Chapter 2 Sequence Quality

#projDir <- "/home/ubuntu/Course_Materials/scRNAseq"
projDir <- params$projDir
projDirLink <- "/Users/baller01/MyMount/svr008ssd/20200511_FernandesM_ME_crukBiSs2020"
inpDirBit <- params$inpDirBit
outDirBit <- params$outDirBit
dirRel <- ".."

library(DT)

2.1 Introduction

We will use two sets of Bone Marrow Mononuclear Cells (BMMC):

• ‘CaronBourque2020’: pediatric samples
• ‘Hca’: HCA Census of Immune Cells for adult BMMCs

Fastq files were retrieved from publicly available archive (SRA and HCA).

Sequencing quality was assessed and visualised using fastQC and MultiQC.

Library structure reminder:

tmpFn <- sprintf("%s/Images/tenxLibStructureV3.png", dirRel)
knitr::include_graphics(tmpFn, auto_pdf = TRUE)

rm(tmpFn)

• The sample index identifies the library, with one I7 index per sample
• The 10X cell barcode (or cell index) identifies the droplet in the library
• The UMI identifies the transcript molecule within a cell and gene
• The insert is the transcript molecule, ie the cDNA sequence

Each sample is described with three sets of fastq files:

• I1: sample index
• R1: 10x barcode + UMI
• R2: insert sequence

The sample index is actually a set of four 8-ntd oligo. For example SIGAB8 is ‘AAAGTGCT-GCTACCTG-TGCTGTAA-CTGCAAGC’. All four are used and identified by a digit, eg 1-4. Depending on the processing pipeline, fastq files may be returned for each 8-ntd index, or combined into a single file.

For the Caron data set they are combined in a single file, and files for separate lanes were also combined into a single fastq file.

Each sample is identified by three fastq files, one per read type:

• sample _ S0 _ L001 _ I1 _ 001 _ .fastq.gz: contains sample index
• sample _ S0 _ L001 _ R1 _ 001 _ .fastq.gz: contains 10x barcode + UMI
• sample _ S0 _ L001 _ R2 _ 001 _ .fastq.gz: contains insert sequence

We kept the same names for the fastqc output. With for example sample ‘SRR9264343’:

• SRR9264343 _ S0 _ L001 _ I1 _ 001 _ fastqc.html
• SRR9264343 _ S0 _ L001 _ R1 _ 001 _ fastqc.html
• SRR9264343 _ S0 _ L001 _ R2 _ 001 _ fastqc.html

fastqcDir <- sprintf("%s/Data/%s/fastqc", projDir, "CaronBourque2020")
fastqcDirLink <- sprintf("%s/Data/%s/fastqc", projDirLink, "CaronBourque2020")

2.2 CaronBourque2020 - fastqc

# CaronBourque2020
cb_sampleSheetFn <- file.path(projDir, "Data/CaronBourque2020/SraRunTable.txt")
cb_sampleSheet <- read.table(cb_sampleSheetFn, header=T, sep=",")
#cb_sampleSheet <-  cb_sampleSheet %>% filter(!Run == "SRR9264351")
cb_sampleSheet

##           Run Assay.Type AvgSpotLen       Bases  BioProject    BioSample
## 1  SRR9264343    RNA-Seq        132 27850288884 PRJNA548203 SAMN12011162
## 2  SRR9264344    RNA-Seq        132 43613421192 PRJNA548203 SAMN12011172
## 3  SRR9264345    RNA-Seq        132 43838527392 PRJNA548203 SAMN12011171
## 4  SRR9264346    RNA-Seq        132 39752529300 PRJNA548203 SAMN12011170
## 5  SRR9264347    RNA-Seq        132 41035092252 PRJNA548203 SAMN12011169
## 6  SRR9264348    RNA-Seq        132 42840756288 PRJNA548203 SAMN12011168
## 7  SRR9264349    RNA-Seq        132 42953865372 PRJNA548203 SAMN12011167
## 8  SRR9264350    RNA-Seq        132 42822420960 PRJNA548203 SAMN12011166
## 9  SRR9264351    RNA-Seq        132 28322630028 PRJNA548203 SAMN12011165
## 10 SRR9264352    RNA-Seq        132 36199482528 PRJNA548203 SAMN12011165
## 11 SRR9264353    RNA-Seq        132 41446760124 PRJNA548203 SAMN12011164
## 12 SRR9264354    RNA-Seq        132 42802129128 PRJNA548203 SAMN12011163
##          Bytes
## 1  18644549905
## 2  27638885644
## 3  28054431102
## 4  25564104997
## 5  24777477094
## 6  27432674292
## 7  27523442193
## 8  27282064655
## 9  19040444664
## 10 22143300246
## 11 26850120365
## 12 27774281557
##                                                            Cell_type
## 1     Pre-B t(12;21) [ETV6-RUNX1] acute lymphoblastic leukemia cells
## 2     Pre-B t(12;21) [ETV6-RUNX1] acute lymphoblastic leukemia cells
## 3     Pre-B t(12;21) [ETV6-RUNX1] acute lymphoblastic leukemia cells
## 4     Pre-B t(12;21) [ETV6-RUNX1] acute lymphoblastic leukemia cells
## 5  Pre-B High hyper diploid [HHD] acute lymphoblastic leukemia cells
## 6  Pre-B High hyper diploid [HHD] acute lymphoblastic leukemia cells
## 7                           Pre-T acute lymphoblastic leukemia cells
## 8                           Pre-T acute lymphoblastic leukemia cells
## 9                    Healthy pediatric bone marrow mononuclear cells
## 10                   Healthy pediatric bone marrow mononuclear cells
## 11                   Healthy pediatric bone marrow mononuclear cells
## 12                   Healthy pediatric bone marrow mononuclear cells
##    Center.Name Consent DATASTORE.filetype DATASTORE.provider
## 1          GEO  public          fastq,sra         gs,ncbi,s3
## 2          GEO  public          fastq,sra         gs,ncbi,s3
## 3          GEO  public          fastq,sra         gs,ncbi,s3
## 4          GEO  public          fastq,sra         gs,ncbi,s3
## 5          GEO  public          fastq,sra         gs,ncbi,s3
## 6          GEO  public          fastq,sra         gs,ncbi,s3
## 7          GEO  public          fastq,sra         gs,ncbi,s3
## 8          GEO  public          fastq,sra         gs,ncbi,s3
## 9          GEO  public          fastq,sra         gs,ncbi,s3
## 10         GEO  public          fastq,sra         gs,ncbi,s3
## 11         GEO  public          fastq,sra         gs,ncbi,s3
## 12         GEO  public          fastq,sra         gs,ncbi,s3
##                  DATASTORE.region                          disease_state
## 1  gs.US,ncbi.public,s3.us-east-1 Childhood acute lymphoblastic leukemia
## 2  gs.US,ncbi.public,s3.us-east-1 Childhood acute lymphoblastic leukemia
## 3  gs.US,ncbi.public,s3.us-east-1 Childhood acute lymphoblastic leukemia
## 4  gs.US,ncbi.public,s3.us-east-1 Childhood acute lymphoblastic leukemia
## 5  gs.US,ncbi.public,s3.us-east-1 Childhood acute lymphoblastic leukemia
## 6  gs.US,ncbi.public,s3.us-east-1 Childhood acute lymphoblastic leukemia
## 7  gs.US,ncbi.public,s3.us-east-1 Childhood acute lymphoblastic leukemia
## 8  gs.US,ncbi.public,s3.us-east-1 Childhood acute lymphoblastic leukemia
## 9  gs.US,ncbi.public,s3.us-east-1              Healthy pediatric control
## 10 gs.US,ncbi.public,s3.us-east-1              Healthy pediatric control
## 11 gs.US,ncbi.public,s3.us-east-1              Healthy pediatric control
## 12 gs.US,ncbi.public,s3.us-east-1              Healthy pediatric control
##    Experiment GEO_Accession..exp.          Instrument LibraryLayout
## 1  SRX6034681          GSM3872434 Illumina HiSeq 4000        PAIRED
## 2  SRX6034682          GSM3872435 Illumina HiSeq 4000        PAIRED
## 3  SRX6034683          GSM3872436 Illumina HiSeq 4000        PAIRED
## 4  SRX6034684          GSM3872437 Illumina HiSeq 4000        PAIRED
## 5  SRX6034685          GSM3872438 Illumina HiSeq 4000        PAIRED
## 6  SRX6034686          GSM3872439 Illumina HiSeq 4000        PAIRED
## 7  SRX6034687          GSM3872440 Illumina HiSeq 4000        PAIRED
## 8  SRX6034688          GSM3872441 Illumina HiSeq 4000        PAIRED
## 9  SRX6034689          GSM3872442 Illumina HiSeq 4000        PAIRED
## 10 SRX6034689          GSM3872442 Illumina HiSeq 4000        PAIRED
## 11 SRX6034690          GSM3872443 Illumina HiSeq 4000        PAIRED
## 12 SRX6034691          GSM3872444 Illumina HiSeq 4000        PAIRED
##    LibrarySelection  LibrarySource     Organism Platform          ReleaseDate
## 1              cDNA TRANSCRIPTOMIC Homo sapiens ILLUMINA 2020-02-14T00:00:00Z
## 2              cDNA TRANSCRIPTOMIC Homo sapiens ILLUMINA 2020-02-14T00:00:00Z
## 3              cDNA TRANSCRIPTOMIC Homo sapiens ILLUMINA 2020-02-14T00:00:00Z
## 4              cDNA TRANSCRIPTOMIC Homo sapiens ILLUMINA 2020-02-14T00:00:00Z
## 5              cDNA TRANSCRIPTOMIC Homo sapiens ILLUMINA 2020-02-14T00:00:00Z
## 6              cDNA TRANSCRIPTOMIC Homo sapiens ILLUMINA 2020-02-14T00:00:00Z
## 7              cDNA TRANSCRIPTOMIC Homo sapiens ILLUMINA 2020-02-14T00:00:00Z
## 8              cDNA TRANSCRIPTOMIC Homo sapiens ILLUMINA 2020-02-14T00:00:00Z
## 9              cDNA TRANSCRIPTOMIC Homo sapiens ILLUMINA 2020-02-14T00:00:00Z
## 10             cDNA TRANSCRIPTOMIC Homo sapiens ILLUMINA 2020-02-14T00:00:00Z
## 11             cDNA TRANSCRIPTOMIC Homo sapiens ILLUMINA 2020-02-14T00:00:00Z
## 12             cDNA TRANSCRIPTOMIC Homo sapiens ILLUMINA 2020-02-14T00:00:00Z
##    Sample.Name source_name SRA.Study
## 1   GSM3872434  ETV6-RUNX1 SRP201012
## 2   GSM3872435  ETV6-RUNX1 SRP201012
## 3   GSM3872436  ETV6-RUNX1 SRP201012
## 4   GSM3872437  ETV6-RUNX1 SRP201012
## 5   GSM3872438         HHD SRP201012
## 6   GSM3872439         HHD SRP201012
## 7   GSM3872440       PRE-T SRP201012
## 8   GSM3872441       PRE-T SRP201012
## 9   GSM3872442       PBMMC SRP201012
## 10  GSM3872442       PBMMC SRP201012
## 11  GSM3872443       PBMMC SRP201012
## 12  GSM3872444       PBMMC SRP201012

htmlVec <- list.files(fastqcDir)
htmlVec <- grep("\\.html$", htmlVec, value=TRUE)

filesDf <- data.frame(
              "I1" = sprintf("%s_S0_L001_%s_001_fastqc.html", cb_sampleSheet$Run, "I1"),
              "R1" = sprintf("%s_S0_L001_%s_001_fastqc.html", cb_sampleSheet$Run, "R1"),
              "R2" = sprintf("%s_S0_L001_%s_001_fastqc.html", cb_sampleSheet$Run, "R2")
)
rownames(filesDf) <- cb_sampleSheet$Run

for (runx in cb_sampleSheet$Run)
{
    cat("Run ", runx, ":\n\n")
    for(i in c("I1", "R1", "R2"))
    {
        #filepath <- file.path(fastqcDir, filesDf[runx,i])
        filepath <- file.path(fastqcDirLink, filesDf[runx,i])
        cat(i, ": [", filesDf[runx,i], "](",filepath,")\n\n")
    }
}

Run SRR9264343 :

I1 : SRR9264343_S0_L001_I1_001_fastqc.html

R1 : SRR9264343_S0_L001_R1_001_fastqc.html

R2 : SRR9264343_S0_L001_R2_001_fastqc.html

Run SRR9264344 :

I1 : SRR9264344_S0_L001_I1_001_fastqc.html

R1 : SRR9264344_S0_L001_R1_001_fastqc.html

R2 : SRR9264344_S0_L001_R2_001_fastqc.html

Run SRR9264345 :

I1 : SRR9264345_S0_L001_I1_001_fastqc.html

R1 : SRR9264345_S0_L001_R1_001_fastqc.html

R2 : SRR9264345_S0_L001_R2_001_fastqc.html

Run SRR9264346 :

I1 : SRR9264346_S0_L001_I1_001_fastqc.html

R1 : SRR9264346_S0_L001_R1_001_fastqc.html

R2 : SRR9264346_S0_L001_R2_001_fastqc.html

Run SRR9264347 :

I1 : SRR9264347_S0_L001_I1_001_fastqc.html

R1 : SRR9264347_S0_L001_R1_001_fastqc.html

R2 : SRR9264347_S0_L001_R2_001_fastqc.html

Run SRR9264348 :

I1 : SRR9264348_S0_L001_I1_001_fastqc.html

R1 : SRR9264348_S0_L001_R1_001_fastqc.html

R2 : SRR9264348_S0_L001_R2_001_fastqc.html

Run SRR9264349 :

I1 : SRR9264349_S0_L001_I1_001_fastqc.html

R1 : SRR9264349_S0_L001_R1_001_fastqc.html

R2 : SRR9264349_S0_L001_R2_001_fastqc.html

Run SRR9264350 :

I1 : SRR9264350_S0_L001_I1_001_fastqc.html

R1 : SRR9264350_S0_L001_R1_001_fastqc.html

R2 : SRR9264350_S0_L001_R2_001_fastqc.html

Run SRR9264351 :

I1 : SRR9264351_S0_L001_I1_001_fastqc.html

R1 : SRR9264351_S0_L001_R1_001_fastqc.html

R2 : SRR9264351_S0_L001_R2_001_fastqc.html

Run SRR9264352 :

I1 : SRR9264352_S0_L001_I1_001_fastqc.html

R1 : SRR9264352_S0_L001_R1_001_fastqc.html

R2 : SRR9264352_S0_L001_R2_001_fastqc.html

Run SRR9264353 :

I1 : SRR9264353_S0_L001_I1_001_fastqc.html

R1 : SRR9264353_S0_L001_R1_001_fastqc.html

R2 : SRR9264353_S0_L001_R2_001_fastqc.html

Run SRR9264354 :

I1 : SRR9264354_S0_L001_I1_001_fastqc.html

R1 : SRR9264354_S0_L001_R1_001_fastqc.html

R2 : SRR9264354_S0_L001_R2_001_fastqc.html

2.3 CaronBourque2020 - MultiQC

2.3.1 sample index: I1

htmlVec <- list.files(paste0(fastqcDir, "/Multiqc/I1"))
htmlVec <- grep("\\.html$", htmlVec, value=TRUE)
for(i in htmlVec){
    filename <- file.path(fastqcDirLink, "/Multiqc/I1", i)
    cat("[", i, "](",filename,")\n\n")
}

multiqc_report.html

2.3.2 cell barcode + UMI: R1

htmlVec <- list.files(paste0(fastqcDir, "/Multiqc/R1"))
htmlVec <- grep("\\.html$", htmlVec, value=TRUE)
for(i in htmlVec){
    filename <- file.path(fastqcDirLink, "/Multiqc/R1", i)
    cat("[", i, "](",filename,")\n\n")
}

multiqc_report.html

2.3.3 insert: R2

htmlVec <- list.files(paste0(fastqcDir, "/Multiqc/R2"))
htmlVec <- grep("\\.html$", htmlVec, value=TRUE)
for(i in htmlVec){
    filename <- file.path(fastqcDirLink, "/Multiqc/R2", i)
    cat("[", i, "](",filename,")\n\n")
}

multiqc_report.html

2.4 HCA adult BMMC - fastqc

For the HCA adult BMMC fastq files were provided for each 8-ntd sample index and lane. We ran fastqc on each separately. We are therefore not listing links to the fastqc reports but only to the MultiQC reports.

fastqcDir <- sprintf("%s/Data/%s/fastqc", projDir, "Hca")
fastqcDirLink <- sprintf("%s/Data/%s/fastqc", projDirLink, "Hca")

# HCA
hca_sampleSheetFn <- file.path(projDir, "Data/Hca/accList_Hca.txt")

hca_sampleSheet <- read.table(hca_sampleSheetFn, header=F, sep=",")
colnames(hca_sampleSheet) <- "Run"
hca_sampleSheet

##         Run
## 1 MantonBM1
## 2 MantonBM2
## 3 MantonBM3
## 4 MantonBM4
## 5 MantonBM5
## 6 MantonBM6
## 7 MantonBM7
## 8 MantonBM8

htmlVec <- list.files(fastqcDir)
htmlVec <- grep("\\.html$", htmlVec, value=TRUE)

378 fastqc reports were compiled in the multiQC reports below.

2.5 HCA adult BMMC - MultiQC

2.5.1 sample index: I1

htmlVec <- list.files(paste0(fastqcDir, "/Multiqc/I1"))
htmlVec <- grep("\\.html$", htmlVec, value=TRUE)
for(i in htmlVec){
    filename <- file.path(fastqcDirLink, "/Multiqc/I1", i)
    cat("[", i, "](",filename,")\n\n")
}

multiqc_report.html

2.5.2 cell barcode + UMI: R1

htmlVec <- list.files(paste0(fastqcDir, "/Multiqc/R1"))
htmlVec <- grep("\\.html$", htmlVec, value=TRUE)
for(i in htmlVec){
    filename <- file.path(fastqcDirLink, "/Multiqc/R1", i)
    cat("[", i, "](",filename,")\n\n")
}

multiqc_report.html

2.5.3 insert: R2

htmlVec <- list.files(paste0(fastqcDir, "/Multiqc/R2"))
htmlVec <- grep("\\.html$", htmlVec, value=TRUE)
for(i in htmlVec){
    filename <- file.path(fastqcDirLink, "/Multiqc/R2", i)
    cat("[", i, "](",filename,")\n\n")
}

multiqc_report.html

WORKING DOCUMENT - IN PROGRESS

library(dplyr)