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DE_pipeline.snake
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## SeA-SnaP differential expression pipeline for RNA-seq analysis
## version: 1.0
## author: J.P.Pett (patrick.pett@bihealth.de)
#TODO: replace config value check by built-in? https://snakemake.readthedocs.io/en/stable/snakefiles/configuration.html#validation
import os, re, yaml, textwrap, pandas as pd
from collections import OrderedDict
from time import asctime, localtime, time
from pathlib import Path
from snakemake.utils import report, format as snakemake_format
from tools.pipeline_tools import DEPipelinePathHandler, ReportTool
yaml.add_representer(OrderedDict, lambda dumper, data: dumper.represent_dict(dict(data)))
SNAKEDIR = Path(workflow.current_basedir)
SNAKEFILE = workflow.snakefile
SCRIPTDIR = str(SNAKEDIR / "external_scripts")
# assemble config
config_file_name = config["file_name"] if "file_name" in config else "DE_config.yaml"
configfile: str(SNAKEDIR / "defaults" / "DE_config_defaults.yaml")
configfile: config_file_name
if config["organism_defaults"]:
configfile: str(SNAKEDIR / "defaults" / config["organism_defaults"])
configfile: config_file_name
# create path handler
conf_ranges = str(SNAKEDIR / "defaults" / "DE_config_ranges.yaml")
test_config = conf_ranges if config["pipeline_param"]["test_config"] else None
pph = DEPipelinePathHandler(workflow, test_config)
# exclude symbols '.' and '/' from wildcards
wildcard_constraints:
sample="[^./]+"
R_SESSION_INFO = r"""
cat("########################### session info ############################","\n")
print(sessionInfo())
cat("#####################################################################","\n\n")
"""
onstart:
# draw a dag
dag_file = pph.file_path(step="pipeline_report", extension="rule_execution.png", contrast="all")
os.makedirs(os.path.dirname(dag_file), exist_ok=True)
shell("snakemake --quiet --snakefile {} --rulegraph | dot -Tpng 1> {}".format(SNAKEFILE, dag_file))
# info about the pipeline run
info_file = pph.file_path(step="pipeline_report", extension="summary.csv", contrast="all")
os.makedirs(os.path.dirname(info_file), exist_ok=True)
shell("snakemake --quiet --snakefile {} --summary | sed 's/\t/, /g' 1> {}".format(SNAKEFILE, info_file))
# save merged config
config_file = pph.file_path(step="pipeline_report", extension="yaml", contrast="all")
with open(config_file, "w") as f: yaml.dump(config, f, default_flow_style=False)
##-------------------- starting point ----------------------------------------------------------------------
def get_inputs_all():
#contrasts
inputs = pph.expand_path(step="contrast", extension="rds")
#functional annotation
inputs.append(pph.expand_path(step = "goseq", extension = "go.rds", if_set = dict(goseq=True) ))
inputs.append(pph.expand_path(step = "goseq", extension = "kegg.rds", if_set = dict(goseq=True) ))
#inputs.append(pph.file_path(step = "annotation", extension = "rds", contrast="all"))
inputs.append(pph.file_path(step = "export_raw_counts", extension = "xlsx", contrast = "all"))
inputs.append(pph.expand_path(step = "cluster_profiler", extension = "rds", if_set = dict(cluster_profiler=dict(run=True)) ))
# XXX uncomment the following line to run tmod_pca
if config["contrasts"]["defaults"]["tmod_pca"]:
inputs.append(pph.file_path(step = "tmod_pca", extension = "rds", contrast="all"))
inputs.append(pph.expand_path(step = "tmod", extension = "rds", if_set = dict(tmod=True)))
#time series
for ts in config["time_series"]:
inputs.append(pph.file_path(step = "rain", extension = "tsv", contrast=ts))
#time series comparisons
for tsc in config["dodr"]["comparisons"]:
inputs.append(pph.file_path(step = "dodr", extension = "tsv", contrast=tsc))
return inputs
shell("rm -f {}".format(pph.file_path(step="pipeline_report", extension="report.html", contrast="all")))
rule all:
input:
get_inputs_all(),
# html generation does not work
#pph.file_path(step="report_html", extension="html", contrast="all"),
pph.file_path("report", "Rmd", contrast = "all")
output:
html = pph.file_path(step="pipeline_report", extension="report.html", contrast="all")
run:
loctime = asctime(localtime(time()))
rule_execution = pph.file_path("pipeline_report", "rule_execution.png", contrast="all")
summary = pph.file_path("pipeline_report", "summary.csv", contrast="all")
version_info = pph.file_path("pipeline_report", "version_info.txt", contrast="all")
conda_info = pph.file_path("pipeline_report", "conda_info.txt", contrast="all")
dag = rule_execution.split("/")[-1]
shell("conda list > {}".format(version_info))
shell("conda info > {}".format(conda_info))
report("""
=======================
RNAseq mapping pipeline
=======================
**Finished: {loctime}**
.. image:: {dag}
File status at pipeline start:
==============================
.. csv-table::
:file: {summary}
Version info:
=============
.. include:: {version_info}
:literal:
Conda info:
===========
.. include:: {conda_info}
:literal:
""", output.html, graph = rule_execution, table = summary)
rule export:
input:
get_inputs_all()
run:
pph.export()
##-------------------- make TxDb -----------------------------------------------------------------------------
rule TxDb_from_GTF:
""" make a TxDb object from GTF """
input:
gtf = config["organism"]["files"]["gtf"]
output:
pph.file_path(step = "TxDb_from_GTF", extension = "sqlite", contrast = "all")
log:
out = pph.file_path("TxDb_from_GTF", "output.log", contrast = "all", log=True)
run:
genus = config["organism"]["genus"]
taxon = config["organism"]["taxon"]
script = textwrap.dedent(r"""
#----- import packages
library(GenomicFeatures)
{R_SESSION_INFO}
#----- make transcript to gene table
TxDb <- makeTxDbFromGFF("{input.gtf}", format="gtf", organism="{genus}", taxonomyId={taxon})
#----- save Db
saveDb(TxDb, "{output}")
""")
script_file = pph.log(log.out, snakemake_format(script), step="TxDb_from_GTF", extension="R", contrast = "all", **wildcards)
shell("Rscript --vanilla '{script_file}' &>> '{log.out}'")
##-------------------- import -------------------------------------------------------------------------------
DESIGN = config["experiment"]["design_formula"]
rule import_gene_counts:
""" collect count files (STAR) and build count matrix """
input:
samples = config["experiment"]["covariate_file"]["star"]
output:
rds=pph.file_path("import_gene_counts", "rds", contrast = "all"),
tsv=pph.file_path("import_gene_counts", "tsv", contrast = "all"),
xlsx=pph.file_path("import_gene_counts", "xlsx", contrast = "all")
log:
out = pph.file_path("import_gene_counts", "output.log", contrast = "all", log=True)
params:
tpm_xlsx=pph.file_path("import_gene_counts", "tpm.xlsx", contrast = "all"),
annot_pkg = config["organism"]["R"]["annotations"]
run:
config_file = pph.file_path(step="pipeline_report", extension="yaml", contrast="all")
column_filter = ""
if config["filters"]["experiment_whitelist"]:
column_filter += "&".join("is.element(sample_df${},{})".format( k, "c({})".format(",".join('"'+l+'"' for l in v)) )
for k,v in config["filters"]["experiment_whitelist"].items())
if config["filters"]["experiment_blacklist"]:
column_filter += "&".join("!is.element(sample_df${},{})".format( k, "c({})".format(",".join('"'+l+'"' for l in v)) )
for k,v in config["filters"]["experiment_blacklist"].items())
if column_filter == "":
column_filter = "TRUE"
r_gene_list = ("'"+config["filters"]["gene_list"]["file"]+"'") if config["filters"]["gene_list"]["file"] else "NULL"
r_gene_list_type = config["filters"]["gene_list"]["type"] or "NULL"
column_lvl = config["experiment"]["columns"] if "columns" in config["experiment"] else {}
level_list = "list({})".format(",".join("{}=c({})".format( n, ",".join('"'+s+'"' for s in l) ) for n,l in column_lvl.items()))
script = textwrap.dedent(r"""
#----- import packages
library(DESeq2)
library(writexl)
library(AnnotationDbi)
library({params.annot_pkg})
{R_SESSION_INFO}
#----- variables
sample_f <- "{input.samples}"
output_xlsx <- "{output.xlsx}"
output_tsv <- "{output.tsv}"
output_tpm_xlsx <- "{params.tpm_xlsx}"
output_rds <- "{output.rds}"
conf.f <- "{config_file}"
gene_list <- {r_gene_list}
subset_genes_type <- "{r_gene_list_type}"
#----- import data
sample_df <- read.table(sample_f, header=TRUE)
files <- as.character(sample_df$filename); print(files)
samples <- as.character(sample_df$label)
config <- yaml::yaml.load_file(conf.f)
#----- merge count files from star
df_list <- lapply(1:length(files), function(i) as.data.frame(read.csv(files[i], skip=4, sep="\t",
col.names=c("ID", samples[i]), check.names=FALSE, header=FALSE)))
count_dat <- Reduce(function(...) merge(..., all=TRUE, by="ID"), df_list)
rownames(count_dat) <- count_dat$ID
#----- subset genes
if (!is.null(gene_list)) {{
subset_genes <- as.character(read.csv(gene_list, header=F)[,1]); print(subset_genes)
if (subset_genes_type == "ENSEMBL"){{
subset_ensembl_genes <- subset_genes
}} else {{
subset_ensembl_genes <- mapIds({params.annot_pkg}, keys=subset_genes, column="ENSEMBL",
keytype=subset_genes_type, multiVals="first"); print(subset_ensembl_genes)
}}
count_dat <- count_dat[subset_ensembl_genes[!is.na(subset_ensembl_genes)],]
}}
#----- if present, merge tpm values (generated from star bam with TPMcalculator) for display along results
if ("tpm" %in% colnames(sample_df)) {{
tpm_files <- as.character(sample_df$tpm)
tpm_df_list <- lapply(1:length(tpm_files), function(i) as.data.frame(read.csv(tpm_files[i], sep="\t",
col.names=c("ID", samples[i]), check.names=FALSE, header=FALSE)))
tpm_dat <- Reduce(function(...) merge(..., all=TRUE, by="ID"), tpm_df_list); rownames(tpm_dat) <- tpm_dat$ID
}}
#----- change levels
level_cols <- {level_list}
for (col in names(level_cols)) {{
sample_df[,col] <- factor(sample_df[,col], levels = level_cols[[col]])
}}
#----- import counts in DESeq2
rownames(sample_df) <- sample_df$label; count_dat <- count_dat[, rownames(sample_df)]
dds <- DESeqDataSetFromMatrix(countData = as.matrix(count_dat), colData = sample_df, design = {DESIGN})
#----- save as rds
saveRDS(dds, file=output_rds)
#----- export counts as tables
colnames(count_dat) <- sample_df$label
write_xlsx(count_dat, path=output_xlsx)
write.table(count_dat, file=output_tsv, sep="\t", quote=F)
if ("tpm" %in% colnames(sample_df)) {{
symbol <- mapIds({params.annot_pkg}, keys=sub("\\.[0-9]+$", "", tpm_dat$ID),
column="SYMBOL", keytype="ENSEMBL", multiVals="first")
tpm_dat <- cbind(symbol, tpm_dat)
colnames(tpm_dat) <- c("Symbol", "geneID", as.character(sample_df$label))
write_xlsx(tpm_dat, path=output_tpm_xlsx)
}}
""")
script_file = pph.log(log.out, snakemake_format(script), step="import_gene_counts", extension="R", contrast = "all", **wildcards)
shell("Rscript --vanilla '{script_file}' &>> '{log.out}'")
rule import_featurecounts:
""" collect count files (featurecounts) and build count matrix """
input:
samples = config["experiment"]["covariate_file"]["star"]
output:
pph.file_path("import_featurecounts", "rds", contrast = "all")
log:
out = pph.file_path("import_featurecounts", "output.log", contrast = "all", log=True)
run:
column_filter = ""
if config["filters"]["experiment_whitelist"]:
column_filter += "&".join("is.element(sample_df${},{})".format( k, "c({})".format(",".join('"'+l+'"' for l in v)) )
for k,v in config["filters"]["experiment_whitelist"].items())
if config["filters"]["experiment_blacklist"]:
column_filter += "&".join("!is.element(sample_df${},{})".format( k, "c({})".format(",".join('"'+l+'"' for l in v)) )
for k,v in config["filters"]["experiment_blacklist"].items())
if column_filter == "":
column_filter = "TRUE"
column_lvl = config["experiment"]["columns"] if "columns" in config["experiment"] else {}
level_list = "list({})".format(",".join("{}=c({})".format( n, ",".join('"'+s+'"' for s in l) ) for n,l in column_lvl.items()))
script = textwrap.dedent(r"""
#----- import packages
library(DESeq2)
{R_SESSION_INFO}
#----- import data
sample_df <- read.table("{input.samples}", header=TRUE)
sample_df <- sample_df[{column_filter},]
files <- as.character(sample_df$filename); print(files)
samples <- as.character(sample_df$label)
#----- merge count files
read_fc <- function(fn, sn) read.csv(fn, comment="#", sep="\t", colClasses=c("character", rep("NULL", 5), "numeric"),
col.names=c("ID", rep("dummy", 5), sn), check.names=FALSE)
df_list <- lapply(1:length(files), function(i) as.data.frame(read_fc(files[i], samples[i])))
count_dat <- Reduce(function(...) merge(..., all=TRUE, by="ID"), df_list); rownames(count_dat) <- count_dat$ID
#----- change levels
level_cols <- {level_list}
for (col in names(level_cols)) {{
sample_df[,col] <- factor(sample_df[,col], levels = level_cols[[col]])
}}
#----- import counts in DESeq2
rownames(sample_df) <- sample_df$label; count_dat <- count_dat[, rownames(sample_df)]
dds <- DESeqDataSetFromMatrix(countData = as.matrix(count_dat), colData = sample_df, design = {DESIGN})
#----- save as rds
saveRDS(dds, file="{output}")
""")
script_file = pph.log(log.out, snakemake_format(script), step="import_featurecounts", extension="R", contrast = "all", **wildcards)
shell("Rscript --vanilla '{script_file}' &>> '{log.out}'")
rule import_sf:
""" collect sf files (Salmon) and build count matrix """
input:
txdb = pph.file_path("TxDb_from_GTF", "sqlite", contrast = "all"),
samples = config["experiment"]["covariate_file"]["salmon"]
output:
pph.file_path("import_sf", "rds", contrast = "all")
log:
out = pph.file_path("import_sf", "output.log", contrast = "all", log=True)
run:
column_filter = ""
if config["filters"]["experiment_whitelist"]:
column_filter += "&".join("is.element(sample_df${},{})".format( k, "c({})".format(",".join('"'+l+'"' for l in v)) )
for k,v in config["filters"]["experiment_whitelist"].items())
if config["filters"]["experiment_blacklist"]:
column_filter += "&".join("!is.element(sample_df${},{})".format( k, "c({})".format(",".join('"'+l+'"' for l in v)) )
for k,v in config["filters"]["experiment_blacklist"].items())
if column_filter == "":
column_filter = "TRUE"
column_lvl = config["experiment"]["columns"] if "columns" in config["experiment"] else {}
level_list = "list({})".format(",".join("{}=c({})".format( k, ",".join('"'+l+'"' for l in v) ) for k,v in column_lvl.items()))
script = textwrap.dedent(r"""
#----- import packages
library(DESeq2)
library(tximport)
library(readr)
library(AnnotationDbi)
{R_SESSION_INFO}
#----- make transcript to gene table
TxDb <- loadDb(file = "{input.txdb}")
k <- keys(TxDb, keytype = "TXNAME")
tx2gene <- select(TxDb, k, "GENEID", "TXNAME")
#----- use tximport on input files
sample_df <- read.table("{input.samples}", header=TRUE)
sample_df <- sample_df[{column_filter},]
files <- as.character(sample_df$filename); print(files)
txi <- tximport(files, type = "salmon", tx2gene = tx2gene)
#----- change levels
level_cols <- {level_list}
for (col in names(level_cols)) {{
sample_df[,col] <- factor(sample_df[,col], levels = level_cols[[col]])
}}
#----- import txi in DESeq2
rownames(sample_df) <- sample_df$label
dds <- DESeqDataSetFromTximport(txi, colData = sample_df, design = {DESIGN})
#----- save as rds
saveRDS(dds, file="{output}")
""")
script_file = pph.log(log.out, snakemake_format(script), step="import_sf", extension="R", contrast = "all", **wildcards)
shell("Rscript --vanilla '{script_file}' &>> '{log.out}'")
##-------------------- export_raw_counts -------------------------------------------------------------------------------
rule export_raw_counts:
""" export DESeq2 raw counts from the DESeq2 object as XLSX"""
input:
rds = pph.choose_input(choice_name = "mapping", options = [
dict(step = "import_gene_counts", extension = "rds", contrast = "all"),
dict(step = "import_featurecounts", extension = "rds", contrast = "all"),
dict(step = "import_sf", extension = "rds", contrast = "all")
])
output:
xlsx = pph.file_path("export_raw_counts", "xlsx", contrast = "all"),
csv = pph.file_path("export_raw_counts", "csv", contrast = "all")
log:
out = pph.file_path("export_raw_counts", "output.log", contrast = "all", log=True)
run:
script = textwrap.dedent(r"""
#----- import packages
library(DESeq2)
library(writexl)
library(tibble)
library(readr)
{R_SESSION_INFO}
#----- variables
output_xlsx <- "{output.xlsx}"
output_csv <- "{output.csv}"
input_rds <- "{input.rds}"
#----- read RDS object, extract count data, save as XLSX
dds <- readRDS(input_rds)
count_dat <- assay(dds)
count_dat <- as.data.frame(count_dat) %>% rownames_to_column("PrimaryID")
col_dat <- colData(dds)
colnames(count_dat)[-1] <- col_dat$label
write_csv(count_dat, path=output_csv)
write_xlsx(count_dat, path=output_xlsx)
""")
script_file = pph.log(log.out, snakemake_format(script), step="export_raw_counts", extension="R", contrast = "all", **wildcards)
shell("Rscript --vanilla '{script_file}' &>> '{log.out}'")
##-------------------- DESeq2 -------------------------------------------------------------------------------
rule DESeq2:
""" run DESeq2 on a count matrix """
input:
pph.choose_input(choice_name = "mapping", options = [
dict(step = "import_gene_counts", extension = "rds", contrast = "all"),
dict(step = "import_featurecounts", extension = "rds", contrast = "all"),
dict(step = "import_sf", extension = "rds", contrast = "all")
])
output:
dds = pph.file_path("DESeq2", "deseq2.rds", contrast = "all"),
rld_blind = pph.file_path("DESeq2", "rld.blind.rds", contrast = "all"),
rld_model = pph.file_path("DESeq2", "rld.model.rds", contrast = "all"),
rld_blind_csv = pph.file_path("DESeq2", "rld.blind.csv", contrast = "all"),
rld_model_csv = pph.file_path("DESeq2", "rld.model.csv", contrast = "all")
log:
out = pph.file_path("DESeq2", "output.log", contrast = "all", log=True)
params:
coef_names = pph.file_path("DESeq2", "coef_names.txt", contrast = "all"),
rld_names = pph.file_path("DESeq2", "rld.model.csv", contrast = "all"),
annot_pkg = config["organism"]["R"]["annotations"]
run:
config_file = pph.file_path(step="pipeline_report", extension="yaml", contrast="all")
count_threshold = config["filters"]["low_counts"]
normalized_expression = config["normalization"]["normalized_expression"]
script = textwrap.dedent(r"""
#----- import packages
library(DESeq2)
library(AnnotationDbi)
library({params.annot_pkg})
{R_SESSION_INFO}
conf.f <- "{config_file}"
#----- load config
config <- yaml::yaml.load_file(conf.f)
#----- load DESeqDataSet
dds <- readRDS("{input}")
#----- pre-filtering
keep <- rowSums(counts(dds)) >= {count_threshold}
## at least min_counts in at least min_count_n number of samples
if(!is.null(config$filters$min_counts) && !is.null(config$filters$min_count_n)) {{
rs <- rowSums(counts(dds) > config$filters$min_counts)
keep <- keep & rs >= config$filters$min_count_n
}}
dds <- dds[keep,]; print(dds)
#----- DESeq2
dds <- DESeq(dds, betaPrior=FALSE, test="Wald")
# ----- Normalised expression values
rld_blind <- switch("{normalized_expression}",
"rld"=rlog(dds, blind=TRUE),
"vst"=vst(dds, blind=TRUE)
)
rld_model <- switch("{normalized_expression}",
"rld"=rlog(dds, blind=FALSE),
"vst"=vst(dds, blind=FALSE)
)
#----- save as rds
saveRDS(dds, file="{output.dds}")
saveRDS(rld_blind, file="{output.rld_blind}")
saveRDS(rld_model, file="{output.rld_model}")
#----- save other files
writeLines(resultsNames(dds), "{params.coef_names}")
write.table(data.frame(
gene_id=rownames(rld_model),
assay(rld_model),
symbol=mapIds({params.annot_pkg}, keys=sub("\\.[0-9]+$", "", row.names(dds)),
column="SYMBOL", keytype="ENSEMBL", multiVals="first"),
entrez=mapIds({params.annot_pkg}, keys=sub("\\.[0-9]+$", "", row.names(dds)),
column="ENTREZID", keytype="ENSEMBL", multiVals="first"),
check.names=FALSE
), file="{output.rld_model_csv}", sep="\t", col.names=TRUE, row.names=FALSE, quote=FALSE)
write.table(data.frame(
gene_id=rownames(rld_blind),
assay(rld_blind),
symbol=mapIds({params.annot_pkg}, keys=sub("\\.[0-9]+$", "", row.names(dds)),
column="SYMBOL", keytype="ENSEMBL", multiVals="first"),
entrez=mapIds({params.annot_pkg}, keys=sub("\\.[0-9]+$", "", row.names(dds)),
column="ENTREZID", keytype="ENSEMBL", multiVals="first"),
check.names=FALSE
), file="{output.rld_blind_csv}", sep="\t", col.names=TRUE, row.names=FALSE, quote=FALSE)
""")
script_file = pph.log(log.out, snakemake_format(script), step="DESeq2", extension="R", contrast = "all", **wildcards)
shell("Rscript --vanilla '{script_file}' &>> '{log.out}'")
##-------------------- contrasts ----------------------------------------------------------------------------
rule contrast:
""" make a contrast """
input:
pph.file_path("DESeq2", "deseq2.rds", contrast = "all")
output:
rds = pph.file_path("contrast", "rds"),
csv = pph.file_path("contrast", "csv")
log:
out = pph.file_path("contrast", "output.log", log=True),
contrast_yaml = pph.file_path("contrast", "contrast.yaml", log=True)
params:
ma_plot = pph.file_path("contrast", "ma.pdf"),
count_plot = pph.file_path("contrast", "counts.pdf"),
annot_pkg = config["organism"]["R"]["annotations"]
run:
contrast = pph.get_contrast(wildcards.contrast)
with open(log.contrast_yaml, "w") as f: yaml.dump(contrast, f, default_flow_style=False)
# variants of contrast definition
contrast_column, contrast_ref = "group", ""
if "coef" in contrast:
contrast_type = "coef"
arg_val = '="{}"'.format(contrast["coef"])
contrast_def, lfc_def = "name" + arg_val, "coef" + arg_val
elif "ratio" in contrast:
contrast_type, ratio = "ratio", contrast["ratio"]
contrast_column, contrast_num, contrast_ref = ratio["column"], ratio["numerator"], ratio["denominator"]
contrast_def = lfc_def = 'contrast=c("{}", "{}", "{}")'.format(contrast_column, contrast_num, contrast_ref)
elif "vector" in contrast:
contrast_type = "vector"
contrast_def = lfc_def = 'contrast=c({})'.format(", ".join(map(str, contrast["vector"])))
else:
raise ValueError("Error in contrast: no valid contrast definition! must be one of {}".format(["coef:", "ratio:", "vector:"]))
# parameters
cutoff_FDR = contrast["max_p_adj"]
lfcThreshold = contrast["results_parameters"]["lfcThreshold"]
altHypothesis = contrast["results_parameters"]["altHypothesis"]
independentFiltering = "TRUE" if contrast["results_parameters"]["independentFiltering"] else "FALSE"
lfc_shrink_type = contrast["lfcShrink_parameters"]["type"]
rank_by = contrast["ranking_by"]
rank_order = contrast["ranking_order"].replace("x", "res$"+rank_by)
script = textwrap.dedent(r"""
#----- import packages
library(DESeq2)
library(AnnotationDbi)
library({params.annot_pkg})
{R_SESSION_INFO}
#----- load DESeqDataSet
dds <- readRDS("{input}")
#----- contrast
if ("{contrast_type}" == "ratio") {{
dds${contrast_column} = relevel(dds${contrast_column}, ref="{contrast_ref}")
dds <- nbinomWaldTest(dds)
}}
res <- results(dds, {contrast_def}, alpha={cutoff_FDR}, lfcThreshold={lfcThreshold},
altHypothesis="{altHypothesis}", independentFiltering={independentFiltering})
#----- log-fold-change shrinkage
#res$lfcShrunk <- res$log2FoldChange
#res$lfcShrunkSE <- res$lfcSE
if ("{lfc_shrink_type}"!="none"){{
lfc <- lfcShrink(dds, {lfc_def}, lfcThreshold={lfcThreshold}, type="{lfc_shrink_type}")
res$log2FoldChange_orig <- res$log2FoldChange
res$log2FoldChange <- lfc$log2FoldChange
res$lfcSE_orig <- res$lfcSE
res$lfcSE <- lfc$lfcSE
}}
#----- ranking
res <- res[order({rank_order}),]
#----- annotation [BEWARE- the in case on multiple mapping, the first entry will be selected]
res$symbol <- mapIds({params.annot_pkg}, keys=sub("\\.[0-9]+$", "", row.names(res)),
column="SYMBOL", keytype="ENSEMBL", multiVals="first")
res$entrez <- mapIds({params.annot_pkg}, keys=sub("\\.[0-9]+$", "", row.names(res)),
column="ENTREZID", keytype="ENSEMBL", multiVals="first")
#----- save as rds & csv
saveRDS(res, file="{output.rds}")
write.table(data.frame(gene_id=rownames(res), res), file="{output.csv}", sep="\t", col.names=TRUE, row.names=FALSE, quote=FALSE)
""")
script_file = pph.log(log.out, snakemake_format(script), step="contrast", extension="R", **wildcards)
shell("Rscript --vanilla '{script_file}' &>> '{log.out}'")
## ------------------------ annotation ---------------------------
## Annotation rule produces a table with mapping between the Primary IDs
## (ENSEMBL usually) and other potentially useful IDs as well as gene
## descriptions.
rule annotation:
""" Adding annotation information """
input:
# we use dds as this is the most reliable source of the primary IDs
dds = pph.file_path("DESeq2", "deseq2.rds", contrast = "all")
output:
res = pph.file_path(step = "annotation", extension = "rds", contrast="all"),
csv = pph.file_path(step = "annotation", extension = "csv", contrast="all")
log:
out = pph.file_path(step = "annotation", extension = "output.log", log=True, contrast="all")
run:
config_file = pph.file_path(step="pipeline_report", extension="yaml", contrast="all")
script = textwrap.dedent(r"""
library(AnnotationDbi)
{R_SESSION_INFO}
res.file <- "{output.res}"
res.csv <- "{output.csv}"
script.d <- "{SCRIPTDIR}"
conf.f <- "{config_file}"
dds <- "{input.dds}"
# ----------------------------------------
# No Snakemake wildcards beyond this point
# ----------------------------------------
library(orthomapper)
config <- yaml::yaml.load_file(conf.f)
gene_ids <- rownames(readRDS(dds))
res <- data.frame(PrimaryID=gene_ids, stringsAsFactors=FALSE)
res$ENSEMBL <- sub("\\..*$", "", res$PrimaryID)
annot <- orthomapper::entrez_annotate(res$ENSEMBL, taxon=as.numeric(config$organism$taxon),
keytype="ENSEMBL", column=c("SYMBOL", "ENTREZID", "REFSEQ", "GENENAME"))
if(!all(annot[,1] == res$ensembl))
stop("object returned by entrez_annotate() does not match query")
## no need to duplicate columns
res <- cbind(res, annot[, -1, drop=FALSE])
write.csv(res, row.names=FALSE, file=res.csv)
saveRDS(res, file=res.file)
""")
script_file = pph.log(log.out, snakemake_format(script), step="annotation", extension="R", contrast="all", **wildcards)
shell("Rscript --vanilla '{script_file}' &>> '{log.out}'")
##-------------------- contrasts ----------------------------------------------------------------------------
## Same as contrasts, but saves all results from DESeq2
## everything named contrasts_full for consistency
rule contrasts_full:
""" make all contrasts (full version) """
input:
pph.file_path("DESeq2", "deseq2.rds", contrast = "all")
output:
rds = pph.file_path("contrasts_full", "rds"),
csv = pph.file_path("contrasts_full", "csv")
log:
out = pph.file_path("contrasts_full", "output.log", log=True),
params:
annot_pkg = config["organism"]["R"]["annotations"]
run:
contrast = pph.get_contrast(wildcards.contrast)
#with open(log.contrast_yaml, "w") as f: yaml.dump(contrast, f, default_flow_style=False)
# variants of contrast definition
contrast_column, contrast_ref = "group", ""
if "coef" in contrast:
contrast_type = "coef"
arg_val = '="{}"'.format(contrast["coef"])
contrast_def, lfc_def = "name" + arg_val, "coef" + arg_val
elif "ratio" in contrast:
contrast_type, ratio = "ratio", contrast["ratio"]
contrast_column, contrast_num, contrast_ref = ratio["column"], ratio["numerator"], ratio["denominator"]
contrast_def = lfc_def = 'contrast=c("{}", "{}", "{}")'.format(contrast_column, contrast_num, contrast_ref)
elif "vector" in contrast:
contrast_type = "vector"
contrast_def = lfc_def = 'contrast=c({})'.format(", ".join(map(str, contrast["vector"])))
else:
raise ValueError("Error in contrast: no valid contrast definition! must be one of {}".format(["coef:", "ratio:", "vector:"]))
# parameters
cutoff_FDR = contrast["max_p_adj"]
lfcThreshold = contrast["results_parameters"]["lfcThreshold"]
altHypothesis = contrast["results_parameters"]["altHypothesis"]
independentFiltering = "TRUE" if contrast["results_parameters"]["independentFiltering"] else "FALSE"
lfc_shrink_type = contrast["lfcShrink_parameters"]["type"]
rank_by = contrast["ranking_by"]
rank_order = contrast["ranking_order"].replace("x", "res$"+rank_by)
script = textwrap.dedent(r"""
#----- import packages
library(AnnotationDbi)
library(DESeq2)
library({params.annot_pkg})
{R_SESSION_INFO}
#----- load DESeqDataSet
dds <- readRDS("{input}")
#----- contrast
if ("{contrast_type}" == "ratio") {{
dds${contrast_column} = relevel(dds${contrast_column}, ref="{contrast_ref}")
dds <- nbinomWaldTest(dds)
}}
res <- results(dds, {contrast_def}, # alpha={cutoff_FDR}, lfcThreshold={lfcThreshold},
altHypothesis="{altHypothesis}") #, independentFiltering={independentFiltering})
#----- log-fold-change shrinkage
#res$lfcShrunk <- res$log2FoldChange
#res$lfcShrunkSE <- res$lfcSE
if ("{lfc_shrink_type}"!="none"){{
lfc <- lfcShrink(dds, {lfc_def}, # lfcThreshold={lfcThreshold},
type="{lfc_shrink_type}")
res$log2FoldChange_orig <- res$log2FoldChange
res$log2FoldChange <- lfc$log2FoldChange
res$lfcSE_orig <- res$lfcSE
res$lfcSE <- lfc$lfcSE
}}
#----- ranking
res <- res[order({rank_order}),]
#----- annotation [BEWARE- the in case on multiple mapping, the first entry will be selected]
res$symbol <- mapIds({params.annot_pkg}, keys=sub("\\.[0-9]+$", "", row.names(res)),
column="SYMBOL", keytype="ENSEMBL", multiVals="first")
res$entrez <- mapIds({params.annot_pkg}, keys=sub("\\.[0-9]+$", "", row.names(res)),
column="ENTREZID", keytype="ENSEMBL", multiVals="first")
#----- save as rds & csv
saveRDS(res, file="{output.rds}")
write.table(data.frame(gene_id=rownames(res), res), file="{output.csv}", sep="\t", col.names=TRUE, row.names=FALSE, quote=FALSE)
""")
script_file = pph.log(log.out, snakemake_format(script), step="contrasts_full", extension="R", **wildcards)
shell("Rscript --vanilla '{script_file}' &>> '{log.out}'")
## ------------------------ preparation of the tmod databases ---------------------------
rule tmod_dbs:
""" Preparing tmod databases """
input:
#rds = pph.file_path("contrasts_full", "rds"),
annotation = pph.file_path(step="annotation", extension="rds", contrast="all")
output:
res = pph.file_path(step = "tmod_dbs", extension = "rds", contrast="all"),
map = pph.file_path(step = "tmod_dbs", extension = "mapping.rds", contrast="all")
log:
out = pph.file_path(step = "tmod_dbs", extension = "output.log", log=True, contrast="all")
run:
config_file = pph.file_path(step="pipeline_report", extension="yaml", contrast="all"),
script = textwrap.dedent(r"""
{R_SESSION_INFO}
res.file <- "{output.res}"
map.file <- "{output.map}"
script.d <- "{SCRIPTDIR}"
conf.f <- "{config_file}"
annot.f <- "{input.annotation}"
## ------------------------------
## no wildcards beyond this point
## ------------------------------
config <- yaml::yaml.load_file(conf.f)
source(file.path(script.d, "tmod_functions.R"))
## read the necessary files
## create the databases
dbs <- process_dbs(config)
saveRDS(dbs, file=res.file)
## provide mapping for the databases
map <- get_mapping(config, dbs, annot.f)
saveRDS(map, file=map.file)
""")
script_file = pph.log(log.out, snakemake_format(script), step="tmod_dbs", extension="R", contrast="all", **wildcards)
shell("Rscript --vanilla '{script_file}' &>> '{log.out}'")
## ------------------------ gene set enrichments with tmod ---------------------------
rule tmod:
""" Gene set enrichment with tmod and MSigDB """
input:
rds = pph.file_path("contrasts_full", "rds"),
dbs = pph.file_path(step = "tmod_dbs", extension = "rds", contrast="all"),
map = pph.file_path(step = "tmod_dbs", extension = "mapping.rds", contrast="all")
output:
tmod_res = pph.file_path(step = "tmod", extension = "rds"),
tmod_gl = pph.file_path(step = "tmod", extension = "gl.rds"),
tmod_xlsx = pph.file_path(step = "tmod", extension = "xlsx")
log:
out = pph.file_path(step = "tmod", extension = "output.log", log=True)
run:
config_file = pph.file_path(step="pipeline_report", extension="yaml", contrast="all")
script = textwrap.dedent(r"""
{R_SESSION_INFO}
script.d <- "{SCRIPTDIR}"
conf.f <- "{config_file}"
de.input <- "{input.rds}"
map.f <- "{input.map}"
dbs.f <- "{input.dbs}"
res.file <- "{output.tmod_res}"
res.xlsx <- "{output.tmod_xlsx}"
res.gl.f <- "{output.tmod_gl}"
## ------------------------------
## no wildcards beyond this point
## ------------------------------
library(DESeq2)
library(tmod)
library(writexl)
library(purrr)
config <- yaml::yaml.load_file(conf.f)
source(file.path(script.d, "tmod_functions.R"))
dbs <- readRDS(dbs.f)
db.map <- readRDS(map.f)
de.res <- readRDS(de.input)
## create ordered gene lists for every database
message(paste0("Starting tmod for contrast ", de.input))
ordered_genelist <- get_ordered_genelist(de.res, config)
genelists <- lapply(dbs, map_genelists, ordered_genelist, db.map)
saveRDS(genelists, file=res.gl.f)
## run tmod using each of the predefined databases
res <- lapply(dbs, run_tmod, config, genelists, db.map)
## res is a list with an element for each db
## each element for a db is a list with an element for each
## of the sorting keys
## each of these elements contains objects `res` (df with results)
## and `gl` (genelist used to generate the results)
saveRDS(res, file=res.file)
message("tmod rds saved")
## shorten results for the human readable XLSX file
## filter the results
res.short <- lapply(res, function(x) lapply(x, reformat_res, pval.thr=Inf, auc.thr=0))
## hard: add lists of significant genes to the results tables
res.short <- imap(res, ~ {{
db.name <- .y
map(.x, ~ {{
add_sign_genes(., dbs[[db.name]], de.res, db.map)
}})
}})
## res.short still has two levels, one for the databases, another one
## for sorting keys, so we need to unlist – one level only
res.short <- unlist(res.short, recursive=FALSE)
## remove if there were no results
res.short <- res.short[!sapply(res.short, is.null)]
write_xlsx(res.short, path=res.xlsx)
""")
script_file = pph.log(log.out, snakemake_format(script), step="tmod", extension="R", **wildcards)
# run R
shell("Rscript --vanilla '{script_file}' &>> '{log.out}'")
##-------------------- multivariate tmod ----------------------------------------------------------------
rule tmod_pca:
""" Gene set enrichment of PCA with tmod and MSigDB """
input:
dds = pph.file_path("DESeq2", "deseq2.rds", contrast = "all"),
dbs = pph.file_path(step = "tmod_dbs", extension = "rds", contrast="all"),
map = pph.file_path(step = "tmod_dbs", extension = "mapping.rds", contrast="all")
output:
res = pph.file_path(step = "tmod_pca", extension = "rds", contrast="all"),
pca = pph.file_path(step = "tmod_pca", extension = "pca.rds", contrast="all"),
xlsx = pph.file_path(step = "tmod_pca", extension = "xlsx", contrast="all")
log:
out = pph.file_path(step = "tmod_pca", extension = "output.log", log=True, contrast="all")
run:
config_file = pph.file_path(step="pipeline_report", extension="yaml", contrast="all")
script = textwrap.dedent(r"""
{R_SESSION_INFO}
script.d <- "{SCRIPTDIR}"
conf.f <- "{config_file}"
dds.f <- "{input.dds}"
map.f <- "{input.map}"
dbs.f <- "{input.dbs}"
res.f <- "{output.res}"
res.xlsx <- "{output.xlsx}"
res.pca.f <- "{output.pca}"
## ------------------------------
## no wildcards beyond this point
## ------------------------------
require(DESeq2)
require(tmod)
require(tidyverse)
require(writexl)
config <- yaml::yaml.load_file(conf.f)
source(file.path(script.d, "tmod_functions.R"))
dbs <- readRDS(dbs.f)
db.map <- readRDS(map.f)
dds <- readRDS(dds.f)
rl <- assay(rlog(dds))
nullvar <- apply(rl, 1, var) < .Machine$double.eps
rl <- rl[ !nullvar, ]
genes <- rownames(rl)
if(is.null(genes)) stop("can't get primary IDs from DESeq2 object!")
pca <- prcomp(t(rl), scale.=TRUE)
saveRDS(pca, file=res.pca.f)
ncomp <- min(4, ncol(pca$x))
res <- lapply(dbs, function(db) {{
mapping.id <- db.map$dbs[[ db$name ]]
mapping <- db.map$maps[[ mapping.id ]]
g.id <- setNames(mapping[genes], genes)
ret <- lapply(1:ncomp, function(i) {{
glists <- list(
up=g.id[order(pca$rotation[,i])],
down=g.id[order(-pca$rotation[,i])],
abs=g.id[order(-abs(pca$rotation[,i]))])
lapply(glists, tmodCERNOtest, mset=db$dbobj)
}})
names(ret) <- paste0("PC.", 1:ncomp)
ret
}})
saveRDS(res, file=res.f)
## shorten results for the human readable XLSX file
## filter the results
res.short <- lapply(res, function(x) lapply(x, function(y) lapply(y, reformat_res, pval.thr=.05, auc.thr=.55)))
## res.short still has two levels, one for the databases, another one