def plotly_alignment_stats(fpaths=[], saveto='', fnames=[]):
'''
Save a plotly box graph with a list of alignment stats files
Parameters
----------
fpaths : list
A list of file paths
saveto : str
File path to save the html file as the plotly box graph
fnames : list
A list of strings to label each ``fpaths``
Returns
-------
bool
True if saving successfully, False otherwise
'''
if not fnames:
fnames = [base_name(f) for f in fpaths]
if len(fnames) != len(fpaths):
fnames = [base_name(f) for f in fpaths]
trace_data = []
# aln_diagnose_item = ["_unmapped",
# "_low_map_qual", '_multimapped', "_uniquemapped",
# "_no_feature", "_ambiguous",
# "_total"]
for i in range(len(fpaths)):
f = fpaths[i]
fname = fnames[i]
stats = pd.read_csv(f, index_col=0)
mapped = stats.loc['_multimapped', :] + stats.loc['_uniquemapped', :]
rate_mapped = mapped / stats.loc['_total', :]
overall_mapped = mapped.sum()
overall_total = stats.loc['_total', :].sum()
stats.fillna(value=0, inplace=True)
trace_data.append(
go.Box(y=rate_mapped,
name='{} (#Mapped={}/#Total={})'.format(
fname, overall_mapped, overall_total)))
layout = go.Layout(xaxis=dict(showticklabels=False),
title='Mapped/Total alignments per BC per item')
fig = go.Figure(data=trace_data, layout=layout)
try:
plot(fig, filename=saveto, auto_open=False)
return (True)
except Exception as e:
print(e, flush=True)
return (False)
def plotly_demultiplexing_stats(fpaths=[], saveto='', fnames=[]):
'''
Save a plotly box graph with a list of demultiplexing stats files
Parameters
----------
fpaths : list
A list of file paths
saveto : str
File path to save the html file as the plotly box graph
fnames : list
A list of strings to label each ``fpaths``
Returns
-------
bool
True if saving successfully, False otherwise
'''
if not fnames:
fnames = [base_name(f) for f in fpaths]
if len(fnames) != len(fpaths):
fnames = [base_name(f) for f in fpaths]
num_reads_data = []
for i in range(len(fpaths)):
f = fpaths[i]
fname = fnames[i]
stats = pd.read_csv(f, index_col=0)
cell_stats = stats.iloc[:-5, :]
# tail 5 lines are fixed as the overall stats
overall_stats = stats.iloc[-5:, :]
num_reads_data.append(
go.Box(y=cell_stats['Reads(#)'],
name='{} (#Saved={}/#Total={})'.format(
fname, overall_stats.loc['saved', 'Reads(#)'],
overall_stats.loc['total', 'Reads(#)'])))
layout = go.Layout(
# legend=dict(x=-.1, y=-.2),
xaxis=dict(showticklabels=False),
title='Number of reads saved per BC per item')
fig = go.Figure(data=num_reads_data, layout=layout)
try:
plot(fig, filename=saveto, auto_open=False)
return (True)
except Exception as e:
print(e, flush=True)
return (False)
def run_bam2sam(x):
x_dirname, x_basename = dir_name(x), base_name(x)
y = join_path(x_dirname, x_basename + '.sam')
_ = popen_communicate('samtools view -h {} -o {}'.format(x, y))
if is_nonempty_file(y):
print_logger('Finished: bam to sam {} to {}'.format(x, y))
rmfile(x)
else:
print_logger('Failed: bam to sam {} to {}'.format(x, y))
def _remove_gz_suffix(x):
xbasename = base_name(x)
xdirname = dir_name(x)
return join_path(xdirname, xbasename.replace('.gz', ''))
def plotly_qc(fpath, saveto, sep=',', name=''):
'''
Generate a plotly html plot for QC of a scRNA-seq data.
QC inlucdes:
- number of total UMIs
- number of detected genes
- percent of MT expression
Input:
fpath: file path (CSV/TSV) to the expression file with genes/features as rows
and cells/samples on columns. First column saves gene names.
saveto: a html file to save the plots using Plot.ly
sep: file sep. Default: ","
'''
bool_success = False
if not is_nonempty_file(fpath):
return bool_success
if not name:
name = base_name(fpath)
expr = pd.read_csv(fpath, index_col=0, sep=sep)
print_logger(('UMI count matrix: '
'{} genes x {} cells').format(expr.shape[0], expr.shape[1]))
total_num_UMIs = expr.sum(axis=0)
num_detected_genes = (expr > 0).sum(axis=0)
mt_index = [x for x in expr.index if x.startswith(
'mt-') or x.startswith('MT-')]
if not mt_index:
percent_mt = 0
else:
mt_umis = expr.loc[pd.Series(mt_index), :].sum(axis=0)
percent_mt = mt_umis / total_num_UMIs
percent_mt = percent_mt.replace(np.inf, 0)
qc = pd.DataFrame(dict(total_num_UMIs=total_num_UMIs,
num_detected_genes=num_detected_genes,
percent_mt=percent_mt))
# 1/5
plotly_g_vs_umi = plotly_scatter(
x=qc.total_num_UMIs,
y=qc.num_detected_genes,
xlab='#Total UMIs (median={})'.format(qc.total_num_UMIs.median()),
ylab='#Detected Genes (median={})'.format(
qc.num_detected_genes.median()),
main=name,
hover_text=qc.index.values)
plotly_g_vs_umi.layout.yaxis.scaleanchor = None
# 2/5
plotly_mt_vs_umi = plotly_scatter(
x=qc.total_num_UMIs,
y=qc.percent_mt,
xlab='#Total UMIs (median={})'.format(qc.total_num_UMIs.median()),
ylab='MT Fraction (median={:6.4f})'.format(qc.percent_mt.median()),
main=name,
hover_text=qc.index.values)
plotly_mt_vs_umi.layout.yaxis.scaleanchor = None
# 3/5
plotly_hist_umis = plotly_hist(
vals=qc.total_num_UMIs,
xlab='#Total UMIs (median={})'.format(qc.total_num_UMIs.median()))
# 4/5
plotly_hist_g = plotly_hist(
vals=qc.num_detected_genes,
xlab=('#Detected Genes '
'(median={})').format(qc.num_detected_genes.median()))
# 5/5
plotly_hist_percent_mt = plotly_hist(
vals=qc.percent_mt,
xlab='MT Fraction (median={:6.4f})'.format(qc.percent_mt.median()))
# Merge the 5 figures together
qc_fig = tools.make_subplots(
rows=2, cols=3,
specs=[[{}, {}, None], [{}, {}, {}]])
qc_fig.append_trace(plotly_g_vs_umi.data[0], 1, 1)
qc_fig.append_trace(plotly_mt_vs_umi.data[0], 1, 2)
qc_fig.append_trace(plotly_hist_umis.data[0], 2, 1)
qc_fig.append_trace(plotly_hist_g.data[0], 2, 2)
qc_fig.append_trace(plotly_hist_percent_mt.data[0], 2, 3)
qc_fig.layout.xaxis1 = {**qc_fig.layout.xaxis1,
**plotly_g_vs_umi.layout.xaxis}
qc_fig.layout.yaxis1 = {**qc_fig.layout.yaxis1,
**plotly_g_vs_umi.layout.yaxis}
qc_fig.layout.xaxis2 = {**qc_fig.layout.xaxis2,
**plotly_mt_vs_umi.layout.xaxis}
qc_fig.layout.yaxis2 = {**qc_fig.layout.yaxis2,
**plotly_mt_vs_umi.layout.yaxis}
qc_fig.layout.xaxis3 = {**qc_fig.layout.xaxis3,
**plotly_hist_umis.layout.xaxis}
qc_fig.layout.yaxis3 = {**qc_fig.layout.yaxis3,
**plotly_hist_umis.layout.yaxis}
qc_fig.layout.xaxis4 = {**qc_fig.layout.xaxis4,
**plotly_hist_g.layout.xaxis}
qc_fig.layout.yaxis4 = {**qc_fig.layout.yaxis4,
**plotly_hist_g.layout.yaxis}
qc_fig.layout.xaxis5 = {**qc_fig.layout.xaxis5,
**plotly_hist_percent_mt.layout.xaxis}
qc_fig.layout.yaxis5 = {**qc_fig.layout.yaxis5,
**plotly_hist_percent_mt.layout.yaxis}
qc_fig['layout'].update(height=800, width=1000, title=name,
showlegend=False)
plot(qc_fig, filename=saveto, auto_open=False)
bool_success = True
return bool_success
def plotly_qc_st(fpath, saveto, sep='\t', name=''):
bool_success = False
if not is_nonempty_file(fpath):
return bool_success
if not name:
name = base_name(fpath)
ST = pd.read_csv(fpath, sep=sep, index_col=0)
print_logger(('ST UMI-count matrix has '
'{} spots x {} genes').format(ST.shape[0], ST.shape[1]))
ST_total_UMIs = ST.sum(axis=1)
ST_detected_genes = (ST > 0).sum(axis=1)
mt_cols = [x for x in ST.columns if x.startswith(
'mt-') or x.startswith('MT-')]
if not mt_cols:
ST_percent_mt = 0
else:
ST_percent_mt = ST[mt_cols].sum(axis=1) / ST_total_UMIs
# ST_percent_mt = ST_percent_mt.replace(np.inf, 0)
# ST_percent_mt = ST_percent_mt.replace(np.NaN, 0)
st_xy = np.array(list(map(lambda xy: xy.strip().split('x'), ST.index)))
st_x = st_xy[:, 0].astype(np.int)
st_y = st_xy[:, 1].astype(np.int)
ST_qc = pd.DataFrame(
dict(Row=st_x, Col=st_y,
total_num_UMIs=ST_total_UMIs,
num_detected_genes=ST_detected_genes,
percent_mt=ST_percent_mt))
# 1/3
plotly_ST_g = plotly_scatter(
x=ST_qc.Row, y=ST_qc.Col,
mask_by=ST_qc.num_detected_genes,
hover_text=ST_qc.num_detected_genes.astype('str'),
colorscale='Viridis',
mask_title=('#Detected Genes '
'(median={})').format(ST_qc.num_detected_genes.median()))
# 2/3
plotly_ST_UMIs = plotly_scatter(
x=ST_qc.Row, y=ST_qc.Col,
mask_by=ST_qc.total_num_UMIs,
hover_text=ST_qc.total_num_UMIs.astype('str'),
colorscale='Viridis',
mask_title=('#Total UMIs '
'(median={})').format(ST_qc.total_num_UMIs.median()))
# 3/3
plotly_ST_mt = plotly_scatter(
x=ST_qc.Row, y=ST_qc.Col,
mask_by=ST_qc.percent_mt,
hover_text=ST_qc.percent_mt.astype('str'),
colorscale='Viridis',
mask_title=('MT Fraction '
'(median={:6.4f})').format(ST_qc.percent_mt.median()))
# Merge the 3 figures together
fig = tools.make_subplots(
rows=1, cols=3,
subplot_titles=('#Total UMIs', '#Detected Genes', 'MT Fraction'))
fig.append_trace(plotly_ST_UMIs.data[0], 1, 1)
fig.append_trace(plotly_ST_g.data[0], 1, 2)
fig.append_trace(plotly_ST_mt.data[0], 1, 3)
fig['layout'].update(height=600, width=1900, title=name)
fig.layout.showlegend = False
# Manually change the locations of other two color bars to proper places
fig.data[0].marker.colorbar.x = 0.28
fig.data[1].marker.colorbar.x = 0.64
plot(fig, filename=saveto, auto_open=False)
bool_success = True
return bool_success