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Holly Ruess
celseq2
Commits
81fad008
Unverified
Commit
81fad008
authored
Apr 16, 2018
by
Yun YAN
Committed by
GitHub
Apr 16, 2018
Browse files
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Browse Files
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Plain Diff
Merge pull request #18 from Puriney/master
v0.5.2
parents
86012144
c69fb44b
Changes
4
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Showing
4 changed files
with
322 additions
and
18 deletions
+322
-18
celseq2/qc.py
celseq2/qc.py
+276
-0
celseq2/version.py
celseq2/version.py
+1
-1
celseq2/workflow/celseq2_beta.snakemake
celseq2/workflow/celseq2_beta.snakemake
+43
-17
setup.py
setup.py
+2
-0
No files found.
celseq2/qc.py
0 → 100644
View file @
81fad008
#!/usr/bin/env python3
import
argparse
import
numpy
as
np
import
pandas
as
pd
from
plotly
import
tools
import
plotly.graph_objs
as
go
from
plotly.offline
import
plot
from
celseq2.helper
import
print_logger
,
base_name
,
is_nonempty_file
def
plotly_scatter
(
x
,
y
,
mask_by
=
None
,
hover_text
=
None
,
xlab
=
''
,
ylab
=
''
,
main
=
''
,
colorscale
=
'Viridis'
,
mask_title
=
''
):
data
=
go
.
Scatter
(
x
=
x
,
y
=
y
,
mode
=
'markers'
)
if
hover_text
is
not
None
:
data
[
'text'
]
=
hover_text
if
mask_by
is
not
None
:
data
.
marker
=
go
.
Marker
(
colorbar
=
go
.
ColorBar
(
title
=
mask_title
,
titleside
=
'right'
),
color
=
mask_by
,
colorscale
=
colorscale
,
showscale
=
True
,
opacity
=
0.7
)
trace
=
[
data
]
layout
=
go
.
Layout
(
title
=
main
,
xaxis
=
dict
(
title
=
xlab
,
autorange
=
True
),
yaxis
=
dict
(
title
=
ylab
,
scaleanchor
=
'x'
,
autorange
=
True
),
height
=
600
,
width
=
600
)
fig
=
go
.
Figure
(
data
=
trace
,
layout
=
layout
)
return
fig
def
plotly_hist
(
vals
,
xlab
=
''
,
ylab
=
'Freq'
,
main
=
''
):
data
=
go
.
Histogram
(
x
=
vals
)
trace
=
[
data
]
layout
=
go
.
Layout
(
title
=
main
,
xaxis
=
dict
(
title
=
xlab
),
yaxis
=
dict
(
title
=
ylab
),
height
=
400
,
width
=
400
)
fig
=
go
.
Figure
(
data
=
trace
,
layout
=
layout
)
return
fig
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
=
pd
.
Series
(
[
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
[
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
)
print_logger
((
'ST UMI-count matrix has '
'{} spots x {} genes'
).
format
(
ST
.
shape
[
0
],
ST
.
shape
[
1
]))
ST_total_UMIs
=
ST
.
iloc
[:,
2
:].
sum
(
axis
=
1
)
ST_detected_genes
=
(
ST
.
iloc
[:,
2
:]
>
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
.
replace
(
np
.
inf
,
0
)
ST_qc
=
pd
.
DataFrame
(
dict
(
Row
=
ST
.
Row
,
Col
=
ST
.
Col
,
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 {})'
.
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
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
def
main
():
parser
=
argparse
.
ArgumentParser
(
add_help
=
True
)
parser
.
add_argument
(
'fpath'
,
type
=
str
,
metavar
=
'FILENAME'
,
help
=
(
'file path (CSV/TSV) to the expression file with genes/features '
'as rows and cells/samples on columns. '
'First column saves gene names.'
))
parser
.
add_argument
(
'saveto'
,
type
=
str
,
metavar
=
'FILENAME'
,
help
=
'File path (html) to save the QC plots.'
)
parser
.
add_argument
(
'--name'
,
type
=
str
,
metavar
=
'STR'
,
default
=
''
)
parser
.
add_argument
(
'--sep'
,
type
=
str
,
default
=
'
\t
'
,
help
=
'File sep (default:
\'\t\'
)'
)
parser
.
add_argument
(
'--st'
,
dest
=
'is_st'
,
action
=
'store_true'
)
parser
.
set_defaults
(
is_st
=
False
)
args
=
parser
.
parse_args
()
if
args
.
is_st
:
plotly_qc_st
(
args
.
fpath
,
args
.
saveto
,
args
.
sep
,
args
.
name
)
else
:
plotly_qc
(
args
.
fpath
,
args
.
saveto
,
args
.
sep
,
args
.
name
)
print_logger
(
'Generate QC for {}'
.
format
(
args
.
fpath
))
print_logger
(
'See {}'
.
format
(
args
.
saveto
))
if
__name__
==
"__main__"
:
main
()
celseq2/version.py
View file @
81fad008
__version__
=
'0.5.
1
'
__version__
=
'0.5.
2
'
celseq2/workflow/celseq2_beta.snakemake
View file @
81fad008
...
...
@@ -104,12 +104,13 @@ SUBDIR_LOG = 'small_log'
SUBDIR_QSUB = 'qsub_log'
SUBDIR_ANNO = 'annotation'
SUBDIR_REPORT = 'report'
SUBDIR_QC_EXPR = 'qc_expr'
SUBDIRS = [SUBDIR_INPUT,
SUBDIR_FASTQ, SUBDIR_ALIGN, SUBDIR_ALIGN_ITEM,
SUBDIR_UMI_CNT, SUBDIR_UMI_SET,
SUBDIR_EXPR,
SUBDIR_REPORT,
SUBDIR_REPORT,
SUBDIR_QC_EXPR,
SUBDIR_LOG, SUBDIR_QSUB, SUBDIR_ANNO
]
...
...
@@ -128,9 +129,7 @@ workdir: DIR_PROJ
if RUN_CELSEQ2_TO_ST:
rule all:
input:
# '_done_annotation',
'_done_UMI',
# '_done_report',
'_done_ST',
output:
touch('_DONE'),
...
...
@@ -149,9 +148,7 @@ if RUN_CELSEQ2_TO_ST:
else:
rule all:
input:
# '_done_annotation',
'_done_UMI',
# '_done_report',
output:
touch('_DONE'),
run:
...
...
@@ -173,32 +170,36 @@ rule COUNT_MATRIX:
expid=list(set(sample_list))),
hdf = expand(join_path(DIR_PROJ, SUBDIR_EXPR, '{expid}', 'expr.h5'),
expid=list(set(sample_list))),
html = expand(join_path(DIR_PROJ, SUBDIR_QC_EXPR,
'{expid}', 'QC.html'),
expid=list(set(sample_list))),
output:
touch('_done_UMI')
message: 'Finished counting UMI-count matrix.'
run:
# if ALIGNER == 'star':
# shell('rm {}'.format('_done_star_genome_loaded'))
# print('Free memory loaded by STAR', flush=True)
# with tempfile.TemporaryDirectory() as tmpdirname:
# cmd = 'STAR '
# cmd += '--genomeLoad Remove '
# cmd += '--genomeDir {STAR_INDEX_DIR} '
# cmd += '--outFileNamePrefix '
# cmd += join_path(tmpdirname, '')
# shell(cmd)
if glob.glob('celseq2_job*.sh*'):
mkfolder(SUBDIR_QSUB)
shell('mv -f celseq2_job*.sh* {}'.format(SUBDIR_QSUB))
print_logger('UMI-count matrix is saved at {}'.format(input.csv))
rule QC_COUNT_MATRIX:
input:
html = expand(join_path(DIR_PROJ, SUBDIR_QC_EXPR,
'{expid}', 'QC.html'),
expid=list(set(sample_list))),
message: 'Finished QC UMI-count matrix.'
# join_path(DIR_PROJ, SUBDIR_QC_EXPR, '{expid}', 'QC_ST.html')
if RUN_CELSEQ2_TO_ST:
rule CELSEQ2_TO_ST:
input:
tsv = expand(join_path(DIR_PROJ, SUBDIR_ST, '{expid}', 'ST.tsv'),
expid=list(set(sample_list))),
html = expand(join_path(DIR_PROJ, SUBDIR_QC_EXPR,
'{expid}', 'QC_ST.html'),
expid=list(set(sample_list))),
message: 'Convert to ST format.'
output:
touch('_done_ST')
...
...
@@ -207,7 +208,6 @@ if RUN_CELSEQ2_TO_ST:
mkfolder(SUBDIR_QSUB)
shell('mv -f celseq2_job*.sh* {}'.format(SUBDIR_QSUB))
rule _celseq2_to_st:
input:
hdf = join_path(DIR_PROJ, SUBDIR_EXPR, '{expid}', 'expr.h5'),
...
...
@@ -228,6 +228,20 @@ if RUN_CELSEQ2_TO_ST:
cmd += ' --exclude-nondetected-genes '
shell(cmd)
rule qc_umi_matrix_per_experiment_ST:
input:
tsv = join_path(DIR_PROJ, SUBDIR_ST, '{expid}', 'ST.tsv'),
output:
html = join_path(DIR_PROJ, SUBDIR_QC_EXPR, '{expid}', 'QC_ST.html'),
params:
expid = '{expid}',
run:
cmd = 'celseq2-qc '
cmd += '{input.tsv} {output.html} '
cmd += '--name {params.expid} '
cmd += '--st'
shell(cmd)
# rule REPORT_ALIGNMENT_LOG:
# input:
...
...
@@ -570,7 +584,19 @@ rule summarize_umi_matrix_per_experiment:
expr_df.to_hdf(join_path(DIR_PROJ, SUBDIR_EXPR,
exp_id, 'expr.h5'), 'table')
rule qc_umi_matrix_per_experiment:
input:
csv = join_path(DIR_PROJ, SUBDIR_EXPR, '{expid}', 'expr.csv'),
output:
html = join_path(DIR_PROJ, SUBDIR_QC_EXPR, '{expid}', 'QC.html'),
params:
expid = '{expid}',
run:
cmd = 'celseq2-qc '
cmd += '{input.csv} {output.html} '
cmd += '--name {params.expid} '
cmd += '--sep {}'.format(',')
shell(cmd)
# rule report_alignment_log:
# input:
...
...
setup.py
View file @
81fad008
...
...
@@ -166,6 +166,8 @@ setup(
'celseq2.diagnose:main'
),
(
'celseq2-to-st = '
'celseq2.support.st_pipeline:main'
),
(
'celseq2-qc = '
'celseq2.qc:main'
),
],
},
...
...
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