def nexus_express(**set_up_kwargs):
# chose sample set from data
# function: choose_samples()
select_samples_from = set_up_kwargs.get('select_samples_from', None)
select_samples_which = parse_arg_type(
set_up_kwargs.get('select_samples_which', None), int)
select_samples_sort_by = set_up_kwargs.get('select_samples_sort_by', None)
if select_samples_sort_by is not None:
select_samples_sort_by = select_samples_sort_by.rsplit(',')
select_samples_title = set_up_kwargs.get('select_samples_title',
'select_all')
clinical_label = select_samples_sort_by[0]
class_labels = set_up_kwargs.get('class_labels', None)
if class_labels is not None:
if ',' in class_labels:
class_labels = class_labels.rsplit(',')
class_values = set_up_kwargs.get('class_values', None)
if class_values is not None:
if ',' in class_values:
class_values = class_values.rsplit(',')
class_values = np.array(class_values).astype(int)
# initialize script params
saveReport = parse_arg_type(set_up_kwargs.get('saveReport', False), bool)
toPrint = parse_arg_type(set_up_kwargs.get('toPrint', False), bool)
toPlotFreq = parse_arg_type(set_up_kwargs.get('toPlotFreq', True), bool)
reportName = set_up_kwargs.get('reportName', script_fname)
txt_label = set_up_kwargs.get('txt_label', 'test_txt_label')
input_fname = set_up_kwargs.get('input_fname', 'data_processed.csv')
gene_info_fname = set_up_kwargs.get('gene_info_fname', None)
chr_col = set_up_kwargs.get('chr_col', 'chr_int')
gene_id_col = set_up_kwargs.get('gene_id_col', 'gene')
sample_info_fname = set_up_kwargs.get('sample_info_fname', None)
if ',' in sample_info_fname:
sample_info_fname = os.path.join(*sample_info_fname.rsplit(','))
sample_info_read_csv_kwargs = set_up_kwargs.get(
'sample_info_read_csv_kwargs', {})
data_uniq_fname = input_fname.rsplit('.')[0]+'__' + \
select_samples_title+'__uniq'
toRemoveDupl = parse_arg_type(set_up_kwargs.get('toRemoveDupl', True),
bool)
# params for diff analysis
min_diff_thres = parse_arg_type(set_up_kwargs.get('min_diff_thres', 0.25),
float)
multtest_alpha = parse_arg_type(set_up_kwargs.get('multtest_alpha', 0.05),
float)
with_perc = parse_arg_type(set_up_kwargs.get('with_perc', 100), int)
multtest_method = set_up_kwargs.get('multtest_method', 'fdr_bh')
# plotting params
plot_kwargs = set_up_kwargs.get('plot_kwargs', {})
function_dict = plot_kwargs.get('function_dict', None)
cmap_custom = plot_kwargs.get('cmap_custom', None)
vmin = parse_arg_type(plot_kwargs.get('vmin', None), int)
vmax = parse_arg_type(plot_kwargs.get('vmax', None), int)
if (cmap_custom is None) and (vmin is not None) and (vmax is not None):
custom_div_cmap_arg = abs(vmin) + abs(vmax)
if (vmin <= 0) and (vmax >= 0):
custom_div_cmap_arg = custom_div_cmap_arg + 1
mincol = plot_kwargs.get('mincol', None)
midcol = plot_kwargs.get('midcol', None)
maxcol = plot_kwargs.get('maxcol', None)
if ((mincol is not None) and (midcol is not None)
and (maxcol is not None)):
cmap_custom = custom_div_cmap(numcolors=custom_div_cmap_arg,
mincol=mincol,
midcol=midcol,
maxcol=maxcol)
else:
cmap_custom = custom_div_cmap(numcolors=custom_div_cmap_arg)
highRes = parse_arg_type(plot_kwargs.get('highRes', False), bool)
if highRes:
img_ext = '.pdf'
else:
img_ext = '.png'
# initialize directories
MainDataDir = set_path(os.path.join(script_path, '..', 'data'))
# data input
input_directory = set_up_kwargs.get('input_directory')
if ',' in input_directory:
input_directory = os.path.join(*input_directory.rsplit(','))
input_directory = os.path.join(MainDataDir, input_directory)
# sample info input
sample_info_directory = set_up_kwargs.get('sample_info_directory')
if ',' in sample_info_directory:
sample_info_directory = os.path.join(
*sample_info_directory.rsplit(','))
sample_info_directory = os.path.join(MainDataDir, sample_info_directory)
# gene info input
gene_info_directory = set_up_kwargs.get('gene_info_directory')
if gene_info_directory is None:
gene_info_directory = input_directory
else:
if ',' in gene_info_directory:
gene_info_directory = os.path.join(
*gene_info_directory.rsplit(','))
gene_info_directory = os.path.join(MainDataDir,
gene_info_directory)
# dupl_genes input
dupl_genes_directory = set_up_kwargs.get('dupl_genes_directory')
dupl_genes_directory = os.path.join(input_directory, dupl_genes_directory)
# data output
output_directory = set_up_kwargs.get('output_directory')
if output_directory is None:
output_directory = set_directory(
os.path.join(input_directory, reportName))
else:
if ',' in output_directory:
output_directory = os.path.join(*output_directory.rsplit(','))
output_directory = set_directory(
os.path.join(MainDataDir, output_directory, reportName))
# save the set_up_kwargs in the output dir for reproducibility
fname = 'set_up_kwargs.json'
f = os.path.join(output_directory, fname)
if toPrint:
logger.info('-save set_up_kwargs dictionary for reproducibility in: ' +
f)
with open(f, 'w') as fp:
json.dump(set_up_kwargs, fp, indent=4)
# load info table of samples
if toPrint:
logger.info('Load info table of samples')
fpath = os.path.join(sample_info_directory, sample_info_fname)
info_table = load_clinical(fpath, **sample_info_read_csv_kwargs)
# load processed data
fpath = os.path.join(input_directory, input_fname)
data = pd.read_csv(fpath, sep='\t', header=0, index_col=0)
empty_pat = data.sum(axis=1).isnull()
if empty_pat.any():
logger.info('Patients with missing values in all genes: ' +
str(data.index[empty_pat]))
data = data.fillna(0)
# load gene info
if gene_info_fname is not None:
fpath = os.path.join(gene_info_directory, gene_info_fname)
genes_positions_table = pd.read_csv(fpath,
sep='\t',
header=0,
index_col=0)
# get gene chrom position
xlabels, xpos = get_chr_ticks(genes_positions_table,
data,
id_col='gene',
chr_col=chr_col)
else:
xlabels, xpos = None, None
# select the samples for the chosen comparison (e.g. all, only TP53wt, etc)
logger.info('select_samples_from: ' + str(select_samples_from) +
', select_samples_which: ' + str(select_samples_which) +
', select_samples_sort_by: ' + str(select_samples_sort_by) +
', select_samples_title: ' + str(select_samples_title))
# keep only info_table with data
temp = info_table.index.name
info_table = info_table.loc[data.index].copy()
info_table.index.name = temp
ids_tmp = choose_samples(info_table.reset_index(),
info_table.index.name,
choose_from=select_samples_from,
choose_what=select_samples_which,
sortby=select_samples_sort_by,
ascending=False)
# keep a subpart of the info_table (rows and columns)
info_table = info_table.loc[ids_tmp, select_samples_sort_by].copy()
# keep only these samples from the data
data = data.loc[ids_tmp, :].copy()
try:
pat_labels_txt = info_table.astype(int).reset_index().values
except:
pat_labels_txt = info_table.reset_index().values
pat_labels_title = str(info_table.reset_index().columns.values)
# plot CNV frequencies of all samples
data_ampl, data_del = _get_ampl_del_from_data(data)
if toPlotFreq:
_plot_oncoscan_frequency_plot(data_ampl, data_del,
select_samples_title, '',
gene_info_fname, xlabels, xpos,
saveReport, img_ext, output_directory)
extra_label = ''
if toRemoveDupl:
# keep a copy of the data with duplicate genes
data_wDupl = data.copy()
xlabels_wDupl = xlabels.copy()
xpos_wDupl = xpos.copy()
data_ampl_wDupl, data_del_wDupl = data_ampl.copy(), data_del.copy()
# load data with uniq genes (this will be the default data from now on)
fpath = os.path.join(dupl_genes_directory, data_uniq_fname + '.txt')
if not os.path.exists(fpath):
logger.warning('The data_uniq file does not exist, ' +
'the analysis will run on the processed data ' +
'only!\nfile path:\n' + fpath)
toRemoveDupl = False
else:
extra_label = '_uniq'
data = pd.read_csv(fpath, sep='\t', header=0, index_col=0)
data = data.fillna(0)
# keep the same samples as before
data = data.loc[data_wDupl.index, :].copy()
# get gene chrom position
if gene_info_fname is not None:
xlabels, xpos = get_chr_ticks(genes_positions_table,
data,
id_col='gene',
chr_col=chr_col)
# plot CNV frequencies of all samples with uniq genes
data_ampl, data_del = _get_ampl_del_from_data(data)
if toPlotFreq:
_plot_oncoscan_frequency_plot(data_ampl, data_del,
select_samples_title,
extra_label, gene_info_fname,
xlabels, xpos, saveReport,
img_ext, output_directory)
# load duplicate genes dictionary
# we will need that for the table we will save later
fpath = os.path.join(dupl_genes_directory,
data_uniq_fname + '.json')
with open(fpath, 'r') as fp:
dupl_genes_dict = json.load(fp)
# separate patient groups and plot their CNV frequencies
group0 = data.loc[info_table.index[info_table[clinical_label] ==
class_values[0]]].copy()
group1 = data.loc[info_table.index[info_table[clinical_label] ==
class_values[1]]].copy()
group0_ampl, group0_del = _get_ampl_del_from_data(group0)
if toPlotFreq:
_plot_oncoscan_frequency_plot(group0_ampl, group0_del,
select_samples_title,
class_labels[0] + extra_label,
gene_info_fname, xlabels, xpos,
saveReport, img_ext, output_directory)
group1_ampl, group1_del = _get_ampl_del_from_data(group1)
if toPlotFreq:
_plot_oncoscan_frequency_plot(group1_ampl, group1_del,
select_samples_title,
class_labels[1] + extra_label,
gene_info_fname, xlabels, xpos,
saveReport, img_ext, output_directory)
if toRemoveDupl:
# plot with the duplicate genes too
group0_wDupl = data_wDupl.loc[info_table.index[
info_table[clinical_label] == class_values[0]]].copy()
group1_wDupl = data_wDupl.loc[info_table.index[
info_table[clinical_label] == class_values[1]]].copy()
group0_ampl_wDupl, group0_del_wDupl = \
_get_ampl_del_from_data(group0_wDupl)
if toPlotFreq:
_plot_oncoscan_frequency_plot(group0_ampl_wDupl, group0_del_wDupl,
select_samples_title,
class_labels[0], gene_info_fname,
xlabels_wDupl, xpos_wDupl,
saveReport, img_ext,
output_directory)
group1_ampl_wDupl, group1_del_wDupl = \
_get_ampl_del_from_data(group1_wDupl)
if toPlotFreq:
_plot_oncoscan_frequency_plot(group1_ampl_wDupl, group1_del_wDupl,
select_samples_title,
class_labels[1], gene_info_fname,
xlabels_wDupl, xpos_wDupl,
saveReport, img_ext,
output_directory)
# run the Nexus Express diff analysis
# select genes with significant p-value (multtest_alpha)
# after mutliple test correction (multtest_method) and
# absolute change higher than the defined threshold (min_diff_thres)
mytitle = select_samples_title+': '+class_labels[0] +\
'['+str(class_values[0])+'] vs. ' +\
class_labels[1]+'['+str(class_values[1])+']'
group0_ampl_new, group1_ampl_new, group0_del_new, group1_del_new, \
pvals, pvals_corrected, pvals_reject, gained, deleted = \
get_NexusExpress_diff_analysis(
group0_ampl, group1_ampl, group0_del, group1_del,
with_perc=with_perc, multtest_method=multtest_method,
multtest_alpha=multtest_alpha, min_diff_thres=min_diff_thres,
mytitle=mytitle
)
# create table with all genes
if gene_info_fname is not None:
diff_genes = genes_positions_table.set_index(
['gene']).loc[data.columns.values][['chr', 'start', 'end']].copy()
else:
diff_genes = pd.DataFrame(index=data.columns.values)
diff_genes.index.name = 'gene'
diff_genes[class_labels[0] + '_' + clinical_label +
'_ampl'] = group0_ampl * with_perc
diff_genes[class_labels[1] + '_' + clinical_label +
'_ampl'] = group1_ampl * with_perc
diff_genes[class_labels[0] + '_' + clinical_label +
'_del'] = group0_del * with_perc
diff_genes[class_labels[1] + '_' + clinical_label +
'_del'] = group1_del * with_perc
diff_genes['pvals'] = pvals
diff_genes['pvals_corrected'] = pvals_corrected
diff_genes['pvals_reject'] = pvals_reject
diff_genes['gained'] = gained
diff_genes['ampl_diff'] = np.abs(
diff_genes[class_labels[0] + '_' + clinical_label + '_ampl'] -
diff_genes[class_labels[1] + '_' + clinical_label + '_ampl'])
diff_genes['deleted'] = deleted
diff_genes['del_diff'] = np.abs(
diff_genes[class_labels[0] + '_' + clinical_label + '_del'] -
diff_genes[class_labels[1] + '_' + clinical_label + '_del'])
# add the dupl_genes column only if there are duplicate genes
if toRemoveDupl:
diff_genes['dupl_genes'] = \
diff_genes.reset_index()['gene'].map(dupl_genes_dict).values
# save also the positions of these duplicate genes
diff_genes['newGeneName'] = diff_genes.index.values
diff_genes.loc[dupl_genes_dict.keys(), 'newGeneName'] += '__wDupl'
if gene_info_fname is not None:
diff_genes['aggChrGene'] = None
diff_genes['aggPos'] = None
diff_genes['aggChrStart'] = None
diff_genes['aggChrEnd'] = None
# for each duplicated gene, aggregate and save
# the name, start, end, chr values in the table
for agene in dupl_genes_dict.keys():
l = [agene]
# if agene in dupl_genes_dict.keys():
l.extend(dupl_genes_dict[agene])
diff_genes.loc[agene, 'aggChrEnd'] = str(
natsorted(
genes_positions_table.set_index('gene').loc[l].
reset_index().groupby(by=['chr'])['end'].apply(
lambda x: list(np.unique(np.append([], x))
)).reset_index().values.tolist()))
diff_genes.loc[agene, 'aggChrStart'] = str(
natsorted(
genes_positions_table.set_index('gene').loc[l].
reset_index().groupby(by=['chr'])['start'].apply(
lambda x: list(np.unique(np.append([], x))
)).reset_index().values.tolist()))
diff_genes.loc[agene, 'aggChrGene'] = str(
natsorted(
genes_positions_table.set_index('gene').loc[l].
reset_index().groupby(by=['chr'])['gene'].apply(
lambda x: list(np.unique(np.append([], x))
)).reset_index().values.tolist()))
aggPos = \
genes_positions_table.set_index('gene').loc[l].groupby(
by=['chr']).agg(
{'start': min, 'end': max}
).reset_index().astype(str).apply(
lambda x: ':'.join(x), axis=1).values
diff_genes.loc[agene, 'aggPos'] = np.apply_along_axis(
lambda x: '__'.join(x), 0, natsorted(aggPos))
# from the above table: select only the selected genes
# according to the Nexus Express diff analysis
diff_genes_selected = diff_genes[(diff_genes['gained'] > 0) |
(diff_genes['deleted'] > 0)].copy()
# save tables
if saveReport:
fname = 'diff_genes_' + select_samples_title + '.csv'
fpath = os.path.join(output_directory, fname)
logger.info("-save all diff genes in :\n" + fpath)
diff_genes.to_csv(fpath, sep='\t', header=True, index=True)
if diff_genes_selected.shape[0] > 0:
# keep only those genes in the data
data = data.loc[:, diff_genes_selected.index]
# change the name of the genes to indicate if they have duplicates
if 'newGeneName' in diff_genes_selected.columns.values:
newgeneNames = diff_genes_selected.loc[data.columns,
'newGeneName'].values
data.columns = newgeneNames
# save this data for future classification
fname = 'data_features_class.csv'
fpath = os.path.join(output_directory, fname)
logger.info("-save data with selected diff features for " +
mytitle + " and samples class labels in :\n" + fpath)
data.to_csv(fpath, sep='\t', header=True, index=True)
# save as tab-delimited csv file
fname = 'diff_genes_selected_' + select_samples_title + '.csv'
fpath = os.path.join(output_directory, fname)
logger.info("-save selected diff genes for " + mytitle +
" in :\n" + fpath)
diff_genes_selected.to_csv(fpath,
sep='\t',
header=True,
index=True)
# save also as excel file
fname = 'diff_genes_selected_' + select_samples_title + '.xlsx'
fpath = os.path.join(output_directory, fname)
logger.info('-save csv file as excel too')
writer = pd.ExcelWriter(fpath)
diff_genes_selected.to_excel(writer,
sheet_name=select_samples_title)
writer.save()
# plot CNV frequencies OF SELECTED GENES for each group in comparison
if toPlotFreq:
if ((group0_ampl_new != 0).any() or (group0_del_new != 0).any()):
_plot_oncoscan_frequency_plot(group0_ampl_new, group0_del_new,
select_samples_title + '_DIFF',
class_labels[0] + extra_label,
gene_info_fname, xlabels, xpos,
saveReport, img_ext,
output_directory)
if ((group1_ampl_new != 0).any() or (group1_del_new != 0).any()):
_plot_oncoscan_frequency_plot(group1_ampl_new, group1_del_new,
select_samples_title + '_DIFF',
class_labels[1] + extra_label,
gene_info_fname, xlabels, xpos,
saveReport, img_ext,
output_directory)
if toRemoveDupl:
group0_ampl_new_wDupl = group0_ampl_wDupl.copy()
group0_ampl_new_wDupl[:] = 0
group1_ampl_new_wDupl = group1_ampl_wDupl.copy()
group1_ampl_new_wDupl[:] = 0
group0_del_new_wDupl = group0_del_wDupl.copy()
group0_del_new_wDupl[:] = 0
group1_del_new_wDupl = group1_del_wDupl.copy()
group1_del_new_wDupl[:] = 0
list__diff_genes_selected_wDupl = []
for i in range(diff_genes_selected.shape[0]):
theGene = diff_genes_selected.index[i]
genes2edit = [theGene]
list__diff_genes_selected_wDupl.extend(genes2edit)
duplgenes_ = diff_genes_selected.loc[theGene]['dupl_genes']
if duplgenes_ is not np.nan:
list__diff_genes_selected_wDupl.extend(duplgenes_)
genes2edit.extend(duplgenes_)
group0_ampl_new_wDupl.loc[genes2edit] = group0_ampl_new.loc[
theGene]
group1_ampl_new_wDupl.loc[genes2edit] = group1_ampl_new.loc[
theGene]
group0_del_new_wDupl.loc[genes2edit] = group0_del_new.loc[theGene]
group1_del_new_wDupl.loc[genes2edit] = group1_del_new.loc[theGene]
if len(list__diff_genes_selected_wDupl) > 0:
# save this data for future classification
fname = 'data_all_genes_class.csv'
fpath = os.path.join(output_directory, fname)
logger.info("-save data with selected diff genes for " + mytitle +
" and samples class labels in :\n" + fpath)
data_wDupl[list__diff_genes_selected_wDupl].to_csv(fpath,
sep='\t',
header=True,
index=True)
if toPlotFreq:
# plot with the duplicate genes too
if ((group0_ampl_new_wDupl != 0).any()
or (group0_del_new_wDupl != 0).any()):
_plot_oncoscan_frequency_plot(
group0_ampl_new_wDupl, group0_del_new_wDupl,
select_samples_title + '_DIFF', class_labels[0],
gene_info_fname, xlabels_wDupl, xpos_wDupl, saveReport,
img_ext, output_directory)
if ((group1_ampl_new_wDupl != 0).any()
or (group1_del_new_wDupl != 0).any()):
_plot_oncoscan_frequency_plot(
group1_ampl_new_wDupl, group1_del_new_wDupl,
select_samples_title + '_DIFF', class_labels[1],
gene_info_fname, xlabels_wDupl, xpos_wDupl, saveReport,
img_ext, output_directory)
# PLOT heatmaps of selected features
if diff_genes_selected.shape[0] > 0:
# get only the CNVs from the selected genes
patientNames2plot = pat_labels_txt
ds_y, ds_x = data.shape
fs_x = 25 if ds_x > 45 else 15 if ds_x > 30 else 10 if ds_x > 3 else 5
fs_y = 20 if ds_y > 40 else 15 if ds_y > 30 else 10
plt.figure(figsize=(fs_x, fs_y))
ax = sns.heatmap(data,
vmin=vmin,
vmax=vmax,
xticklabels=True,
yticklabels=patientNames2plot,
cmap=cmap_custom,
cbar=False)
ax.set_ylabel(pat_labels_title)
plt.xticks(rotation=90)
cbar = ax.figure.colorbar(ax.collections[0])
set_cbar_ticks(cbar, function_dict, custom_div_cmap_arg)
plt.title(mytitle)
if saveReport:
fpath = os.path.join(
output_directory,
'Fig_Heatmap_' + select_samples_title + extra_label + img_ext)
logger.info('Save Heatmap of selected features as ' + img_ext +
' in:\n' + fpath)
plt.savefig(fpath,
transparent=True,
bbox_inches='tight',
pad_inches=0.1,
frameon=False)
plt.close("all")
else:
plt.show()
if toRemoveDupl:
data2plot = data_wDupl[list__diff_genes_selected_wDupl]
patientNames2plot = pat_labels_txt
ds_y, ds_x = data2plot.shape
fs_x = 25 if ds_x > 45 else 15 if ds_x > 30 else 10
fs_y = 20 if ds_y > 40 else 15 if ds_y > 30 else 10
plt.figure(figsize=(fs_x, fs_y))
ax = sns.heatmap(data2plot,
vmin=vmin,
vmax=vmax,
xticklabels=True,
yticklabels=patientNames2plot,
cmap=cmap_custom,
cbar=False)
ax.set_ylabel(pat_labels_title)
plt.xticks(rotation=90)
cbar = ax.figure.colorbar(ax.collections[0])
set_cbar_ticks(cbar, function_dict, custom_div_cmap_arg)
plt.title(mytitle)
if saveReport:
fpath = os.path.join(
output_directory,
'Fig_Heatmap_' + select_samples_title + '_wDupl' + img_ext)
logger.info('Save Heatmap of selected features as ' + img_ext +
' in:\n' + fpath)
plt.savefig(fpath,
transparent=True,
bbox_inches='tight',
pad_inches=0.1,
frameon=False)
plt.close("all")
else:
plt.show()
def combine_features(**set_up_kwargs):
# initialize script params
saveReport = parse_arg_type(set_up_kwargs.get('saveReport', False), bool)
toPrint = parse_arg_type(set_up_kwargs.get('toPrint', False), bool)
reportName = set_up_kwargs.get('reportName', script_fname)
txt_label = set_up_kwargs.get('txt_label', 'test_txt_label')
sample_final_id = set_up_kwargs.get('sample_final_id')
sample_data_ids = set_up_kwargs.get('sample_data_ids')
if ',' in sample_data_ids:
sample_data_ids = sample_data_ids.rsplit(',')
# plotting params
plot_kwargs = set_up_kwargs.get('plot_kwargs', {})
highRes = parse_arg_type(plot_kwargs.get('highRes', False), bool)
if highRes:
img_ext = '.pdf'
else:
img_ext = '.png'
cmap_custom = plot_kwargs.get('cmap_custom', None)
vmin = parse_arg_type(plot_kwargs.get('vmin', None), int)
vmax = parse_arg_type(plot_kwargs.get('vmax', None), int)
if (cmap_custom is None) and (vmin is not None) and (vmax is not None):
custom_div_cmap_arg = abs(vmin) + abs(vmax)
if (vmin <= 0) and (vmax >= 0):
custom_div_cmap_arg = custom_div_cmap_arg + 1
mincol = plot_kwargs.get('mincol', None)
midcol = plot_kwargs.get('midcol', None)
maxcol = plot_kwargs.get('maxcol', None)
if ((mincol is not None) and (midcol is not None)
and (maxcol is not None)):
cmap_custom = custom_div_cmap(numcolors=custom_div_cmap_arg,
mincol=mincol,
midcol=midcol,
maxcol=maxcol)
else:
cmap_custom = custom_div_cmap(numcolors=custom_div_cmap_arg)
# initialize directories
MainDataDir = set_path(os.path.join(script_path, '..', 'data'))
# data input
file_short_ids = set_up_kwargs.get('file_short_ids', None)
if ',' in file_short_ids:
file_short_ids = file_short_ids.rsplit(',')
else:
file_short_ids = [file_short_ids]
data_fpaths = _split_argument_to_list(set_up_kwargs,
'files_to_combine_features',
asPath=True,
MainDataDir=MainDataDir)
# sample info input
sample_info_directory = set_up_kwargs.get('sample_info_directory')
if ',' in sample_info_directory:
sample_info_directory = os.path.join(
*sample_info_directory.rsplit(','))
sample_info_directory = os.path.join(MainDataDir, sample_info_directory)
sample_info_fname = set_up_kwargs.get('sample_info_fname')
if ',' in sample_info_fname:
sample_info_fname = os.path.join(*sample_info_fname.rsplit(','))
sample_info_read_csv_kwargs = set_up_kwargs.get(
'sample_info_read_csv_kwargs', {})
# data output
output_directory = set_up_kwargs.get('output_directory')
if ',' in output_directory:
output_directory = os.path.join(*output_directory.rsplit(','))
output_directory = set_directory(
os.path.join(MainDataDir, output_directory, reportName))
# save the set_up_kwargs in the output dir for reproducibility
fname = 'set_up_kwargs.json'
f = os.path.join(output_directory, fname)
if toPrint:
logger.info('-save set_up_kwargs dictionary for reproducibility in: ' +
f)
with open(f, 'w') as fp:
json.dump(set_up_kwargs, fp, indent=4)
# load info table of samples
try:
fpath = os.path.join(sample_info_directory, sample_info_fname)
sample_info_read_csv_kwargs['col_as_index'] = sample_final_id
info_table = load_clinical(fpath, **sample_info_read_csv_kwargs)
except Exception as ex:
logger.error('Load info table of samples FAILED!')
logger.error(ex)
raise
# load data
data_dfs = []
for i, fpath in enumerate(data_fpaths):
try:
df = pd.read_csv(fpath, sep='\t', header=0, index_col=0)
logger.info('loaded data file with shape: ' + str(df.shape))
except Exception as ex:
logger.error('failed to read data file from: ' + str(fpath))
logger.error(ex)
raise
# if datasets have different sample IDs
# map them to a user defined common one
if sample_data_ids[i] != sample_final_id:
# get the two ids from the info_table
matching_ids = info_table.reset_index()\
.set_index(sample_data_ids[i])[sample_final_id]
# add the new id and drop the old one
# join help with the one-to-one mapping
df = df.join(matching_ids, how='right')\
.set_index(sample_final_id, drop=True)
# add suffix to separate common genes between datasets
df.columns += "__" + file_short_ids[i]
data_dfs.append(df)
# now we join the data features from the multiple datasets
# on the common samples (inner join)
data = pd.concat(data_dfs, axis=1, join='inner', sort=False)
# sort the samples by name
all_samples = natsorted(data.index.values)
data = data.loc[all_samples, :]
# heatmap of combined data (on features)
_figure_x_size, _figure_y_size, _show_gene_names, _show_sample_names = \
set_heatmap_size(data)
plt.figure(figsize=(_figure_x_size, _figure_y_size))
ax = sns.heatmap(data,
vmin=vmin,
vmax=vmax,
yticklabels=_show_sample_names,
xticklabels=_show_gene_names,
cmap=cmap_custom,
cbar=True)
plt.xticks(rotation=90)
plt.title(txt_label)
if saveReport:
logger.info('Save heatmap')
fpath = os.path.join(
output_directory,
'Fig_heatmap_with_' + sample_final_id + '_id' + img_ext)
plt.savefig(fpath,
transparent=True,
bbox_inches='tight',
pad_inches=0.1,
frameon=False)
plt.close("all")
else:
plt.show()
# save the combined data
fname = 'data_with_' + sample_final_id + '_id.csv'
fpath = os.path.join(output_directory, fname)
logger.info('-save the combined data with different features ' +
'with shape:\n' + str(data.shape))
data.to_csv(fpath, sep='\t')
def combine_cohorts(**set_up_kwargs):
# initialize script params
saveReport = parse_arg_type(set_up_kwargs.get('saveReport', False), bool)
toPrint = parse_arg_type(set_up_kwargs.get('toPrint', False), bool)
reportName = set_up_kwargs.get('reportName', script_fname)
txt_label = set_up_kwargs.get('txt_label', 'test_txt_label')
# plotting params
plot_kwargs = set_up_kwargs.get('plot_kwargs', {})
function_dict = plot_kwargs.get('function_dict', None)
highRes = parse_arg_type(plot_kwargs.get('highRes', False), bool)
if highRes:
img_ext = '.pdf'
else:
img_ext = '.png'
cmap_custom = plot_kwargs.get('cmap_custom', None)
vmin = parse_arg_type(plot_kwargs.get('vmin', None), int)
vmax = parse_arg_type(plot_kwargs.get('vmax', None), int)
if (cmap_custom is None) and (vmin is not None) and (vmax is not None):
custom_div_cmap_arg = abs(vmin) + abs(vmax)
if (vmin <= 0) and (vmax >= 0):
custom_div_cmap_arg = custom_div_cmap_arg + 1
mincol = plot_kwargs.get('mincol', None)
midcol = plot_kwargs.get('midcol', None)
maxcol = plot_kwargs.get('maxcol', None)
if ((mincol is not None) and (midcol is not None)
and (maxcol is not None)):
cmap_custom = custom_div_cmap(numcolors=custom_div_cmap_arg,
mincol=mincol,
midcol=midcol,
maxcol=maxcol)
else:
cmap_custom = custom_div_cmap(numcolors=custom_div_cmap_arg)
# initialize directories
MainDataDir = set_path(os.path.join(script_path, '..', 'data'))
# data input
data_fpaths = _split_argument_to_list(set_up_kwargs,
'files_to_combine_samples',
asPath=True,
MainDataDir=MainDataDir)
# data output
_output_directory = set_up_kwargs.get('output_directory')
if ',' in _output_directory:
_output_directory = os.path.join(*_output_directory.rsplit(','))
_output_directory = os.path.join(MainDataDir, _output_directory)
output_directory = set_directory(
os.path.join(_output_directory, reportName))
# save the set_up_kwargs in the output dir for reproducibility
fname = 'set_up_kwargs.json'
f = os.path.join(output_directory, fname)
if toPrint:
logger.info('-save set_up_kwargs dictionary for reproducibility in: ' +
f)
with open(f, 'w') as fp:
json.dump(set_up_kwargs, fp, indent=4)
# sample_info params
sample_info_kwargs = set_up_kwargs.get('sample_info_kwargs', {})
save_new_sample_info = False
if sample_info_kwargs:
save_new_sample_info = True
# sample info input
sample_info_fpaths = _split_argument_to_list(sample_info_kwargs,
'sample_info_fpaths',
asPath=True,
MainDataDir=MainDataDir)
sample_info_read_csv_kwargs = sample_info_kwargs.get(
'sample_info_read_csv_kwargs', {})
sample_final_id = _split_argument_to_list(sample_info_kwargs,
'sample_final_id',
asPath=False)
sample_info_new_label = _split_argument_to_list(
sample_info_kwargs, 'sample_info_new_label', asPath=False)
sample_info_combine_labels = _split_argument_to_list(
sample_info_kwargs, 'sample_info_combine_labels', asPath=False)
sample_info_swap_class_label = _split_argument_to_list(
sample_info_kwargs, 'sample_info_swap_class_label', asPath=False)
# if not isinstance(sample_info_swap_class_label, list):
# sample_info_swap_class_label = \
# [sample_info_swap_class_label]
# new sample_info output dir
new_sample_info_fpath = sample_info_kwargs.get('new_sample_info_fpath')
if new_sample_info_fpath is None:
new_sample_info_fpath = _output_directory
else:
if ',' in new_sample_info_fpath:
new_sample_info_fpath = os.path.join(
*new_sample_info_fpath.rsplit(','))
new_sample_info_fpath = os.path.join(MainDataDir,
new_sample_info_fpath)
data_dfs = []
if save_new_sample_info:
sample_info_tables = []
# load info table of samples
for i, fpath in enumerate(data_fpaths):
if save_new_sample_info:
try:
sample_info_read_csv_kwargs[str(i)]['col_as_index'] = \
sample_final_id[i]
info_table = load_clinical(
sample_info_fpaths[i],
**sample_info_read_csv_kwargs[str(i)])
except Exception as ex:
logger.error('Load info table of samples FAILED!')
logger.error(ex)
raise
if isinstance(sample_info_swap_class_label[i], list):
_sample_info_swap_class_label_list = \
sample_info_swap_class_label[i]
else:
_sample_info_swap_class_label_list = \
[sample_info_swap_class_label[i]]
for j in range(len(_sample_info_swap_class_label_list)):
if _sample_info_swap_class_label_list[j] == '':
continue
logger.warning('The user requested to swap the ' +
str(_sample_info_swap_class_label_list[j]) +
' label in the ' + str(i) + ' dataset')
info_table[_sample_info_swap_class_label_list[j]] = (
~info_table[_sample_info_swap_class_label_list[j]].astype(
bool)).astype(int)
info_table['dataset'] = i
# load data
fpath = data_fpaths[i]
try:
df = pd.read_csv(fpath, sep='\t', header=0, index_col=0)
logger.info('loaded data file with shape: ' + str(df.shape))
except Exception as ex:
logger.error('failed to read data file from: ' + str(fpath))
logger.error(ex)
raise
data_dfs.append(df)
if save_new_sample_info:
sample_info_tables.append(info_table)
# now we join the cohort samples from the multiple datasets
# on the common features (inner join)
data = pd.concat(data_dfs, axis=0, join='inner', sort=False)
# gene info input
gene_info_fpath = set_up_kwargs.get('gene_info_fpath')
if gene_info_fpath is not None:
if ',' in gene_info_fpath:
gene_info_fpath = os.path.join(*gene_info_fpath.rsplit(','))
gene_info_fpath = os.path.join(MainDataDir, gene_info_fpath)
chr_col = set_up_kwargs.get('chr_col', 'chr_int')
gene_id_col = set_up_kwargs.get('gene_id_col', 'gene')
# load gene info
try:
genes_positions_table = pd.read_csv(gene_info_fpath,
sep='\t',
header=0,
index_col=0)
# get gene chrom position
xlabels, xpos = get_chr_ticks(genes_positions_table,
data,
id_col='gene',
chr_col=chr_col)
except Exception as ex:
logger.warning('could not get genes position info')
logger.warning(ex)
xlabels, xpos = None, None
else:
xlabels, xpos = None, None
if save_new_sample_info:
# do the same for the info_tables
# but keep all collumns (outer join)
sample_info = pd.concat(sample_info_tables,
axis=0,
join='outer',
sort=False)
sample_info.index.name = 'patientID'
# create new label name by merging existing labels
# (when no common label name between cohorts)
if sample_info_new_label is not None:
if isinstance(sample_info_new_label, list):
_sample_info_new_label_list = \
sample_info_new_label
_sample_info_combine_labels_list = \
sample_info_combine_labels
else:
_sample_info_new_label_list = \
[sample_info_new_label]
_sample_info_combine_labels_list = \
[sample_info_combine_labels]
for l, new_label in enumerate(_sample_info_new_label_list):
combine_labels = _sample_info_combine_labels_list[l]
sample_info[new_label] = sample_info[combine_labels].sum(axis=1)
logger.info('combined labels: ' + str(combine_labels) +
'into the new label: ' + str(new_label))
# sort the samples by name
all_samples = natsorted(data.index.values)
data = data.loc[all_samples, :]
# heatmap of combined data (on samples)
_figure_x_size, _figure_y_size, _show_gene_names, _show_sample_names = \
set_heatmap_size(data)
plt.figure(figsize=(_figure_x_size, _figure_y_size))
ax = sns.heatmap(data,
vmin=vmin,
vmax=vmax,
yticklabels=_show_sample_names,
xticklabels=_show_gene_names,
cmap=cmap_custom,
cbar=False)
plt.xticks(rotation=90)
cbar = ax.figure.colorbar(ax.collections[0])
set_cbar_ticks(cbar, function_dict, custom_div_cmap_arg)
plt.title(txt_label)
if saveReport:
logger.info('Save heatmap')
fpath = os.path.join(output_directory, 'Fig_heatmap' + img_ext)
plt.savefig(fpath,
transparent=True,
bbox_inches='tight',
pad_inches=0.1,
frameon=False)
plt.close("all")
else:
plt.show()
#########################################
if (xlabels is not None) and (xpos is not None):
# ORDER genes
if toPrint:
logger.info('Order data according to genomic position')
# extract the gene relative order
gene_order = genes_positions_table.set_index(
gene_id_col).loc[:, 'order'].copy()
# keep only gene_order with data
ids_tmp = set(gene_order.index.values).intersection(
set(data.columns.values))
# keep only the order of these genes
gene_order = gene_order.loc[ids_tmp].copy()
gene_order = gene_order.sort_values()
# then keep only these genes from the data
data2plot = data.loc[:, gene_order.index].copy()
# PLOT heatmap after gene ordering
if toPrint:
logger.info('Plot heatmap after gene ordering')
_figure_x_size, _figure_y_size, \
_show_gene_names, _show_sample_names = \
set_heatmap_size(data2plot)
plt.figure(figsize=(_figure_x_size, _figure_y_size))
ax = sns.heatmap(data2plot,
vmin=vmin,
vmax=vmax,
xticklabels=_show_gene_names,
yticklabels=_show_sample_names,
cmap=cmap_custom,
cbar=False)
ax.set_xticks(xpos)
ax.set_xticklabels(xlabels, rotation=0)
cbar = ax.figure.colorbar(ax.collections[0])
set_cbar_ticks(cbar, function_dict, custom_div_cmap_arg)
if saveReport:
if toPrint:
logger.info('Save heatmap')
plt.savefig(os.path.join(output_directory,
'Fig_heatmap_ordered' + img_ext),
transparent=True,
bbox_inches='tight',
pad_inches=0.1,
frameon=False)
plt.close("all")
else:
plt.show()
#########################################
# save the combined data
fname = 'integrated_data.csv'
fpath = os.path.join(output_directory, fname)
logger.info('-save the combined data from different cohorts ' +
'with shape:\n' + str(data.shape))
data.to_csv(fpath, sep='\t')
if save_new_sample_info:
# save the sample_info
fname = 'integrated_sample_info.csv'
fpath = os.path.join(new_sample_info_fpath, fname)
logger.info('-save the combined sample_info from different cohorts ' +
'with shape:\n' + str(sample_info.shape))
sample_info.to_csv(fpath, sep='\t')
def feature_selection(**set_up_kwargs):
# initialize script params
saveReport = parse_arg_type(
set_up_kwargs.get('saveReport', False),
bool
)
toPrint = parse_arg_type(
set_up_kwargs.get('toPrint', False),
bool
)
reportName = set_up_kwargs.get('reportName', script_fname)
txt_label = set_up_kwargs.get('txt_label', 'test_txt_label')
sample_class_column = set_up_kwargs.get('sample_class_column', None)
if sample_class_column is None:
logger.error("NO class label was defined!")
raise
class_labels = set_up_kwargs.get('class_labels', None)
if class_labels is not None:
if ',' in class_labels:
class_labels = class_labels.rsplit(',')
class_labels = np.array(class_labels)
class_values = set_up_kwargs.get('class_values', None)
if class_values is not None:
if ',' in class_values:
class_values = class_values.rsplit(',')
class_values = np.array(class_values).astype(int)
# feature_selection_args
feature_selection_args = set_up_kwargs.get('feature_selection_args', {})
pval_thres = parse_arg_type(
feature_selection_args.pop('pval_thres', 0.05),
float
)
topN = parse_arg_type(
feature_selection_args.pop('topN', 10),
int
)
# plotting params
plot_kwargs = set_up_kwargs.get('plot_kwargs', {})
function_dict = plot_kwargs.get('function_dict', None)
with_swarm = parse_arg_type(
plot_kwargs.get('with_swarm', False),
bool
)
highRes = parse_arg_type(
plot_kwargs.get('highRes', False),
bool
)
if highRes:
img_ext = '.pdf'
else:
img_ext = '.png'
cmap_custom = plot_kwargs.get('cmap_custom', None)
vmin = parse_arg_type(
plot_kwargs.get('vmin', None),
int
)
vmax = parse_arg_type(
plot_kwargs.get('vmax', None),
int
)
if (cmap_custom is None) and (vmin is not None) and (vmax is not None):
custom_div_cmap_arg = abs(vmin)+abs(vmax)
if (vmin <= 0) and (vmax >= 0):
custom_div_cmap_arg = custom_div_cmap_arg + 1
mincol = plot_kwargs.get('mincol', None)
midcol = plot_kwargs.get('midcol', None)
maxcol = plot_kwargs.get('maxcol', None)
if (
(mincol is not None) and
(midcol is not None) and
(maxcol is not None)
):
cmap_custom = custom_div_cmap(
numcolors=custom_div_cmap_arg,
mincol=mincol, midcol=midcol, maxcol=maxcol)
else:
cmap_custom = custom_div_cmap(numcolors=custom_div_cmap_arg)
# initialize directories
MainDataDir = set_path(os.path.join(script_path, '..', 'data'))
# data input
data_fpath = set_up_kwargs.get('data_fpath')
if ',' in data_fpath:
data_fpath = os.path.join(*data_fpath.rsplit(','))
data_fpath = os.path.join(MainDataDir, data_fpath)
# sample info input
sample_info_fpath = set_up_kwargs.get('sample_info_fpath')
if ',' in sample_info_fpath:
sample_info_fpath = os.path.join(*sample_info_fpath.rsplit(','))
sample_info_fpath = os.path.join(MainDataDir, sample_info_fpath)
sample_info_read_csv_kwargs = set_up_kwargs.get(
'sample_info_read_csv_kwargs', {})
# dupl_genes_dict
dupl_genes_dict_fpath = set_up_kwargs.get('dupl_genes_dict_fpath', None)
if dupl_genes_dict_fpath is not None:
if ',' in dupl_genes_dict_fpath:
dupl_genes_dict_fpath = os.path.join(
*dupl_genes_dict_fpath.rsplit(','))
dupl_genes_dict_fpath = os.path.join(
MainDataDir, dupl_genes_dict_fpath)
# data output
output_directory = set_up_kwargs.get('output_directory')
if ',' in output_directory:
output_directory = os.path.join(*output_directory.rsplit(','))
output_directory = set_directory(
os.path.join(MainDataDir, output_directory, reportName)
)
# save the set_up_kwargs in the output dir for reproducibility
fname = 'set_up_kwargs.json'
f = os.path.join(output_directory, fname)
if toPrint:
logger.info(
'-save set_up_kwargs dictionary for reproducibility in: '+f)
with open(f, 'w') as fp:
json.dump(set_up_kwargs, fp, indent=4)
# load data
try:
data = pd.read_csv(data_fpath, sep='\t', header=0, index_col=0)
logger.info('loaded data file with shape: '+str(data.shape))
except:
logger.error('failed to read data file from: '+str(data_fpath))
raise
# load info table of samples
try:
info_table = load_clinical(
sample_info_fpath, **sample_info_read_csv_kwargs)
except:
logger.error('Load info table of samples FAILED!')
raise
# set the ground truth
ground_truth = info_table.loc[data.index, sample_class_column]
ground_truth.sort_values(inplace=True)
data = data.reindex(ground_truth.index, axis=0)
try:
yticklabels = ground_truth.index.values+',' + \
ground_truth.values.astype(int).flatten().astype(str)
except:
yticklabels = ground_truth.index.values+',' + \
ground_truth.values.flatten().astype(str)
# load duplicate genes dictionary
# we will need that for the featsel results table we will save later
if dupl_genes_dict_fpath is not None:
with open(dupl_genes_dict_fpath, 'r') as fp:
dupl_genes_dict = json.load(fp)
else:
dupl_genes_dict = None
# Feature Selection
model, all_coefs, printed_results, \
(pval, correct, wrong), _sample_pred_diffs = \
_feature_selection_by_classification(
data, ground_truth, **feature_selection_args)
# save sample prediction scores
compare_predictions = pd.concat(
[ground_truth, _sample_pred_diffs], axis=1)
compare_predictions.loc[:, 'pred_diffs'].fillna(1, inplace=True)
compare_predictions = compare_predictions.astype(int)
fname = 'sample_prediciton_scores.csv'
fpath = os.path.join(output_directory, fname)
logger.info("-save sample prediction scores in :\n"+fpath)
compare_predictions.to_csv(
fpath, sep='\t', header=True, index=True)
# plot count of correct/wrong predictions per class
y_maxlim = np.histogram(
compare_predictions[sample_class_column], bins=2)[0].max()
axes = compare_predictions.hist(
by=sample_class_column, column='pred_diffs',
bins=2, rwidth=0.4, figsize=(10, 6))
for ax in axes:
ax.set_ylim(0, y_maxlim+1)
ax.set_xlim(0, 1)
ax.set_xticks([0.25, 0.75])
ax.set_xticklabels(['correct', 'wrong'], rotation=0, fontsize=18)
ax_title = class_labels[np.where(
class_values == float(
ax.get_title()))[0][0]]+':'+str(ax.get_title())
ax.set_title(ax_title, fontsize=18)
plt.suptitle(sample_class_column+' predictions', fontsize=20)
if saveReport:
logger.info('Save count plot')
fpath = os.path.join(
output_directory, 'Fig_count_plot'+img_ext
)
plt.savefig(
fpath, transparent=True, bbox_inches='tight',
pad_inches=0.1, frameon=False)
plt.close("all")
else:
plt.show()
# plot confusion matrix
# Compute confusion matrix
cnf_matrix = confusion_matrix(
compare_predictions[sample_class_column],
np.abs(
compare_predictions[sample_class_column] -
compare_predictions['pred_diffs']))
np.set_printoptions(precision=2)
_classes = [
class_labels[class_values == 0][0],
class_labels[class_values == 1][0]]
# Plot non-normalized confusion matrix
plt.figure()
plot_confusion_matrix(
cnf_matrix, classes=_classes,
title='Confusion matrix, without normalization')
if saveReport:
logger.info('Save count plot')
fpath = os.path.join(
output_directory, 'Fig_confusion_matrix'+img_ext
)
plt.savefig(
fpath, transparent=True, bbox_inches='tight',
pad_inches=0.1, frameon=False)
plt.close("all")
else:
plt.show()
# Plot normalized confusion matrix
plt.figure()
plot_confusion_matrix(
cnf_matrix, classes=_classes, normalize=True,
title='Normalized confusion matrix')
if saveReport:
logger.info('Save count plot')
fpath = os.path.join(
output_directory, 'Fig_confusion_matrix_normalized'+img_ext
)
plt.savefig(
fpath, transparent=True, bbox_inches='tight',
pad_inches=0.1, frameon=False)
plt.close("all")
else:
plt.show()
# Save to model in the output_directory
fname = 'joblib_model.pkl'
fpath = os.path.join(output_directory, fname)
logger.info('-save model with joblib')
joblib.dump(model, fpath)
# get the genes with the nnz coefficients in classification
featsel_results = pd.DataFrame(index=data.columns.values)
featsel_results.index.name = 'gene'
featsel_results['nnz'] = 0
featsel_results['mean_coef'] = all_coefs.mean(axis=0)
featsel_results['std_coef'] = all_coefs.std(axis=0)
nnz_coef_gene_names = (
np.abs(all_coefs).max(axis=0) > 0
).index.values
featsel_results.loc[nnz_coef_gene_names, 'nnz'] = 1
n_names = nnz_coef_gene_names.shape[0]
if dupl_genes_dict is not None:
featsel_results = edit_names_with_duplicates(
featsel_results, dupl_genes_dict)
# change the name of the genes to indicate if they have duplicates
newgeneNames_data = featsel_results.loc[
data.columns, 'newGeneName'].values
newgeneNames_coefs = featsel_results.loc[
all_coefs.columns, 'newGeneName'].values
featsel_results.reset_index(inplace=True, drop=False)
featsel_results.set_index('newGeneName', inplace=True)
data.columns = newgeneNames_data
all_coefs.columns = newgeneNames_coefs
nnz_coef_gene_names = (
np.abs(all_coefs).max(axis=0) > 0
).index.values
# boxplot of all nnz coefs
coefs_to_plot = all_coefs.loc[:, nnz_coef_gene_names]
boxplot(
coefs_to_plot, coefs_to_plot.shape[1],
coefs_to_plot.columns.values,
title=txt_label+" - nnz coef genes",
txtbox=printed_results, sidespace=2,
swarm=False, n_names=coefs_to_plot.shape[1]
)
if saveReport:
logger.info('Save boxplot')
fpath = os.path.join(
output_directory, 'Fig_boxplot_with_nnz_coefs'+img_ext
)
plt.savefig(
fpath, transparent=True, bbox_inches='tight',
pad_inches=0.1, frameon=False)
plt.close("all")
else:
plt.show()
# heatmap of genes with nnz coefs in classification
fs_x, fs_y, _show_gene_names, _ = set_heatmap_size(data)
plt.figure(figsize=(fs_x, fs_y))
ax = sns.heatmap(data.loc[:, nnz_coef_gene_names], vmin=vmin, vmax=vmax,
yticklabels=yticklabels,
xticklabels=_show_gene_names,
cmap=cmap_custom, cbar=False)
plt.xticks(rotation=90)
cbar = ax.figure.colorbar(ax.collections[0])
set_cbar_ticks(cbar, function_dict, custom_div_cmap_arg)
plt.title(
str(n_names)+' genes with nnz coefs in classification: ' +
class_labels[0]+'['+str(class_values[0])+'] vs. ' +
class_labels[1]+'['+str(class_values[1])+']'
)
if saveReport:
logger.info('Save heatmap')
fpath = os.path.join(
output_directory, 'Fig_heatmap_with_nnz_coefs'+img_ext
)
plt.savefig(fpath,
transparent=True, bbox_inches='tight',
pad_inches=0.1, frameon=False)
plt.close("all")
else:
plt.show()
if (pval < pval_thres) and (correct > wrong):
logger.info(
"selecting genes because bionomial test with pValue < " +
str(pval_thres)+" and #correct("+str(correct) +
") > #wrong("+str(wrong)+") answers"
)
featsel_results['abs_mean_coef'] = np.abs(featsel_results['mean_coef'])
if topN > data.shape[1]:
topN = data.shape[1]
featsel_results['top'+str(topN)] = 0
featsel_results.sort_values(
by=['abs_mean_coef'], inplace=True, ascending=False)
selected_gene_names = featsel_results.index.values[:topN]
featsel_results.loc[selected_gene_names, 'top'+str(topN)] = 1
# keep only those genes in the data
data = data.loc[:, selected_gene_names]
# save this data for future classification
fname = 'data_features_class.csv'
fpath = os.path.join(output_directory, fname)
logger.info("-save data with selected genes\n"+fpath)
data.to_csv(fpath, sep='\t', header=True, index=True)
# save as tab-delimited csv file
fname = 'featsel_results.csv'
fpath = os.path.join(output_directory, fname)
logger.info("-save selected genes in :\n"+fpath)
featsel_results.to_csv(
fpath, sep='\t', header=True, index=True)
# save also as excel file
fname = 'featsel_results.xlsx'
fpath = os.path.join(output_directory, fname)
logger.info('-save csv file as excel too')
writer = pd.ExcelWriter(fpath)
featsel_results.to_excel(writer)
writer.save()
# boxplot of selected coefs
coefs_to_plot = all_coefs.loc[:, selected_gene_names]
boxplot(
coefs_to_plot, coefs_to_plot.shape[1],
coefs_to_plot.columns.values,
title=txt_label+" - selected top"+str(topN)+" genes",
txtbox=printed_results, sidespace=2,
swarm=False, n_names=coefs_to_plot.shape[1]
)
if saveReport:
logger.info('Save boxplot')
fpath = os.path.join(
output_directory, 'Fig_boxplot_with_selected_genes'+img_ext
)
plt.savefig(
fpath, transparent=True, bbox_inches='tight',
pad_inches=0.1, frameon=False)
plt.close("all")
else:
plt.show()
# heatmap of selected genes
fs_x, fs_y, _show_gene_names, _ = set_heatmap_size(data)
plt.figure(figsize=(fs_x, fs_y))
ax = sns.heatmap(
data.loc[:, selected_gene_names], vmin=vmin, vmax=vmax,
yticklabels=yticklabels,
xticklabels=_show_gene_names,
cmap=cmap_custom, cbar=False)
plt.xticks(rotation=90)
cbar = ax.figure.colorbar(ax.collections[0])
set_cbar_ticks(cbar, function_dict, custom_div_cmap_arg)
plt.title(
'selected top'+str(topN)+' genes: ' +
class_labels[0]+'['+str(class_values[0])+'] vs. ' +
class_labels[1]+'['+str(class_values[1])+']'
)
if saveReport:
logger.info('Save heatmap')
fpath = os.path.join(
output_directory, 'Fig_heatmap_with_selected_genes'+img_ext
)
plt.savefig(fpath,
transparent=True, bbox_inches='tight',
pad_inches=0.1, frameon=False)
plt.close("all")
else:
plt.show()
else:
selected_gene_names = None
if with_swarm:
# swarmplots
coefs_to_plot = all_coefs.loc[:, nnz_coef_gene_names]
boxplot(
coefs_to_plot, coefs_to_plot.shape[1],
coefs_to_plot.columns.values,
title=txt_label+" - nnz coef genes",
txtbox=printed_results, sidespace=2,
swarm=True, n_names=coefs_to_plot.shape[1]
)
if saveReport:
logger.info('Save swarmplot')
fpath = os.path.join(
output_directory, 'Fig_swarmplot_with_nnz_coefs'+img_ext
)
plt.savefig(
fpath, transparent=True, bbox_inches='tight',
pad_inches=0.1, frameon=False)
plt.close("all")
else:
plt.show()
if selected_gene_names is not None:
coefs_to_plot = all_coefs.loc[:, selected_gene_names]
boxplot(
coefs_to_plot, coefs_to_plot.shape[1],
coefs_to_plot.columns.values,
title=txt_label+" - nnz coef genes",
txtbox=printed_results, sidespace=2,
swarm=True, n_names=coefs_to_plot.shape[1]
)
if saveReport:
logger.info('Save swarmplot')
fpath = os.path.join(
output_directory,
'Fig_swarmplot_with_selected_genes'+img_ext
)
plt.savefig(
fpath, transparent=True, bbox_inches='tight',
pad_inches=0.1, frameon=False)
plt.close("all")
else:
plt.show()
def classification(**set_up_kwargs):
# initialize script params
saveReport = parse_arg_type(
set_up_kwargs.get('saveReport', False),
bool
)
toPrint = parse_arg_type(
set_up_kwargs.get('toPrint', False),
bool
)
reportName = set_up_kwargs.get('reportName', script_fname)
txt_label = set_up_kwargs.get('txt_label', 'test_txt_label')
sample_class_column = set_up_kwargs.get('sample_class_column', None)
if sample_class_column is None:
logger.error("NO class label was defined!")
raise
class_labels = set_up_kwargs.get('class_labels', None)
if class_labels is not None:
if ',' in class_labels:
class_labels = class_labels.rsplit(',')
class_values = set_up_kwargs.get('class_values', None)
if class_values is not None:
if ',' in class_values:
class_values = class_values.rsplit(',')
class_values = np.array(class_values).astype(int)
# feature_selection_args
classification_args = set_up_kwargs.get('classification_args', {})
split_train_size = classification_args.pop('split_train_size', 20)
try:
if '.' in split_train_size:
split_train_size = parse_arg_type(split_train_size, float)
else:
split_train_size = parse_arg_type(split_train_size, int)
except:
pass
split_random_state = parse_arg_type(
classification_args.pop('split_random_state', 0),
int
)
# plotting params
plot_kwargs = set_up_kwargs.get('plot_kwargs', {})
function_dict = plot_kwargs.get('function_dict', None)
with_swarm = parse_arg_type(
plot_kwargs.get('with_swarm', False),
bool
)
highRes = parse_arg_type(
plot_kwargs.get('highRes', False),
bool
)
if highRes:
img_ext = '.pdf'
else:
img_ext = '.png'
cmap_custom = plot_kwargs.get('cmap_custom', None)
vmin = parse_arg_type(
plot_kwargs.get('vmin', None),
int
)
vmax = parse_arg_type(
plot_kwargs.get('vmax', None),
int
)
if (cmap_custom is None) and (vmin is not None) and (vmax is not None):
custom_div_cmap_arg = abs(vmin)+abs(vmax)
if (vmin <= 0) and (vmax >= 0):
custom_div_cmap_arg = custom_div_cmap_arg + 1
mincol = plot_kwargs.get('mincol', None)
midcol = plot_kwargs.get('midcol', None)
maxcol = plot_kwargs.get('maxcol', None)
if (
(mincol is not None) and
(midcol is not None) and
(maxcol is not None)
):
cmap_custom = custom_div_cmap(
numcolors=custom_div_cmap_arg,
mincol=mincol, midcol=midcol, maxcol=maxcol)
else:
cmap_custom = custom_div_cmap(numcolors=custom_div_cmap_arg)
# initialize directories
MainDataDir = set_path(os.path.join(script_path, '..', 'data'))
# data input
data_fpath = set_up_kwargs.get('data_fpath')
if ',' in data_fpath:
data_fpath = os.path.join(*data_fpath.rsplit(','))
data_fpath = os.path.join(MainDataDir, data_fpath)
# sample info input
sample_info_fpath = set_up_kwargs.get('sample_info_fpath')
if ',' in sample_info_fpath:
sample_info_fpath = os.path.join(*sample_info_fpath.rsplit(','))
sample_info_fpath = os.path.join(MainDataDir, sample_info_fpath)
sample_info_read_csv_kwargs = set_up_kwargs.get(
'sample_info_read_csv_kwargs', {})
# dupl_genes_dict
dupl_genes_dict_fpath = set_up_kwargs.get('dupl_genes_dict_fpath', None)
if dupl_genes_dict_fpath is not None:
if ',' in dupl_genes_dict_fpath:
dupl_genes_dict_fpath = os.path.join(
*dupl_genes_dict_fpath.rsplit(','))
dupl_genes_dict_fpath = os.path.join(
MainDataDir, dupl_genes_dict_fpath)
# data output
output_directory = set_up_kwargs.get('output_directory')
if ',' in output_directory:
output_directory = os.path.join(*output_directory.rsplit(','))
output_directory = set_directory(
os.path.join(MainDataDir, output_directory, reportName)
)
# save the set_up_kwargs in the output dir for reproducibility
fname = 'set_up_kwargs.json'
f = os.path.join(output_directory, fname)
if toPrint:
logger.info(
'-save set_up_kwargs dictionary for reproducibility in: '+f)
with open(f, 'w') as fp:
json.dump(set_up_kwargs, fp, indent=4)
# load data
try:
data = pd.read_csv(data_fpath, sep='\t', header=0, index_col=0)
logger.info('loaded data file with shape: '+str(data.shape))
except:
logger.error('failed to read data file from: '+str(data_fpath))
raise
# load info table of samples
try:
info_table = load_clinical(
sample_info_fpath, **sample_info_read_csv_kwargs)
except:
logger.error('Load info table of samples FAILED!')
raise
# set the ground truth
ground_truth = info_table.loc[data.index, sample_class_column]
# load duplicate genes dictionary
# we will need that for the featsel results table we will save later
if dupl_genes_dict_fpath is not None:
with open(dupl_genes_dict_fpath, 'r') as fp:
dupl_genes_dict = json.load(fp)
else:
dupl_genes_dict = None
# Classification
if toTrain:
# choose labels to stratify train_test_split
if 'dataset' in info_table.columns.values:
stratify_by = pd.concat(
[ground_truth, info_table['dataset']], axis=1)
else:
stratify_by = ground_truth
# split data in train and test
data_train, data_test, y_train, y_test = train_test_split(
data, ground_truth,
train_size=split_train_size,
test_size=None,
random_state=split_random_state,
stratify=stratify_by)
try:
yticklabels_train = y_train.index.values+',' + \
y_train.values.astype(int).flatten().astype(str)
except:
yticklabels_train = y_train.index.values+',' + \
y_train.values.flatten().astype(str)
# train model
model, all_coefs, y_train_predictions, y_train_scores = \
_run_classification(
data_train, y_train, **classification_args)
else:
# load model from file
model = joblib.load(model_fpath)
# load features of trained model
data_test = data
y_test = ground_truth
try:
yticklabels_test = y_test.index.values+',' + \
y_test.values.astype(int).flatten().astype(str)
except:
yticklabels_test = y_test.index.values+',' + \
y_test.values.flatten().astype(str)
# Test the model
y_test_score = model.score(data_test, y_test)
y_test_predictions = model.predict(data_test)
y_test_predictions = pd.Series(y_test_predictions, index=data_test.index)
y_test_predictions.name = 'test_predictions'
if toTrain:
#################################################
# plot accuracy scores of the train and test data
plt.figure(figsize=(10, 6))
plt.scatter(
np.arange(len(y_train_scores))+1, y_train_scores, color='black')
plt.scatter(0, y_test_score, color='red')
plt.xlim(-1, len(y_train_scores)+1)
plt.ylim(0, 1)
plt.xlabel("test and train kfolds")
plt.ylabel("accuracy scores")
if saveReport:
logger.info('Save boxplot')
fpath = os.path.join(
output_directory, 'Fig_scatter'+img_ext
)
plt.savefig(
fpath, transparent=True, bbox_inches='tight',
pad_inches=0.1, frameon=False)
plt.close("all")
else:
plt.show()
# save train sample cross prediction scores
y_train_all_labels = pd.concat(
[y_train, y_train_predictions], axis=1)
fname = 'y_train_all_labels.csv'
fpath = os.path.join(output_directory, fname)
logger.info("-save train labels in :\n"+fpath)
y_train_all_labels.to_csv(
fpath, sep='\t', header=True, index=True)
# Save to model in the output_directory
fname = 'joblib_model.pkl'
fpath = os.path.join(output_directory, fname)
logger.info('-save model with joblib')
joblib.dump(model, fpath)
# training classification results
classification_results = pd.DataFrame(index=data.columns.values)
classification_results.index.name = 'gene'
classification_results['mean_coef'] = all_coefs.mean(axis=0)
classification_results['std_coef'] = all_coefs.std(axis=0)
if dupl_genes_dict is not None:
classification_results = edit_names_with_duplicates(
classification_results, dupl_genes_dict)
# change the name of the genes to indicate if they have duplicates
newgeneNames_data = classification_results.loc[
data.columns, 'newGeneName'].values
newgeneNames_coefs = classification_results.loc[
all_coefs.columns, 'newGeneName'].values
classification_results.reset_index(inplace=True, drop=False)
classification_results.set_index('newGeneName', inplace=True)
data.columns = newgeneNames_data
all_coefs.columns = newgeneNames_coefs
# boxplot of coefs
coefs_to_plot = all_coefs
boxplot(
coefs_to_plot, coefs_to_plot.shape[1],
coefs_to_plot.columns.values,
title=txt_label,
txtbox='', sidespace=2,
swarm=False, n_names=coefs_to_plot.shape[1]
)
if saveReport:
logger.info('Save boxplot')
fpath = os.path.join(
output_directory, 'Fig_boxplot_train'+img_ext
)
plt.savefig(
fpath, transparent=True, bbox_inches='tight',
pad_inches=0.1, frameon=False)
plt.close("all")
else:
plt.show()
# heatmap of genes in classification - train
fs_x, fs_y, _show_gene_names, _ = set_heatmap_size(data_train)
plt.figure(figsize=(fs_x, fs_y))
ax = sns.heatmap(
data_train, vmin=vmin, vmax=vmax,
yticklabels=yticklabels_train,
xticklabels=_show_gene_names,
cmap=cmap_custom, cbar=False)
plt.xticks(rotation=90)
cbar = ax.figure.colorbar(ax.collections[0])
set_cbar_ticks(cbar, function_dict, custom_div_cmap_arg)
plt.title(
'train data classification: ' +
class_labels[0]+'['+str(class_values[0])+'] vs. ' +
class_labels[1]+'['+str(class_values[1])+']'
)
if saveReport:
logger.info('Save heatmap')
fpath = os.path.join(
output_directory, 'Fig_heatmap_train'+img_ext
)
plt.savefig(fpath,
transparent=True, bbox_inches='tight',
pad_inches=0.1, frameon=False)
plt.close("all")
else:
plt.show()
# save as tab-delimited csv file
fname = 'classification_results.csv'
fpath = os.path.join(output_directory, fname)
logger.info("-save selected genes in :\n"+fpath)
classification_results.to_csv(
fpath, sep='\t', header=True, index=True)
# save also as excel file
fname = 'classification_results.xlsx'
fpath = os.path.join(output_directory, fname)
logger.info('-save csv file as excel too')
writer = pd.ExcelWriter(fpath)
classification_results.to_excel(writer)
writer.save()
if with_swarm:
# swarmplots
coefs_to_plot = all_coefs.loc[:, nnz_coef_gene_names]
boxplot(
coefs_to_plot, coefs_to_plot.shape[1],
coefs_to_plot.columns.values,
title=txt_label,
txtbox='', sidespace=2,
swarm=True, n_names=coefs_to_plot.shape[1]
)
if saveReport:
logger.info('Save swarmplot')
fpath = os.path.join(
output_directory, 'Fig_swarmplot_train'+img_ext
)
plt.savefig(
fpath, transparent=True, bbox_inches='tight',
pad_inches=0.1, frameon=False)
plt.close("all")
else:
plt.show()
#################################################
# save test sample prediction scores
y_test_all_labels = pd.concat(
[y_test, y_test_predictions], axis=1)
fname = 'y_test_all_labels.csv'
fpath = os.path.join(output_directory, fname)
logger.info("-save test labels in :\n"+fpath)
y_test_all_labels.to_csv(
fpath, sep='\t', header=True, index=True)
# heatmap of genes in classification - test
fs_x, fs_y, _show_gene_names, _ = set_heatmap_size(data_test)
plt.figure(figsize=(fs_x, fs_y))
ax = sns.heatmap(data_test, vmin=vmin, vmax=vmax,
yticklabels=yticklabels_test,
xticklabels=_show_gene_names,
cmap=cmap_custom, cbar=False)
plt.xticks(rotation=90)
cbar = ax.figure.colorbar(ax.collections[0])
set_cbar_ticks(cbar, function_dict, custom_div_cmap_arg)
plt.title(
'test data classification: ' +
class_labels[0]+'['+str(class_values[0])+'] vs. ' +
class_labels[1]+'['+str(class_values[1])+']'
)
if saveReport:
logger.info('Save heatmap')
fpath = os.path.join(
output_directory, 'Fig_heatmap_test'+img_ext
)
plt.savefig(fpath,
transparent=True, bbox_inches='tight',
pad_inches=0.1, frameon=False)
plt.close("all")
else:
plt.show()
def process_data(**set_up_kwargs):
# initialize script params
saveReport = parse_arg_type(set_up_kwargs.get('saveReport', False), bool)
toPrint = parse_arg_type(set_up_kwargs.get('toPrint', False), bool)
reportName = set_up_kwargs.get('reportName', script_fname)
input_fname = set_up_kwargs.get('input_fname', 'data_processed.csv')
gene_info_fname = set_up_kwargs.get('gene_info_fname',
'gene_info_fname.csv')
txt_label = set_up_kwargs.get('txt_label', 'test_txt_label')
chr_col = set_up_kwargs.get('chr_col', 'chr_int')
gene_id_col = set_up_kwargs.get('gene_id_col', 'gene')
remove_patients = set_up_kwargs.get('remove_patients', None)
if remove_patients is None or remove_patients == "":
remove_patients_list = []
else:
remove_patients_list = remove_patients.rsplit(',')
select_genes = set_up_kwargs.get('select_genes', None)
if select_genes is None or select_genes == "":
select_genes_list = []
else:
select_genes_list = select_genes.rsplit(',')
sample_info_fname = set_up_kwargs.get('sample_info_fname',
'20180704_emca.csv')
if ',' in sample_info_fname:
sample_info_fname = os.path.join(*sample_info_fname.rsplit(','))
sample_info_read_csv_kwargs = set_up_kwargs.get(
'sample_info_read_csv_kwargs', {})
old_data_sample_id = set_up_kwargs.get('old_data_sample_id', None)
if old_data_sample_id is not None:
change_id = True
else:
change_id = False
# chose sample set from data
# function: choose_samples()
select_samples_from = set_up_kwargs.get('select_samples_from', None)
select_samples_which = parse_arg_type(
set_up_kwargs.get('select_samples_which', None), int)
select_samples_sort_by = set_up_kwargs.get('select_samples_sort_by', None)
if select_samples_sort_by is not None:
select_samples_sort_by = select_samples_sort_by.rsplit(',')
# map_values_dict
map_values = set_up_kwargs.get('map_values', None)
if map_values is not None:
map_values_dict = None
if isinstance(map_values, dict):
map_values_dict = {int(k): int(v) for k, v in map_values.items()}
# plotting params
plot_kwargs = set_up_kwargs.get('plot_kwargs', {})
function_dict = plot_kwargs.get('function_dict', None)
cmap_custom = plot_kwargs.get('cmap_custom', None)
vmin = parse_arg_type(plot_kwargs.get('vmin', None), int)
vmax = parse_arg_type(plot_kwargs.get('vmax', None), int)
if (cmap_custom is None) and (vmin is not None) and (vmax is not None):
custom_div_cmap_arg = abs(vmin) + abs(vmax)
if (vmin <= 0) and (vmax >= 0):
custom_div_cmap_arg = custom_div_cmap_arg + 1
mincol = plot_kwargs.get('mincol', None)
midcol = plot_kwargs.get('midcol', None)
maxcol = plot_kwargs.get('maxcol', None)
if ((mincol is not None) and (midcol is not None)
and (maxcol is not None)):
cmap_custom = custom_div_cmap(numcolors=custom_div_cmap_arg,
mincol=mincol,
midcol=midcol,
maxcol=maxcol)
else:
cmap_custom = custom_div_cmap(numcolors=custom_div_cmap_arg)
highRes = parse_arg_type(plot_kwargs.get('highRes', False), bool)
if highRes:
img_ext = '.pdf'
else:
img_ext = '.png'
# initialize directories
MainDataDir = set_path(os.path.join(script_path, '..', 'data'))
# data input
input_directory = set_up_kwargs.get('input_directory')
if ',' in input_directory:
input_directory = os.path.join(*input_directory.rsplit(','))
input_directory = os.path.join(MainDataDir, input_directory)
# sample info input
sample_info_directory = set_up_kwargs.get('sample_info_directory')
if ',' in sample_info_directory:
sample_info_directory = os.path.join(
*sample_info_directory.rsplit(','))
sample_info_directory = os.path.join(MainDataDir, sample_info_directory)
# gene info input
gene_info_directory = set_up_kwargs.get('gene_info_directory')
if gene_info_directory is None:
gene_info_directory = input_directory
else:
if ',' in gene_info_directory:
gene_info_directory = os.path.join(
*gene_info_directory.rsplit(','))
gene_info_directory = os.path.join(MainDataDir,
gene_info_directory)
# data output
output_directory = set_up_kwargs.get('output_directory')
if output_directory is None:
output_directory = set_directory(
os.path.join(input_directory, reportName))
else:
if ',' in output_directory:
output_directory = os.path.join(*output_directory.rsplit(','))
output_directory = set_directory(
os.path.join(MainDataDir, output_directory, reportName))
# save the set_up_kwargs in the output dir for reproducibility
fname = 'set_up_kwargs.json'
f = os.path.join(output_directory, fname)
if toPrint:
logger.info('-save set_up_kwargs dictionary for reproducibility in: ' +
f)
with open(f, 'w') as fp:
json.dump(set_up_kwargs, fp, indent=4)
#########################################
# load input_data
fpath = os.path.join(input_directory, input_fname)
data = pd.read_csv(fpath, sep='\t', header=0, index_col=0)
data = data.fillna(0)
# load info table of samples
if toPrint:
logger.info('Load info table of samples')
fpath = os.path.join(sample_info_directory, sample_info_fname)
info_table = load_clinical(fpath, **sample_info_read_csv_kwargs)
# load gene info
fpath = os.path.join(gene_info_directory, gene_info_fname)
try:
genes_positions_table = pd.read_csv(fpath,
sep='\t',
header=0,
index_col=0)
# get gene chrom position
xlabels, xpos = get_chr_ticks(genes_positions_table,
data,
id_col='gene',
chr_col=chr_col)
except:
logger.warning('could not get genes position info')
xlabels, xpos = None, None
#########################################
# CHECK if there are empty genes and remove them
is_empty = (data.isnull()).all(axis=0)
if is_empty.any():
genes2remove = data.columns[is_empty]
data.drop(genes2remove, axis=1, inplace=True)
if toPrint:
logger.info('remove the following genes because ' +
'they have no values in the table: ' +
str(genes2remove))
# CHECK if there are empty patients BUT don't remove them
empty_pat = data.sum(axis=1).isnull()
if empty_pat.any():
logger.info('Patients with missing values in all genes: ' +
str(data.index[empty_pat]))
# SELECT specific genes (optional)
if len(select_genes_list) > 0:
# first take intersection of with data
select_genes_list = list(
set(data.columns.values).intersection(set(select_genes_list)))
# then keep only these genes from in the data
data = data.loc[:, select_genes_list].copy()
if genes_positions_table is not None:
xlabels, xpos = get_chr_ticks(genes_positions_table,
data,
id_col='gene',
chr_col=chr_col)
# MAP values with a dictionary (optional)
if map_values is not None:
if map_values_dict is not None:
_diff_set = set(np.unique(data.values.flatten().astype(int)))\
.difference(set([0]))\
.difference(set(list(map_values_dict.keys())))
if _diff_set:
logger.warning(
"the user\'s dict to replace data values is incomplete " +
"the following values in the data are not accounted for " +
"and will remain the same:\n" + str(_diff_set))
logger.info("replacing data values with user\'s dictionary:\n" +
str(map_values_dict))
data.replace(map_values_dict, inplace=True)
elif map_values in ['bin', 'binary', 'binarize']:
logger.info("binarizing data values" + str(map_values_dict))
binarize(data, copy=False)
else:
logger.warning(
"invalid map_values argument, no action will be taken: \n" +
str(map_values))
# SELECT sample groups (optional)
if change_id:
temp = info_table.index.name
info_table = (
info_table
# delete NaNs in the data id
.dropna(subset=[old_data_sample_id])
# do not lose original index
.reset_index()
# same index as data
.set_index(old_data_sample_id, drop=False)
# keep same as data ids
.loc[data.index]
# return back to original index
.set_index(temp)
)
else:
temp = info_table.index.name
info_table = info_table.loc[data.index].copy()
info_table.index.name = temp
ids_tmp = choose_samples(info_table.reset_index(),
info_table.index.name,
choose_from=select_samples_from,
choose_what=select_samples_which,
sortby=select_samples_sort_by,
ascending=False)
info_table = info_table.loc[ids_tmp, :].copy()
if change_id:
old_index_sorted = info_table[old_data_sample_id].values.copy()
data = data.loc[old_index_sorted, :].copy()
new_ids = info_table.index.values
# data = data.reindex(new_ids, axis=0) # gives me nan values!
data.index = new_ids
# in case there are patients with no data
data.dropna(axis=0, inplace=True)
info_table = info_table.loc[data.index, :].copy()
else:
data = data.loc[ids_tmp, :].copy()
pat_labels = info_table[select_samples_sort_by].copy()
try:
pat_labels_txt = pat_labels.astype(int).reset_index().values
except:
pat_labels_txt = pat_labels.reset_index().values
pat_labels_title = str(pat_labels.reset_index().columns.values)
# PLOT heatmap without gene ordering
if toPrint:
logger.info('Plot heatmap before gene ordering')
_figure_x_size, _figure_y_size, _show_gene_names, _ = \
set_heatmap_size(data)
plt.figure(figsize=(_figure_x_size, _figure_y_size))
ax = sns.heatmap(data,
vmin=vmin,
vmax=vmax,
xticklabels=_show_gene_names,
yticklabels=pat_labels_txt,
cmap=cmap_custom,
cbar=False)
ax.set_ylabel(pat_labels_title)
plt.xticks(rotation=90)
cbar = ax.figure.colorbar(ax.collections[0])
set_cbar_ticks(cbar, function_dict, custom_div_cmap_arg)
if saveReport:
if toPrint:
logger.info('Save heatmap')
plt.savefig(os.path.join(output_directory, 'Fig_heatmap' + img_ext),
transparent=True,
bbox_inches='tight',
pad_inches=0.1,
frameon=False)
plt.close("all")
else:
plt.show()
#########################################
if (xlabels is not None) and (xpos is not None):
# ORDER genes
if toPrint:
logger.info('Order data according to genomic position')
# extract the gene relative order
gene_order = genes_positions_table.set_index(
gene_id_col).loc[:, 'order'].copy()
# keep only gene_order with data
ids_tmp = set(gene_order.index.values).intersection(
set(data.columns.values))
# keep only the order of these genes
gene_order = gene_order.loc[ids_tmp].copy()
gene_order = gene_order.sort_values()
# then keep only these genes from the data
data = data.loc[:, gene_order.index].copy()
# data = pd.DataFrame(data, columns=sorted(
# gene_order_dict, key=gene_order_dict.get))
# PLOT heatmap after gene ordering
if toPrint:
logger.info('Plot heatmap after gene ordering')
_figure_x_size, _figure_y_size, _show_gene_names, _ = \
set_heatmap_size(data)
plt.figure(figsize=(_figure_x_size, _figure_y_size))
ax = sns.heatmap(data,
vmin=vmin,
vmax=vmax,
xticklabels=_show_gene_names,
yticklabels=pat_labels_txt,
cmap=cmap_custom,
cbar=False)
ax.set_xticks(xpos)
ax.set_xticklabels(xlabels, rotation=90)
ax.set_ylabel(pat_labels_title)
cbar = ax.figure.colorbar(ax.collections[0])
set_cbar_ticks(cbar, function_dict, custom_div_cmap_arg)
if saveReport:
if toPrint:
logger.info('Save heatmap')
plt.savefig(os.path.join(output_directory,
'Fig_heatmap_ordered' + img_ext),
transparent=True,
bbox_inches='tight',
pad_inches=0.1,
frameon=False)
plt.close("all")
else:
plt.show()
#########################################
# SAVE filtered data
if saveReport:
# save files
fname = 'data_processed.csv'
f = os.path.join(output_directory, fname)
if toPrint:
logger.info('-save ordered data: ' + f)
data.to_csv(f, sep='\t', header=True, index=True)
def set_up_data(**set_up_kwargs):
# initialize script params
saveReport = parse_arg_type(set_up_kwargs.get('saveReport', False), bool)
toPrint = parse_arg_type(set_up_kwargs.get('toPrint', False), bool)
reportName = set_up_kwargs.get('reportName', script_fname)
# load_data_csv_kwargs = set_up_kwargs.get(
# 'load_data_csv_kwargs', {}
# )
editWith = set_up_kwargs.get('editWith', 'Oncoscan')
if 'VCF' in editWith:
_edit_kwargs = set_up_kwargs.get('edit_kwargs', {})
function_dict = _edit_kwargs.get('function_dict', None)
txt_label = set_up_kwargs.get('txt_label', 'test_txt_label')
select_genes = set_up_kwargs.get('select_genes', None)
if select_genes is None or select_genes == "":
select_genes_list = []
else:
select_genes_list = select_genes.rsplit(',')
chr_col = set_up_kwargs.get('chr_col', 'chr_int')
gene_id_col = set_up_kwargs.get('gene_id_col', 'gene')
sample_info_fname = set_up_kwargs.get('sample_info_fname',
'20180704_emca.csv')
if ',' in sample_info_fname:
sample_info_fname = os.path.join(*sample_info_fname.rsplit(','))
sample_info_read_csv_kwargs = set_up_kwargs.get(
'sample_info_read_csv_kwargs', {})
sample_info_table_sortLabels = \
set_up_kwargs.get('sample_info_table_sortLabels', None)
sample_info_table_sortLabels_list = \
sample_info_table_sortLabels.rsplit(',')
# plotting params
plot_kwargs = set_up_kwargs.get('plot_kwargs', {})
highRes = parse_arg_type(plot_kwargs.get('highRes', False), bool)
if highRes:
img_ext = '.pdf'
else:
img_ext = '.png'
# initialize directories
MainDataDir = set_path(os.path.join(script_path, '..', 'data'))
input_directory = set_up_kwargs.get('input_directory')
if ',' in input_directory:
input_directory = os.path.join(*input_directory.rsplit(','))
input_directory = os.path.join(MainDataDir, input_directory)
output_directory = set_up_kwargs.get('output_directory')
if output_directory is None:
output_directory = set_directory(
os.path.join(input_directory, reportName))
else:
if ',' in output_directory:
output_directory = os.path.join(*output_directory.rsplit(','))
output_directory = set_directory(
os.path.join(MainDataDir, output_directory, reportName))
# save the set_up_kwargs in the output dir for reproducibility
fname = 'set_up_kwargs.json'
f = os.path.join(output_directory, fname)
if toPrint:
logger.info('-save set_up_kwargs dictionary for reproducibility in: ' +
f)
with open(f, 'w') as fp:
json.dump(set_up_kwargs, fp, indent=4)
data_directory = set_up_kwargs.get('data_directory', None)
if data_directory is None:
data_directory = input_directory
else:
if ',' in data_directory:
data_directory = os.path.join(*data_directory.rsplit(','))
data_files = set_up_kwargs.get('data_files', '')
data_files_list = data_files.rsplit(',')
if len(data_files_list) > 0:
fpaths = [
os.path.join(input_directory, data_directory, aFile)
for aFile in data_files_list
]
else:
fpaths = os.path.join(input_directory, data_directory)
# load info table of samples
if toPrint:
logger.info('Load info table of samples')
fpath = os.path.join(input_directory, sample_info_fname)
info_table = load_clinical(fpath, **sample_info_read_csv_kwargs)
if toPrint:
logger.info('Missing values for each column:\n')
info_table_isna_sum = info_table.isna().sum()
for _i in range(info_table_isna_sum.shape[0]):
logger.info(
str(info_table_isna_sum.index[_i]) + '\t' +
str(info_table_isna_sum.iloc[_i]))
#########################################
if 'genepanel' in editWith:
# print('ERROR: undefined scenario!')
# raise
edit_kwargs = set_up_kwargs.get('edit_kwargs', {})
# load data table
if len(fpaths) > 0:
logger.error('more than one data files were given!\n' +
str(fpaths))
variants = pd.read_csv(fpaths[0], sep='\t', header=0)
# EDIT:
# map function impact to value with function_dict
# substitute allele frequencies with impact values
# aggregate rows to unique genes, choose how to merge
# remove patients
data = edit_genepanel(variants, **edit_kwargs)
xlabels, xpos = None, None
# plot heatmap
# save formated data
# (optional) format data to binary
# plot heatmap
# save formated data
else:
load_files = parse_arg_type(set_up_kwargs.get('load_files', False),
bool)
# load data/files from each patient
if load_files:
if toPrint:
logger.info(txt_label + ': load files from all patients\n')
pat_data_list, pat_data_or_dict, dropped_rows_filter, \
dropped_rows_process, dropped_rows_edit, info_table = \
load_and_process_files(
fpaths, info_table, **set_up_kwargs)
else:
if toPrint:
logger.info(txt_label + ': load data from all patients\n')
pat_data_list, pat_data_or_dict, dropped_rows_filter, \
dropped_rows_process, dropped_rows_edit, info_table = \
load_and_process_summary_file(
fpaths, info_table, **set_up_kwargs)
if (dropped_rows_filter.shape[0] > 0) and (saveReport):
f_new = 'allsamples__dropped_rows_filter.txt'
if toPrint:
logger.info('-save dropped rows from filtering in:\n' + f_new)
dropped_rows_filter.to_csv(os.path.join(output_directory, f_new),
sep='\t',
header=True,
index=True)
if (dropped_rows_process.shape[0] > 0) and (saveReport):
f_new = 'allsamples__dropped_rows_process.txt'
if toPrint:
logger.info('-save dropped rows from processing in:\n' + f_new)
dropped_rows_process.to_csv(os.path.join(output_directory, f_new),
sep='\t',
header=True,
index=True)
if (dropped_rows_edit.shape[0] > 0) and (saveReport):
f_new = 'allsamples__dropped_rows_edit.txt'
if toPrint:
logger.info('-save dropped rows from editing in:\n' + f_new)
dropped_rows_edit.to_csv(os.path.join(output_directory, f_new),
sep='\t',
header=True,
index=True)
# get size of each sample
# (i.e. abundance of genes with in each sample)
# and plot it
counts = []
sample_labels = []
for df in pat_data_list:
counts.append(df.shape[0])
sample_labels.append(df.columns[0].rsplit(':')[0])
##################################################
# concat all samples in one table and keep union of all genes,
# then fill NaNs with zero
if toPrint:
logger.info('Concantanate all ' + editWith +
' samples in 2 tables (with position, only values)\n')
# samples in rows, genes in columns
table_withPos = pd.concat(pat_data_list,
join='outer',
axis=1,
sort=False).T
# CLEAN THE data FROM ALL SAMPLES
# extract the start, end and chrom info from the table
# and keep only the functions values
start_table = \
table_withPos[table_withPos.index.str.contains('start')].copy()
end_table = table_withPos[table_withPos.index.str.contains(
'end')].copy()
chr_table = table_withPos[table_withPos.index.str.contains(
'chr')].copy()
data = table_withPos.drop(np.concatenate(
[start_table.index, end_table.index, chr_table.index], axis=0),
axis=0)
if toPrint:
logger.info('Dimensions of data (samples,genes): ' +
str(data.shape))
data.index = [index_name.rsplit(':')[0] for index_name in data.index]
start_table.index = [
index_name.rsplit(':')[0] for index_name in start_table.index
]
end_table.index = [
index_name.rsplit(':')[0] for index_name in end_table.index
]
chr_table.index = [
index_name.rsplit(':')[0] for index_name in chr_table.index
]
# remove genes that exist in multiple chromosomes across samples
ll = [
list(chr_table[col].dropna().unique()) for col in chr_table.columns
]
n, m = max(map(len, ll)), len(ll)
uniq_chr_per_gene = pd.DataFrame(
[[_uniq_chr_per_gene(j, i) for j in ll] for i in range(n)],
columns=chr_table.columns)
genes2drop = uniq_chr_per_gene.columns[(
~uniq_chr_per_gene.isnull()).sum() > 1].values
if toPrint:
logger.info('Remove ' + str(genes2drop.shape[0]) +
' genes that exist in multiple chromosomes ' +
'across samples:\n' + str(genes2drop))
if (genes2drop.shape[0] > 0):
# if saveReport:
# fname = 'chr_table.csv'
# f = os.path.join(output_directory, fname)
# if toPrint:
# logger.info('-save chromosomes in: '+f)
# chr_table.to_csv(f, sep='\t', header=True, index=True)
# fname = 'chr_table_uniq.csv'
# f = os.path.join(output_directory, fname)
# if toPrint:
# logger.info('-save unique chromosomes in: '+f)
# uniq_chr_per_gene.to_csv(
# f, sep='\t', header=True, index=True)
# fname = 'chr_table_uniq_genes2drop.csv'
# f = os.path.join(output_directory, fname)
# if toPrint:
# logger.info('-save unique chromosomes ' +
# 'from genes to drop in: '+f)
# uniq_chr_per_gene.loc[:, genes2drop].to_csv(f, sep='\t',
# header=True,
# index=True)
start_table.drop(genes2drop, axis=1, inplace=True)
end_table.drop(genes2drop, axis=1, inplace=True)
chr_table.drop(genes2drop, axis=1, inplace=True)
data.drop(genes2drop, axis=1, inplace=True)
uniq_chr_per_gene.drop(genes2drop, axis=1, inplace=True)
uniq_chr_per_gene = uniq_chr_per_gene.iloc[0, :].copy()
if toPrint:
logger.info('Dimensions of data (samples,genes):' +
str(data.shape))
else:
uniq_chr_per_gene = uniq_chr_per_gene.iloc[0, :].copy()
# ORDER THE GENES FROM ALL SAMPLES (SLOW?)
if toPrint:
logger.info('Create a Dataframe with the genes ' +
'and their genomic positions')
gene_pos = pd.concat([
start_table.apply(lambda x: pd.to_numeric(
x, errors='ignore', downcast='integer')).min().astype(int),
end_table.apply(lambda x: pd.to_numeric(
x, errors='ignore', downcast='integer')).max().astype(int),
uniq_chr_per_gene
],
axis=1,
sort=False)
gene_pos.columns = ['start', 'end', 'chr']
gene_pos.index.name = gene_id_col
gene_pos.reset_index(inplace=True)
gene_pos['chr_gene'] = gene_pos['chr'] + ':' + gene_pos[gene_id_col]
gene_pos[chr_col] = gene_pos['chr'].str.split('chr', 2).str[1]
gene_pos['toNatSort'] = [
':'.join([
str(gene_pos[chr_col][row]),
str(gene_pos['start'][row]),
str(gene_pos['end'][row])
]) for row in range(gene_pos.shape[0])
]
if toPrint:
logger.info('Dataframes agree (?): ' +
str(gene_pos.shape[0] == data.shape[1]))
# are the genes duplicated ?
dupl_genes = gene_pos[gene_id_col].duplicated()
if dupl_genes.any():
logger.error('genes are duplicated, check your data first!')
logger.info('duplicated genes:' +
gene_pos[gene_id_col][dupl_genes].values)
raise ()
else:
if toPrint:
logger.info('gene names are unique, continue..')
if toPrint:
logger.info('Order genes according to genomic position')
gene_order = index_natsorted(gene_pos['toNatSort'])
gene_pos = gene_pos.iloc[gene_order, :].copy()
gene_pos.reset_index(drop=True, inplace=True)
gene_pos.index.name = 'order'
gene_pos.reset_index(inplace=True)
#########################################
# CREATE dictionary of gene names and their order
gene_order_dict = dict(
(gene_pos[gene_id_col][i], int(gene_pos['order'][i]))
for i in range(gene_pos.shape[0]))
xlabels, xpos = get_chr_ticks(gene_pos,
data,
id_col=gene_id_col,
chr_col=chr_col)
# SAVE ordered table and gene pos info table
if saveReport:
fname = 'genes_info.csv'
f = os.path.join(output_directory, fname)
if toPrint:
logger.info('-save genes info: ' + f)
gene_pos.to_csv(f, sep='\t', header=True, index=True)
fname = 'gene_order_dict.json'
f = os.path.join(output_directory, fname)
if toPrint:
logger.info('-save genes order dictionary: ' + f)
with open(f, 'w') as fp:
json.dump(gene_order_dict, fp, indent=4)
#########################################
for label in [
'rows_in_sample', 'rows_in_sample_filt',
'rows_in_sample_processed', 'rows_in_sample_editted'
]:
if label in info_table.columns:
# PLOT Abundance of gene data per sample
if toPrint:
logger.info('Plot ' + label + ' for each sample')
mutCount = info_table[[label]].copy()
patient_new_order = info_table.loc[mutCount.index].sort_values(
by=sample_info_table_sortLabels_list)
xticklabels = list(
zip(
patient_new_order.index.values, info_table.loc[
patient_new_order.index,
sample_info_table_sortLabels_list].values))
mutCount = mutCount.loc[patient_new_order.index]
rank = mutCount[label].argsort().argsort().values
pal = sns.cubehelix_palette(mutCount.shape[0],
reverse=True,
dark=.40,
light=.95)
plt.figure(figsize=(10, 5))
g = sns.barplot(np.arange(mutCount.shape[0]),
mutCount[label],
palette=np.array(pal[::-1])[rank])
g.set_xticklabels(xticklabels, rotation=90)
g.set(xlabel='samples', ylabel='count')
g.set_title('Abundance of ' + label + ' per sample: ' +
str((mutCount[label] <= 0).sum()) +
' empty samples')
if saveReport:
logger.info('Save figure')
plt.savefig(os.path.join(output_directory,
'Fig_samples_' + label + img_ext),
transparent=True,
bbox_inches='tight',
pad_inches=0.1,
frameon=False)
plt.close("all")
else:
plt.show()
#########################################
# # SAVE data w/ and w/o positions
# if saveReport:
# # save data
# fname = 'table_withPos.csv'
# f = os.path.join(output_directory, fname)
# if toPrint:
# logger.info('-save data in: '+f)
# table_withPos.to_csv(f, sep='\t', header=True, index=True)
# if saveReport:
# # save data
# fname = 'data.csv'
# f = os.path.join(output_directory, fname)
# if toPrint:
# logger.info('-save data in: '+f)
# data.to_csv(f, sep='\t', header=True, index=True)
# if toPrint:
# logger.info(
# 'Dimensions of data (samples,genes):'+str(data.shape))
# -- END IF -- #
#########################################
# SAVE data and sample_info
if saveReport:
# save files
fname = 'data.csv'
f = os.path.join(output_directory, fname)
if toPrint:
logger.info('-save ordered data: ' + f)
data.to_csv(f, sep='\t', header=True, index=True)
fname = 'sample_info.csv'
f = os.path.join(output_directory, fname)
if toPrint:
logger.info('-save sample_info: ' + f)
info_table.to_csv(f, sep='\t', header=True, index=True)
def remove_duplicate_genes(**set_up_kwargs):
# chose sample set from data
select_samples_from = set_up_kwargs.get('select_samples_from', None)
select_samples_which = parse_arg_type(
set_up_kwargs.get('select_samples_which', None),
int
)
select_samples_sort_by = set_up_kwargs.get('select_samples_sort_by',
None)
if select_samples_sort_by is not None:
select_samples_sort_by = select_samples_sort_by.rsplit(',')
select_samples_title = set_up_kwargs.get('select_samples_title',
'select_all')
# initialize script params
saveReport = parse_arg_type(
set_up_kwargs.get('saveReport', False),
bool
)
toPrint = parse_arg_type(
set_up_kwargs.get('toPrint', False),
bool
)
reportName = set_up_kwargs.get('reportName', script_fname)
txt_label = set_up_kwargs.get('txt_label', 'test_txt_label')
input_fname = set_up_kwargs.get('input_fname',
'data_processed.csv')
gene_info_fname = set_up_kwargs.get('gene_info_fname',
None)
chr_col = set_up_kwargs.get('chr_col', 'chr_int')
gene_id_col = set_up_kwargs.get('gene_id_col', 'gene')
sample_info_fname = set_up_kwargs.get('sample_info_fname',
None)
if ',' in sample_info_fname:
sample_info_fname = os.path.join(*sample_info_fname.rsplit(','))
sample_info_read_csv_kwargs = set_up_kwargs.get(
'sample_info_read_csv_kwargs', {})
# plotting params
plot_kwargs = set_up_kwargs.get('plot_kwargs', {})
function_dict = plot_kwargs.get('function_dict', None)
cmap_custom = plot_kwargs.get('cmap_custom', None)
vmin = parse_arg_type(
plot_kwargs.get('vmin', None),
int
)
vmax = parse_arg_type(
plot_kwargs.get('vmax', None),
int
)
if (cmap_custom is None) and (vmin is not None) and (vmax is not None):
custom_div_cmap_arg = abs(vmin)+abs(vmax)
if (vmin <= 0) and (vmax >= 0):
custom_div_cmap_arg = custom_div_cmap_arg + 1
mincol = plot_kwargs.get('mincol', None)
midcol = plot_kwargs.get('midcol', None)
maxcol = plot_kwargs.get('maxcol', None)
if (
(mincol is not None) and
(midcol is not None) and
(maxcol is not None)
):
cmap_custom = custom_div_cmap(
numcolors=custom_div_cmap_arg,
mincol=mincol, midcol=midcol, maxcol=maxcol)
else:
cmap_custom = custom_div_cmap(numcolors=custom_div_cmap_arg)
highRes = parse_arg_type(
plot_kwargs.get('highRes', False),
bool
)
if highRes:
img_ext = '.pdf'
else:
img_ext = '.png'
# initialize directories
MainDataDir = set_path(os.path.join(script_path, '..', 'data'))
# data input
input_directory = set_up_kwargs.get('input_directory')
if ',' in input_directory:
input_directory = os.path.join(*input_directory.rsplit(','))
input_directory = os.path.join(MainDataDir, input_directory)
# sample info input
sample_info_directory = set_up_kwargs.get('sample_info_directory')
if ',' in sample_info_directory:
sample_info_directory = os.path.join(
*sample_info_directory.rsplit(','))
sample_info_directory = os.path.join(MainDataDir, sample_info_directory)
# gene info input
gene_info_directory = set_up_kwargs.get('gene_info_directory')
if gene_info_directory is None:
gene_info_directory = input_directory
else:
if ',' in gene_info_directory:
gene_info_directory = os.path.join(
*gene_info_directory.rsplit(','))
gene_info_directory = os.path.join(
MainDataDir, gene_info_directory)
# data output
output_directory = set_up_kwargs.get('output_directory')
if output_directory is None:
output_directory = set_directory(
os.path.join(input_directory, reportName)
)
else:
if ',' in output_directory:
output_directory = os.path.join(*output_directory.rsplit(','))
output_directory = set_directory(
os.path.join(MainDataDir, output_directory, reportName)
)
# save the set_up_kwargs in the output dir for reproducibility
fname = 'set_up_kwargs.json'
f = os.path.join(output_directory, fname)
if toPrint:
logger.info(
'-save set_up_kwargs dictionary for reproducibility in: '+f)
with open(f, 'w') as fp:
json.dump(set_up_kwargs, fp, indent=4)
# pairwise distances params
compute_pdist = parse_arg_type(
set_up_kwargs.get('compute_pdist', False),
bool
)
pdist_fname = 'data_'+select_samples_title+'__genes_pdist.h5'
pdist_fpath = os.path.join(input_directory, pdist_fname)
if not os.path.exists(pdist_fpath):
compute_pdist = True
# load info table of samples
if toPrint:
logger.info('Load info table of samples')
fpath = os.path.join(sample_info_directory, sample_info_fname)
info_table = load_clinical(fpath, **sample_info_read_csv_kwargs)
# load input_data
fpath = os.path.join(input_directory, input_fname)
input_data = pd.read_csv(fpath, sep='\t', header=0, index_col=0)
empty_pat = input_data.sum(axis=1).isnull()
if empty_pat.any():
logger.info('Patients with missing values in all genes: ' +
str(input_data.index[empty_pat]))
input_data = input_data.fillna(0)
# keep only info_table with input_data
ids_tmp = set(info_table.index.values
).intersection(set(input_data.index.values))
info_table = info_table.loc[ids_tmp].copy()
# info_table = info_table.reset_index()
# load gene info
fpath = os.path.join(gene_info_directory, gene_info_fname)
genes_positions_table = pd.read_csv(fpath, sep='\t', header=0,
index_col=0)
# get gene chrom position
xlabels, xpos = get_chr_ticks(genes_positions_table, input_data,
id_col=gene_id_col, chr_col=chr_col)
logger.info('select_samples_from: '+str(select_samples_from) +
'select_samples_which: '+str(select_samples_which) +
'select_samples_sort_by: '+str(select_samples_sort_by) +
'select_samples_title: '+str(select_samples_title))
# keep only info_table with data
temp = info_table.index.name
info_table = info_table.loc[input_data.index].copy()
info_table.index.name = temp
ids_tmp = choose_samples(info_table.reset_index(),
info_table.index.name,
choose_from=select_samples_from,
choose_what=select_samples_which,
sortby=select_samples_sort_by,
ascending=False)
# keep a subpart of the info_table (rows and columns)
info_table = info_table.loc[ids_tmp, select_samples_sort_by].copy()
# keep only these samples from the data
data = input_data.loc[ids_tmp, :].copy()
try:
pat_labels_txt = info_table.astype(int).reset_index().values
except:
pat_labels_txt = info_table.reset_index().values
pat_labels_title = str(info_table.reset_index().columns.values)
# remove all zero columns!
orphancols = np.where(abs(data).sum(axis=0) == 0)[0]
if len(orphancols) > 0:
logger.warning(
'removing '+str(len(orphancols)) +
' genes from data with zero columns!')
cols2drop = data.columns.values[orphancols]
data = data.drop(cols2drop, axis=1).copy()
# REMOVE DUPLICATES!!!!
uniqdata, dupldict, _, _ = remove_andSave_duplicates(
data, to_compute_euclidean_distances=compute_pdist,
to_save_euclidean_distances=saveReport, to_save_output=saveReport,
output_filename=input_fname.rsplit('.')[0]+'__'+select_samples_title,
output_directory=output_directory)
# get gene chrom position
xlabels_uniq, xpos_uniq = get_chr_ticks(
genes_positions_table, uniqdata,
id_col=gene_id_col, chr_col=chr_col)
fext = ['', '_uniq']
xlabels_choose = [xlabels, xlabels_uniq]
xpos_choose = [xpos, xpos_uniq]
for i_data, choose_data in enumerate([data, uniqdata]):
if select_samples_which is None:
# distplot DO NOT break Y-axis
logger.info('Plotting distplot..')
sns.distplot(choose_data.values.flatten(),
hist=True, kde=False, color='b')
plt.title("Copy number abundance in "+txt_label+" (uniq genes)")
if saveReport:
logger.info('Save distplot')
plt.savefig(os.path.join(
output_directory, 'Fig_distplot_' +
select_samples_title+fext[i_data]+img_ext),
transparent=True, bbox_inches='tight',
pad_inches=0.1, frameon=False)
plt.close("all")
else:
plt.show()
# distplot break Y-axis
logger.info('Plotting break Y-axis distplot..')
_, uniq_count = np.unique(choose_data.values.flatten(),
return_counts=True)
ymax_bottom = int(math.ceil(
np.sort(uniq_count)[-2] / 1000.0)
) * 1000
ymax_top = int(math.ceil(
np.sort(uniq_count)[-1] / 10000.0)
) * 10000
distplot_breakYaxis(choose_data.values, ymax_bottom,
ymax_top, color='b', d=0.005,
pad=1.5, figsize=(10, 6),
mytitle='Copy number abundance in '+txt_label +
'with cropped y axis (uniq genes)')
if saveReport:
logger.info('Save distplot')
plt.savefig(os.path.join(
output_directory, 'Fig_distplot_breakYaxis_' +
select_samples_title+fext[i_data]+img_ext),
transparent=True, bbox_inches='tight',
pad_inches=0.1, frameon=False)
plt.close("all")
else:
plt.show()
# Plot heatmap
_figure_x_size, _figure_y_size, _show_gene_names, _ = \
set_heatmap_size(data)
plt.figure(figsize=(_figure_x_size, _figure_y_size))
ax = sns.heatmap(choose_data, vmin=vmin, vmax=vmax,
yticklabels=pat_labels_txt, xticklabels=False,
cmap=cmap_custom, cbar=False)
if (_show_gene_names and (
(xpos_choose[i_data] is None) or
(xlabels_choose[i_data] is None))):
plt.xticks(rotation=90)
elif (
(xpos_choose[i_data] is not None) and
(xlabels_choose[i_data] is not None)):
plt.xticks(xpos_choose[i_data], xlabels_choose[i_data], rotation=0)
plt.xlabel('chromosomes (the number is aligned at the end ' +
'of the chr region)')
plt.ylabel('samples '+select_samples_title+'\n'+pat_labels_title)
cbar = ax.figure.colorbar(ax.collections[0])
set_cbar_ticks(cbar, function_dict, custom_div_cmap_arg)
plt.title(
txt_label+'\nheatmap of ' +
select_samples_title+' samples')
if saveReport:
logger.info('Save heatmap')
plt.savefig(os.path.join(
output_directory, 'Fig_heatmap_'+select_samples_title +
fext[i_data]+img_ext),
transparent=True, bbox_inches='tight',
pad_inches=0.1, frameon=False)
plt.close("all")
else:
plt.show()
# Plot pairwise sample correlations
data_cor = 1-squareform(pdist(choose_data, 'correlation'))
plt.figure(figsize=(15, 10))
sns.heatmap(data_cor, vmin=-1, vmax=1, yticklabels=pat_labels_txt,
xticklabels=pat_labels_txt, cmap='PiYG', square=True)
plt.xlabel("samples "+select_samples_title)
plt.ylabel(pat_labels_title)
plt.title("Auto-corerelation of "+select_samples_title +
" samples - "+txt_label)
if saveReport:
logger.info('Save heatmap')
plt.savefig(os.path.join(
output_directory, 'Fig_corr_'+select_samples_title +
fext[i_data]+img_ext),
transparent=True, bbox_inches='tight',
pad_inches=0.1, frameon=False)
plt.close("all")
else:
plt.show()
