Last updated: 2019-04-03

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Knit directory: Comparative_eQTL/analysis/

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library(corrplot)
library(ggfortify)
library(readxl)
library(tidyverse)
library(psych)
library(ggrepel)
library(knitr)
library(reshape2)
library(gplots)

The RNA-seq libraries were sequenced across many sequencing lanes on a few different flow cells. Here want to check that the data clusters by RNA-seq library (biological sample) rather than flow cell or sequencing lane. This is just a QC-check before I merge fastq files by biological sample, which is perhaps the most straightforward way to process the data.

The data below were processed by individual fastq file (1 file for every individual:flowcell:lane). Gene expression was quantified with kallisto for each fastq and merged into a single matrix of TPM values.

# Read in count table, filtering out rows that are all zeros
CountTable <- read.table(gzfile('../output/CountTable.SeparatedByFastq.tpm.txt.gz'), header=T, check.names=FALSE, row.names = 1) %>%
  filter_all(any_vars(.>0)) %>% rename_all(funs(basename(.)))

# Read in metadata
Metadata <- as.data.frame(read_excel("../data/Metadata.xlsx"))

# Read in lane info
LaneInfo <- read.table('../output/RNA_seq_FlowCellInfo.txt', header=T, stringsAsFactors = F, sep='\t' )
LaneInfo$basename <- basename(LaneInfo$file)

# Read in fastq to sample dictionary
FastqToSample <- read.table('../code/snakemake_workflow/RNASeqFileList.tsv', header=T, stringsAsFactors = F)
FastqToSample$basename <- basename(FastqToSample$fastq)

In total I had 191 fastq files from 39 libraries (1 per biological sample/ individual) which were collectively spread across 22 lanes across 3 HiSeq flow cells.

# Create spearman correlation matrix of log(TPM) for top 1000 expressed genes.
CorMatrix <- CountTable %>%
  +0.1 %>%
  mutate(sumVar = rowSums(.)) %>%
  arrange(desc(sumVar)) %>%
  head(1000) %>%
  select(-sumVar) %>%
  log() %>%
  scale() %>%
  cor(method = c("spearman"))

BiologicalSample <- as.character(unclass(factor(plyr::mapvalues(row.names(CorMatrix), from=FastqToSample$basename, to=FastqToSample$sample))))

FlowCell <- as.character(unclass(factor(plyr::mapvalues(row.names(CorMatrix), from=LaneInfo$basename, to=LaneInfo$flowcellid))))

# Biological samples colored columns. FlowCell colored as rows.
heatmap.2(CorMatrix, trace="none", ColSideColors=BiologicalSample, RowSideColors = FlowCell)

The samples segregate by biological sample rather than flow cell or lane. For downstream processing, I simply combined fastq files from different flow cells and lanes by biological sample and remapped the combined-by-sample fastq files to make new count table.



sessionInfo()
R version 3.5.1 (2018-07-02)
Platform: x86_64-apple-darwin15.6.0 (64-bit)
Running under: macOS  10.14

Matrix products: default
BLAS: /Library/Frameworks/R.framework/Versions/3.5/Resources/lib/libRblas.0.dylib
LAPACK: /Library/Frameworks/R.framework/Versions/3.5/Resources/lib/libRlapack.dylib

locale:
[1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8

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

other attached packages:
 [1] gplots_3.0.1    reshape2_1.4.3  knitr_1.22      ggrepel_0.8.0  
 [5] psych_1.8.10    forcats_0.4.0   stringr_1.4.0   dplyr_0.8.0.1  
 [9] purrr_0.3.2     readr_1.3.1     tidyr_0.8.2     tibble_2.1.1   
[13] tidyverse_1.2.1 readxl_1.1.0    ggfortify_0.4.5 ggplot2_3.1.0  
[17] corrplot_0.84  

loaded via a namespace (and not attached):
 [1] Rcpp_1.0.1         lubridate_1.7.4    lattice_0.20-38   
 [4] gtools_3.8.1       assertthat_0.2.1   rprojroot_1.3-2   
 [7] digest_0.6.18      R6_2.4.0           cellranger_1.1.0  
[10] plyr_1.8.4         backports_1.1.3    evaluate_0.13     
[13] httr_1.4.0         pillar_1.3.1       rlang_0.3.3       
[16] lazyeval_0.2.2     rstudioapi_0.10    gdata_2.18.0      
[19] rmarkdown_1.11     foreign_0.8-71     munsell_0.5.0     
[22] broom_0.5.1        compiler_3.5.1     modelr_0.1.4      
[25] xfun_0.6           pkgconfig_2.0.2    mnormt_1.5-5      
[28] htmltools_0.3.6    tidyselect_0.2.5   gridExtra_2.3     
[31] workflowr_1.2.0    crayon_1.3.4       withr_2.1.2       
[34] bitops_1.0-6       grid_3.5.1         nlme_3.1-137      
[37] jsonlite_1.6       gtable_0.3.0       git2r_0.24.0      
[40] magrittr_1.5       scales_1.0.0       KernSmooth_2.23-15
[43] cli_1.1.0          stringi_1.4.3      fs_1.2.6          
[46] xml2_1.2.0         generics_0.0.2     tools_3.5.1       
[49] glue_1.3.1         hms_0.4.2          parallel_3.5.1    
[52] yaml_2.2.0         colorspace_1.4-1   caTools_1.17.1.1  
[55] rvest_0.3.2        haven_2.1.0