Snakemake pipeline for Gene Set Enrichment: pathway and GO enrichment analysis

public public 1yr ago 0 bookmarks
View Workflow
Report
genesetenrichment
genesetenrichment
View Workflow

Snakemake Workflow for Gene Set Enrichment: Pathway and GO Analysis

Author: Sherine Awad

Change the config.yaml file appropriately according to your data. Update parameters of Genrich in the config file. Also, change workdir where the reference genome, etc.

Then run: snakemake -jnumber_of_cores, for example for 5 cores use: 

snakemake -j5

and for a dry run use: 

snakemake -j1 -n

and to print the commands in a dry run use: 

snakemake -j1 -n -p

For the sake reproducibility, use conda to pull same versions of tools. Snakemake and conda have to be installed in your system: 

snakemake --cores --use-conda

TO DO

  1. Add more Genome for enrichment

  2. Add more parameters for a genric pipeline

Code Snippets

 1
 2
 3
 4
 5
 6
 7
 8
 9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
library("edgeR")
library(biomaRt)
library(dplyr)
library("AnnotationDbi")
library("org.Hs.eg.db")
library(data.table)
library("gplots")
library(magrittr)
library(pathview)
library("org.Mm.eg.db")
library(gage)
library(gageData)
library(data.table)


args <- commandArgs(trailingOnly = TRUE)
dge = args[1]
ORGANISM  =args[2] 
compare_type = args[3]
outname = args[4]

res <- read.csv(file = dge, header = TRUE)
summary(res) 
foldchanges = res$logFC
names(foldchanges) = res$entrez
outname = paste(outname, compare_type, sep ="-")
outGO = paste(outname, "GO.csv", sep ="-")

if (ORGANISM == "HUMAN")
{
data(kegg.sets.hs)
data(go.sets.hs)
data(carta.hs)
data(sigmet.idx.hs)
data(go.subs.hs)
kegg.sets.hs = kegg.sets.hs[sigmet.idx.hs]
head(kegg.sets.hs,3)
#GO
data(go.sets.hs)
data(go.subs.hs)
gobpsets = go.sets.hs[go.subs.hs$BP]
gobpres = gage(foldchanges, gsets=kegg.sets.hs, same.dir =TRUE, compare =compare_type,cutoff=0.05)
lapply(gobpres, head)
write.csv(gobpres, outGO)

} else if (ORGANISM == "MOUSE"){
data(kegg.sets.mm)
data(go.sets.mm)
data(carta.mm)
data(sigmet.idx.mm)
data(go.subs.mm)


kegg.sets.mm = kegg.sets.mm[sigmet.idx.mm]
head(kegg.sets.mm,3)
data(go.sets.mm)
data(go.subs.mm)
gobpsets = go.sets.mm[go.subs.mm$BP]
gobpres = gage(foldchanges, gsets=kegg.sets.mm, same.dir =TRUE, compare =compare_type,cutoff=0.05)
lapply(gobpres, head)
write.csv(gobpres, outGO)
}
R dplyr data.table org.Hs.eg.db magrittr org.Mm.eg.db biomaRt gplots AnnotationDbi gage From line 1 of scripts/GO.R 
  1
  2
  3
  4
  5
  6
  7
  8
  9
 10
 11
 12
 13
 14
 15
 16
 17
 18
 19
 20
 21
 22
 23
 24
 25
 26
 27
 28
 29
 30
 31
 32
 33
 34
 35
 36
 37
 38
 39
 40
 41
 42
 43
 44
 45
 46
 47
 48
 49
 50
 51
 52
 53
 54
 55
 56
 57
 58
 59
 60
 61
 62
 63
 64
 65
 66
 67
 68
 69
 70
 71
 72
 73
 74
 75
 76
 77
 78
 79
 80
 81
 82
 83
 84
 85
 86
 87
 88
 89
 90
 91
 92
 93
 94
 95
 96
 97
 98
 99
100
101
102
103
104
105
106
107
108
library("edgeR")
library(biomaRt)
library(dplyr)
library("AnnotationDbi")
library("org.Hs.eg.db")
library(data.table)
library("gplots")
library(magrittr)
library(pathview)
library("org.Mm.eg.db")
library(gage)
library(gageData)
library(data.table)


args <- commandArgs(trailingOnly = TRUE)
dge = args[1]
ORGANISM  =args[2] 
compare_type = args[3]
outname = args[4]
outname 

res <- read.csv(file = dge, header = TRUE)
outname = paste(outname, compare_type, sep ="-")
out = paste(outname, "KEGG.csv", sep="-")
outUP = paste(outname, "KEGG_UP.csv", sep ="-")
outDOWN = paste(outname, "KEGG_DOWN.csv", sep ="-")

foldchanges = res$logFC
names(foldchanges) <- res$entrez
summary(foldchanges)
#---------------------------
#KEGG Analysis 
#---------------------------
if (ORGANISM == "HUMAN")
{
data(kegg.sets.hs)
data(go.sets.hs)
data(carta.hs)
data(sigmet.idx.hs)
data(go.subs.hs)
kegg.sets.hs = kegg.sets.hs[sigmet.idx.hs]
head(kegg.sets.hs,3)
#---------------------------------------------------Use Kegg and gage to get upregulated and downregulated pathways
data(kegg.gs)
keggres = gage(foldchanges, gsets =kegg.sets.hs, same.dir = TRUE, compare=compare_type,make.plot = TRUE)
lapply(keggres, head)
write.csv(keggres,out)
keggrespathwaysup = data.frame(id=rownames(keggres$greater), keggres$greater) %>%
  tbl_df() %>%
  filter(row_number()<=30) %>%
  .$id %>%
  as.character()
keggrespathwaysdn = data.frame(id=rownames(keggres$less), keggres$less) %>%
  tbl_df() %>%
  filter(row_number()<=30) %>%
  .$id %>%
  as.character()
write.csv(keggrespathwaysup, outUP)
write.csv(keggrespathwaysdn, outDOWN)
#-------------------------------------------------------------------------------------------------------------------------------
keggresidsup = substr(keggrespathwaysup, start=1, stop=8)
keggresidsup
keggresidsdn = substr(keggrespathwaysdn, start=1, stop=8)
#gobpres = gage(foldchanges, gsets=kegg.sets.hs, same.dir =FALSE, compare ="paired",cutoff=0.05)
#lapply(gobpres, head)
#---------------------------------------------------------Define plotting function for applying later
plot_pathway = function(pid) pathview(gene.data=foldchanges,gene.idtype="ENTREZID", pathway.id=pid, species="human", new.signature=FALSE)
#---------------------------------------------------------plot multiple pathways ups and downs 
tmpup = sapply(keggresidsup, function(pid) pathview(gene.data=foldchanges,gene.idtype="ENTREZID", pathway.id=pid, species="human"))
tmpdn = sapply(keggresidsdn, function(pid) pathview(gene.data=foldchanges,gene.idtype="ENTREZID", pathway.id=pid, species="human"))
} else if (ORGANISM == "MOUSE"){
data(kegg.sets.mm)
data(go.sets.mm)
data(carta.mm)
data(sigmet.idx.mm)
data(go.subs.mm)
kegg.sets.mm = kegg.sets.mm[sigmet.idx.mm]
head(kegg.sets.mm,3)
#---------------------------------------------------Use Kegg and gage to get upregulated and downregulated pathways
data(kegg.gs)
keggres = gage(foldchanges, gsets =kegg.sets.mm, same.dir = TRUE, compare=compare_type,make.plot = TRUE)
lapply(keggres, head)
write.csv(keggres,out)
keggrespathwaysup = data.frame(id=rownames(keggres$greater), keggres$greater) %>%
  tbl_df() %>%
  filter(row_number()<=30) %>%
  .$id %>%
  as.character()
keggrespathwaysdn = data.frame(id=rownames(keggres$less), keggres$less) %>%
  tbl_df() %>%
  filter(row_number()<=30) %>%
  .$id %>%
  as.character()
write.csv(keggrespathwaysup, outUP)
write.csv(keggrespathwaysdn, outDOWN)
#-------------------------------------------------------------------------------------------------------------------------------
keggresidsup = substr(keggrespathwaysup, start=1, stop=8)
keggresidsup
keggresidsdn = substr(keggrespathwaysdn, start=1, stop=8)
#gobpres = gage(foldchanges, gsets=kegg.sets.mm, same.dir =FALSE, compare ="paired",cutoff=0.05)
#lapply(gobpres, head)
#---------------------------------------------------------Define plotting function for applying later
plot_pathway = function(pid) pathview(gene.data=foldchanges,gene.idtype="ENTREZID", pathway.id=pid, species="mouse", new.signature=FALSE)
#---------------------------------------------------------plot multiple pathways ups and downs 
tmpup = sapply(keggresidsup, function(pid) pathview(gene.data=foldchanges,gene.idtype="ENTREZID", pathway.id=pid, species="mouse"))
tmpdn = sapply(keggresidsdn, function(pid) pathview(gene.data=foldchanges,gene.idtype="ENTREZID", pathway.id=pid, species="mouse"))
}
R dplyr data.table org.Hs.eg.db magrittr org.Mm.eg.db biomaRt gplots AnnotationDbi gage pathview From line 1 of scripts/pathway.R 
20
21
22
23
shell: 
    """
    Rscript scripts/pathway.R {input} {params.organims} {params.comparison} {params.outname}
    """
SnakeMake From line 20 of master/Snakefile 
35
36
37
38
shell:
    """
    Rscript scripts/GO.R {input} {params.organims} {params.comparison} {params.outname}
    """
SnakeMake From line 35 of master/Snakefile
ShowHide 2 more snippets with no or duplicated tags.

Login to post a comment if you would like to share your experience with this workflow.

Do you know this workflow well? If so, you can request seller status , and start supporting this workflow.

Free

Created: 1yr ago
Updated: 1yr ago
Maitainers: public
URL: https://github.com/SherineAwad/GeneSetEnrichment
Name: genesetenrichment
Version: 1
Badge:
workflow icon

Insert copied code into your website to add a link to this workflow.

Downloaded: 0
Copyright: Public Domain
License: BSD 3-Clause "New" or "Revised" License
  • Future updates

Related Workflows

psychip_snakemake
Show Details View Workflow
ENCODE pipeline for histone marks developed for the psychENCODE project
public

public

psychip pipeline is an improved version of the ENCODE pipeline for histone marks developed for the psychENCODE project. The o...
raw sequence reads Alignment Sequence alignment report macs2 ucsc-bedclip bedGraphToBigWig BEDTools BWA Picard SAMtools Snakemake
Free
74
ncov_2
Show Details View Workflow
Near-real time tracking of SARS-CoV-2 in Connecticut
public

public

Repository containing scripts to perform near-real time tracking of SARS-CoV-2 in Connecticut using genomic data. This pipeli...
JSON nextclade Augur Biopython FOCUS Pandas Snakemake bs4 epiweeks geopy matplotlib numpy pycountry pycountry-convert uszipcode
Free
28
cellranger-snakemake-gke
Show Details View Workflow
snakemake workflow to run cellranger on a given bucket using gke.
public

public

A Snakemake workflow for running cellranger on a given bucket using Google Kubernetes Engine. The usage of this workflow ...
macs2 ucsc-bedclip bedGraphToBigWig BEDTools BWA Picard SAMtools Snakemake
Free
74
atlas
Show Details View Workflow
ATLAS - Three commands to start analyzing your metagenome data
public

public

Metagenome-atlas is a easy-to-use metagenomic pipeline based on snakemake. It handles all steps from QC, Assembly, Binning, t...
raw sequence reads Genome assembly Annotation track checkm2 gunc prodigal snakemake-wrapper-utils MEGAHIT Atlas BBMap Biopython BioRuby Bwa-mem2 cd-hit CheckM DAS Diamond eggNOG-mapper v2 MetaBAT 2 Minimap2 MMseqs MultiQC Pandas Picard pyfastx SAMtools SemiBin Snakemake SPAdes SqueezeMeta TADpole VAMB CONCOCT ete3 gtdbtk h5py networkx numpy plotly psutil utils metagenomics
Free
175
rna-seq-star-deseq2
Show Details View Workflow
RNA-seq workflow using STAR and DESeq2
public

public

This workflow performs a differential gene expression analysis with STAR and Deseq2. The usage of this workflow is described ...
raw sequence reads Gene expression profiling ID list snakemake-wrapper-utils biomaRt PCAtools BiocParallel BWA Cutadapt DESeq2 GFFutils MultiQC Pandas SAMtools Snakemake STAR cli stringr tidyverse RNA-Seq
Free
41
dna-seq-gatk-variant-calling
Show Details View Workflow
This Snakemake pipeline implements the GATK best-practices workflow
public

public

This Snakemake pipeline implements the GATK best-practices workflow for calling small germline variants. The usage of thi...
VCF raw sequence reads Variant calling genetic variants gatk rust-bio-tools snakemake-wrapper-utils tabix BCFtools BWA FastQC MultiQC Pandas Picard SAMtools Snakemake Trimmomatic Variant Effect Predictor (VEP) common matplotlib numpy seaborn DNA
Free
57