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suppa.snakefile
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##############################################################################
#
# Snakemake pipeline:
# SUPPA2
#
# AUTHOR: Maciej_Bak
# AFFILIATION: Swiss_Institute_of_Bioinformatics
# CONTACT: maciej.bak@unibas.ch
# CREATED: 22-11-2019
# LICENSE: Apache_2.0
#
##############################################################################
# imports
import sys
import os
import pandas as pd
# local rules
localrules: suppa_all, suppa_create_result_dir, suppa_extract_expression, \
suppa_merge_SE, suppa_merge_MX, suppa_merge_RI, suppa_merge_AF, suppa_merge_AL, suppa_merge_A3, suppa_merge_A5, \
suppa_merge_isoform, suppa_merge_differential_splicing
configfile: "treated_vs_untreated.yaml"
##############################################################################
### Target rule with final output of the pipeline
##############################################################################
rule suppa_all:
input:
TSV_SE_ALL = expand(os.path.join("{output_dir}", "SE_ALL.psi"), \
output_dir=config["result_dir"]),
TSV_RI_ALL = expand(os.path.join("{output_dir}", "RI_ALL.psi"), \
output_dir=config["result_dir"]),
TSV_MX_ALL = expand(os.path.join("{output_dir}", "MX_ALL.psi"), \
output_dir=config["result_dir"]),
TSV_AF_ALL = expand(os.path.join("{output_dir}", "AF_ALL.psi"), \
output_dir=config["result_dir"]),
TSV_AL_ALL = expand(os.path.join("{output_dir}", "AL_ALL.psi"), \
output_dir=config["result_dir"]),
TSV_A3_ALL = expand(os.path.join("{output_dir}", "A3_ALL.psi"), \
output_dir=config["result_dir"]),
TSV_A5_ALL = expand(os.path.join("{output_dir}", "A5_ALL.psi"), \
output_dir=config["result_dir"]),
TSV_isoform_psivec = \
expand(os.path.join("{output_dir}", "Delta_psi_isoform.psivec"), \
output_dir=config["result_dir"]),
TSV_isoform_dpsi = \
expand(os.path.join("{output_dir}", "Delta_psi_isoform.dpsi"), \
output_dir=config["result_dir"]),
TSV_merged_AS_table = \
expand(os.path.join("{output_dir}", "local_AS.tsv"), \
output_dir=config["result_dir"])
##############################################################################
### Create directories for the result
##############################################################################
rule suppa_create_result_dir:
output:
TMP_output = temp(os.path.join("{output_dir}", "dir_created"))
params:
DIR_main_dir = "{output_dir}",
LOG_cluster_log = "{output_dir}/cluster_log",
log:
DIR_local_log = "{output_dir}/local_log"
shell:
"""
mkdir -p {params.DIR_main_dir}; \
mkdir -p {params.LOG_cluster_log}; \
mkdir -p {log.DIR_local_log}; \
touch {output.TMP_output}
"""
##############################################################################
### SUPPA: prepare local AS event type and transcript events tables
##############################################################################
rule suppa_generate_local_events:
input:
TMP_output = os.path.join("{output_dir}", "dir_created")
output:
TSV_SE_ioe = os.path.join("{output_dir}", "AS_events", \
"events_SE_variable_"+config["threshold"]+".ioe"),
TSV_A5_ioe = os.path.join("{output_dir}", "AS_events", \
"events_A5_variable_"+config["threshold"]+".ioe"),
TSV_A3_ioe = os.path.join("{output_dir}", "AS_events", \
"events_A3_variable_"+config["threshold"]+".ioe"),
TSV_RI_ioe = os.path.join("{output_dir}", "AS_events", \
"events_RI_variable_"+config["threshold"]+".ioe"),
TSV_MX_ioe = os.path.join("{output_dir}", "AS_events", \
"events_MX_variable_"+config["threshold"]+".ioe"),
TSV_AF_ioe = os.path.join("{output_dir}", "AS_events", \
"events_AF_variable_"+config["threshold"]+".ioe"),
TSV_AL_ioe = os.path.join("{output_dir}", "AS_events", \
"events_AL_variable_"+config["threshold"]+".ioe")
params:
GTF_gtf = config["gtf"],
threshold = config["threshold"],
# workaround due to suppa file creation:
STRING_output_prefix = "{output_dir}/AS_events/events",
LOG_cluster_log = \
os.path.join("{output_dir}", "cluster_log", \
"suppa_generate_local_events.log"),
queue = "30min",
time = "00:30:00"
log:
LOG_local_log = \
os.path.join("{output_dir}", "local_log", \
"suppa_generate_local_events.log"),
resources:
threads = 1,
mem = 10000
benchmark:
os.path.join("{output_dir}", \
"cluster_log", "suppa_generate_local_events_benchmark.log")
conda:
"packages.yaml"
shell:
"""
suppa.py generateEvents \
--input-file {params.GTF_gtf} \
--format ioe \
--output-file {params.STRING_output_prefix} \
--event-type {{SE,SS,MX,RI,FL}} \
--boundary V \
--threshold {params.threshold} \
&> {log.LOG_local_log}
"""
rule suppa_generate_transcript_events:
input:
TMP_output = os.path.join("{output_dir}", "dir_created")
output:
TSV_transcript_events_ioi = \
os.path.join("{output_dir}", "AS_events", "transcript_events.ioi")
params:
GTF_gtf = config["gtf"],
# workaround due to suppa file creation:
STRING_output_prefix = \
os.path.join("{output_dir}", "AS_events", "transcript_events"),
LOG_cluster_log = \
os.path.join("{output_dir}", "cluster_log", \
"suppa_generate_transcript_events.log"),
queue = "30min",
time = "00:30:00"
log:
LOG_local_log = \
os.path.join("{output_dir}", "local_log", \
"suppa_generate_transcript_events.log"),
resources:
threads = 1,
mem = 10000
benchmark:
os.path.join("{output_dir}", \
"cluster_log", "suppa_generate_transcript_events_benchmark.log")
conda:
"packages.yaml"
shell:
"""
suppa.py generateEvents \
--input-file {params.GTF_gtf} \
--format ioi \
--output-file {params.STRING_output_prefix} \
&> {log.LOG_local_log}
"""
##############################################################################
### Extract transcripts expression per sample
##############################################################################
rule suppa_extract_expression:
input:
TMP_output = os.path.join("{output_dir}", "dir_created")
output:
DIR_quantification_dir = \
directory(os.path.join("{output_dir}", "quantification"))
params:
TSV_transcript_quantification = config["transcript_quantification"],
LIST_control_samples = config["CONTROL"],
LIST_experiment_samples = config["EXPERIMENT"],
log:
LOG_local_log = \
os.path.join("{output_dir}", "local_log", \
"suppa_extract_expression.log"),
run:
# split transcript expression table column-wise and condition-wise
os.mkdir(output.DIR_quantification_dir)
quant = pd.read_csv(config["transcript_quantification"], \
sep="\t", index_col=0)
for s in quant.columns.values:
outfile_name = \
os.path.join(output.DIR_quantification_dir, s+".tsv")
quant[[s]].to_csv(outfile_name, sep="\t", index_label=False)
temp_str = \
os.path.join(output.DIR_quantification_dir,"CONTROL.tsv")
quant[params.LIST_control_samples].to_csv(temp_str, \
sep="\t", index_label=False)
temp_str = \
os.path.join(output.DIR_quantification_dir,"EXPERIMENT.tsv")
quant[params.LIST_experiment_samples].to_csv(temp_str, \
sep="\t", index_label=False)
##############################################################################
### SUPPA: PSI calculation per local events
##############################################################################
rule suppa_psi_per_event_SE:
input:
TSV_SE_ioe = os.path.join("{output_dir}", "AS_events", \
"events_SE_variable_"+config["threshold"]+".ioe"),
DIR_quantification_dir = \
os.path.join("{output_dir}", "quantification")
output:
TSV_PSI_output = os.path.join("{output_dir}", "{sample}", "SE.psi")
params:
STRING_output_prefix = os.path.join("{output_dir}", "{sample}", "SE"),
TSV_sample_quant = \
os.path.join("{output_dir}", "quantification", "{sample}.tsv"),
tpm_filter = config["total_filter"],
LOG_cluster_log = \
os.path.join("{output_dir}", "cluster_log", \
"suppa_psi_per_event_SE_{sample}.log"),
queue = "30min",
time = "00:10:00"
log:
LOG_local_log = \
os.path.join("{output_dir}", "local_log", \
"suppa_psi_per_event_SE_{sample}.log")
resources:
threads = 1,
mem = 5000
benchmark:
os.path.join("{output_dir}", \
"cluster_log", "suppa_psi_per_event_SE_{sample}_benchmark.log")
conda:
"packages.yaml"
shell:
"""
suppa.py psiPerEvent \
--ioe-file {input.TSV_SE_ioe} \
--expression-file {params.TSV_sample_quant} \
--total-filter {params.tpm_filter} \
--output {params.STRING_output_prefix} \
&> {log.LOG_local_log}
"""
rule suppa_psi_per_event_MX:
input:
TSV_MX_ioe = os.path.join("{output_dir}", "AS_events", \
"events_MX_variable_"+config["threshold"]+".ioe"),
DIR_quantification_dir = \
os.path.join("{output_dir}", "quantification")
output:
TSV_PSI_output = os.path.join("{output_dir}", "{sample}", "MX.psi")
params:
STRING_output_prefix = os.path.join("{output_dir}", "{sample}", "MX"),
TSV_sample_quant = \
os.path.join("{output_dir}", "quantification", "{sample}.tsv"),
tpm_filter = config["total_filter"],
LOG_cluster_log = \
os.path.join("{output_dir}", "cluster_log", \
"suppa_psi_per_event_MX_{sample}.log"),
queue = "30min",
time = "00:10:00"
log:
LOG_local_log = \
os.path.join("{output_dir}", "local_log", \
"suppa_psi_per_event_MX_{sample}.log")
resources:
threads = 1,
mem = 5000
benchmark:
os.path.join("{output_dir}", \
"cluster_log", "suppa_psi_per_event_MX_{sample}_benchmark.log")
conda:
"packages.yaml"
shell:
"""
suppa.py psiPerEvent \
--ioe-file {input.TSV_MX_ioe} \
--expression-file {params.TSV_sample_quant} \
--total-filter {params.tpm_filter} \
--output {params.STRING_output_prefix} \
&> {log.LOG_local_log}
"""
rule suppa_psi_per_event_RI:
input:
TSV_RI_ioe = os.path.join("{output_dir}", "AS_events", \
"events_RI_variable_"+config["threshold"]+".ioe"),
DIR_quantification_dir = \
os.path.join("{output_dir}", "quantification")
output:
TSV_PSI_output = os.path.join("{output_dir}", "{sample}", "RI.psi")
params:
STRING_output_prefix = os.path.join("{output_dir}", "{sample}", "RI"),
TSV_sample_quant = \
os.path.join("{output_dir}", "quantification", "{sample}.tsv"),
tpm_filter = config["total_filter"],
LOG_cluster_log = \
os.path.join("{output_dir}", "cluster_log", \
"suppa_psi_per_event_RI_{sample}.log"),
queue = "30min",
time = "00:10:00"
log:
LOG_local_log = \
os.path.join("{output_dir}", "local_log", \
"suppa_psi_per_event_RI_{sample}.log")
resources:
threads = 1,
mem = 5000
benchmark:
os.path.join("{output_dir}", \
"cluster_log", "suppa_psi_per_event_RI_{sample}_benchmark.log")
conda:
"packages.yaml"
shell:
"""
suppa.py psiPerEvent \
--ioe-file {input.TSV_RI_ioe} \
--expression-file {params.TSV_sample_quant} \
--total-filter {params.tpm_filter} \
--output {params.STRING_output_prefix} \
&> {log.LOG_local_log}
"""
rule suppa_psi_per_event_AF:
input:
TSV_AF_ioe = os.path.join("{output_dir}", "AS_events", \
"events_AF_variable_"+config["threshold"]+".ioe"),
DIR_quantification_dir = \
os.path.join("{output_dir}", "quantification")
output:
TSV_PSI_output = os.path.join("{output_dir}", "{sample}", "AF.psi")
params:
STRING_output_prefix = os.path.join("{output_dir}", "{sample}", "AF"),
TSV_sample_quant = \
os.path.join("{output_dir}", "quantification", "{sample}.tsv"),
tpm_filter = config["total_filter"],
LOG_cluster_log = \
os.path.join("{output_dir}", "cluster_log", \
"suppa_psi_per_event_AF_{sample}.log"),
queue = "30min",
time = "00:10:00"
log:
LOG_local_log = \
os.path.join("{output_dir}", "local_log", \
"suppa_psi_per_event_AF_{sample}.log")
resources:
threads = 1,
mem = 5000
benchmark:
os.path.join("{output_dir}", \
"cluster_log", "suppa_psi_per_event_AF_{sample}_benchmark.log")
conda:
"packages.yaml"
shell:
"""
suppa.py psiPerEvent \
--ioe-file {input.TSV_AF_ioe} \
--expression-file {params.TSV_sample_quant} \
--total-filter {params.tpm_filter} \
--output {params.STRING_output_prefix} \
&> {log.LOG_local_log}
"""
rule suppa_psi_per_event_AL:
input:
TSV_AL_ioe = os.path.join("{output_dir}", "AS_events", \
"events_AL_variable_"+config["threshold"]+".ioe"),
DIR_quantification_dir = \
os.path.join("{output_dir}", "quantification")
output:
TSV_PSI_output = os.path.join("{output_dir}", "{sample}", "AL.psi")
params:
STRING_output_prefix = os.path.join("{output_dir}", "{sample}", "AL"),
TSV_sample_quant = \
os.path.join("{output_dir}", "quantification", "{sample}.tsv"),
tpm_filter = config["total_filter"],
LOG_cluster_log = \
os.path.join("{output_dir}", "cluster_log", \
"suppa_psi_per_event_AL_{sample}.log"),
queue = "30min",
time = "00:10:00"
log:
LOG_local_log = \
os.path.join("{output_dir}", "local_log", \
"suppa_psi_per_event_AL_{sample}.log")
resources:
threads = 1,
mem = 5000
benchmark:
os.path.join("{output_dir}", \
"cluster_log", "suppa_psi_per_event_AL_{sample}_benchmark.log")
conda:
"packages.yaml"
shell:
"""
suppa.py psiPerEvent \
--ioe-file {input.TSV_AL_ioe} \
--expression-file {params.TSV_sample_quant} \
--total-filter {params.tpm_filter} \
--output {params.STRING_output_prefix} \
&> {log.LOG_local_log}
"""
rule suppa_psi_per_event_A3:
input:
TSV_A3_ioe = os.path.join("{output_dir}", "AS_events", \
"events_A3_variable_"+config["threshold"]+".ioe"),
DIR_quantification_dir = \
os.path.join("{output_dir}", "quantification")
output:
TSV_PSI_output = os.path.join("{output_dir}", "{sample}", "A3.psi")
params:
STRING_output_prefix = os.path.join("{output_dir}", "{sample}", "A3"),
TSV_sample_quant = \
os.path.join("{output_dir}", "quantification", "{sample}.tsv"),
tpm_filter = config["total_filter"],
LOG_cluster_log = \
os.path.join("{output_dir}", "cluster_log", \
"suppa_psi_per_event_A3_{sample}.log"),
queue = "30min",
time = "00:10:00"
log:
LOG_local_log = \
os.path.join("{output_dir}", "local_log", \
"suppa_psi_per_event_A3_{sample}.log")
resources:
threads = 1,
mem = 5000
benchmark:
os.path.join("{output_dir}", \
"cluster_log", "suppa_psi_per_event_A3_{sample}_benchmark.log")
conda:
"packages.yaml"
shell:
"""
suppa.py psiPerEvent \
--ioe-file {input.TSV_A3_ioe} \
--expression-file {params.TSV_sample_quant} \
--total-filter {params.tpm_filter} \
--output {params.STRING_output_prefix} \
&> {log.LOG_local_log}
"""
rule suppa_psi_per_event_A5:
input:
TSV_A5_ioe = os.path.join("{output_dir}", "AS_events", \
"events_A5_variable_"+config["threshold"]+".ioe"),
DIR_quantification_dir = \
os.path.join("{output_dir}", "quantification")
output:
TSV_PSI_output = os.path.join("{output_dir}", "{sample}", "A5.psi")
params:
STRING_output_prefix = os.path.join("{output_dir}", "{sample}", "A5"),
TSV_sample_quant = \
os.path.join("{output_dir}", "quantification", "{sample}.tsv"),
tpm_filter = config["total_filter"],
LOG_cluster_log = \
os.path.join("{output_dir}", "cluster_log", \
"suppa_psi_per_event_A5_{sample}.log"),
queue = "30min",
time = "00:10:00"
log:
LOG_local_log = \
os.path.join("{output_dir}", "local_log", \
"suppa_psi_per_event_A5_{sample}.log")
resources:
threads = 1,
mem = 5000
benchmark:
os.path.join("{output_dir}", \
"cluster_log", "suppa_psi_per_event_A5_{sample}_benchmark.log")
conda:
"packages.yaml"
shell:
"""
suppa.py psiPerEvent \
--ioe-file {input.TSV_A5_ioe} \
--expression-file {params.TSV_sample_quant} \
--total-filter {params.tpm_filter} \
--output {params.STRING_output_prefix} \
&> {log.LOG_local_log}
"""
##############################################################################
### SUPPA: PSI calculation per transcript events
##############################################################################
rule suppa_psi_per_isoform:
input:
TSV_transcript_events_ioi = \
os.path.join("{output_dir}", "AS_events", \
"transcript_events.ioi"),
DIR_quantification_dir = \
os.path.join("{output_dir}", "quantification")
output:
TSV_PSI_output = \
os.path.join("{output_dir}", "{sample}_isoform.psi")
params:
GTF_gtf = config["gtf"],
STRING_output_prefix = os.path.join("{output_dir}", "{sample}"),
TSV_sample_quant = \
os.path.join("{output_dir}", "quantification", "{sample}.tsv"),
LOG_cluster_log = \
os.path.join("{output_dir}", "cluster_log", \
"suppa_psi_per_isoform_{sample}.log"),
queue = "30min",
time = "00:10:00"
log:
LOG_local_log = \
os.path.join("{output_dir}", "local_log", \
"suppa_psi_per_isoform_{sample}.log")
resources:
threads = 1,
mem = 5000
benchmark:
os.path.join("{output_dir}", \
"cluster_log", "suppa_psi_per_isoform_{sample}_benchmark.log")
conda:
"packages.yaml"
shell:
"""
suppa.py psiPerIsoform \
--gtf {params.GTF_gtf} \
--expression-file {params.TSV_sample_quant} \
--output-file {params.STRING_output_prefix} \
&> {log.LOG_local_log}
"""
##############################################################################
### Merge multiple tables
##############################################################################
rule suppa_merge_SE:
input:
expand(os.path.join("{output_dir}", "{sample}", "SE.psi"), \
output_dir=config["result_dir"], \
sample=config["CONTROL"]+config["EXPERIMENT"])
output:
TSV_SE_CONTROL = os.path.join("{output_dir}", "SE_CONTROL.psi"),
TSV_SE_EXPERIMENT = os.path.join("{output_dir}", "SE_EXPERIMENT.psi"),
TSV_SE_ALL = os.path.join("{output_dir}", "SE_ALL.psi"),
params:
DIR_output_dir = "{output_dir}",
LIST_control_samples = config["CONTROL"],
LIST_experiment_samples = config["EXPERIMENT"],
log:
LOG_local_log = \
os.path.join("{output_dir}", "local_log", "merge_SE.log")
run:
# merge PSI tables for both conditions and for all samples
df_list = []
for i in params.LIST_control_samples:
fname = os.path.join(params.DIR_output_dir,i,"SE.psi")
df_list.append(pd.read_csv(fname, sep="\t", index_col=0))
merged_df = pd.concat(df_list, axis=1).fillna("nan")
fname = os.path.join(params.DIR_output_dir,"SE_CONTROL.psi")
merged_df.to_csv(fname, sep="\t", index_label=False)
df_list = []
for i in params.LIST_experiment_samples:
fname = os.path.join(params.DIR_output_dir,i,"SE.psi")
df_list.append(pd.read_csv(fname, sep="\t", index_col=0))
merged_df = pd.concat(df_list, axis=1).fillna("nan")
fname = os.path.join(params.DIR_output_dir,"SE_EXPERIMENT.psi")
merged_df.to_csv(fname, sep="\t", index_label=False)
df_list = []
for i in params.LIST_control_samples+params.LIST_experiment_samples:
fname = os.path.join(params.DIR_output_dir,i,"SE.psi")
df_list.append(pd.read_csv(fname, sep="\t", index_col=0))
merged_df = pd.concat(df_list, axis=1).fillna("nan")
fname = os.path.join(params.DIR_output_dir,"SE_ALL.psi")
merged_df.to_csv(fname, sep="\t", index_label=False)
rule suppa_merge_MX:
input:
expand(os.path.join("{output_dir}", "{sample}", "MX.psi"), \
output_dir=config["result_dir"], \
sample=config["CONTROL"]+config["EXPERIMENT"])
output:
TSV_MX_CONTROL = os.path.join("{output_dir}", "MX_CONTROL.psi"),
TSV_MX_EXPERIMENT = os.path.join("{output_dir}", "MX_EXPERIMENT.psi"),
TSV_MX_ALL = os.path.join("{output_dir}", "MX_ALL.psi"),
params:
DIR_output_dir = "{output_dir}",
LIST_control_samples = config["CONTROL"],
LIST_experiment_samples = config["EXPERIMENT"],
log:
LOG_local_log = \
os.path.join("{output_dir}", "local_log", "merge_MX.log")
run:
# merge PSI tables for both conditions and for all samples
df_list = []
for i in params.LIST_control_samples:
fname = os.path.join(params.DIR_output_dir,i,"MX.psi")
df_list.append(pd.read_csv(fname, sep="\t", index_col=0))
merged_df = pd.concat(df_list, axis=1).fillna("nan")
fname = os.path.join(params.DIR_output_dir,"MX_CONTROL.psi")
merged_df.to_csv(fname, sep="\t", index_label=False)
df_list = []
for i in params.LIST_experiment_samples:
fname = os.path.join(params.DIR_output_dir,i,"MX.psi")
df_list.append(pd.read_csv(fname, sep="\t", index_col=0))
merged_df = pd.concat(df_list, axis=1).fillna("nan")
fname = os.path.join(params.DIR_output_dir,"MX_EXPERIMENT.psi")
merged_df.to_csv(fname, sep="\t", index_label=False)
df_list = []
for i in params.LIST_control_samples+params.LIST_experiment_samples:
fname = os.path.join(params.DIR_output_dir,i,"MX.psi")
df_list.append(pd.read_csv(fname, sep="\t", index_col=0))
merged_df = pd.concat(df_list, axis=1).fillna("nan")
fname = os.path.join(params.DIR_output_dir,"MX_ALL.psi")
merged_df.to_csv(fname, sep="\t", index_label=False)
rule suppa_merge_RI:
input:
expand(os.path.join("{output_dir}", "{sample}", "RI.psi"), \
output_dir=config["result_dir"], \
sample=config["CONTROL"]+config["EXPERIMENT"])
output:
TSV_RI_CONTROL = os.path.join("{output_dir}", "RI_CONTROL.psi"),
TSV_RI_EXPERIMENT = os.path.join("{output_dir}", "RI_EXPERIMENT.psi"),
TSV_RI_ALL = os.path.join("{output_dir}", "RI_ALL.psi"),
params:
DIR_output_dir = "{output_dir}",
LIST_control_samples = config["CONTROL"],
LIST_experiment_samples = config["EXPERIMENT"],
log:
LOG_local_log = \
os.path.join("{output_dir}", "local_log", "merge_RI.log")
run:
# merge PSI tables for both conditions and for all samples
df_list = []
for i in params.LIST_control_samples:
fname = os.path.join(params.DIR_output_dir,i,"RI.psi")
df_list.append(pd.read_csv(fname, sep="\t", index_col=0))
merged_df = pd.concat(df_list, axis=1).fillna("nan")
fname = os.path.join(params.DIR_output_dir,"RI_CONTROL.psi")
merged_df.to_csv(fname, sep="\t", index_label=False)
df_list = []
for i in params.LIST_experiment_samples:
fname = os.path.join(params.DIR_output_dir,i,"RI.psi")
df_list.append(pd.read_csv(fname, sep="\t", index_col=0))
merged_df = pd.concat(df_list, axis=1).fillna("nan")
fname = os.path.join(params.DIR_output_dir,"RI_EXPERIMENT.psi")
merged_df.to_csv(fname, sep="\t", index_label=False)
df_list = []
for i in params.LIST_control_samples+params.LIST_experiment_samples:
fname = os.path.join(params.DIR_output_dir,i,"RI.psi")
df_list.append(pd.read_csv(fname, sep="\t", index_col=0))
merged_df = pd.concat(df_list, axis=1).fillna("nan")
fname = os.path.join(params.DIR_output_dir,"RI_ALL.psi")
merged_df.to_csv(fname, sep="\t", index_label=False)
rule suppa_merge_AF:
input:
expand(os.path.join("{output_dir}", "{sample}", "AF.psi"), \
output_dir=config["result_dir"], \
sample=config["CONTROL"]+config["EXPERIMENT"])
output:
TSV_AF_CONTROL = os.path.join("{output_dir}", "AF_CONTROL.psi"),
TSV_AF_EXPERIMENT = os.path.join("{output_dir}", "AF_EXPERIMENT.psi"),
TSV_AF_ALL = os.path.join("{output_dir}", "AF_ALL.psi"),
params:
DIR_output_dir = "{output_dir}",
LIST_control_samples = config["CONTROL"],
LIST_experiment_samples = config["EXPERIMENT"],
log:
LOG_local_log = \
os.path.join("{output_dir}", "local_log", "merge_AF.log")
run:
# merge PSI tables for both conditions and for all samples
df_list = []
for i in params.LIST_control_samples:
fname = os.path.join(params.DIR_output_dir,i,"AF.psi")
df_list.append(pd.read_csv(fname, sep="\t", index_col=0))
merged_df = pd.concat(df_list, axis=1).fillna("nan")
fname = os.path.join(params.DIR_output_dir,"AF_CONTROL.psi")
merged_df.to_csv(fname, sep="\t", index_label=False)
df_list = []
for i in params.LIST_experiment_samples:
fname = os.path.join(params.DIR_output_dir,i,"AF.psi")
df_list.append(pd.read_csv(fname, sep="\t", index_col=0))
merged_df = pd.concat(df_list, axis=1).fillna("nan")
fname = os.path.join(params.DIR_output_dir,"AF_EXPERIMENT.psi")
merged_df.to_csv(fname, sep="\t", index_label=False)
df_list = []
for i in params.LIST_control_samples+params.LIST_experiment_samples:
fname = os.path.join(params.DIR_output_dir,i,"AF.psi")
df_list.append(pd.read_csv(fname, sep="\t", index_col=0))
merged_df = pd.concat(df_list, axis=1).fillna("nan")
fname = os.path.join(params.DIR_output_dir,"AF_ALL.psi")
merged_df.to_csv(fname, sep="\t", index_label=False)
rule suppa_merge_AL:
input:
expand(os.path.join("{output_dir}", "{sample}", "AL.psi"), \
output_dir=config["result_dir"], \
sample=config["CONTROL"]+config["EXPERIMENT"])
output:
TSV_AL_CONTROL = os.path.join("{output_dir}", "AL_CONTROL.psi"),
TSV_AL_EXPERIMENT = os.path.join("{output_dir}", "AL_EXPERIMENT.psi"),
TSV_AL_ALL = os.path.join("{output_dir}", "AL_ALL.psi"),
params:
DIR_output_dir = "{output_dir}",
LIST_control_samples = config["CONTROL"],
LIST_experiment_samples = config["EXPERIMENT"],
log:
LOG_local_log = \
os.path.join("{output_dir}", "local_log", "merge_AL.log")
run:
# merge PSI tables for both conditions and for all samples
df_list = []
for i in params.LIST_control_samples:
fname = os.path.join(params.DIR_output_dir,i,"AL.psi")
df_list.append(pd.read_csv(fname, sep="\t", index_col=0))
merged_df = pd.concat(df_list, axis=1).fillna("nan")
fname = os.path.join(params.DIR_output_dir,"AL_CONTROL.psi")
merged_df.to_csv(fname, sep="\t", index_label=False)
df_list = []
for i in params.LIST_experiment_samples:
fname = os.path.join(params.DIR_output_dir,i,"AL.psi")
df_list.append(pd.read_csv(fname, sep="\t", index_col=0))
merged_df = pd.concat(df_list, axis=1).fillna("nan")
fname = os.path.join(params.DIR_output_dir,"AL_EXPERIMENT.psi")
merged_df.to_csv(fname, sep="\t", index_label=False)
df_list = []
for i in params.LIST_control_samples+params.LIST_experiment_samples:
fname = os.path.join(params.DIR_output_dir,i,"AL.psi")
df_list.append(pd.read_csv(fname, sep="\t", index_col=0))
merged_df = pd.concat(df_list, axis=1).fillna("nan")
fname = os.path.join(params.DIR_output_dir,"AL_ALL.psi")
merged_df.to_csv(fname, sep="\t", index_label=False)
rule suppa_merge_A3:
input:
expand(os.path.join("{output_dir}", "{sample}", "A3.psi"), \
output_dir=config["result_dir"], \
sample=config["CONTROL"]+config["EXPERIMENT"])
output:
TSV_A3_CONTROL = os.path.join("{output_dir}", "A3_CONTROL.psi"),
TSV_A3_EXPERIMENT = os.path.join("{output_dir}", "A3_EXPERIMENT.psi"),
TSV_A3_ALL = os.path.join("{output_dir}", "A3_ALL.psi"),
params:
DIR_output_dir = "{output_dir}",
LIST_control_samples = config["CONTROL"],
LIST_experiment_samples = config["EXPERIMENT"],
log:
LOG_local_log = \
os.path.join("{output_dir}", "local_log", "merge_A3.log")
run:
# merge PSI tables for both conditions and for all samples
df_list = []
for i in params.LIST_control_samples:
fname = os.path.join(params.DIR_output_dir,i,"A3.psi")
df_list.append(pd.read_csv(fname, sep="\t", index_col=0))
merged_df = pd.concat(df_list, axis=1).fillna("nan")
fname = os.path.join(params.DIR_output_dir,"A3_CONTROL.psi")
merged_df.to_csv(fname, sep="\t", index_label=False)
df_list = []
for i in params.LIST_experiment_samples:
fname = os.path.join(params.DIR_output_dir,i,"A3.psi")
df_list.append(pd.read_csv(fname, sep="\t", index_col=0))
merged_df = pd.concat(df_list, axis=1).fillna("nan")
fname = os.path.join(params.DIR_output_dir,"A3_EXPERIMENT.psi")
merged_df.to_csv(fname, sep="\t", index_label=False)
df_list = []
for i in params.LIST_control_samples+params.LIST_experiment_samples:
fname = os.path.join(params.DIR_output_dir,i,"A3.psi")
df_list.append(pd.read_csv(fname, sep="\t", index_col=0))
merged_df = pd.concat(df_list, axis=1).fillna("nan")
fname = os.path.join(params.DIR_output_dir,"A3_ALL.psi")
merged_df.to_csv(fname, sep="\t", index_label=False)
rule suppa_merge_A5:
input:
expand(os.path.join("{output_dir}", "{sample}", "A5.psi"), \
output_dir=config["result_dir"], \
sample=config["CONTROL"]+config["EXPERIMENT"])
output:
TSV_A5_CONTROL = os.path.join("{output_dir}", "A5_CONTROL.psi"),
TSV_A5_EXPERIMENT = os.path.join("{output_dir}", "A5_EXPERIMENT.psi"),
TSV_A5_ALL = os.path.join("{output_dir}", "A5_ALL.psi"),
params:
DIR_output_dir = "{output_dir}",
LIST_control_samples = config["CONTROL"],
LIST_experiment_samples = config["EXPERIMENT"],
log:
LOG_local_log = \
os.path.join("{output_dir}", "local_log", "merge_A5.log")
run:
# merge PSI tables for both conditions and for all samples
df_list = []
for i in params.LIST_control_samples:
fname = os.path.join(params.DIR_output_dir,i,"A5.psi")
df_list.append(pd.read_csv(fname, sep="\t", index_col=0))
merged_df = pd.concat(df_list, axis=1).fillna("nan")
fname = os.path.join(params.DIR_output_dir,"A5_CONTROL.psi")
merged_df.to_csv(fname, sep="\t", index_label=False)
df_list = []
for i in params.LIST_experiment_samples:
fname = os.path.join(params.DIR_output_dir,i,"A5.psi")
df_list.append(pd.read_csv(fname, sep="\t", index_col=0))
merged_df = pd.concat(df_list, axis=1).fillna("nan")
fname = os.path.join(params.DIR_output_dir,"A5_EXPERIMENT.psi")
merged_df.to_csv(fname, sep="\t", index_label=False)
df_list = []
for i in params.LIST_control_samples+params.LIST_experiment_samples:
fname = os.path.join(params.DIR_output_dir,i,"A5.psi")
df_list.append(pd.read_csv(fname, sep="\t", index_col=0))
merged_df = pd.concat(df_list, axis=1).fillna("nan")
fname = os.path.join(params.DIR_output_dir,"A5_ALL.psi")
merged_df.to_csv(fname, sep="\t", index_label=False)
rule suppa_merge_isoform:
input:
expand(os.path.join("{output_dir}", "{sample}_isoform.psi"), \
output_dir=config["result_dir"], \
sample=config["CONTROL"]+config["EXPERIMENT"])
output:
TSV_isoform_CONTROL = \
os.path.join("{output_dir}", "isoform_CONTROL.psi"),
TSV_isoform_EXPERIMENT = \
os.path.join("{output_dir}", "isoform_EXPERIMENT.psi"),
TSV_isoform_ALL = \
os.path.join("{output_dir}", "isoform_ALL.psi"),
params:
DIR_output_dir = "{output_dir}",
LIST_control_samples = config["CONTROL"],
LIST_experiment_samples = config["EXPERIMENT"],
log:
LOG_local_log = \
os.path.join("{output_dir}", "local_log", "merge_isoform.log")
run:
# merge PSI tables for both conditions and for all samples
df_list = []
for i in params.LIST_control_samples:
fname = os.path.join(params.DIR_output_dir,i+"_isoform.psi")
df_list.append(pd.read_csv(fname, sep="\t", index_col=0))
merged_df = pd.concat(df_list, axis=1).fillna("nan")
fname = os.path.join(params.DIR_output_dir,"isoform_CONTROL.psi")
merged_df.to_csv(fname, sep="\t", index_label=False)
df_list = []
for i in params.LIST_experiment_samples:
fname = os.path.join(params.DIR_output_dir,i+"_isoform.psi")
df_list.append(pd.read_csv(fname, sep="\t", index_col=0))
merged_df = pd.concat(df_list, axis=1).fillna("nan")
fname = os.path.join(params.DIR_output_dir,"isoform_EXPERIMENT.psi")
merged_df.to_csv(fname, sep="\t", index_label=False)
df_list = []
for i in params.LIST_control_samples+params.LIST_experiment_samples:
fname = os.path.join(params.DIR_output_dir,i+"_isoform.psi")
df_list.append(pd.read_csv(fname, sep="\t", index_col=0))
merged_df = pd.concat(df_list, axis=1).fillna("nan")
fname = os.path.join(params.DIR_output_dir,"isoform_ALL.psi")
merged_df.to_csv(fname, sep="\t", index_label=False)
##############################################################################
### Differential splicing analysis for local AS events
##############################################################################
rule suppa_diffSplice_SE:
input:
TSV_SE_CONTROL = os.path.join("{output_dir}", "SE_CONTROL.psi"),
TSV_SE_EXPERIMENT = os.path.join("{output_dir}", "SE_EXPERIMENT.psi"),
output:
TSV_SE_psivec = os.path.join("{output_dir}", "Delta_psi_SE.psivec"),
TSV_SE_dpsi = os.path.join("{output_dir}", "Delta_psi_SE.dpsi")
params:
TSV_SE_ioe = os.path.join("{output_dir}", "AS_events", \
"events_SE_variable_"+config["threshold"]+".ioe"),
DIR_output_dir = "{output_dir}",
STRING_output_prefix = "Delta_psi_SE",
method = "empirical",
area = "1000",
lower_bound = config["lower_bound"],
gene_correction = "--gene-correction",
nan_threshold = config["nan_threshold"],
TSV_control_quant = \
os.path.join("{output_dir}", "quantification", "CONTROL.tsv"),
TSV_experiment_quant = \
os.path.join("{output_dir}", "quantification", "EXPERIMENT.tsv"),
LOG_cluster_log = \
os.path.join("{output_dir}", "cluster_log", \
"suppa_diffSplice_SE.log"),
queue = "30min",
time = "00:30:00"
log:
LOG_local_log = os.path.join("{output_dir}", "local_log", \
"suppa_diffSplice_SE.log")
resources:
threads = 1,
mem = 5000
benchmark:
os.path.join("{output_dir}", \
"cluster_log", "suppa_diffSplice_SE_benchmark.log")
conda:
"packages.yaml"
shell:
"""
suppa.py diffSplice \
--method {params.method} \
--input {params.TSV_SE_ioe} \
--psi {input.TSV_SE_CONTROL} {input.TSV_SE_EXPERIMENT} \
--tpm {params.TSV_control_quant} {params.TSV_experiment_quant} \
--area {params.area} \
--nan-threshold {params.nan_threshold} \
--lower-bound {params.lower_bound} \
{params.gene_correction} \
--output {params.STRING_output_prefix} \
&> {log.LOG_local_log} &&
sleep 5 &&
mv Delta_psi_SE.psivec {params.DIR_output_dir}/Delta_psi_SE.psivec &&
mv Delta_psi_SE.dpsi {params.DIR_output_dir}/Delta_psi_SE.dpsi;
"""
rule suppa_diffSplice_MX:
input:
TSV_MX_CONTROL = os.path.join("{output_dir}", "MX_CONTROL.psi"),
TSV_MX_EXPERIMENT = os.path.join("{output_dir}", "MX_EXPERIMENT.psi"),
output:
TSV_MX_psivec = os.path.join("{output_dir}", "Delta_psi_MX.psivec"),
TSV_MX_dpsi = os.path.join("{output_dir}", "Delta_psi_MX.dpsi")
params:
TSV_MX_ioe = os.path.join("{output_dir}", "AS_events", \
"events_MX_variable_"+config["threshold"]+".ioe"),
DIR_output_dir = "{output_dir}",
STRING_output_prefix = "Delta_psi_MX",
method = "empirical",
area = "1000",
lower_bound = config["lower_bound"],
gene_correction = "--gene-correction",
TSV_control_quant = \
os.path.join("{output_dir}", "quantification", "CONTROL.tsv"),
TSV_experiment_quant = \
os.path.join("{output_dir}", "quantification", "EXPERIMENT.tsv"),
LOG_cluster_log = \
os.path.join("{output_dir}", "cluster_log", \
"suppa_diffSplice_MX.log"),
queue = "30min",
time = "00:30:00"
log:
LOG_local_log = os.path.join("{output_dir}", "local_log", \
"suppa_diffSplice_MX.log")
resources:
threads = 1,
mem = 5000
benchmark:
os.path.join("{output_dir}", \
"cluster_log", "suppa_diffSplice_MX_benchmark.log")
conda:
"packages.yaml"
shell:
"""
suppa.py diffSplice \
--method {params.method} \
--input {params.TSV_MX_ioe} \
--psi {input.TSV_MX_CONTROL} {input.TSV_MX_EXPERIMENT} \
--tpm {params.TSV_control_quant} {params.TSV_experiment_quant} \
--area {params.area} \
-gc \
--lower-bound {params.lower_bound} \
{params.gene_correction} \
--output {params.STRING_output_prefix} \
&> {log.LOG_local_log} &&
sleep 5 &&
mv Delta_psi_MX.psivec {params.DIR_output_dir}/Delta_psi_MX.psivec &&
mv Delta_psi_MX.dpsi {params.DIR_output_dir}/Delta_psi_MX.dpsi;
"""
rule suppa_diffSplice_RI:
input:
TSV_RI_CONTROL = os.path.join("{output_dir}", "RI_CONTROL.psi"),
TSV_RI_EXPERIMENT = os.path.join("{output_dir}", "RI_EXPERIMENT.psi"),
output:
TSV_RI_psivec = os.path.join("{output_dir}", "Delta_psi_RI.psivec"),
TSV_RI_dpsi = os.path.join("{output_dir}", "Delta_psi_RI.dpsi")
params:
TSV_RI_ioe = os.path.join("{output_dir}", "AS_events", \
"events_RI_variable_"+config["threshold"]+".ioe"),
DIR_output_dir = "{output_dir}",
STRING_output_prefix = "Delta_psi_RI",
method = "empirical",
area = "1000",
lower_bound = config["lower_bound"],
gene_correction = "--gene-correction",
TSV_control_quant = \
os.path.join("{output_dir}", "quantification", "CONTROL.tsv"),
TSV_experiment_quant = \
os.path.join("{output_dir}", "quantification", "EXPERIMENT.tsv"),
LOG_cluster_log = \
os.path.join("{output_dir}", "cluster_log", \
"suppa_diffSplice_RI.log"),
queue = "30min",
time = "00:30:00"
log:
LOG_local_log = os.path.join("{output_dir}", "local_log", \