for some features in some cells).
##
## . All of this is absolutely expected.


What are the features most affected by the soup removal ?

# soupfeatures

## Compute the fraction matrix (PRE / POST)
##  NOTE : we may have 0 counts in the divider, 
##  so we increment both matrix by +1 !
cont_frac <- (scmat_cells + 1) / (scmat_unsoup + 1)

## Fraction of counts decreased by soup removal, per feature (ordered)
feat_frac <- sort(sparseMatrixStats::rowMeans2(cont_frac), decreasing = TRUE)

### Display Top 5 features
utils::head(feat_frac, decreasing = TRUE)
Show output 
     LYZ   S100A9   S100A8  HLA-DRA     CD74     CST3 
1.478310 1.453317 1.384649 1.343360 1.279586 1.204137 
## Removing objects to free some RAM
rm(cont_frac, feat_frac)
# qsoupfeat

Does something about these genes strike you ?

Hint : fraction or difference ?

# soupfeaturesdiff

## Compute the DIFFERENCE matrix (PRE / POST)
##  NOTE : we may have 0 counts in the divider, 
##  so we increment both matrix by +1 !
cont_dif <- scmat_cells - scmat_unsoup

## Fraction of counts decreased by soup removal, per feature (ordered)
feat_dif <- sort(sparseMatrixStats::rowMeans2(cont_dif), decreasing = TRUE)

### Display Top 5 features
utils::head(feat_dif, decreasing = TRUE)
Show output 
  MALAT1      B2M   TMSB4X   EEF1A1    RPL21    RPS27 
7.533714 4.528000 3.885714 3.060571 2.435429 2.372571 
## Removing objects to free some RAM
rm(cont_dif, feat_dif)


8.3.2 Plot the “top 1” feature

We want to visualize the expression of the “top” soup gene, LYZ, in our dataset.

To do so, we will generate a projection of the data into a reduced (2-D) mathematical space. This is done by using a series of R function from the Seurat package, that we won’t describe here and now : understanding these commands below is not the scope of our current session, but it will be during the next few days.

Actually, these commands perform all the steps we’ll see in the next few days, in a row, but with default values (thus, almost certainly inadequate to our current data).

In consequence, please just consider the output as a way to quickly and dirtily visualize the data : the depicted topology should be considered as very rough and mostly imperfect.

8.3.2.1 Before SoupX

# bsoupx

## Roll process, using a temporary "sobj_presoupx" Seurat object
sobj_presoupx <- Seurat::CreateSeuratObject(counts = scmat_cells, project = "PBMC10K_BeforeSoupX")
sobj_presoupx <- Seurat::NormalizeData(object = sobj_presoupx, verbose = FALSE)
sobj_presoupx <- Seurat::ScaleData(object = sobj_presoupx, verbose = FALSE)
sobj_presoupx <- Seurat::FindVariableFeatures(object = sobj_presoupx, verbose = FALSE)
sobj_presoupx <- Seurat::RunPCA(object = sobj_presoupx, npcs = 21, verbose = FALSE)
sobj_presoupx <- Seurat::RunUMAP(object = sobj_presoupx, dims = c(1:20), verbose = FALSE)

## Top1 feature plot
fp_pre <- Seurat::FeaturePlot(object = sobj_presoupx, features = 'LYZ') + Seurat::DarkTheme()
print(fp_pre)
Show plot


8.3.2.2 After SoupX

# asoupx

## Roll process, using a temporary "sobj_postsoupx" Seurat object
sobj_postsoupx <- Seurat::CreateSeuratObject(counts = scmat_unsoup, project = "PBMC10K_AfterSoupX")
sobj_postsoupx <- Seurat::NormalizeData(object = sobj_postsoupx, verbose = FALSE)
sobj_postsoupx <- Seurat::ScaleData(object = sobj_postsoupx, verbose = FALSE)
sobj_postsoupx <- Seurat::FindVariableFeatures(object = sobj_postsoupx, verbose = FALSE)
sobj_postsoupx <- Seurat::RunPCA(object = sobj_postsoupx, npcs = 21, verbose = FALSE)
sobj_postsoupx <- Seurat::RunUMAP(object = sobj_postsoupx, dims = c(1:20), verbose = FALSE)

## Top1 feature plot
fp_post <- Seurat::FeaturePlot(object = sobj_postsoupx, features = 'LYZ') + Seurat::DarkTheme()
print(fp_post)
Show plot


Merging plots for ease of use :

# soupx_umaps

## Using the patchwork package to merge plots (and ggplot2 to add titles)
patchwork::wrap_plots(
  list(
    fp_pre & ggplot2::ggtitle(label = "LYZ before SoupX"), 
    fp_post & ggplot2::ggtitle(label = "LYZ after SoupX")), 
  nrow = 1)
Show plot


  • Question

    # q_umapsoupx

Can you compare the two plots :
  - for the LYZ expression across the 2-D space ?
  - about the space topologies ?
  - for the LYZ expression globally ?


    # a_umapsoupx

## . The level of expression measured in cells with smaller expression
##   before SoupX has gone to almost none after : the ambient expression
##   of this gene has efficiently been removed.
##
## . Hopefully, the cluster(s) with higher expression remain(s) high.
##
## . The expression scale upper bound after SoupX is higher than before ?! 
##   But we REMOVED some counts ?! While being counter-intuitive, this is
##   a positive consequence of the ambient removal on the scaling of
##   barcodes expression (see later). 
##
## . The range of both X and Y axes have increased, depicting a better
##   separation of / distance between some of the clusters.
##
## . The global topology of clusters remains close (but not identical).
## 
## . Clusters compacity has increased.


8.4 Conclusion

  • Removing ambient RNA has a positive effect :

    • on the quality of the expression level measurements

    • on the observed topology

    • … despite an estimation of only ~5% !

  • Actually, ambient RNA level and composition is (one of) the major source(s) of the batch effect bias that may alter the integration of different samples.





Beyond :

  1. One can check if the “unsouped” (ie, post-SoupX) matrix retain some soup ?. How would you do it ?

    # b_soupx2

## SoupX for ambient fraction estimation
soup2_frac <- SoupX_auto(
  scmat_filt = scmat_unsoup, 
  scmat_raw = scmat)

## Run SoupX in "removal" mode
scmat_unsoup2 <- SoupX_auto(
  scmat_filt = scmat_unsoup, 
  scmat_raw = scmat, 
  return_object = TRUE)

  2. Save the sobj_presoupx R object on disk.

    # b_save_answer1

## A single object : use RDS format with saveRDS()
saveRDS(object = sobj_presoupx, file = paste0(output_dir, "/sobj_presoupx.RDS"))

  3. Save both the sobj_presoupx and sobj_postsoupx objects in a single archive on disk.

    # b_save_answer2

## Multiple objects : use Rdata format with save()
save(list = c(sobj_presoupx, sobj_postsoupx), file = paste0(output_dir, "/sobjects.RDA"))

  4. Same as 1. but use the bzip2 compression algorithm

    # b_save_answer3

## Compress with bzip2
saveRDS(object = sobj, file = paste0(output_dir, "/sobj.RDS"), compress = "bzip2")





9 Rsession

For reproducibility and context, it is recommended to include in your RMarkdown the list of loaded packages and their version.

# rsession

utils::sessionInfo()
Show output 
R version 4.4.1 (2024-06-14)
Platform: x86_64-conda-linux-gnu
Running under: Ubuntu 22.04.5 LTS

Matrix products: default
BLAS/LAPACK: /shared/ifbstor1/software/miniconda/envs/r-4.4.1/lib/libopenblasp-r0.3.29.so;  LAPACK version 3.12.0

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: Europe/Paris
tzcode source: system (glibc)

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

loaded via a namespace (and not attached):
  [1] RcppAnnoy_0.0.22            splines_4.4.1              
  [3] later_1.4.2                 tibble_3.2.1               
  [5] R.oo_1.27.0                 polyclip_1.10-7            
  [7] fastDummies_1.7.5           lifecycle_1.0.4            
  [9] edgeR_4.2.2                 globals_0.18.0             
 [11] lattice_0.22-6              MASS_7.3-65                
 [13] magrittr_2.0.3              limma_3.60.6               
 [15] plotly_4.10.4               sass_0.4.10                
 [17] rmarkdown_2.29              jquerylib_0.1.4            
 [19] yaml_2.3.10                 metapod_1.12.0             
 [21] httpuv_1.6.15               Seurat_5.3.0               
 [23] sctransform_0.4.2           spam_2.11-1                
 [25] sp_2.2-0                    spatstat.sparse_3.1-0      
 [27] reticulate_1.42.0           cowplot_1.1.3              
 [29] pbapply_1.7-2               RColorBrewer_1.1-3         
 [31] abind_1.4-8                 zlibbioc_1.50.0            
 [33] Rtsne_0.17                  GenomicRanges_1.56.2       
 [35] purrr_1.0.4                 R.utils_2.13.0             
 [37] BiocGenerics_0.50.0         GenomeInfoDbData_1.2.12    
 [39] IRanges_2.38.1              S4Vectors_0.42.1           
 [41] ggrepel_0.9.6               irlba_2.3.5.1              
 [43] listenv_0.9.1               spatstat.utils_3.1-4       
 [45] goftest_1.2-3               RSpectra_0.16-2            
 [47] spatstat.random_3.4-1       dqrng_0.4.1                
 [49] fitdistrplus_1.2-2          parallelly_1.45.0          
 [51] DelayedMatrixStats_1.26.0   codetools_0.2-20           
 [53] DropletUtils_1.24.0         DelayedArray_0.30.1        
 [55] scuttle_1.14.0              tidyselect_1.2.1           
 [57] UCSC.utils_1.0.0            farver_2.1.2               
 [59] ScaledMatrix_1.12.0         SoupX_1.6.2                
 [61] matrixStats_1.5.0           stats4_4.4.1               
 [63] rmdformats_1.0.4            spatstat.explore_3.4-3     
 [65] jsonlite_2.0.0              BiocNeighbors_1.22.0       
 [67] progressr_0.15.1            ggridges_0.5.6             
 [69] survival_3.7-0              tools_4.4.1                
 [71] ica_1.0-3                   Rcpp_1.0.14                
 [73] glue_1.8.0                  gridExtra_2.3              
 [75] SparseArray_1.4.8           xfun_0.52                  
 [77] MatrixGenerics_1.16.0       GenomeInfoDb_1.40.1        
 [79] dplyr_1.1.4                 HDF5Array_1.32.0           
 [81] withr_3.0.2                 fastmap_1.2.0              
 [83] bluster_1.14.0              rhdf5filters_1.16.0        
 [85] rsvd_1.0.5                  digest_0.6.37              
 [87] R6_2.6.1                    mime_0.13                  
 [89] scattermore_1.2             tensor_1.5                 
 [91] dichromat_2.0-0.1           spatstat.data_3.1-6        
 [93] R.methodsS3_1.8.2           tidyr_1.3.1                
 [95] generics_0.1.4              data.table_1.17.4          
 [97] httr_1.4.7                  htmlwidgets_1.6.4          
 [99] S4Arrays_1.4.1              uwot_0.2.3                 
[101] pkgconfig_2.0.3             gtable_0.3.6               
[103] lmtest_0.9-40               SingleCellExperiment_1.26.0
[105] XVector_0.44.0              htmltools_0.5.8.1          
[107] dotCall64_1.2               bookdown_0.39              
[109] SeuratObject_5.1.0          scales_1.4.0               
[111] Biobase_2.64.0              png_0.1-8                  
[113] spatstat.univar_3.1-3       scran_1.32.0               
[115] knitr_1.50                  rstudioapi_0.17.1          
[117] reshape2_1.4.4              nlme_3.1-165               
[119] cachem_1.1.0                zoo_1.8-14                 
[121] rhdf5_2.48.0                stringr_1.5.1              
[123] KernSmooth_2.23-24          parallel_4.4.1             
[125] miniUI_0.1.2                pillar_1.10.2              
[127] grid_4.4.1                  vctrs_0.6.5                
[129] RANN_2.6.2                  promises_1.3.2             
[131] BiocSingular_1.20.0         beachmat_2.20.0            
[133] xtable_1.8-4                cluster_2.1.6              
[135] evaluate_1.0.3              cli_3.6.5                  
[137] locfit_1.5-9.9              compiler_4.4.1             
[139] rlang_1.1.6                 crayon_1.5.3               
[141] future.apply_1.11.3         labeling_0.4.3             
[143] plyr_1.8.9                  stringi_1.8.7              
[145] viridisLite_0.4.2           deldir_2.0-4               
[147] BiocParallel_1.38.0         lazyeval_0.2.2             
[149] spatstat.geom_3.4-1         Matrix_1.7-3               
[151] RcppHNSW_0.6.0              patchwork_1.3.0            
[153] sparseMatrixStats_1.16.0    future_1.49.0              
[155] ggplot2_3.5.2               Rhdf5lib_1.26.0            
[157] statmod_1.5.0               shiny_1.10.0               
[159] SummarizedExperiment_1.34.0 ROCR_1.0-11                
[161] igraph_2.1.4                bslib_0.9.0
---
title: "<CENTER>EB3I n1 2025 scRNAseq<BR>-<BR><B>PRE-PROCESSING (I)</B><BR>-<BR>Load a count matrix, empty droplets & ambient RNA filtering</CENTER>"
date: "2025-16-21.22"
author:
  - name: "EB3I n1 scRNAseq Team"
  - name: "Bastien JOB"
    email: "bastien.job@gustaveroussy.fr"
  - name: "William JARASSIER"
    email: "w.jarassier@gmail.com" 
output:
  rmdformats::readthedown:
    fig_width: 8
    fig_height: 6
    highlight: tango  ## Theme for the code chunks
    embed_fonts: TRUE
    number_sections: true  ## Adds number to headers (sections)
    theme: flatly  ## CSS theme for the HTML page
    collapsed: true  ## By default, the TOC is folded
    toc_depth: 3
    smooth_scroll: true ## Smooth scroll of the HTML page
    self_contained: true ## Includes all plots/images within the HTML
    code_download: true ## Adds a button to download the Rmd
    code_folding: show
    thumbnails: false
    lightbox: true
    fig_caption: false
    gallery: true
    use_bookdown: true
always_allow_html: true ## Allow plain HTML code in the Rmd
editor_options: 
  markdown: 
    wrap: 72
---

<!-- knit setup -->

```{r knit_setup, echo = FALSE}
knitr::opts_chunk$set(
  echo = TRUE,        # Print the code
  eval = TRUE,        # Run command lines
  message = FALSE,    # Print messages
  prompt = FALSE,     # Do not display prompt
  comment = NA,       # No comments on this section
  warning = FALSE,    # Display warnings
  tidy = FALSE,
  fig.align="center", 
  # results = 'hide',
  width = 100       # Number of characters per line
)
```

<!-- CSS to color chunks and outputs -->

```{css, echo=FALSE}
.notrun {
  background-color: lightgrey !important;
  border: 3px solid black !important;
}
.notruno {
  background-color: lightgrey !important;
  color : black !important;
}
.question {
  background-color: aquamarine !important;
  color : black !important;
  border: 3px solid limegreen !important;
}
.questiono {
  background-color: aquamarine !important;
  color : black !important;
}
.answer {
  background-color: navajowhite !important;
  border: 3px solid brown !important;
}
.answero {
  background-color: navajowhite !important;
  color : black !important;
}
.beyond {
  background-color: violet !important;
  border: 3px solid purple !important;
}
.beyondo {
  background-color: violet !important;
  color : black !important;
}
```

<!-- Hook to handle code blocks output folding -->

```{r knit_hook, echo = FALSE}
hooks = knitr::knit_hooks$get()
hook_foldable = function(type) {
  force(type)
  function(x, options) {
    res = hooks[[type]](x, options)
    
    if (isFALSE(options[[paste0("fold.", type)]])) return(res)
    
    paste0(
      "<details><summary>Show ", type, "</summary>\n\n",
      res,
      "\n\n</details>"
    )
  }
}
knitr::knit_hooks$set(
  output = hook_foldable("output"),
  plot = hook_foldable("plot")
)
```

------------------------------------------------------------------------

------------------------------------------------------------------------

<center>![](eb3i_banner.png)</center>

------------------------------------------------------------------------

------------------------------------------------------------------------

# PREAMBLE

## Purpose of this session

This file describes the different steps to perform the **first part** of
the **single cell RNAseq** data analysis training course for the **EB3I
n1 2025**, covering these steps :

-   **Load** a single cell raw counts **matrix**

-   **Remove** barcodes from **empty droplets**

-   **Estimate** and remove **ambient RNA** contamination *("soup")*

## An important notice

<center>![](gyro.png)</center>

-   These early (but mandatory) steps of the analysis are not covered by
    the renowned, higher-level analysis frameworks like **Seurat** nor
    **scran/scater**.

-   Thus, we will write/use a certain amount of **lines of code**, some
    of which of a higher complexity for R beginners.

-   **Newcomer, do not fear** !

    -   While you are **welcome to try** writing some by yourself,
        depending on your R knowledge and self-confidence in it ...

    -   ... you do not have to know [**how**]{.underline} these code
        work ...

    -   ... thus you **should not feel ashamed** copy-pasting the
        pre-written code hereafter ...

    -   ... but in both cases, please try **your best understanding**
        [**what**]{.underline} these code do, and even more,
        [**why**]{.underline} !

    -   In this purpose, you may use whatever **help** you can **by**
        **searching**.

## What to run ?

-   This training presentation, written in Rmarkdown, contains **many**
    chunks ( = code blocks)

-   You **do not have to** run them all !

-   Chunks follow a **color code** :

### Chunks you **ARE** expected to run :

```{r chunk_run}
# chunk_run

## Example of chunk to run
getwd()

```

<br>

### Chunks you **ARE NOT** expected to run

Hopefully, we will demo them. You may run them by yourself, young
padawan, but if you get late or worst, break something, *you*'ll be the
one to blame !

```{r chunk_demo, class.source="notrun", class.output="notruno"}
# chunk_demo

## Example of chunk to NOT run
utils::sessionInfo()

```

<br>

### Questions

Some questions we would like you to answer during the training course.
Please be fair and do not take a look at the answer before we tell you
so :)

```{r chunk_question, class.source="question", eval = FALSE}
# chunk_question

## What's the Answer to Life ? The Universe ? Everything ??

```

<br>

### Answers

They usually follow questions...

```{r chunk_answer, class.source = c("fold-hide", "answer"), class.output="answero"}
# chunk_answer

cat("The Answer to the Great Question... is ... 
Forty-two. Six by nine : forty-two. That's it. 
That's all there is. (I always thought something was 
fundamentally wrong with the universe.)")

```

<br>

### Additional exercises / questions

These are beyond the scope of the training course, but you may play it
*if you feel bored*

```{r chunk_beyond, class.source="beyond", class.output="beyondo"}
# chunk_beyond

## Example of chunk to play with but beyond the scope
getwd()

```

<br>

------------------------------------------------------------------------

------------------------------------------------------------------------

# Start Rstudio

-   Using the [OpenOnDemand/Rstudio cheat
    sheet](https://moodle.france-bioinformatique.fr/pluginfile.php/1475/mod_folder/content/0/OoD_R_Rstudio.html){target="_blank"},
    connect to the [OpenOnDemand
    portal](https://ondemand.cluster.france-bioinformatique.fr){target="_blank"}
    and **create a Rstudio session** with the right resource
    requirements.

------------------------------------------------------------------------

------------------------------------------------------------------------

# Warm-up

-   We now set **common parameters** as new variables, once and for all
    for this session :

```{r setparam}
# setparam


## Set your project name
# WARNING : Do not just copy-paste this ! It's MY project name ! Put YOURS !!
project_name <- "ebaii_sc_teachers"


## Control if the project_name exists on the cluster
cat('PATH CHECK : ', dir.exists(paste0('/shared/projects/', project_name)))

## Seed for the RNG
my_seed <- 1337L

## Empty droplets max p-value
max_p <- 1E-03

```

------------------------------------------------------------------------

------------------------------------------------------------------------

# Prepare the data structure

-   During the current and remaining training sessions, we will work on
    different **data** **objects**, some retrieved from **external**
    resources, some we will **create**.

-   In order to reuse these for further steps, or other sessions, we
    need to **store** them, thus create a **directory structure** on the
    cluster.

-   We will create this structure in **your project** folder (the one
    you registered at the IFB Core, in which you stored your own data
    for the tutoring).

-   To ensure that *you* will always use the same structure for *your*
    work, the code hereafter has been writen so that you just have to
    define a single **variable** that corresponds to your project name.

    -   In *MY* case, as a SC training teacher, it is
        **`ebaii_sc_teachers`**.

## Main directory

-   Create the training course directory

```{r maindir, fold.output = FALSE}
# maindir

## Prepare the path in a "TD_dir" variable
TD_dir <- paste0("/shared/projects/", project_name, "/SC_TD")

## Create the root directory
dir.create(path = TD_dir, recursive = TRUE)

## Print the root directory on-screen
print(TD_dir)

```

## Current session

```{r sessiondir, fold.output = FALSE}
# sessiondir

## Create the session (Preproc.1) directory
session_dir <- paste0(TD_dir, "/01_Preproc.1")
dir.create(path = session_dir, recursive = TRUE)

## Print the session directory on-screen
print(session_dir)
```

## Input data directory

```{r indir, fold.output=FALSE}
# indir

## Create the INPUT data directory
input_dir <- paste0(session_dir, "/DATA")
dir.create(path = input_dir, recursive = TRUE)

## Print the input directory on-screen
print(input_dir)
```

## Output results directory

```{r outdir, fold.output=FALSE}
# outdir

## Create the OUTPUT data directory
output_dir <- paste0(session_dir, "/RESULTS")
dir.create(path = output_dir, recursive = TRUE)

## Print the output directory on-screen
print(output_dir)
```

<br><br>

[**Note :**]{.underline} *Actually, for this session we won't export any
result, so creating this **`RESULTS`** folder was just for practice
purpose.*

<br>

Now, the we have everything needed to :

-   **load** data (`input_dir`)

-   **save** the results we will generate (`output_dir`).

------------------------------------------------------------------------

------------------------------------------------------------------------

# Additional resources

**However**, some upcoming steps require a bit complicated methods that
are not fun to write, nor copy-paste multiple times. So, we will now
prepare code **functions** that will ease these steps. After executing
these functions code, we may call (invoke) them when needed.

## Functions

### SoupX helper function

This function was written to ease the use of SoupX on a count matrix
It was tested on `SoupX`=1.6.2 with `igraph`=1.5.1, `Seurat`=4.4.0,5.1.0

**Parameters** :

-   `scmat_filt` : (sparse)matrix corresponding to an empty droplets -filtered count matrix

-   `scmat_raw` : (sparse)matrix corresponding to an NON- empty droplets -filtered count matrix

-   `soupQuantile`, `contaminationRange` : see `?SoupX::autoEstCont`

-   `contaminationRange` : range of the *expected* soup proportion

-   `soupRange` : estimate soup fraction from features with total reads
comprised in this range of counts (only used when `scmat_raw != NULL`)

-   `return_object` : if `TRUE`, return the "unsouped" count matrix ; if
`FALSE`, only perform soup estimation and return the estimated
proportion value (rho)

-   `doPlot` : Perform the SoupX estimation plot (rho distribution)

```{r soupx_func}
# soupx_func

SoupX_auto <- function(scmat_filt = NULL, scmat_raw = NULL, soupQuantile = 0.9,
                       contaminationRange = c(.01, .8), soupRange = c(0,100),
                       return_object = FALSE, doPlot = FALSE) {
  
  ## Checks
  if(is.null(scmat_filt)) stop('A filtered count matrix is required !')
  
  if(is.null(scmat_raw)) message('No unfiltered raw counts matrix provided. Estimation will be based on filtered matrix only.')
  
  ## If no raw matrix
  if (is.null(scmat_raw)) {
    spChanRaw <- SoupX::SoupChannel(
      tod = scmat_filt, 
      toc = scmat_filt, 
      calcSoupProfile = FALSE)
    sc_rowsum <- sparseMatrixStats::rowSums2(scmat_filt)
    spProf <- data.frame(
      row.names = rownames(scmat_filt), 
      est = sc_rowsum/sum(scmat_filt), 
      counts = sc_rowsum)
    spChan <- SoupX::setSoupProfile(spChanRaw, spProf)
  } else {
    spChan <- SoupX::SoupChannel(
      tod = scmat_raw, 
      toc = scmat_filt, 
      calcSoupProfile = FALSE)
    if (min(spChan$nDropUMIs) > max(soupRange)) stop(
      'Minimum found counts per barcode is : ', 
      min(spChan$nDropUMIs), 
      ', which is smaller than the upper bound of soupRange ! Please increase soupRange max !')
    spChan <- SoupX::estimateSoup(sc = spChan, soupRange = soupRange)
  }
  ## Display Top 20 contributing genes
  if (!return_object) {
    cat('\nSoup-contributing features (Top 20) :\n')
    print(knitr::kable(head(
      spChan$soupProfile[order(spChan$soupProfile$est, decreasing = TRUE), ], 
      n = 20)))
  }
  ## Quick clustering needed
  spClust <- scran::quickCluster(scmat_filt, method = "igraph")
  ## Adding clusters to the SoupChannel object
  spChan <- SoupX::setClusters(sc = spChan, clusters = spClust)
  ## Estimating soup
  sX <- SoupX::autoEstCont(sc = spChan, doPlot = doPlot, tfidfMin = 1, 
                           soupQuantile = soupQuantile, maxMarkers = 100, 
                           contaminationRange = contaminationRange, 
                           rhoMaxFDR = .2, priorRho = .05, priorRhoStdDev = .1, 
                           forceAccept = FALSE)
  
  ## Removing soup (adjusting counts)
  if(return_object) {
    cat('Counts BEFORE SoupX : ', sum(scmat_filt), '\n')
    scmat_soupx <- SoupX::adjustCounts(
      sX, method = 'subtraction', roundToInt = TRUE, 
      tol = .001, pCut = .01)
    cat('Counts AFTER SoupX : ', sum(scmat_soupx), '\n')
    rm(scmat_filt)
    return(scmat_soupx)
  } else return(sX$fit$rhoEst)
}

```

<br>

------------------------------------------------------------------------

------------------------------------------------------------------------

# Load a 10X Cell Ranger raw count matrix

## The dataset

-   The data we will work on is hosted on
    [Zenodo](https://zenodo.org){target="_blank"}. To ease its
    retrieval, we can define its ID as a variable

    ```{r zid}
    # zid
    
    ## This is actually to help me updating this training without hassle
    zen_id <- '14034221'
    
    ## This is the path to the current EB3I backup
    sessionid <- '2538_eb3i_n1_2025'
    
    ```

-   Here will we train ourselves to load into R a single cell RNAseq
    data produced by 10X Genomics' software **`Cell Ranger`**.

-   We will work with a public dataset provided by the manufacturer,
    that consists in \~ **10,000 PBMC** (peripheral bone marrow cells)
    from a human donor (downsampled to 1/10th, for smaller computation time).

-   The experiment was performed with the **3' capture kit v3**.

-   The analysis was performed with **`Cell Ranger v3`**, mapping on the
    **`GRCh38-2020-A`** manufacturer reference.

-   The data consists into the typical 10x 3-files structure, hosted in
    a Zenodo respository (Id :
    [`r zen_id`](https://zenodo.org/records/%60r%20zen_id%60 "Zenodo Preproc.1"){target="_blank"})

    -   `barcodes.tsv.gz` : the list of putative droplet **barcodes**
    -   `features.tsv.gz` : the list of **features** (genes)
    -   `matrix.mtx.gz` : the **feature** x **barcode** expression count
        **matrix**

## Download data

-   The relatively complex code chunk hereafter has been written to :

    -   let you download the data files from :

        -   Zenodo by default

        -   if `backup = TRUE`, a local backup instead (in case we loose the
            internet connection)

    -   if the requested data are already available locally :

        -   load this from the local source

        -   if `force = TRUE` a new download is forced

```{r dlzen10x, message = TRUE, echo = TRUE}
# dlzen10x

### Named files (will be used later on !)
mtx_file <- "matrix.mtx.gz"
features_file <- "features.tsv.gz"
barcodes_file <- "barcodes.tsv.gz"
summary_file <- "pbmc_10k_v3_summary.html"

## Filename(s) to retrieve
toget_files <- c(mtx_file,
                 features_file,
                 barcodes_file,
                 summary_file)

## Folder to store retrieved files
local_folder <- input_dir

## Use local backup ?
backup <- FALSE
if(backup) message("Using local backup !")

## Force download ?
force <- FALSE
if(force) message("Forcing (re)download !")

### Define remote folder
remote_folder <- if (backup) paste("/shared/projects/", sessionid, "/atelier_scrnaseq/TD/BACKUP/10X/") else paste0("https://zenodo.org/records/", zen_id, "/files/")

### Reconstruct the input paths
remote_path <- paste0(remote_folder, "/", toget_files)

### Reconstruct the output paths
local_path <- paste0(local_folder, "/", toget_files)

## Retrieve files (if they don't exist), in loop
for (tg in seq_along(toget_files)) {
  ## If the file does not locally exist
  if (!file.exists(local_path[tg]) | force) {
    ## Retrieve data
    if(backup) {
      file.copy(from = remote_path[tg],
                to = local_path[tg])
    } else {
      download.file(url = remote_path[tg], 
                    destfile = local_path[tg])
    }
    ## Check if downloaded files exist locally
    if(file.exists(local_path[tg])) message("\tOK")
  } else message(paste0(toget_files[tg], " already downloaded !"))
}

```

## Load into R

-   **Load** the **10X** **data** files into R :

    ```{r q_r10X, eval = FALSE, class.source="question", eval = FALSE}
    # q_r10X
    
    How ?
    
    ```

    -   A good starter

    ```{r a_r10X1, class.source = c("fold-hide", "answer"), echo = FALSE}
    # a_r10X1
    
    cat('https://lmddgtfy.net/?q=load%2010x%20data%20in%20R')
    
    ```

    -   Back to your local help ressource

    ```{r a_r10X2, class.source = c("fold-hide", "answer"), eval = FALSE}
    # a_r10X2
    
    ## Reading the function help page
    ?Seurat::Read10X
    
    ```

    <br><br>
    
    We can now apply it to our data : 
    
    ```{r load10x, class = "fold-hide"}
    # load10x
    
    ## Loading unfiltered 10X matrix
    scmat <- Seurat::Read10X(data.dir = input_dir)
    
    ```

    <br>

-   However, we don't know what the loaded object actually looks like in
    R...

-   **Questions** :

    -   Do you know a way to ...

    ```{r q_whatis, class.source="question", eval = FALSE}
    # q_whatis
    
    ... know what IS the type of object that was created ?
    
    ```

    <br>

    ```{r a_whatis, class.source = c("fold-hide", "answer"), eval = FALSE}
    # a_whatis
    
    ## To know the type of an R object : methods::is()
    ?methods::is
    
    ```

    ```{r gois, class.source = "fold-hide"}
    # gois
    
    ## Get the type
    methods::is(scmat)
    
    ```

    <br><br>

    ```{r q_struc, class.source="question", eval = FALSE}
    # q_struc
    
    ... observe the STRucture of the resulting object ?
    
    ```

    <br>

    ```{r a_struc, class.source = c("fold-hide", "answer"), eval = FALSE}
    # a_struc
    
    ## To get a basic structure description : utils::str()
    ?utils::str
    
    ```

    ```{r go_str, class.source = "fold-hide"}
    # go_str
    
    ## Get the basic structure
    utils::str(scmat)
    
    ```
    
    <br>
    
    Not that meaningful, though... At least we know we have a matrix, so one can
    get its dimensions !
    
    ```{r scmat_dim}
    # scmat_dim
    
    ## Dimensions of a >1D object
    dim(scmat)
    
    ```
    
    <br>

## Features cleanup

`Seurat` does not like at all **underscores** (`_`) in feature names ...

We will have to :

-   **control** if our dataset contains some ? Here, we will use the `base::grep()` function that looks in character strings `x` if they contain a provided set of characters as a `pattern`, and returns their position (when `value = FALSE`, default) or value itself (`value = TRUE`)

    ```{r us_check1}
    # us_check1
    
    ## Check feature names with underscore(s)
    base::grep(pattern = '_', 
         x = rownames(scmat), 
         value = TRUE)
    
    ```

    <br>

-   if so, **clean** it ! Here we will use the `base::gsub()` function that looks for a `pattern` in a character string `x` and substitutes it with a `replacement`  character string.

    ```{r us_clean}
    # us_clean
    
    ## Clean features : replacing underscores by dashes
    rownames(scmat) <- base::gsub(
      pattern = "_", 
      replacement = "-", 
      x = rownames(scmat))
    
    ```

-   then, **control** the efficiency of our cleaning (with a new use of `base::grep`)

    ```{r us_check2, class.source="notrun", class.output="notruno"}
    # us_check2
    
    ## Post-cleanup control
    base::grep(pattern = "_", 
               x = rownames(scmat), 
               value = TRUE)
    
    ```

<br>

------------------------------------------------------------------------

------------------------------------------------------------------------

# Empty droplets

-   While a large part of our barcodes contain **no count** at all,
    barcodes with at least one total count **may not** mandatorily
    correspond to **true cells**, due to ambient RNA.

-   Here we want to **identify** empty droplets, then **remove** them
    from the matrix.

## Detection

-   To detect the margins of "true" cells versus empty droplets, we will
    rely on the so-called **`double-kneeplot`** : a plot of **UMI counts per 
    barcode**, in function of their increasingly **ordered rank**.

<br>
<center>![](elbow_knee_KNEE.png)</center>
<br>

-   To draw such plot, we need to **rank the barcodes** thanks to their 
    total counts.

### Ranking barcodes

-   We will use the
    [**DropletUtils**](https://bioconductor.org/packages/release/bioc/html/DropletUtils.html){target="_blank"} package

    ```{r h_barcodeRanks, eval = FALSE}
    # h_barcodeRanks
    
    ## Read the function help page
    ?DropletUtils::barcodeRanks()
    
    ```

    ```{r bcranks}
    # bcranks
    
    ## Generate the rank statistics
    bc_rank <- DropletUtils::barcodeRanks(scmat)
    
    ```

-   Check the ranking of barcodes, according to their counts

    ```{r bcrank_top, class.source="notrun", class.output="notruno", fold.output = FALSE}
    # bcrank_top
    
    ## Show a summary of the ranks (ordered for display convenience)
    print(bc_rank[order(bc_rank$rank),])
    
    ```

<br>

### Kneeplot

Now, we can **draw** our kneeplot :

```{r kneeplot1, class.source="notrun", class.output="notruno", fold.plot = FALSE}
# kneeplot1

## Plot
graphics::plot(x = bc_rank$rank, 
               y = bc_rank$total + 1, 
               log = "xy", 
               xlab = "Barcode rank", 
               ylab = "Total UMIs", 
               col = "black", 
               pch = ".", 
               cex = 5, 
               main = "Kneeplot")

```

<br>

-   **Question** :

    ```{r q_knee, class.source="question", eval = FALSE}
    # q_knee
    
    Looking at the kneeplot, can you roughly predict how many barcodes will be 
    kept as cells (ie, as non-empty droplets) ?
    
    ```

    <br>

    ```{r a_knee, class.source = c("fold-hide", "answer"), eval = FALSE}
    # a_knee
    
    ## . The "cliff" in the kneeplot is at a 
    ##  rank value a bit below ~ 1,000.
    
    ```

Then, we can use these ranks to **identify "true" cells** :

```{r h_emptyDrops, class.source="notrun", class.output="notruno", eval = FALSE}
# h_emptyDrops

## Read the function help page
?DropletUtils::emptyDrops

```

The function we will use relies on random number generation, so we will fix the RNG seed, for reproducibility

```{r emptydrops1}
# emptydrops1

## Set the RNG seed
set.seed(my_seed)

## Identify empty droplets
bc_rank2 <- DropletUtils::emptyDrops(scmat)

## Whats is the structure of the returned output ?
str(bc_rank2)
```

We can show a summary of the ordered barcodes :

```{r emptysmry, class.source="notrun", class.output="notruno"}
# emptysmry

## Show a summary of the qualified ranks (ordered for display convenience)
print(bc_rank2[order(bc_rank2$Total, decreasing = TRUE),])

```

<br>

### Selection

Barcodes considered as empty droplets have **no p-value** (`FDR` = NA)

```{r bcr2_na}
# bcr2_na

## Creating a validation column (empty droplets have NA as p-value)
bc_rank2$VALID <- !is.na(bc_rank2$FDR) 

## Quantify "real cells"
table(bc_rank2$VALID)

```

<br>

We can **control the stringency** by setting a FDR-adjusted p-value
**threshold**

```{r valid_fdr}
# valid_fdr

## Recall the max p-value we set as a variable early on
max_p

## We've set the max p-value to 1E-03
bc_rank2$VALID[bc_rank2$FDR >= max_p] <- FALSE

## Control
table(bc_rank2$VALID)

```

<br>

Now, we can **display** our **true cells** on the kneeplot :

```{r kneeplot2, fig.align="center"}
# kneeplot2

## Plot with highlight of the selected barcodes (red)
graphics::plot(x = bc_rank$rank, 
               y = bc_rank$total+1, 
               log = "xy", 
               xlab = "Barcode rank", 
               ylab = "Total UMIs", 
               col = ifelse(bc_rank2$VALID, "red", "black"), 
               pch = ".", 
               cex = ifelse(bc_rank2$VALID, 7, 3), 
               main = paste0("Kneeplot (", 
                             length(which(bc_rank2$VALID)), 
                             " barcodes kept)"))

```

<br>

-   **Question** :

    ```{r q_kneered, class.source="question", eval = FALSE}
    # q_kneered
    
    Is there something unexpected for the retained "TRUE" cells (red) in this kneeplot ?

    ```

    <br>

    ```{r a_kneered, class.source = c("fold-hide", "answer"), eval = FALSE}
    # a_kneered
    
    ## . You may observe that the selection of "true" cells (red)
    ##   does not strictly correspond to a "cut" in the kneeplot
    ##   descending curve.
    ##
    ## . This is because emptyDrops does not only "follow" the curve
    ##   to determine a threshold corresponding to a "minimal count" 
    ##   value to consider a barcode as a cell, but also performs a 
    ##   statistical analysis for each barcode, considering how much 
    ##   its expression profile ressembles the one of other cells, 
    ##   even with a very different (lower) global level of expression.
    
    ```

<br><br>

## Removal

We just need to restrict our count matrix to "true" cells.

-   How many barcodes do we have at the moment ?

    ```{r scdim3, class.source="notrun", class.output="notruno"}
    # scdim3
    
    dim(scmat)
    
    ```

    <br>

-   Filter empty droplets out :

    ```{r edfilter}
    # edfilter
    
    ## Restrict to valid barcodes
    scmat_cells <- scmat[, bc_rank2$VALID]
    
    ## Control
    dim(scmat_cells)
    
    ```

<br>

------------------------------------------------------------------------

------------------------------------------------------------------------

# Ambient RNA

-   The counts measured in this matrix of (now considered "true") single
    cells do **not** reflect the measurement of these cells
    expression **only**

-   Due to other **over-lysed / dying cells** in the emulsion, we also
    measured fraction of **ambient RNA** that adds up to the "real"
    expression

-   We may **estimate** it by observing counts existing at a **minimal
    level** across cells that greatly differ in their expression profile
    
-   Just in case, due to the fun tool name we will use, `SoupX`, I'll
    write down "soup" as an alias for "ambient RNA", but both terms have
    the same meaning.

<br>

## Estimate the "soup"

[**SoupX**](https://cran.r-project.org/web/packages/SoupX/readme/README.html){target="_blank"}
can estimate the rate and composition of ambient RNA, in several ways :

-   **Manual mode** : providing a list of features expected to reflect
    the soup (genes expressed at high levels in a majority of the
    expected cell types)

-   **Automatic modes**, using the empty droplets-**filtered** raw count
    matrix ...

    -   ... along **with the unfiltered** matrix (ie, containing all
        quantified droplets). This is the most efficient mode.

    -   ... **solely** : this is less efficient, but useful when the
        unfiltered matrix is not available.

```{r h_SoupX_auto, eval = FALSE, class.source="notrun", class.output="notruno"}
# h_SoupX_auto

## Reading the SoupX package main help page
?SoupX::SoupX

```

Let's run SoupX, just asking for an estimation of the "soup" proportion
:

```{r soupxrhoE}
# soupxrhoE

## TIP : In case of error :
# install.packages("irlba", type="source", force=TRUE)

## SoupX for ambient fraction estimation
soup_frac <- SoupX_auto(
  scmat_filt = scmat_cells, 
  scmat_raw = scmat)

```

<br>

***NOTE*** : We can observe
[**MALAT1**](https://www.biorxiv.org/content/10.1101/2024.07.14.603469v2){target="_blank"} as the top first gene (as well as multiple riboprotein-coding genes).

<br>

What's the estimated fraction of soup in our counts ?

```{r soupxfrac}
# soupxfrac

cat("Soup fraction : ", soup_frac)

```

<br>

-   **Question** :

    ```{r q_souprate, class.source="question", eval = FALSE}
    # q_souprate
    
    Should we try to remove such an amount of ambient RNA ?
    
    ```

    <br>

    ```{r a_souprate, class.source = c("fold-hide", "answer"), eval = FALSE}
    # a_souprate
    
    The best way to know is to try it out =D
    
    ```

<br>

## Remove the "soup"

SoupX can **remove** the soup (subtract counts for identified soup genes
to all barcodes/cells) using different methods :

-   **Automatically** (safer)

-   Specifying a **fraction of counts** to remove.

    -   *Pros* :

        -   By removing X % of reads :

            -   if soup was effectively present (at a rate \<= X), this
                will **mostly affect soup** genes first.

            -   If not, it will **decrease counts** globally, thus have
                negligible effect in differences between cells / cell
                types.

    -   *Cons* :

        -   **Counts are** already **rare** in droplet-based single cell
            technologies... Removing them "by default" makes them **even
            more rare**...

        -   If actual soup **rate is higher** than the defined rate X to
            remove, this mode will remain **uneffective**...

```{r soupxrm}
# soupxrm

## Run SoupX in "removal" mode
scmat_unsoup <- SoupX_auto(
  scmat_filt = scmat_cells, 
  scmat_raw = scmat, 
  return_object = TRUE)

```

<br>

## Effect of the "soup" removal

-   Now, we want to **characterize the effect** of the removal of the
    ambient RNA, by comparing the pre- and post- soupX matrices.
    
-   An easy way is to **describe**, then **visualize** such an effect.

<br><br>

### Describe and visualize {.tabset .tabset-fade .tabset-pills}

#### Before SoupX (DEMO)

We can describe our count matrix thanks to some useful metrics :

-   Total counts

```{r descBS_totcount, class.source="notrun", class.output="notruno"}
# descBS_totcount

## All counts
base::sum(scmat_cells)

```

-   Sparsity (fraction of `0`s)

```{r descBS_sparsity, class.source="notrun", class.output="notruno"}
# descBS_sparsity

## Compute sparsity (number of matrix values at 0 / cross-product of matrix dim)
base::sum(sparseMatrixStats::colCounts(
  x = scmat_cells, value = 0)
  ) / base::prod(dim(scmat_cells))

```

-   Distribution of counts in cells

```{r descBS_nCount, class.source="notrun", class.output="notruno"}
# descBS_nCount

## Sums up counts for each cell
base::summary(sparseMatrixStats::colSums2(x = scmat_cells, na.rm = TRUE))

```

-   Distribution of expressed genes in cells

```{r descBS_nFeature, class.source="notrun", class.output="notruno"}
# descBS_nFeature

## All genes minus 0-count genes
base::summary(nrow(scmat_cells) - sparseMatrixStats::colCounts(
  x = scmat_cells, 
  value = 0, 
  na.rm = TRUE))

```

#### After SoupX

We can describe our count matrix thanks to some useful metrics :

-   Total counts

```{r descAS_totcount}
# descAS_totcount

## All counts
base::sum(scmat_unsoup)

```

-   Sparsity (fraction of `0`s)

```{r descAS_sparsity}
# descAS_sparsity

## Compute sparsity (number of matrix values at 0 / cross-product of matrix dim)
base::sum(sparseMatrixStats::colCounts(
  x = scmat_unsoup, value = 0)
  ) / base::prod(dim(scmat_unsoup))

```

-   Distribution of counts in cells

```{r descAS_nCount}
# descAS_nCount

## Sums up counts for each cell
base::summary(sparseMatrixStats::colSums2(x = scmat_unsoup, na.rm = TRUE))

```

-   Distribution of expressed genes in cells

```{r descAS_nFeature}
# descAS_nFeature

## Compute sparsity (number of matrix values at 0 / cross-product of matrix dim)
base::summary(nrow(scmat_cells) - sparseMatrixStats::colCounts(
  x = scmat_unsoup, 
  value = 0, 
  na.rm = TRUE))

```

##  {.unnumbered}

<br>

------------------------------------------------------------------------

------------------------------------------------------------------------

<br>

-   **Questions** :

    ```{r q_descdiff, class.source="question", eval = FALSE}
    # q_descdiff
    
    Can you describe the difference (especially for counts and sparsity) ?
      
    Was it expected ?
    
    ```

    <br>

    ```{r a_descdiff, class.source = c("fold-hide", "answer"), eval = FALSE}
    # a_descdiff
    
    ## . Total counts, max value, counts per cell and number of
    ##   expressed features per cell, all have decreased.
    ##
    ## . The sparsity level has slightly increased (removing the soup
    ##   obliterated all counts for some features in some cells).
    ##
    ## . All of this is absolutely expected.
    
    ```

<br>

What are the features most affected by the soup removal ?

```{r soupfeatures, class.source="notrun", class.output="notruno"}
# soupfeatures

## Compute the fraction matrix (PRE / POST)
##  NOTE : we may have 0 counts in the divider, 
##  so we increment both matrix by +1 !
cont_frac <- (scmat_cells + 1) / (scmat_unsoup + 1)

## Fraction of counts decreased by soup removal, per feature (ordered)
feat_frac <- sort(sparseMatrixStats::rowMeans2(cont_frac), decreasing = TRUE)

### Display Top 5 features
utils::head(feat_frac, decreasing = TRUE)

## Removing objects to free some RAM
rm(cont_frac, feat_frac)

```

```{r qsoupfeat, class.source="question", eval = FALSE}
# qsoupfeat

Does something about these genes strike you ?

```

Hint : **fraction** or **difference** ?


```{r soupfeaturesdiff, class.source=c("fold-hide", "answer"), class.output="answero"}
# soupfeaturesdiff

## Compute the DIFFERENCE matrix (PRE / POST)
##  NOTE : we may have 0 counts in the divider, 
##  so we increment both matrix by +1 !
cont_dif <- scmat_cells - scmat_unsoup

## Fraction of counts decreased by soup removal, per feature (ordered)
feat_dif <- sort(sparseMatrixStats::rowMeans2(cont_dif), decreasing = TRUE)

### Display Top 5 features
utils::head(feat_dif, decreasing = TRUE)

## Removing objects to free some RAM
rm(cont_dif, feat_dif)

```

<br>

### Plot the "top 1" feature {.tabset .tabset-fade .tabset-pills}

We want to visualize the expression of the "top" soup gene, LYZ, in our dataset.

To do so, we will generate a projection of the data into a reduced (2-D) 
mathematical space. This is done by using a series of R function from the 
Seurat package, that we won't describe here and now : understanding these 
commands below is not the scope of our current session, but it will be during 
the next few days.

Actually, these commands perform all the steps we'll see in the next few days, 
in a row, but with _default values_ (thus, almost certainly inadequate to our 
current data).

In consequence, please just consider the output as a way to quickly and dirtily 
visualize the data : the depicted topology should be considered as very rough 
and mostly imperfect.

#### Before SoupX

```{r bsoupx, fig.height = 6, fig.width = 6, class.source="notrun", class.output="notruno"}
# bsoupx

## Roll process, using a temporary "sobj_presoupx" Seurat object
sobj_presoupx <- Seurat::CreateSeuratObject(counts = scmat_cells, project = "PBMC10K_BeforeSoupX")
sobj_presoupx <- Seurat::NormalizeData(object = sobj_presoupx, verbose = FALSE)
sobj_presoupx <- Seurat::ScaleData(object = sobj_presoupx, verbose = FALSE)
sobj_presoupx <- Seurat::FindVariableFeatures(object = sobj_presoupx, verbose = FALSE)
sobj_presoupx <- Seurat::RunPCA(object = sobj_presoupx, npcs = 21, verbose = FALSE)
sobj_presoupx <- Seurat::RunUMAP(object = sobj_presoupx, dims = c(1:20), verbose = FALSE)

## Top1 feature plot
fp_pre <- Seurat::FeaturePlot(object = sobj_presoupx, features = 'LYZ') + Seurat::DarkTheme()
print(fp_pre)

```

<br>

#### After SoupX

```{r asoupx, fig.height = 6, fig.width = 6}
# asoupx

## Roll process, using a temporary "sobj_postsoupx" Seurat object
sobj_postsoupx <- Seurat::CreateSeuratObject(counts = scmat_unsoup, project = "PBMC10K_AfterSoupX")
sobj_postsoupx <- Seurat::NormalizeData(object = sobj_postsoupx, verbose = FALSE)
sobj_postsoupx <- Seurat::ScaleData(object = sobj_postsoupx, verbose = FALSE)
sobj_postsoupx <- Seurat::FindVariableFeatures(object = sobj_postsoupx, verbose = FALSE)
sobj_postsoupx <- Seurat::RunPCA(object = sobj_postsoupx, npcs = 21, verbose = FALSE)
sobj_postsoupx <- Seurat::RunUMAP(object = sobj_postsoupx, dims = c(1:20), verbose = FALSE)

## Top1 feature plot
fp_post <- Seurat::FeaturePlot(object = sobj_postsoupx, features = 'LYZ') + Seurat::DarkTheme()
print(fp_post)

```

<br>

##  {.unnumbered}

------------------------------------------------------------------------

------------------------------------------------------------------------

Merging plots for ease of use :

```{r soupx_umaps, fig.width = 12, fig.height = 6, class.source="beyond", class.output="beyondo"}
# soupx_umaps

## Using the patchwork package to merge plots (and ggplot2 to add titles)
patchwork::wrap_plots(
  list(
    fp_pre & ggplot2::ggtitle(label = "LYZ before SoupX"), 
    fp_post & ggplot2::ggtitle(label = "LYZ after SoupX")), 
  nrow = 1)

```

<br>

-   **Question**

    ```{r q_umapsoupx, class.source="question", eval = FALSE}
    # q_umapsoupx
    
    Can you compare the two plots :
      - for the LYZ expression across the 2-D space ?
      - about the space topologies ?
      - for the LYZ expression globally ?
      
    ```

    <br>

    ```{r a_umapsoupx, class.source = c("fold-hide", "answer"), eval = FALSE}
    # a_umapsoupx
    
    ## . The level of expression measured in cells with smaller expression
    ##   before SoupX has gone to almost none after : the ambient expression
    ##   of this gene has efficiently been removed.
    ##
    ## . Hopefully, the cluster(s) with higher expression remain(s) high.
    ##
    ## . The expression scale upper bound after SoupX is higher than before ?! 
    ##   But we REMOVED some counts ?! While being counter-intuitive, this is
    ##   a positive consequence of the ambient removal on the scaling of
    ##   barcodes expression (see later). 
    ##
    ## . The range of both X and Y axes have increased, depicting a better
    ##   separation of / distance between some of the clusters.
    ##
    ## . The global topology of clusters remains close (but not identical).
    ## 
    ## . Clusters compacity has increased.
    
    ```

<br>

## Conclusion

-   Removing ambient RNA has a **positive effect** :

    -   on the **quality of the expression** level measurements
    
    -   on the observed **topology**
    
    -   ... despite an estimation of only ~5% !

-   Actually, ambient RNA level and composition is (one of) the major source(s) 
of the **batch effect bias** that may alter the integration of different 
samples.

<br>

------------------------------------------------------------------------

------------------------------------------------------------------------

<br><br><br>

**Beyond** :

1.  One can check if the "unsouped" (ie, post-SoupX) matrix retain some soup ?. How would you do it ?

    ```{r b_soupx2, class.source = c("fold-hide", "beyond"), eval = FALSE}
    # b_soupx2
    
    ## SoupX for ambient fraction estimation
    soup2_frac <- SoupX_auto(
      scmat_filt = scmat_unsoup, 
      scmat_raw = scmat)
    
    ## Run SoupX in "removal" mode
    scmat_unsoup2 <- SoupX_auto(
      scmat_filt = scmat_unsoup, 
      scmat_raw = scmat, 
      return_object = TRUE)
    
    ```

2.  Save the `sobj_presoupx` R object on disk.

    ```{r b_save_answer1, class.source = c("fold-hide", "beyond"), eval = FALSE}
    # b_save_answer1
    
    ## A single object : use RDS format with saveRDS()
    saveRDS(object = sobj_presoupx, file = paste0(output_dir, "/sobj_presoupx.RDS"))
    
    ```

3.  Save both the `sobj_presoupx` and `sobj_postsoupx` objects in a single archive
    on disk.

    ```{r b_save_answer2, class.source = c("fold-hide", "beyond"), eval = FALSE}
    # b_save_answer2
    
    ## Multiple objects : use Rdata format with save()
    save(list = c(sobj_presoupx, sobj_postsoupx), file = paste0(output_dir, "/sobjects.RDA"))
    
    ```

4.  Same as 1. but use the `bzip2` compression algorithm

    ```{r b_save_answer3, class.source = c("fold-hide", "beyond"), eval = FALSE}
    # b_save_answer3
    
    ## Compress with bzip2
    saveRDS(object = sobj, file = paste0(output_dir, "/sobj.RDS"), compress = "bzip2")
    
    ```

<br>

------------------------------------------------------------------------

------------------------------------------------------------------------

<br><br><br>

# Rsession

For reproducibility and context, it is recommended to include in your RMarkdown the list of loaded packages and their version.

```{r rsession, class.source="notrun", class.output="notruno"}
# rsession

utils::sessionInfo()

```
