pipeline i used for calling sQTL in chimps

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Authors

  • Benjamin Fair (@bfairkun)

Overview

This pipeline includes read mapping (STAR), preparation of a phenotype table of splicing traits (leafcutter), and sQTL calling (MatrixEQTL calculate nominal associations, and run permutations, saving the best P-value for each intron for each permutation). Then due a permutation test on a per cluster basis. Depending on your downstream analysis, you may want intron level, or gene level Pvalues. If you want those, you will have to edit the script in the pipeline that does the permutation testing.

Usage

Step 1: Install workflow

If you simply want to use this workflow, clone the latest release . If you intend to modify and further develop this workflow, fork this repository. Please consider providing any generally applicable modifications via a pull request.

Install dependencies with conda:

conda env create --file environment.yaml

Other dependencies that I could not include on conda include the scripts for leafcutter . I have my own fork with small modifications that are required for this pipeline to work:

leafcutter : modified script to allow nonconventional chromosome names (eg: 2A)

Clone my forks linked above, and add the necessary scripts to $PATH by appending the following to .bashrc: 

export PATH=$PATH:PathToLeacutterClonedRepo/scripts
export PATH=$PATH:PathToLeacutterClonedRepo/clustering

re-source the .bashrc: source ~/.bashrc

Make sure tidyverse, qvalue, stats, and MatrixEQTLlibraries are installed for R... I have been using RCC's R/3.4.3 ( module load R/3.4.3 ), and installed these with install.packages() once in R.

activate the conda environment: conda activate my_Chimp_EQTL_env

and create rule-specic environments: snakemake --use-conda --create-envs-only

Step 2: Configure workflow

Configure the workflow according to your needs via editing the file config.yaml . Configure cluster settings in cluster-config.json

Step 3: Execute workflow

Test your configuration by performing a dry-run via 

snakemake -n

Execute the workflow locally via 

snakemake --cores $N

using $N cores or run it in a cluster environment via 

snakemake --cluster --cluster-config cluster-config.json --cluster "sbatch --partition={cluster.partition} --job-name={cluster.name} --output=/dev/null --job-name={cluster.name} --nodes={cluster.n} --mem={cluster.mem}"

or by executing the included sbatch script to execute the snakemake process from a cluster 

sbatch snakemake.sbatch

See the Snakemake documentation for further details.

Code Snippets

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shell:
    """
    plink --id-delim '-' --vcf {input.vcf} --vcf-half-call m --allow-extra-chr --make-bed --out eQTL_mapping/plink/Unfiltered &> {log}
    """
SnakeMake pLink From line 8 of rules/eqtl_calling.smk 
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shell:
    """
    plink --bfile eQTL_mapping/plink/Unfiltered  --allow-extra-chr --memory 28000 --make-bed --out eQTL_mapping/plink/ForAssociationTesting {params.keepfam} {params.extra} {params.remove_ind} {params.maf} &> {log}
    """
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shell:
    """
    plink --bfile eQTL_mapping/plink/Unfiltered  --allow-extra-chr --make-bed --out eQTL_mapping/Kinship/ForGRM {params.keepfam} {params.maf} {params.remove_ind} {params.extra}
    sed -i 's/-9$/1/' {output.fam}
    """
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shell:
    """
    plink --bfile eQTL_mapping/Kinship/ForGRM --allow-extra-chr --indep-pairwise 50 5 0.5 &> {log}
    plink --bfile eQTL_mapping/Kinship/ForGRM  --allow-extra-chr --extract plink.prune.in --make-bed --out eQTL_mapping/Kinship/ForAssociationTesting.pruned &> {log}
    sed -i 's/-9$/1/' {output.fam}
    rm plink.prune.in plink.prune.out
    """
SnakeMake pLink From line 64 of rules/eqtl_calling.smk 
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shell:
    """
    gemma -gk 1 -bfile eQTL_mapping/Kinship/ForAssociationTesting.pruned -o GRM -outdir eQTL_mapping/Kinship &> {log}
    """
SnakeMake Gemma From line 80 of rules/eqtl_calling.smk 
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shell:
    """
    plink --bfile  eQTL_mapping/plink/ForAssociationTesting --allow-extra-chr --recode A-transpose --tab --geno --memory 40000 --out eQTL_mapping/MatrixEQTL/ForAssociationTesting.snps
    perl -lne '/^.+?\\t(.+?\\t).+?\\t.+?\\t.+?\\t.+?\\t(.+$)/ and print "$1$2" ' eQTL_mapping/MatrixEQTL/ForAssociationTesting.snps.traw | sed '1s/_{params}//g' > {output}
    """
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shell:
    """
    awk -F'\\t' -v OFS='\\t' 'BEGIN {{ print "snp","chrom","pos" }} {{ print $2, $1,$4 }}' {input.snps} > {output.snp_locs}
    """
SnakeMake From line 104 of rules/eqtl_calling.smk 
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shell:
    "cat {input} > {output}"
SnakeMake From line 6 of rules/RNASeqMapping.smk 
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shell:
    """
    STAR --runMode genomeGenerate --runThreadN 4 --genomeDir MiscOutput/STAR_index/ --sjdbGTFfile {input.gtf} --genomeFastaFiles {input.fasta} &> {log}
    """
SnakeMake STAR From line 17 of rules/RNASeqMapping.smk 
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shell:
    "STAR --runThreadN {threads} --genomeDir MiscOutput/STAR_index/ --readFilesIn {input.fastq} --outSAMtype BAM SortedByCoordinate --outWigStrand Unstranded --outWigType wiggle --alignEndsType EndToEnd --quantMode GeneCounts --twopassMode Basic --readFilesCommand zcat --outFileNamePrefix RNASeq/STAR/{wildcards.RNASeqSample}/ &> {log}"
SnakeMake STAR From line 33 of rules/RNASeqMapping.smk 
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shell:
    "samtools index {input}"
SnakeMake SAMtools From line 41 of rules/RNASeqMapping.smk 
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shell: "bash scripts/GetFastqIdentifierInfo.sh {input} > {output} 2> {log}"
SnakeMake From line 48 of rules/RNASeqMapping.smk 
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shell:
    """
    awk -F'\\t' -v OFS='\\t' '$4==1 && $1!="MT" {{ print $1,$2,$3,".",$7,"+" }} $4==2&& $1!="MT" {{ print $1,$2,$3,".",$7,"-" }}' {input} > {output}
    """
SnakeMake From line 8 of rules/sqtl_calling.smk 
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run:
    import os
    SamplesToRemove = open(params.SamplesToRemove, 'r').read().split('\n')
    with open(output[0], "w") as out: 
        for filepath in input:
            samplename = os.path.basename(filepath).split(".junc")[0]
            if samplename not in  SamplesToRemove:
                out.write(filepath + '\n')
SnakeMake From line 20 of rules/sqtl_calling.smk 
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shell:
    """
    leafcutter_cluster.py -j {input} -r sQTL_mapping/leafcutter/clustering/ &> {log}
    """
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shell:
    """
    ~/miniconda3/bin/python2.7 ~/CurrentProjects/leafcutter/scripts/prepare_phenotype_table.py -p 13 --ChromosomeBlackList {input.blacklist_chromosomes}  {input.counts}

    cat <(head -1 sQTL_mapping/leafcutter/clustering/leafcutter_perind.counts.gz.qqnorm_chr3) <(awk 'FNR>1' sQTL_mapping/leafcutter/clustering/leafcutter_perind.counts.gz.qqnorm_chr*) > {output.phenotypes}
    """
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shell:
    """
    cut -d $'\\t' -f 4- {input.phenotypes} > {output.phenotypes}
    awk -F'\\t' -v OFS='\\t' 'BEGIN {{ print "gene", "chr", "start", "stop" }} NR>1 {{ split($4,a,":"); print $4, a[1], a[2], a[3] }}' {input.phenotypes} > {output.intron_locs}
    Rscript scripts/ReorderPhenotypeTableForMatrixEQTL.R {output.phenotypes} {input.fam} {output.phenotypesReordered}
    """
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shell:
    """
    Rscript scripts/SelectNtoM_PCs_toCovariateFiles.R  {input.leafcutterPCs} {input.fam} sQTL_mapping/Covariates/FromLeafcutter.PCs. {params.PC_min} {params.PC_max}
    """
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shell:
    """
    Rscript scripts/MatrixEqtl_Cis.R {input.snps} {input.snp_locs} {input.phenotypes} {input.gene_loc} {input.covariates} {input.GRM} {output.results} {output.fig} {output.permuted_results} {output.permutated_fig} {params.cis_window} &> {log}
    """
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shell:
    """
    awk -F'\\t' -v OFS='\\t' 'FNR>1 && $6<0.3 {{ print $1,$2,$3,$4,$5,$6,FILENAME  }}' {input} > {output.CattedResult}
    Rscript scripts/Plot_EQTLs_vs_PCs.R {output.CattedResult} {output.Plot}
    """
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shell:
    """
    Rscript scripts/MatrixEqtl_Cis.AllPvals.R {input.snps} {input.snp_locs} {input.phenotypes} {input.gene_loc} {input.covariates} {input.GRM} {output.results} {output.fig} {params.cis_window} {output.BestGenePvals} &> {log}
    """
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shell:
    """
    awk -F'\\t' -v OFS='\\t' 'NR==1 || /^ID\.{wildcards.chromosome}\./' {input.snps} > {output.snps}
    awk -F'\\t' -v OFS='\\t' 'NR==1 || /^ID\.{wildcards.chromosome}\./' {input.snp_locs} > {output.snp_locs}
    awk -F'\\t' -v OFS='\\t' 'NR==1 || /^ID\.{wildcards.chromosome}\./ {{print $1, $2, $3, $4, $5, $6}}' {input.results} > {output.results}
    """
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shell:
    """
    eigenMT.py --QTL {input.results} --GEN {input.snps} --GENPOS {input.snp_locs} --PHEPOS {input.gene_loc} --cis_dist 250000 --OUT {output} --CHROM {wildcards.chromosome} &> {log}
    """
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shell:
    """
    (cat <(cat {input} | head -1) <(cat {input} | grep -v '^snps') | gzip - > {output}) &> {log}
    """
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shell:
    """
    Rscript scripts/MatrixEqtl_Cis_Permutations.R {input.snps} {input.snp_locs} {input.phenotypes} {input.gene_loc} {input.covariates} {input.GRM} {output.results} {params.NumberPermutations} {params.InitialSeed} {params.cis_window} > {log}
    """
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shell:
    """
    cat sQTL_mapping/MatrixEQTL/ConfigCovariateModelResults/Permutations/Chunk.0.txt > {output}
    for i in {{1..{LastChunk}}}; do
        tail -n +2 sQTL_mapping/MatrixEQTL/ConfigCovariateModelResults/Permutations/Chunk.${{i}}.txt >> {output}
    done
    """
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shell:
    """
    Rscript scripts/PermutationTest.R {input.Permutations_MinPvaluePerTrait} {input.Actual_MinPvaluePerTrait} {output.PermutationTestResults} &> {log}
    """
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printf "file\tinstrument\trunid\tflowcellid\tflowcelllane\tbarcode"
for MYFILE in "$@"
do
    samplename=$(basename -s ".fastq.gz" $MYFILE)
    printf "\n$MYFILE\t"
    barcode=$(zcat $MYFILE | paste -d'\t'  - - - - | awk -F'\t' '{split($1,a,":"); print a[10]}' | head -10000 | sort | uniq -c | sort -nr | head -1)
    outstr=$(zcat $MYFILE | head -1 | awk -F'\t' -v OFS='\t' '{split($1,a,":"); print a[1], a[2], a[3], a[4]}')
    printf "$outstr\t$barcode"
done
Shell From line 6 of scripts/GetFastqIdentifierInfo.sh 
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library(MatrixEQTL)
library(tidyverse)
library(qvalue)

args = commandArgs(trailingOnly=TRUE)
SNP_file_name <- args[1]
snps_location_file_name <- args[2]
expression_file_name <- args[3]
gene_location_file_name <- args[4]
covariates_file_name <- args[5]
errorCovariance_file <- args[6]
output_file_name_cis <- args[7]
ouput_QQ <- args[8]
cisDistance <- args[9]
output_best_p <- args[10]

# SNP_file_name <- "~/CurrentProjects/Comparative_eQTL/code/snakemake_workflow/scratch/Test.snps"
# snps_location_file_name <- "~/CurrentProjects/Comparative_eQTL/code/snakemake_workflow/scratch/Test.snploc"
# expression_file_name <- "code/snakemake_workflow/eQTL_mapping/MatrixEQTL/ForAssociationTesting.phenotypes.txt"
# gene_location_file_name <- "code/snakemake_workflow/eQTL_mapping/MatrixEQTL/ForAssociationTesting.geneloc.txt"
# covariates_file_name <- "output/Covariates/0GenotypePCs_and_11RNASeqPCs.covariates"
# errorCovariance_file <- "code/snakemake_workflow/eQTL_mapping/Kinship/GRM.cXX.txt"
# output_file_name_cis = tempfile()
# cisDistance = 250000

# SNP_file_name <- "code/snakemake_workflow/eQTL_mapping/MatrixEQTL/ForAssociationTesting.snps"
# snps_location_file_name <- "code/snakemake_workflow/eQTL_mapping/MatrixEQTL/ForAssociationTesting.snploc"
# expression_file_name <- "code/snakemake_workflow/eQTL_mapping/MatrixEQTL/ForAssociationTesting.phenotypes.txt"
# gene_location_file_name <- "code/snakemake_workflow/eQTL_mapping/MatrixEQTL/ForAssociationTesting.geneloc.txt"
# covariates_file_name <- "output/Covariates/0GenotypePCs_and_10RNASeqPCs.covariates"
# errorCovariance_file <- "code/snakemake_workflow/eQTL_mapping/Kinship/GRM.cXX.txt"
# output_file_name_cis = tempfile()
# cisDistance = 250000

# Linear model to use, modelANOVA, modelLINEAR, or modelLINEAR_CROSS
useModel = modelLINEAR; # modelANOVA, modelLINEAR, or modelLINEAR_CROSS


output_file_name_tra = tempfile();

# Only associations significant at this level will be saved
pvOutputThreshold_cis = 1;

# Error covariance matrix
# Set to numeric() for identity.
errorCovariance <- as.matrix(read.table(errorCovariance_file,sep='\t'))


# Distance for local gene-SNP pairs
cisDist = as.numeric(cisDistance);
print(cisDist)

## Load genotype data

snps = SlicedData$new();
snps$fileDelimiter = "\t";      # the TAB character
snps$fileOmitCharacters = "NA"; # denote missing values;
snps$fileSkipRows = 1;          # one row of column labels
snps$fileSkipColumns = 1;       # one column of row labels
snps$fileSliceSize = 2000;      # read file in slices of 2,000 rows
snps$LoadFile(SNP_file_name);

## Load gene expression data
print("reading phenotype table")
gene = SlicedData$new();
gene$fileDelimiter = "\t";      # the TAB character
gene$fileOmitCharacters = "NA"; # denote missing values;
gene$fileSkipRows = 1;          # one row of column labels
gene$fileSkipColumns = 1;       # one column of row labels
gene$fileSliceSize = 2000;      # read file in slices of 2,000 rows
gene$LoadFile(expression_file_name);

## Load covariates
print("reading covariates")
cvrt = SlicedData$new();
cvrt$fileDelimiter = "\t";      # the TAB character
cvrt$fileOmitCharacters = "NA"; # denote missing values;
cvrt$fileSkipRows = 1;          # one row of column labels
cvrt$fileSkipColumns = 1;       # one column of row labels
if(length(covariates_file_name)>0) {
  cvrt$LoadFile(covariates_file_name);
}

## Run the analysis
snpspos = read.table(snps_location_file_name, header = TRUE, stringsAsFactors = FALSE);
genepos = read.table(gene_location_file_name, header = TRUE, stringsAsFactors = FALSE);

#Include all pvalues in output so that qvalues can be calculated. Filter after.
me = Matrix_eQTL_main(
  snps = snps,
  gene = gene,
  cvrt = cvrt,
  output_file_name     = NULL,
  pvOutputThreshold     = 0,
  useModel = useModel,
  errorCovariance = errorCovariance,
  verbose = TRUE,
  output_file_name.cis = NULL,
  pvOutputThreshold.cis = 1,
  snpspos = snpspos,
  genepos = genepos,
  cisDist = cisDist,
  pvalue.hist = "qqplot",
  min.pv.by.genesnp = TRUE,
  noFDRsaveMemory = FALSE);
print('done with real pass')

#Transform Pvalues to Qvalues
#If pi_0 is close to 1 (which is often case in eQTL calling), BH-p will be equal to Q-value within precision of saved digits.
me$cis$eqtls$qvalue <- qvalue(me$cis$eqtls$pvalue)$qvalues

print('done with qval stuff')
#Write out results, while filtering by pvalue
me$cis$eqtls %>%
  select(snps, gene, beta, statistic, pvalue, FDR, qvalue) %>%
  write.table(file=output_file_name_cis, sep='\t', quote = F, row.names = F)

cat('Analysis done in: ', me$time.in.sec, ' seconds', '\n');

## Plot the Q-Q plot of local p-values

ggsave(file=ouput_QQ, plot(me))

## Write out best p-value for each phenotype.
me$cis$min.pv.gene %>% as.data.frame() %>%
    rownames_to_column() %>%
write.table(file=output_best_p, sep='\t', quote=F, col.names=c("Gene", "MinP"), row.names = FALSE)
R tidyverse qvalue MatrixEQTL From line 7 of scripts/MatrixEqtl_Cis.AllPvals.R 
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library(MatrixEQTL)
library(tidyverse)

args = commandArgs(trailingOnly=TRUE)
SNP_file_name <- args[1]
snps_location_file_name <- args[2]
expression_file_name <- args[3]
gene_location_file_name <- args[4]
covariates_file_name <- args[5]
errorCovariance_file <- args[6]
permutation_matrix_output_filename <- args[7]
Npermutations <- as.numeric(args[8])
InitialSeed <- as.numeric(args[9])
cisDistance <- args[10]

# setwd("/project2/gilad/bjf79_project1/projects/Comparative_eQTL/")
# SNP_file_name <- "~/CurrentProjects/Comparative_eQTL/code/snakemake_workflow/scratch/Test.snps"
# snps_location_file_name <- "~/CurrentProjects/Comparative_eQTL/code/snakemake_workflow/scratch/Test.snploc"
# expression_file_name <- "code/snakemake_workflow/eQTL_mapping/MatrixEQTL/ForAssociationTesting.phenotypes.txt"
# gene_location_file_name <- "code/snakemake_workflow/eQTL_mapping/MatrixEQTL/ForAssociationTesting.geneloc.txt"
# covariates_file_name <- "output/Covariates/0GenotypePCs_and_11RNASeqPCs.covariates"
# errorCovariance_file <- "code/snakemake_workflow/eQTL_mapping/Kinship/GRM.cXX.txt"
# permutation_matrix_output_filename <- "/project2/gilad/bjf79/temp/PvalueMatrix.txt"
# cisDistance<-100000
# InitialSeed <- 1
# Npermutations <- 5


# Linear model to use, modelANOVA, modelLINEAR, or modelLINEAR_CROSS
useModel = modelLINEAR; # modelANOVA, modelLINEAR, or modelLINEAR_CROSS


output_file_name_tra = tempfile();

# Only associations significant at this level will be saved

# Error covariance matrix
# Set to numeric() for identity.
errorCovariance <- as.matrix(read.table(errorCovariance_file,sep='\t'))


# Distance for local gene-SNP pairs
cisDist = as.numeric(cisDistance);
print(paste("Cis distance setting:",cisDist))

## Load genotype data
print("Loading genotype data...")
snps = SlicedData$new();
snps$fileDelimiter = "\t";      # the TAB character
snps$fileOmitCharacters = "NA"; # denote missing values;
snps$fileSkipRows = 1;          # one row of column labels
snps$fileSkipColumns = 1;       # one column of row labels
snps$fileSliceSize = 2000;      # read file in slices of 2,000 rows
snps$LoadFile(SNP_file_name);

## Load gene expression data
print("Loading expression data...")
gene = SlicedData$new();
gene$fileDelimiter = "\t";      # the TAB character
gene$fileOmitCharacters = "NA"; # denote missing values;
gene$fileSkipRows = 1;          # one row of column labels
gene$fileSkipColumns = 1;       # one column of row labels
gene$fileSliceSize = 2000;      # read file in slices of 2,000 rows
gene$LoadFile(expression_file_name);

## Load covariates
print("Loading covariate data...")
cvrt = SlicedData$new();
cvrt$fileDelimiter = "\t";      # the TAB character
cvrt$fileOmitCharacters = "NA"; # denote missing values;
cvrt$fileSkipRows = 1;          # one row of column labels
cvrt$fileSkipColumns = 1;       # one column of row labels
if(length(covariates_file_name)>0) {
  cvrt$LoadFile(covariates_file_name);
}

## Other initialization stuff
print("Loading other stuff...")
snpspos = read.table(snps_location_file_name, header = TRUE, stringsAsFactors = FALSE);
genepos = read.table(gene_location_file_name, header = TRUE, stringsAsFactors = FALSE);

### Permute the sample labels for expression ###
# Read in actual data expression matrix and covariates
ActualData.ExpressionMatrix <- read.table(expression_file_name, header=T, row.names = 1, check.names = FALSE )
ActualData.Cov <- read.table(covariates_file_name, header=T, row.names = 1, check.names = FALSE )

#Initialize empty matrix for results. Rows are genes. Columns are permutations
PermutatePvalueMatrix <- matrix(data=NA, nrow = nrow(ActualData.ExpressionMatrix), ncol = Npermutations)

### Run permutations
print("Running permutations...")
for (i in 1:Npermutations){
  # Permute column labels for both (using same seed for randomization)
  # technically I am permuting the column data, and preserving the column labels. That way, I do not have to do the same for the (huge) genotype file, which would take more computational time
  set.seed(i+InitialSeed)
  Temp.df <- ActualData.ExpressionMatrix %>% select(sample(colnames(ActualData.ExpressionMatrix), length(colnames(ActualData.ExpressionMatrix))))
  set.seed(i+InitialSeed)
  Temp.df.cov <- ActualData.Cov %>% select(sample(colnames(ActualData.ExpressionMatrix), length(colnames(ActualData.ExpressionMatrix))))
  colnames(Temp.df) <- colnames(ActualData.ExpressionMatrix)
  colnames(Temp.df.cov) <- colnames(ActualData.ExpressionMatrix)

  # Write out permutated expression matrix and reload it
  TempFilepath.ExpressionMatrix <- tempfile("ExpressionMatrix.")
  write.table(Temp.df, file=TempFilepath.ExpressionMatrix, sep='\t', quote=F, col.names =NA)
  gene$LoadFile(TempFilepath.ExpressionMatrix);

  TempFilepath.Covariates <- tempfile("Covariates.")
  write.table(Temp.df.cov, file=TempFilepath.Covariates, sep='\t', quote=F, col.names =NA)
  cvrt$LoadFile(TempFilepath.Covariates);

  #Calculate Pvalues from permutated data
  permuted = Matrix_eQTL_main(
    snps = snps,
    gene = gene,
    cvrt = cvrt,
    output_file_name     = NULL,
    pvOutputThreshold     = 0,
    useModel = useModel,
    errorCovariance = errorCovariance,
    verbose = F,
    output_file_name.cis = NULL,
    pvOutputThreshold.cis = 1,
    snpspos = snpspos,
    genepos = genepos,
    cisDist = cisDist,
    pvalue.hist = FALSE,
    min.pv.by.genesnp = TRUE,
    noFDRsaveMemory = FALSE);

  print(paste('done with permutation pass', i))
  PermutatePvalueMatrix[,i]=permuted$cis$min.pv.gene
}

row.names(PermutatePvalueMatrix) <- names(permuted$cis$min.pv.gene)
write.table(t(PermutatePvalueMatrix), permutation_matrix_output_filename, quote=F, sep='\t', col.names = T, row.names=F)
R tidyverse MatrixEQTL From line 7 of scripts/MatrixEqtl_Cis_Permutations.R 
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library(MatrixEQTL)
library(tidyverse)
library(qvalue)

args = commandArgs(trailingOnly=TRUE)
SNP_file_name <- args[1]
snps_location_file_name <- args[2]
expression_file_name <- args[3]
gene_location_file_name <- args[4]
covariates_file_name <- args[5]
errorCovariance_file <- args[6]
output_file_name_cis <- args[7]
ouput_QQ <- args[8]
permuted_output_filename <- args[9]
permuted_output_QQ <- args[10]
cisDistance <- args[11]

# setwd("/project2/gilad/bjf79_project1/projects/Comparative_eQTL/")
# SNP_file_name <- "/project2/gilad/bjf79_project1/projects/Comparative_eQTL/code/snakemake_workflow/scratch/Test.snps"
# snps_location_file_name <- "/project2/gilad/bjf79_project1/projects/Comparative_eQTL/code/snakemake_workflow/scratch/Test.snploc"
# expression_file_name <- "code/snakemake_workflow/eQTL_mapping/MatrixEQTL/ForAssociationTesting.phenotypes.txt"
# gene_location_file_name <- "code/snakemake_workflow/eQTL_mapping/MatrixEQTL/ForAssociationTesting.geneloc.txt"
# covariates_file_name <- "output/Covariates/0GenotypePCs_and_11RNASeqPCs.covariates"
# errorCovariance_file <- "code/snakemake_workflow/eQTL_mapping/Kinship/GRM.cXX.txt"
# cisDistance<-100000
# output_file_name_cis = tempfile()

# Linear model to use, modelANOVA, modelLINEAR, or modelLINEAR_CROSS
useModel = modelLINEAR; # modelANOVA, modelLINEAR, or modelLINEAR_CROSS


output_file_name_tra = tempfile();

# Only associations significant at this level will be saved
pvOutputThreshold_cis = 2e-2;

# Error covariance matrix
# Set to numeric() for identity.
errorCovariance <- as.matrix(read.table(errorCovariance_file,sep='\t'))


# Distance for local gene-SNP pairs
cisDist = as.numeric(cisDistance);
print(cisDist)

## Load genotype data

snps = SlicedData$new();
snps$fileDelimiter = "\t";      # the TAB character
snps$fileOmitCharacters = "NA"; # denote missing values;
snps$fileSkipRows = 1;          # one row of column labels
snps$fileSkipColumns = 1;       # one column of row labels
snps$fileSliceSize = 2000;      # read file in slices of 2,000 rows
snps$LoadFile(SNP_file_name);

## Load gene expression data

gene = SlicedData$new();
gene$fileDelimiter = "\t";      # the TAB character
gene$fileOmitCharacters = "NA"; # denote missing values;
gene$fileSkipRows = 1;          # one row of column labels
gene$fileSkipColumns = 1;       # one column of row labels
gene$fileSliceSize = 2000;      # read file in slices of 2,000 rows
gene$LoadFile(expression_file_name);

## Load covariates

cvrt = SlicedData$new();
cvrt$fileDelimiter = "\t";      # the TAB character
cvrt$fileOmitCharacters = "NA"; # denote missing values;
cvrt$fileSkipRows = 1;          # one row of column labels
cvrt$fileSkipColumns = 1;       # one column of row labels
if(length(covariates_file_name)>0) {
  cvrt$LoadFile(covariates_file_name);
}

## Run the analysis
snpspos = read.table(snps_location_file_name, header = TRUE, stringsAsFactors = FALSE);
genepos = read.table(gene_location_file_name, header = TRUE, stringsAsFactors = FALSE);

#Include all pvalues in output so that qvalues can be calculated. Filter after.
me = Matrix_eQTL_main(
  snps = snps,
  gene = gene,
  cvrt = cvrt,
  output_file_name     = NULL,
  pvOutputThreshold     = 0,
  useModel = useModel,
  errorCovariance = errorCovariance,
  verbose = TRUE,
  output_file_name.cis = NULL,
  pvOutputThreshold.cis = 1,
  snpspos = snpspos,
  genepos = genepos,
  cisDist = cisDist,
  pvalue.hist = "qqplot",
  min.pv.by.genesnp = TRUE,
  noFDRsaveMemory = FALSE);
print('done with real pass')

#Transform Pvalues to Qvalues
#If pi_0 is close to 1 (which is often case in eQTL calling), BH-p will be equal to Q-value within precision of saved digits.
me$cis$eqtls$qvalue <-
  qvalue(me$cis$eqtls$pvalue)$qvalues

#Write out results, while filtering by pvalue
me$cis$eqtls %>%
  filter(pvalue < pvOutputThreshold_cis ) %>%
  select(snps, gene, beta, statistic, pvalue, FDR, qvalue) %>%
  write.table(file=output_file_name_cis, sep='\t', quote = F, row.names = F)


# Permute the sample labels for expression
ActualData <- read.table(expression_file_name, header=T, row.names = 1)
Temp.df <- ActualData %>% select(sample(colnames(ActualData), length(colnames(ActualData))))
colnames(Temp.df) <- colnames(ActualData)
TempFilepath <- tempfile()
write.table(Temp.df, file=TempFilepath, sep='\t', quote=F, col.names =NA)

gene$LoadFile(TempFilepath);

permuted = Matrix_eQTL_main(
  snps = snps,
  gene = gene,
  cvrt = cvrt,
  output_file_name     = NULL,
  pvOutputThreshold     = 0,
  useModel = useModel,
  errorCovariance = errorCovariance,
  verbose = TRUE,
  output_file_name.cis = NULL,
  pvOutputThreshold.cis = 1,
  snpspos = snpspos,
  genepos = genepos,
  cisDist = cisDist,
  pvalue.hist = "qqplot",
  min.pv.by.genesnp = FALSE,
  noFDRsaveMemory = FALSE);
print('done with permutation pass')

permuted$cis$eqtls %>%
  filter(pvalue < pvOutputThreshold_cis ) %>%
  select(snps, gene, beta, statistic, pvalue, FDR) %>%
  write.table(file=permuted_output_filename, sep='\t', quote = F, row.names = F)

## Results:

cat('Analysis done in: ', me$time.in.sec, ' seconds', '\n');

## Plot the Q-Q plot of local p-values

ggsave(file=ouput_QQ, plot(me))
ggsave(file=permuted_output_QQ, plot(permuted))
R tidyverse qvalue MatrixEQTL From line 7 of scripts/MatrixEqtl_Cis.R 
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library(tidyverse)
library(data.table)
library(qvalue)
library(stats)


args = commandArgs(trailingOnly=TRUE)
PermutationsTableFile <- args[1]
ActualTableFile <- args[2]
TableOutFile <- args[3]

## Files for testing
# setwd("~/CurrentProjects/sQTL_pipeline/")
# PermutationsTableFile <- "sQTL_mapping/MatrixEQTL/ConfigCovariateModelResults/PermutationsCombined.txt"
# ActualTableFile <- "sQTL_mapping/MatrixEQTL/ConfigCovariateModelResults/BestPvalueNonPermuted.txt"
# TableOutFile <- "~/temp/test.txt"

# read table of min p values from permutations
permutationTable <- t(fread(PermutationsTableFile, sep='\t', header=T))

# read table of min p values from real data
ActualTable <- fread(ActualTableFile, sep='\t', header=T) %>%
  as.data.frame() %>% tibble::column_to_rownames("Gene")

# Merge the two tables, putting the real data as the first column.
# If you are interested in gene level or cluster-level tests, this where you
# should group and find minimum Pval. Here I did cluster level P-values.
MergedTable <- merge(ActualTable, permutationTable, by="row.names") %>%
  mutate(clusterName=gsub("^(.+?):.+?:.+?:(clu_\\d+).+$", "\\1.\\2", Row.names, perl=T)) %>%
  dplyr::group_by(clusterName) %>% dplyr::summarise_all(dplyr::funs(min)) %>%
  dplyr::ungroup() %>% dplyr::select(-Row.names) %>% as.data.frame() %>%
  tibble::column_to_rownames("clusterName")

PermutationTest <- function(PvalVector){
  return(ecdf(PvalVector)(PvalVector[1]))
}


OutTable <- data.frame(
  BestActualPval=MergedTable$MinP,
  PermutationTestPval=apply(MergedTable, 1, PermutationTest))
OutTable$PermutationTestQvalue <- qvalue(OutTable$PermutationTestPval)$qvalue

OutTable %>%
  tibble::rownames_to_column(var = "GroupedTrait") %>%
  write.table(file=TableOutFile, quote=F, sep='\t')
R tidyverse data.table qvalue From line 1 of scripts/PermutationTest.R 
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library(tidyverse)
library(gridExtra)
library(reshape2)

args <- commandArgs(trailingOnly = T)
FileIn <- args[1]
PlotOut <- args[2]

results <- read.table(FileIn, sep='\t', header=F, col.names = c("SNP", "gene", "beta","t", "p", "q", "Filename"))
head(results)
## eGene count
eGenes_perPC <- results %>%
  filter(q<0.2) %>%
  distinct(gene, Filename, .keep_all = TRUE) %>%
  group_by(Filename) %>%
  tally() %>%
  mutate(PCcount = gsub("^(.+?)_and_(.+?)RNASeqPCs.covariates.txt","\\2",Filename, perl=TRUE)) %>%
  select(-Filename, FDR_20=n)

eGenes_perPC$FDR_10 <- results %>%
  filter(q<0.1) %>%
  distinct(gene, Filename, .keep_all = TRUE) %>%
  group_by(Filename) %>%
  tally() %>%
  mutate(PCcount = gsub("^(.+?)_and_(.+?)RNASeqPCs.covariates.txt","\\2",Filename, perl=TRUE)) %>%
  pull(n)

eGenes_perPC$FDR_5 <- results %>%
  filter(q<0.05) %>%
  distinct(gene, Filename, .keep_all = TRUE) %>%
  group_by(Filename) %>%
  tally() %>%
  mutate(PCcount = gsub("^(.+?)_and_(.+?)RNASeqPCs.covariates.txt","\\2",Filename, perl=TRUE)) %>%
  pull(n)

P1 <- eGenes_perPC %>%
  melt(id.vars="PCcount") %>%
  transform(PCcount=as.numeric(PCcount)) %>%
  mutate(FDR = plyr::mapvalues(variable, from=c("FDR_10", "FDR_20", "FDR_5"), to=c("0.1", "0.2", "0.05"))) %>%
  ggplot(aes(x=PCcount, y=value, color=FDR)) +
  geom_point() +
  geom_line() +
  xlab("Number PCs") +
  ylab("Number eGenes") +
  theme_bw()

## eQTL count
eGenes_perPC <- results %>%
  filter(q<0.2) %>%
  group_by(Filename) %>%
  tally() %>%
  mutate(PCcount = gsub("^(.+?)_and_(.+?)RNASeqPCs.covariates.txt","\\2",Filename, perl=TRUE)) %>%
  select(-Filename, FDR_20=n)

eGenes_perPC$FDR_10 <- results %>%
  filter(q<0.1) %>%
  group_by(Filename) %>%
  tally() %>%
  mutate(PCcount = gsub("^(.+?)_and_(.+?)RNASeqPCs.covariates.txt","\\2",Filename, perl=TRUE)) %>%
  pull(n)

eGenes_perPC$FDR_5 <- results %>%
  filter(q<0.05) %>%
  group_by(Filename) %>%
  tally() %>%
  mutate(PCcount = gsub("^(.+?)_and_(.+?)RNASeqPCs.covariates.txt","\\2",Filename, perl=TRUE)) %>%
  pull(n)

P2 <- eGenes_perPC %>%
  melt(id.vars="PCcount") %>%
  transform(PCcount=as.numeric(PCcount)) %>%
  mutate(FDR = plyr::mapvalues(variable, from=c("FDR_10", "FDR_20", "FDR_5"), to=c("0.1", "0.2", "0.05"))) %>%
  ggplot(aes(x=PCcount, y=value, color=FDR)) +
  geom_point() +
  geom_line() +
  xlab("Number PCs") +
  ylab("Number eGene-SNP pairs") +
  theme_bw()

g<-arrangeGrob(P1, P2, nrow=2)
ggsave(PlotOut, g)
R ggplot2 tidyverse reshape2 gridExtra From line 1 of scripts/Plot_EQTLs_vs_PCs.R 
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library(tidyverse)

args <- commandArgs(trailingOnly = T)
PhenotypeTableFilepath <- args[1]
EmptyFamFilepath <- args[2]
PhenotypeOutFilepath <- args[3]
EmptyFamFile <- read.table(EmptyFamFilepath, col.names=c("FID", "IID", "Father", "Mother", "SX", "Pheno"), stringsAsFactors = F) %>%
  select(-Pheno)

PhenotypeFile <- read.table(PhenotypeTableFilepath, header=T, check.names = F)
PhenotypeFile %>%
  select(ID, EmptyFamFile$IID) %>%
  write.table(file=PhenotypeOutFilepath, col.names = T, row.names = F, quote=F, sep='\t')
R tidyverse From line 1 of scripts/ReorderPhenotypeTableForMatrixEQTL.R 
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library(tidyverse)

args <- commandArgs(trailingOnly = T)
PCsFromLeafcutter <- args[1]
EmptyFamFilepath <- args[2]
PhenotypeOutBaseFilepath <- args[3]
N <- args[4]
M <- args[5]

EmptyFamFile <- read.table(EmptyFamFilepath, col.names=c("FID", "IID", "Father", "Mother", "SX", "Pheno"), stringsAsFactors = F, sep=" ") %>%
  select(-Pheno)

Covariates <- read.table(PCsFromLeafcutter, header=T, check.names = F)

for (i in N:M){
  Covariates %>%
    select(id, EmptyFamFile$IID) %>%
    filter(id %in% N:i) %>%
    write.table(file=paste0(PhenotypeOutBaseFilepath, i, ".covariates.txt"), quote=F, row.names=F, sep='\t')
}
R tidyverse From line 1 of scripts/SelectNtoM_PCs_toCovariateFiles.R
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Created: 1yr ago
Updated: 1yr ago
Maitainers: public
URL: https://github.com/bfairkun/sQTL
Name: sqtl
Version: 1
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Copyright: Public Domain
License: MIT License
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