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[FEATURE] Separate colorblind friendly feature into separate PR from P…
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…yProphet#101
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@singjc
singjc committed Mar 24, 2022
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22 changes: 11 additions & 11 deletions pyprophet/levels_contexts.py
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Original file line number Diff line number Diff line change
Expand Up @@ -11,7 +11,7 @@
from .data_handling import check_sqlite_table


def statistics_report(data, outfile, context, analyte, parametric, pfdr, pi0_lambda, pi0_method, pi0_smooth_df, pi0_smooth_log_pi0, lfdr_truncate, lfdr_monotone, lfdr_transformation, lfdr_adj, lfdr_eps):
def statistics_report(data, outfile, context, analyte, parametric, pfdr, pi0_lambda, pi0_method, pi0_smooth_df, pi0_smooth_log_pi0, lfdr_truncate, lfdr_monotone, lfdr_transformation, lfdr_adj, lfdr_eps, color_palette):

error_stat, pi0 = error_statistics(data[data.decoy==0]['score'], data[data.decoy==1]['score'], parametric, pfdr, pi0_lambda, pi0_method, pi0_smooth_df, pi0_smooth_log_pi0, True, lfdr_truncate, lfdr_monotone, lfdr_transformation, lfdr_adj, lfdr_eps)

Expand All @@ -33,11 +33,11 @@ def statistics_report(data, outfile, context, analyte, parametric, pfdr, pi0_lam
outfile = outfile + "_" + str(data['run_id'].unique()[0])

# export PDF report
save_report(outfile + "_" + context + "_" + analyte + ".pdf", outfile + ": " + context + " " + analyte + "-level error-rate control", data[data.decoy==1]["score"], data[data.decoy==0]["score"], stat_table["cutoff"], stat_table["svalue"], stat_table["qvalue"], data[data.decoy==0]["p_value"], pi0)
save_report(outfile + "_" + context + "_" + analyte + ".pdf", outfile + ": " + context + " " + analyte + "-level error-rate control", data[data.decoy==1]["score"], data[data.decoy==0]["score"], stat_table["cutoff"], stat_table["svalue"], stat_table["qvalue"], data[data.decoy==0]["p_value"], pi0, color_palette)

return(data)

def infer_genes(infile, outfile, context, parametric, pfdr, pi0_lambda, pi0_method, pi0_smooth_df, pi0_smooth_log_pi0, lfdr_truncate, lfdr_monotone, lfdr_transformation, lfdr_adj, lfdr_eps):
def infer_genes(infile, outfile, context, parametric, pfdr, pi0_lambda, pi0_method, pi0_smooth_df, pi0_smooth_log_pi0, lfdr_truncate, lfdr_monotone, lfdr_transformation, lfdr_adj, lfdr_eps, color_palette):

con = sqlite3.connect(infile)

Expand Down Expand Up @@ -98,10 +98,10 @@ def infer_genes(infile, outfile, context, parametric, pfdr, pi0_lambda, pi0_meth
con.close()

if context == 'run-specific':
data = data.groupby('run_id').apply(statistics_report, outfile, context, "gene", parametric, pfdr, pi0_lambda, pi0_method, pi0_smooth_df, pi0_smooth_log_pi0, lfdr_truncate, lfdr_monotone, lfdr_transformation, lfdr_adj, lfdr_eps).reset_index()
data = data.groupby('run_id').apply(statistics_report, outfile, context, "gene", parametric, pfdr, pi0_lambda, pi0_method, pi0_smooth_df, pi0_smooth_log_pi0, lfdr_truncate, lfdr_monotone, lfdr_transformation, lfdr_adj, lfdr_eps, color_palette).reset_index()

elif context in ['global', 'experiment-wide']:
data = statistics_report(data, outfile, context, "gene", parametric, pfdr, pi0_lambda, pi0_method, pi0_smooth_df, pi0_smooth_log_pi0, lfdr_truncate, lfdr_monotone, lfdr_transformation, lfdr_adj, lfdr_eps)
data = statistics_report(data, outfile, context, "gene", parametric, pfdr, pi0_lambda, pi0_method, pi0_smooth_df, pi0_smooth_log_pi0, lfdr_truncate, lfdr_monotone, lfdr_transformation, lfdr_adj, lfdr_eps, color_palette)

# store data in table
if infile != outfile:
Expand All @@ -123,7 +123,7 @@ def infer_genes(infile, outfile, context, parametric, pfdr, pi0_lambda, pi0_meth
con.close()


def infer_proteins(infile, outfile, context, parametric, pfdr, pi0_lambda, pi0_method, pi0_smooth_df, pi0_smooth_log_pi0, lfdr_truncate, lfdr_monotone, lfdr_transformation, lfdr_adj, lfdr_eps):
def infer_proteins(infile, outfile, context, parametric, pfdr, pi0_lambda, pi0_method, pi0_smooth_df, pi0_smooth_log_pi0, lfdr_truncate, lfdr_monotone, lfdr_transformation, lfdr_adj, lfdr_eps, color_palette):

con = sqlite3.connect(infile)

Expand Down Expand Up @@ -184,10 +184,10 @@ def infer_proteins(infile, outfile, context, parametric, pfdr, pi0_lambda, pi0_m
con.close()

if context == 'run-specific':
data = data.groupby('run_id').apply(statistics_report, outfile, context, "protein", parametric, pfdr, pi0_lambda, pi0_method, pi0_smooth_df, pi0_smooth_log_pi0, lfdr_truncate, lfdr_monotone, lfdr_transformation, lfdr_adj, lfdr_eps).reset_index()
data = data.groupby('run_id').apply(statistics_report, outfile, context, "protein", parametric, pfdr, pi0_lambda, pi0_method, pi0_smooth_df, pi0_smooth_log_pi0, lfdr_truncate, lfdr_monotone, lfdr_transformation, lfdr_adj, lfdr_eps, color_palette).reset_index()

elif context in ['global', 'experiment-wide']:
data = statistics_report(data, outfile, context, "protein", parametric, pfdr, pi0_lambda, pi0_method, pi0_smooth_df, pi0_smooth_log_pi0, lfdr_truncate, lfdr_monotone, lfdr_transformation, lfdr_adj, lfdr_eps)
data = statistics_report(data, outfile, context, "protein", parametric, pfdr, pi0_lambda, pi0_method, pi0_smooth_df, pi0_smooth_log_pi0, lfdr_truncate, lfdr_monotone, lfdr_transformation, lfdr_adj, lfdr_eps, color_palette)

# store data in table
if infile != outfile:
Expand All @@ -209,7 +209,7 @@ def infer_proteins(infile, outfile, context, parametric, pfdr, pi0_lambda, pi0_m
con.close()


def infer_peptides(infile, outfile, context, parametric, pfdr, pi0_lambda, pi0_method, pi0_smooth_df, pi0_smooth_log_pi0, lfdr_truncate, lfdr_monotone, lfdr_transformation, lfdr_adj, lfdr_eps):
def infer_peptides(infile, outfile, context, parametric, pfdr, pi0_lambda, pi0_method, pi0_smooth_df, pi0_smooth_log_pi0, lfdr_truncate, lfdr_monotone, lfdr_transformation, lfdr_adj, lfdr_eps, color_palette):

con = sqlite3.connect(infile)

Expand Down Expand Up @@ -257,10 +257,10 @@ def infer_peptides(infile, outfile, context, parametric, pfdr, pi0_lambda, pi0_m
con.close()

if context == 'run-specific':
data = data.groupby('run_id').apply(statistics_report, outfile, context, "peptide", parametric, pfdr, pi0_lambda, pi0_method, pi0_smooth_df, pi0_smooth_log_pi0, lfdr_truncate, lfdr_monotone, lfdr_transformation, lfdr_adj, lfdr_eps).reset_index()
data = data.groupby('run_id').apply(statistics_report, outfile, context, "peptide", parametric, pfdr, pi0_lambda, pi0_method, pi0_smooth_df, pi0_smooth_log_pi0, lfdr_truncate, lfdr_monotone, lfdr_transformation, lfdr_adj, lfdr_eps, color_palette).reset_index()

elif context in ['global', 'experiment-wide']:
data = statistics_report(data, outfile, context, "peptide", parametric, pfdr, pi0_lambda, pi0_method, pi0_smooth_df, pi0_smooth_log_pi0, lfdr_truncate, lfdr_monotone, lfdr_transformation, lfdr_adj, lfdr_eps)
data = statistics_report(data, outfile, context, "peptide", parametric, pfdr, pi0_lambda, pi0_method, pi0_smooth_df, pi0_smooth_log_pi0, lfdr_truncate, lfdr_monotone, lfdr_transformation, lfdr_adj, lfdr_eps, color_palette)

# store data in table
if infile != outfile:
Expand Down
26 changes: 17 additions & 9 deletions pyprophet/main.py
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Original file line number Diff line number Diff line change
Expand Up @@ -71,10 +71,12 @@ def cli():
@click.option('--ipf_min_transition_sn', default=0, show_default=True, type=float, help='Minimum log signal-to-noise level to consider transitions in IPF. Set -1 to disable this filter.')
# TRIC
@click.option('--tric_chromprob/--no-tric_chromprob', default=False, show_default=True, help='Whether chromatogram probabilities for TRIC should be computed.')
# Visualization
@click.option('--color_palette', default='normal', show_default=True, type=click.Choice(['normal', 'protan', 'deutran', 'tritan']), help='Color palette to use in reports.')
# Processing
@click.option('--threads', default=1, show_default=True, type=int, help='Number of threads used for semi-supervised learning. -1 means all available CPUs.', callback=transform_threads)
@click.option('--test/--no-test', default=False, show_default=True, help='Run in test mode with fixed seed.')
def score(infile, outfile, classifier, xgb_autotune, apply_weights, xeval_fraction, xeval_num_iter, ss_initial_fdr, ss_iteration_fdr, ss_num_iter, ss_main_score, group_id, parametric, pfdr, pi0_lambda, pi0_method, pi0_smooth_df, pi0_smooth_log_pi0, lfdr_truncate, lfdr_monotone, lfdr_transformation, lfdr_adj, lfdr_eps, level, ipf_max_peakgroup_rank, ipf_max_peakgroup_pep, ipf_max_transition_isotope_overlap, ipf_min_transition_sn, tric_chromprob, threads, test, ss_score_filter):
def score(infile, outfile, classifier, xgb_autotune, apply_weights, xeval_fraction, xeval_num_iter, ss_initial_fdr, ss_iteration_fdr, ss_num_iter, ss_main_score, group_id, parametric, pfdr, pi0_lambda, pi0_method, pi0_smooth_df, pi0_smooth_log_pi0, lfdr_truncate, lfdr_monotone, lfdr_transformation, lfdr_adj, lfdr_eps, level, ipf_max_peakgroup_rank, ipf_max_peakgroup_pep, ipf_max_transition_isotope_overlap, ipf_min_transition_sn, tric_chromprob, threads, test, ss_score_filter, color_palette):
"""
Conduct semi-supervised learning and error-rate estimation for MS1, MS2 and transition-level data.
"""
Expand All @@ -92,9 +94,9 @@ def score(infile, outfile, classifier, xgb_autotune, apply_weights, xeval_fracti
xgb_params_space = {'eta': hp.uniform('eta', 0.0, 0.3), 'gamma': hp.uniform('gamma', 0.0, 0.5), 'max_depth': hp.quniform('max_depth', 2, 8, 1), 'min_child_weight': hp.quniform('min_child_weight', 1, 5, 1), 'subsample': 1, 'colsample_bytree': 1, 'colsample_bylevel': 1, 'colsample_bynode': 1, 'lambda': hp.uniform('lambda', 0.0, 1.0), 'alpha': hp.uniform('alpha', 0.0, 1.0), 'scale_pos_weight': 1.0, 'silent': 1, 'objective': 'binary:logitraw', 'nthread': 1, 'eval_metric': 'auc'}

if not apply_weights:
PyProphetLearner(infile, outfile, classifier, xgb_hyperparams, xgb_params, xgb_params_space, xeval_fraction, xeval_num_iter, ss_initial_fdr, ss_iteration_fdr, ss_num_iter, ss_main_score, group_id, parametric, pfdr, pi0_lambda, pi0_method, pi0_smooth_df, pi0_smooth_log_pi0, lfdr_truncate, lfdr_monotone, lfdr_transformation, lfdr_adj, lfdr_eps, level, ipf_max_peakgroup_rank, ipf_max_peakgroup_pep, ipf_max_transition_isotope_overlap, ipf_min_transition_sn, tric_chromprob, threads, test, ss_score_filter).run()
PyProphetLearner(infile, outfile, classifier, xgb_hyperparams, xgb_params, xgb_params_space, xeval_fraction, xeval_num_iter, ss_initial_fdr, ss_iteration_fdr, ss_num_iter, ss_main_score, group_id, parametric, pfdr, pi0_lambda, pi0_method, pi0_smooth_df, pi0_smooth_log_pi0, lfdr_truncate, lfdr_monotone, lfdr_transformation, lfdr_adj, lfdr_eps, level, ipf_max_peakgroup_rank, ipf_max_peakgroup_pep, ipf_max_transition_isotope_overlap, ipf_min_transition_sn, tric_chromprob, threads, test, ss_score_filter, color_palette).run()
else:
PyProphetWeightApplier(infile, outfile, classifier, xgb_hyperparams, xgb_params, xgb_params_space, xeval_fraction, xeval_num_iter, ss_initial_fdr, ss_iteration_fdr, ss_num_iter, ss_main_score, group_id, parametric, pfdr, pi0_lambda, pi0_method, pi0_smooth_df, pi0_smooth_log_pi0, lfdr_truncate, lfdr_monotone, lfdr_transformation, lfdr_adj, lfdr_eps, level, ipf_max_peakgroup_rank, ipf_max_peakgroup_pep, ipf_max_transition_isotope_overlap, ipf_min_transition_sn, tric_chromprob, threads, test, apply_weights, ss_score_filter).run()
PyProphetWeightApplier(infile, outfile, classifier, xgb_hyperparams, xgb_params, xgb_params_space, xeval_fraction, xeval_num_iter, ss_initial_fdr, ss_iteration_fdr, ss_num_iter, ss_main_score, group_id, parametric, pfdr, pi0_lambda, pi0_method, pi0_smooth_df, pi0_smooth_log_pi0, lfdr_truncate, lfdr_monotone, lfdr_transformation, lfdr_adj, lfdr_eps, level, ipf_max_peakgroup_rank, ipf_max_peakgroup_pep, ipf_max_transition_isotope_overlap, ipf_min_transition_sn, tric_chromprob, threads, test, apply_weights, ss_score_filter, color_palette).run()


# IPF
Expand Down Expand Up @@ -143,7 +145,9 @@ def ipf(infile, outfile, ipf_ms1_scoring, ipf_ms2_scoring, ipf_h0, ipf_grouped_f
@click.option('--lfdr_transformation', default='probit', show_default=True, type=click.Choice(['probit', 'logit']), help='Either a "probit" or "logit" transformation is applied to the p-values so that a local FDR estimate can be formed that does not involve edge effects of the [0,1] interval in which the p-values lie.')
@click.option('--lfdr_adj', default=1.5, show_default=True, type=float, help='Numeric value that is applied as a multiple of the smoothing bandwidth used in the density estimation.')
@click.option('--lfdr_eps', default=np.power(10.0,-8), show_default=True, type=float, help='Numeric value that is threshold for the tails of the empirical p-value distribution.')
def peptide(infile, outfile, context, parametric, pfdr, pi0_lambda, pi0_method, pi0_smooth_df, pi0_smooth_log_pi0, lfdr_truncate, lfdr_monotone, lfdr_transformation, lfdr_adj, lfdr_eps):
# Visualization
@click.option('--color_palette', default='normal', show_default=True, type=click.Choice(['normal', 'protan', 'deutran', 'tritan']), help='Color palette to use in reports.')
def peptide(infile, outfile, context, parametric, pfdr, pi0_lambda, pi0_method, pi0_smooth_df, pi0_smooth_log_pi0, lfdr_truncate, lfdr_monotone, lfdr_transformation, lfdr_adj, lfdr_eps, color_palette):
"""
Infer peptides and conduct error-rate estimation in different contexts.
"""
Expand All @@ -153,7 +157,7 @@ def peptide(infile, outfile, context, parametric, pfdr, pi0_lambda, pi0_method,
else:
outfile = outfile

infer_peptides(infile, outfile, context, parametric, pfdr, pi0_lambda, pi0_method, pi0_smooth_df, pi0_smooth_log_pi0, lfdr_truncate, lfdr_monotone, lfdr_transformation, lfdr_adj, lfdr_eps)
infer_peptides(infile, outfile, context, parametric, pfdr, pi0_lambda, pi0_method, pi0_smooth_df, pi0_smooth_log_pi0, lfdr_truncate, lfdr_monotone, lfdr_transformation, lfdr_adj, lfdr_eps, color_palette)


# Gene-level inference
Expand All @@ -175,7 +179,9 @@ def peptide(infile, outfile, context, parametric, pfdr, pi0_lambda, pi0_method,
@click.option('--lfdr_transformation', default='probit', show_default=True, type=click.Choice(['probit', 'logit']), help='Either a "probit" or "logit" transformation is applied to the p-values so that a local FDR estimate can be formed that does not involve edge effects of the [0,1] interval in which the p-values lie.')
@click.option('--lfdr_adj', default=1.5, show_default=True, type=float, help='Numeric value that is applied as a multiple of the smoothing bandwidth used in the density estimation.')
@click.option('--lfdr_eps', default=np.power(10.0,-8), show_default=True, type=float, help='Numeric value that is threshold for the tails of the empirical p-value distribution.')
def gene(infile, outfile, context, parametric, pfdr, pi0_lambda, pi0_method, pi0_smooth_df, pi0_smooth_log_pi0, lfdr_truncate, lfdr_monotone, lfdr_transformation, lfdr_adj, lfdr_eps):
# Visualization
@click.option('--color_palette', default='normal', show_default=True, type=click.Choice(['normal', 'protan', 'deutran', 'tritan']), help='Color palette to use in reports.')
def gene(infile, outfile, context, parametric, pfdr, pi0_lambda, pi0_method, pi0_smooth_df, pi0_smooth_log_pi0, lfdr_truncate, lfdr_monotone, lfdr_transformation, lfdr_adj, lfdr_eps, color_palette):
"""
Infer genes and conduct error-rate estimation in different contexts.
"""
Expand All @@ -185,7 +191,7 @@ def gene(infile, outfile, context, parametric, pfdr, pi0_lambda, pi0_method, pi0
else:
outfile = outfile

infer_genes(infile, outfile, context, parametric, pfdr, pi0_lambda, pi0_method, pi0_smooth_df, pi0_smooth_log_pi0, lfdr_truncate, lfdr_monotone, lfdr_transformation, lfdr_adj, lfdr_eps)
infer_genes(infile, outfile, context, parametric, pfdr, pi0_lambda, pi0_method, pi0_smooth_df, pi0_smooth_log_pi0, lfdr_truncate, lfdr_monotone, lfdr_transformation, lfdr_adj, lfdr_eps, color_palette)

# Protein-level inference
@cli.command()
Expand All @@ -206,7 +212,9 @@ def gene(infile, outfile, context, parametric, pfdr, pi0_lambda, pi0_method, pi0
@click.option('--lfdr_transformation', default='probit', show_default=True, type=click.Choice(['probit', 'logit']), help='Either a "probit" or "logit" transformation is applied to the p-values so that a local FDR estimate can be formed that does not involve edge effects of the [0,1] interval in which the p-values lie.')
@click.option('--lfdr_adj', default=1.5, show_default=True, type=float, help='Numeric value that is applied as a multiple of the smoothing bandwidth used in the density estimation.')
@click.option('--lfdr_eps', default=np.power(10.0,-8), show_default=True, type=float, help='Numeric value that is threshold for the tails of the empirical p-value distribution.')
def protein(infile, outfile, context, parametric, pfdr, pi0_lambda, pi0_method, pi0_smooth_df, pi0_smooth_log_pi0, lfdr_truncate, lfdr_monotone, lfdr_transformation, lfdr_adj, lfdr_eps):
# Visualization
@click.option('--color_palette', default='normal', show_default=True, type=click.Choice(['normal', 'protan', 'deutran', 'tritan']), help='Color palette to use in reports.')
def protein(infile, outfile, context, parametric, pfdr, pi0_lambda, pi0_method, pi0_smooth_df, pi0_smooth_log_pi0, lfdr_truncate, lfdr_monotone, lfdr_transformation, lfdr_adj, lfdr_eps, color_palette):
"""
Infer proteins and conduct error-rate estimation in different contexts.
"""
Expand All @@ -216,7 +224,7 @@ def protein(infile, outfile, context, parametric, pfdr, pi0_lambda, pi0_method,
else:
outfile = outfile

infer_proteins(infile, outfile, context, parametric, pfdr, pi0_lambda, pi0_method, pi0_smooth_df, pi0_smooth_log_pi0, lfdr_truncate, lfdr_monotone, lfdr_transformation, lfdr_adj, lfdr_eps)
infer_proteins(infile, outfile, context, parametric, pfdr, pi0_lambda, pi0_method, pi0_smooth_df, pi0_smooth_log_pi0, lfdr_truncate, lfdr_monotone, lfdr_transformation, lfdr_adj, lfdr_eps, color_palette)


# Subsample OpenSWATH file to minimum for integrated scoring
Expand Down
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