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pipeline_pyscenic_r.py
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pipeline_pyscenic_r.py
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"""
=============================
Pipeline pySCENIC R analysis
=============================
Authors: Devika Agarwal and Lucy Garner
Overview
========
This pipeline performs pySCENIC downstream analysis steps (https://pyscenic.readthedocs.io/en/latest/index.html)
including:
1. AUCell score based UMAP projection and clustering in seurat
2. AUCell score based heatmaps and violin plots
3. Binarized score projection on UMAP for each regulon
4. Wilcoxon (or chosen test) and KS statistical test for each regulon for celltype and condition
5. Average AUCell score heatmaps based on wilcoxon (ot chosen test)/KS test results
6. Pathway analyses and plots for top regulons per condition/celltype for regulon target genes
"""
from ruffus import *
import sys
import os
import re
from cgatcore import pipeline as P
PARAMS = P.get_parameters(
["%s/pipeline.yml" % os.path.splitext(__file__)[0],
"../pipeline.yml",
"pipeline.yml"])
@follows(mkdir("reports.dir"))
@transform("pyscenic_results.dir/*.dir/*.dir/aucell.csv",
regex(r"pyscenic_results.dir/(r.*|n.*).dir/([^_]+).dir/aucell.csv"),
r"reports.dir/\1.dir/\2.dir/scenic_seurat.html")
def scenic_seurat(infile, outfile):
''' Seurat based analyses for scenic results'''
if "normalised" in infile:
datatype = "normalised"
elif "raw" in infile:
datatype = "raw"
sample = infile.split("/")[2]
sample = sample.replace(".dir", "")
R_PATH = os.path.join(os.getcwd(), "R")
outbase = P.snip(outfile, ".html")
dir = re.sub("aucell.csv", "", infile)
aucell_zscores = dir + PARAMS["aucell_zscores"]
aucell_zscores_celltype = dir + "aucell_zscores_" + PARAMS["annotation_celltype"] + "-annotation.csv"
aucell_zscores_condition = dir + "aucell_zscores_" + PARAMS["annotation_condition"] + "-annotation.csv"
binary_matrix = dir + PARAMS["binary_matrix"]
working_dir = PARAMS["working_dir"]
seurat_object = "data.dir/" + sample + "_" + PARAMS["rseurat_seurat_object"]
results_directory = "pyscenic_results.dir/" + datatype + ".dir/" + sample + ".dir/pyscenic_r.dir"
plots_directory = "plots.dir/" + datatype + ".dir/" + sample + ".dir/pyscenic_r.dir"
umap_pcs = PARAMS["rseurat_umap_pcs"]
celltype = PARAMS["rseurat_celltype"]
clustering_resolution = PARAMS["rseurat_clustering_resolution"]
stacked_vln_function = PARAMS["rseurat_stacked_vln_function"]
dotplot_function = PARAMS["rseurat_dotplot_function"]
diff_exp_test = PARAMS["rseurat_diff_exp_test"]
latent_variables = PARAMS["rseurat_latent_variables"]
reference_condition = PARAMS["rseurat_reference_condition"]
celltype_condition = PARAMS["rseurat_celltype_condition"]
FDR_threshold = PARAMS["rseurat_FDR_threshold"]
condition = PARAMS["rseurat_condition"]
condition_exclusion = PARAMS["rseurat_condition_exclusion"]
if condition_exclusion != "None" and sample in condition_exclusion:
condition = "None"
os.makedirs(results_directory)
statement = """Rscript -e "rmarkdown::render('%(R_PATH)s/scenic_seurat.Rmd',
params = list(working_dir = '%(working_dir)s',
seurat_object = '%(working_dir)s/%(seurat_object)s',
aucell_scores = '%(working_dir)s/%(infile)s',
aucell_zscores = '%(working_dir)s/%(aucell_zscores)s',
aucell_zscores_celltype = '%(working_dir)s/%(aucell_zscores_celltype)s',
aucell_zscores_condition = '%(working_dir)s/%(aucell_zscores_condition)s',
binary_matrix = '%(working_dir)s/%(binary_matrix)s',
results_directory = '%(working_dir)s/%(results_directory)s',
plots_directory = '%(working_dir)s/%(plots_directory)s',
datatype = '%(datatype)s',
umap_pcs = '%(umap_pcs)s',
celltype = '%(celltype)s',
condition = '%(condition)s',
clustering_resolution = '%(clustering_resolution)s',
stacked_vln_function = '%(working_dir)s/%(stacked_vln_function)s',
dotplot_function = '%(working_dir)s/%(dotplot_function)s',
diff_exp_test = '%(diff_exp_test)s',
latent_variables = '%(latent_variables)s',
reference_condition = '%(reference_condition)s',
celltype_condition = '%(celltype_condition)s',
FDR_threshold = '%(FDR_threshold)s'),
output_file = '%(working_dir)s/%(outfile)s')"
> %(working_dir)s/%(results_directory)s/scenic_seurat.log
2> %(working_dir)s/%(results_directory)s/scenic_seurat.err"""
P.run(statement, job_threads = PARAMS["rseurat_threads"], job_memory = '10G',
job_queue = PARAMS["cluster_queue"], job_condaenv = PARAMS["conda_env"])
@follows(scenic_seurat)
@transform("pyscenic_results.dir/*.dir/*.dir/binary_matrix.csv",
regex(r"pyscenic_results.dir/(r.*|n.*).dir/([^_]+).dir/binary_matrix.csv"),
r"reports.dir/\1.dir/\2.dir/scenic_analysis_R.html")
def rscenic(infile, outfile):
''' R based analyses for scenic results'''
if "normalised" in infile:
datatype = "normalised"
elif "raw" in infile:
datatype = "raw"
sample = infile.split("/")[2]
sample = sample.replace(".dir", "")
R_PATH = os.path.join(os.getcwd(), "R")
outbase = P.snip(outfile, ".html")
dir = re.sub("binary_matrix.csv", "", infile)
exp_matrix = dir + "filtered-expression.csv"
celltype = PARAMS["rscenic_celltype"]
condition = PARAMS["rscenic_condition"]
condition_exclusion = PARAMS["rscenic_condition_exclusion"]
if condition_exclusion != "None" and sample in condition_exclusion:
condition = "None"
celltype_condition = PARAMS["rscenic_celltype_condition"]
annotation_celltype = PARAMS["rscenic_annotation_celltype"]
annotation_condition = PARAMS["rscenic_annotation_condition"]
annotation_celltype_condition = "data.dir/" + sample + "_" + PARAMS["rscenic_annotation_celltype_condition"]
zscores_celltype = dir + "aucell_zscores_" + PARAMS["rscenic_annotation_celltype"]
rss_celltype = dir + PARAMS["rscenic_annotation_celltype"].split(".")[0] + "_RSS.csv"
zscore_filter_threshold_celltype = PARAMS["rscenic_zscore_filter_threshold_celltype"]
celltype_top = PARAMS["rseurat_diff_exp_test"] + '_celltype_top10.csv'
if condition != "None":
zscores_condition = dir + "aucell_zscores_" + PARAMS["rscenic_annotation_condition"]
rss_condition = dir + PARAMS["rscenic_annotation_condition"].split(".")[0] + "_RSS.csv"
zscore_filter_threshold_condition = PARAMS["rscenic_zscore_filter_threshold_condition"]
condition_reference_top = PARAMS["rseurat_diff_exp_test"] + '_condition_reference_top10.csv'
condition_pairwise_top = PARAMS["rseurat_diff_exp_test"] + '_condition_pairwise_top10.csv'
ks_condition_top = 'ks_condition_top10.csv'
else:
zscores_condition = "None"
rss_condition = "None"
zscore_filter_threshold_condition = "None"
condition_reference_top = "None"
condition_pairwise_top = "None"
ks_condition_top = "None"
zscores_celltype_condition = dir + PARAMS["rscenic_zscores_celltype_condition"]
rss_celltype_condition = dir + PARAMS["rscenic_rss_celltype_condition"]
zscore_filter_threshold_celltype_condition = PARAMS["rscenic_zscore_filter_threshold_celltype_condition"]
regulon_genes = dir + "regulons.csv"
species = PARAMS["rscenic_species"]
working_dir = PARAMS["working_dir"]
num_workers = PARAMS["rscenic_num_workers"]
results_directory = "pyscenic_results.dir/" + datatype + ".dir/" + sample + ".dir/pyscenic_r.dir"
plots_directory = "plots.dir/" + datatype + ".dir/" + sample + ".dir/pyscenic_r.dir"
binary_heatmap_cell_annotations = sample + "_" + annotation_celltype
go_ont_option = PARAMS["rscenic_go_ont_option"]
pvaluecutoff = PARAMS["rscenic_pvaluecutoff"]
qvaluecutoff = PARAMS["rscenic_qvaluecutoff"]
maxGSSize = PARAMS["rscenic_maxGSSize"]
msigdb_geneset = PARAMS["rscenic_msigdb_geneset"]
statement = """Rscript -e "rmarkdown::render('%(R_PATH)s/scenic_analysis_R.Rmd',
params = list(working_dir = '%(working_dir)s',
num_workers = '%(num_workers)s',
datatype = '%(datatype)s',
results_directory = '%(working_dir)s/%(results_directory)s',
plots_directory = '%(working_dir)s/%(plots_directory)s',
celltype = '%(celltype)s',
condition = '%(condition)s',
celltype_condition = '%(celltype_condition)s',
zscores_celltype = '%(working_dir)s/%(zscores_celltype)s',
zscores_condition = '%(working_dir)s/%(zscores_condition)s',
zscores_celltype_condition = '%(working_dir)s/%(zscores_celltype_condition)s',
zscore_filter_threshold_celltype = '%(zscore_filter_threshold_celltype)s',
zscore_filter_threshold_condition = '%(zscore_filter_threshold_condition)s',
zscore_filter_threshold_celltype_condition = '%(zscore_filter_threshold_celltype_condition)s',
binary_mtx = '%(working_dir)s/%(infile)s',
annotation_celltype = '%(working_dir)s/data.dir/%(sample)s_%(annotation_celltype)s',
annotation_condition = '%(working_dir)s/data.dir/%(sample)s_%(annotation_condition)s',
annotation_celltype_condition = '%(working_dir)s/%(annotation_celltype_condition)s',
binary_heatmap_cell_annotations ='%(working_dir)s/data.dir/%(binary_heatmap_cell_annotations)s',
rss_celltype = '%(working_dir)s/%(rss_celltype)s',
rss_condition = '%(working_dir)s/%(rss_condition)s',
rss_celltype_condition = '%(working_dir)s/%(rss_celltype_condition)s',
species = '%(species)s',
regulon_genes = '%(working_dir)s/%(regulon_genes)s',
exp_matrix = '%(working_dir)s/%(exp_matrix)s',
go_ont_option = '%(go_ont_option)s',
diff_exp_test = '%(rseurat_diff_exp_test)s',
celltype_top = '%(celltype_top)s',
condition_reference_top = '%(condition_reference_top)s',
condition_pairwise_top = '%(condition_pairwise_top)s',
ks_celltype_top = 'ks_celltype_top10.csv',
ks_condition_top = '%(ks_condition_top)s',
pvaluecutoff = '%(pvaluecutoff)s',
qvaluecutoff = '%(qvaluecutoff)s',
maxGSSize = '%(maxGSSize)s',
msigdb_geneset = '%(msigdb_geneset)s'),
output_file = '%(working_dir)s/%(outfile)s')"
> %(working_dir)s/%(results_directory)s/scenic_analysis_R.log
2> %(working_dir)s/%(results_directory)s/scenic_analysis_R.err"""
P.run(statement, job_threads = PARAMS["rscenic_num_workers"], job_memory = '15G',
job_queue = PARAMS["cluster_queue"], job_condaenv = PARAMS["conda_env"])
@follows(scenic_seurat, rscenic)
def full():
pass
def main(argv = None):
if argv is None:
argv = sys.argv
P.main(argv)
if __name__ == "__main__":
sys.exit(P.main(sys.argv))