def run_eVIP(infile=None, zscore_gct = None, out_directory=None, sig_info =None, c=None, r=None, num_reps=None,
ie_filter=None,ie_col=None, i=None, allele_col=None, conn_null=None, conn_thresh=None,
mut_wt_rep_rank_diff=None, use_c_pval=None, cell_id=None, plate_id=None, ref_allele_mode=None,
x_thresh=None, y_thresh=None, annotate=None, by_gene_color=None, pdf=None, xmin=None,
xmax=None, ymin=None, ymax=None, viz_ymin=None, viz_ymax=None, corr_val=None,mut_wt_rep_thresh=None,disting_thresh=None,sparkler_off=None,viz_off=None,cond_median_max_diff_thresh=None):
#different sig_gctx for exp an z inputs used in viz
sig_gctx_val = out_directory+ "/z_scores.gct"
# if args.zscore_gct :
# sig_gctx_val = args.zscore_gct
# run eVIP_corr.py
# print('calculating correlations...')
run_corr = eVIP_corr.run_main(input=infile,zscore_gct=zscore_gct, out_dir= out_directory)
# print('comparing...')
run_compare = eVIP_compare.run_main(sig_info=sig_info, gctx = out_directory+"/spearman_rank_matrix.gct",
allele_col = allele_col, o= out_directory+"/compare", r = r,
c = c, i = i, conn_null = conn_null, ie_col = ie_col,
ie_filter = ie_filter, num_reps = num_reps, cell_id = cell_id, plate_id = plate_id)
# print('predicting...')
run_predict = eVIP_predict.run_main(out_directory+"/compare.txt", out_directory+"/predict", conn_thresh,
mut_wt_rep_thresh, mut_wt_rep_rank_diff,
disting_thresh, use_c_pval,cond_median_max_diff_thresh)
if not sparkler_off:
# print "making sparkler plots..."
run_sparkler = eVIP_sparkler.eVIP_run_main(pred_file = out_directory+"/predict.txt", ref_allele_mode=ref_allele_mode,
y_thresh = y_thresh , x_thresh = x_thresh,
use_c_pval= use_c_pval,annotate=annotate, by_gene_color= by_gene_color, pdf= pdf,
xmin= xmin, xmax = xmax, ymin = ymin, ymax = ymax, out_dir = out_directory+"/sparkler_plots")
if not viz_off:
# print "making visualizations..."
if conn_null:
null_conn = conn_null
else:
null_conn = out_directory + "/compare_conn_null.txt"
run_viz = eVIP_viz.eVIP_run_main(pred_file= out_directory+"/predict.txt", sig_info = sig_info, gctx=out_directory+"/spearman_rank_matrix.gct",
sig_gctx = sig_gctx_val, ref_allele_mode = ref_allele_mode, null_conn = null_conn,
out_dir = out_directory+"/viz",ymin = viz_ymin, ymax= viz_ymax, allele_col = allele_col, use_c_pval = use_c_pval,
pdf = pdf, cell_id = cell_id, plate_id = plate_id, corr_val_str= corr_val)
def run_eVIP_multiple_testing_pt1(infile=None, zscore_gct = None, out_directory=None, sig_info =None, c=None, r=None, num_reps=None,
ie_filter=None,ie_col=None, i=None, allele_col=None, conn_null=None, conn_thresh=None,
mut_wt_rep_rank_diff=None, use_c_pval=None, cell_id=None, plate_id=None, ref_allele_mode=None,
x_thresh=None, y_thresh=None, annotate=None, by_gene_color=None, pdf=None, xmin=None,
xmax=None, ymin=None, ymax=None, viz_ymin=None, viz_ymax=None, corr_val=None,sparkler_off=None,viz_off=None):
#runs eVIP_corr and compare and returns the p values from compare
#different sig_gctx for exp an z inputs used in viz
sig_gctx_val = out_directory+ "/z_scores.gct"
#run eVIP_corr.py
# print('calculating correlations...')
run_corr = eVIP_corr.run_main(input=infile,zscore_gct=zscore_gct, out_dir= out_directory)
#run eVIP_compare.py
# print('comparing...')
run_compare = eVIP_compare.run_main(sig_info, out_directory+"/spearman_rank_matrix.gct", allele_col , out_directory+"/compare", r ,c , i , conn_null , ie_col, ie_filter, num_reps, cell_id , plate_id)
#getting the p values from the pathway compare file
mut_wt_rep_pvals_from_compare = []
mut_wt_conn_null_pvals_from_compare = []
wt_mut_rep_vs_wt_mut_conn_pvals_from_compare = []
num_test_alleles = None
with open(out_directory+"/compare.txt", "r") as compare_file:
file_reader = csv.DictReader(compare_file, delimiter="\t")
for row in file_reader:
mut_wt_rep_pvals_from_compare.append(row['mut_wt_rep_pval'])
mut_wt_conn_null_pvals_from_compare.append(row['mut_wt_conn_null_pval'])
wt_mut_rep_vs_wt_mut_conn_pvals_from_compare.append(row['wt_mut_rep_vs_wt_mut_conn_pval'])
#counting the number of mutations being tested
with open(out_directory + "/compare.txt", "r") as compare_file:
file_reader = csv.DictReader(compare_file, delimiter="\t")
each_line = list(file_reader)
num_test_alleles=len(each_line)
return mut_wt_rep_pvals_from_compare, mut_wt_conn_null_pvals_from_compare, wt_mut_rep_vs_wt_mut_conn_pvals_from_compare,num_test_alleles
def run_eVIP(infile=None,
zscore_gct=None,
out_directory=None,
sig_info=None,
c=None,
r=None,
num_reps=None,
ie_filter=None,
ie_col=None,
i=None,
allele_col=None,
conn_null=None,
conn_thresh=None,
mut_wt_rep_rank_diff=None,
use_c_pval=None,
cell_id=None,
plate_id=None,
ref_allele_mode=None,
x_thresh=None,
y_thresh=None,
annotate=None,
by_gene_color=None,
pdf=None,
xmin=None,
xmax=None,
ymin=None,
ymax=None,
viz_ymin=None,
viz_ymax=None,
corr_val=None):
#different sig_gctx for exp an z inputs used in viz
if args.infile:
sig_gctx_val = out_directory + "/z_scores.gct"
if args.zscore_gct:
sig_gctx_val = args.zscore_gct
# run eVIP_corr.py
print('calculating correlations...')
run_corr = eVIP_corr.run_main(input=infile,
zscore_gct=zscore_gct,
out_dir=out_directory)
print('comparing...')
run_compare = eVIP_compare.run_main(sig_info=sig_info,
gctx=out_directory +
"/spearman_rank_matrix.gct",
allele_col=args.allele_col,
o=out_directory + "/compare",
r=args.r,
c=args.c,
i=args.i,
conn_null=args.conn_null,
ie_col=args.ie_col,
ie_filter=args.ie_filter,
num_reps=args.num_reps,
cell_id=args.cell_id,
plate_id=args.plate_id)
print('predicting...')
run_predict = eVIP_predict.run_main(
i=out_directory + "/compare.txt",
o=out_directory + "/predict",
conn_thresh=args.conn_thresh,
mut_wt_rep_thresh=args.mut_wt_rep_thresh,
mut_wt_rep_rank_diff=args.mut_wt_rep_rank_diff,
disting_thresh=args.disting_thresh,
use_c_pval=args.use_c_pval)
if not args.sparkler_off:
print "making sparkler plots..."
run_sparkler = eVIP_sparkler.eVIP_run_main(
pred_file=out_directory + "/predict.txt",
ref_allele_mode=args.ref_allele_mode,
y_thresh=args.y_thresh,
x_thresh=args.x_thresh,
use_c_pval=args.use_c_pval,
annotate=args.annotate,
by_gene_color=args.by_gene_color,
pdf=args.pdf,
xmin=args.xmin,
xmax=args.xmax,
ymin=args.ymin,
ymax=args.ymax,
out_dir=out_directory + "/sparkler_plots")
if not args.viz_off:
print "making visualizations..."
if args.conn_null:
null_conn = args.conn_null
else:
null_conn = out_directory + "/compare_conn_null.txt"
run_viz = eVIP_viz.eVIP_run_main(
pred_file=out_directory + "/predict.txt",
sig_info=args.sig_info,
gctx=out_directory + "/spearman_rank_matrix.gct",
sig_gctx=sig_gctx_val,
ref_allele_mode=args.ref_allele_mode,
null_conn=null_conn,
out_dir=out_directory + "/viz",
ymin=args.viz_ymin,
ymax=args.viz_ymax,
allele_col=args.allele_col,
use_c_pval=args.use_c_pval,
pdf=args.pdf,
cell_id=args.cell_id,
plate_id=args.plate_id,
corr_val_str=args.corr_val)