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Statistical-Genetics
jass
Commits
eda41353
Commit
eda41353
authored
2 years ago
by
Hanna JULIENNE
Browse files
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Merge branch 'improve_plots' into 'master'
Improve plots See merge request
!74
parents
e732afa5
9f24874c
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1 merge request
!74
Improve plots
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2 changed files
jass/models/plots.py
+35
-44
35 additions, 44 deletions
jass/models/plots.py
requirements.txt
+2
-1
2 additions, 1 deletion
requirements.txt
with
37 additions
and
45 deletions
jass/models/plots.py
+
35
−
44
View file @
eda41353
...
@@ -16,10 +16,11 @@ import matplotlib.pyplot as plt
...
@@ -16,10 +16,11 @@ import matplotlib.pyplot as plt
from
matplotlib
import
colors
from
matplotlib
import
colors
import
matplotlib.patches
as
mpatches
import
matplotlib.patches
as
mpatches
from
scipy.stats
import
norm
,
chi2
from
scipy.stats
import
norm
,
chi2
import
seaborn
as
sns
import
os
import
os
from
pandas
import
DataFrame
,
read_hdf
from
pandas
import
DataFrame
,
read_hdf
from
scipy.stats
import
chi2
import
pandas
as
pd
def
replaceZeroes
(
df
):
def
replaceZeroes
(
df
):
"""
"""
...
@@ -233,7 +234,7 @@ def create_quadrant_plot(work_file_path: str,
...
@@ -233,7 +234,7 @@ def create_quadrant_plot(work_file_path: str,
alim
=
np
.
ceil
(
pv_t
.
JASS_PVAL
.
loc
[
pv_t
.
color
==
"
#f77189
"
].
max
()
+
2
)
alim
=
np
.
ceil
(
pv_t
.
JASS_PVAL
.
loc
[
pv_t
.
color
==
"
#f77189
"
].
max
()
+
2
)
if
np
.
isnan
(
alim
):
if
np
.
isnan
(
alim
):
alim
=
10
alim
=
10
plt
.
axis
([
0
,
alim
,
0
,
alim
])
plt
.
plot
([
0
,
alim
,
0
,
alim
])
# légendes abcisse et ordonnee
# légendes abcisse et ordonnee
plt
.
xlabel
(
'
-log10(P) for univariate tests
'
,
fontsize
=
12
)
plt
.
xlabel
(
'
-log10(P) for univariate tests
'
,
fontsize
=
12
)
# plt.show()
# plt.show()
...
@@ -251,60 +252,50 @@ def create_quadrant_plot(work_file_path: str,
...
@@ -251,60 +252,50 @@ def create_quadrant_plot(work_file_path: str,
def
create_qq_plot
(
work_file_path
:
str
,
qq_plot_path
:
str
):
def
create_qq_plot
(
work_file_path
:
str
,
qq_plot_path
:
str
):
df
=
read_hdf
(
work_file_path
,
"
SumStatTab
"
,
columns
=
[
'
JASS_PVAL
'
,
'
UNIVARIATE_MIN_PVAL
'
])
df
=
read_hdf
(
work_file_path
,
"
SumStatTab
"
,
columns
=
[
'
JASS_PVAL
'
,
'
UNIVARIATE_MIN_PVAL
'
,
'
UNIVARIATE_MIN_QVAL
'
])
df
[[
'
JASS_PVAL
'
,
'
UNIVARIATE_MIN_PVAL
'
]]
=
replaceZeroes
(
df
[[
'
JASS_PVAL
'
,
'
UNIVARIATE_MIN_PVAL
'
]])
dr
=
read_hdf
(
work_file_path
,
"
Regions
"
)
#count the number of traits
Ntrait
=
len
([
i
for
i
in
dr
.
columns
if
i
[:
2
]
==
'
z_
'
])
pvalue
=
-
np
.
log10
(
df
.
JASS_PVAL
)
<<<<<<<
jass
/
models
/
plots
.
py
# Cast values between 0 and 1, 0 and 1 excluded
x
=
-
np
.
log10
(
np
.
arange
(
1
,
pvalue
.
shape
[
0
]
+
1
)
/
(
pvalue
.
shape
[
0
]
+
2
))
y
=
pvalue
.
sort_values
()
plt
.
scatter
(
x
[::
-
1
],
y
,
s
=
5
)
pval_median
=
np
.
nanmedian
(
df
.
JASS_PVAL
)
lambda_value_omni
=
chi2
.
isf
(
pval_median
,
df
=
Ntrait
)
/
chi2
.
isf
(
0.5
,
df
=
Ntrait
)
lambda_value_sumZ
=
chi2
.
isf
(
pval_median
,
df
=
1
)
/
chi2
.
isf
(
0.5
,
df
=
1
)
print
(
"
Number of trait analyzed = {}
"
.
format
(
Ntrait
))
print
(
"
JASS median p-val = {:.3f}
"
.
format
(
pval_median
))
print
(
"
Inflation Factor if the test is omnibus= {:.3f}
"
.
format
(
lambda_value_omni
))
print
(
"
Inflation Factor if the test is a SumZ= {:.3f}
"
.
format
(
lambda_value_sumZ
))
x_1
=
np
.
linspace
(
0
,
6
)
y_1
=
(
pval_median
/
0.5
)
*
x_1
x_2
=
np
.
linspace
(
0
,
6
)
plt
.
plot
(
x_1
,
y_1
,
c
=
"
red
"
)
plt
.
plot
(
x_2
,
x_2
)
plt
.
title
(
"
Median p-val = {:.2f}
"
.
format
(
pval_median
))
plt
.
xlabel
(
"
expected quantile of -log10(P)
"
)
plt
.
ylabel
(
"
observed quantile of -log10(P)
"
)
plt
.
savefig
(
qq_plot_path
,
dpi
=
600
)
df
[[
'
JASS_PVAL
'
,
'
UNIVARIATE_MIN_PVAL
'
,
"
UNIVARIATE_MIN_QVAL
"
]]
=
replaceZeroes
(
df
[[
'
JASS_PVAL
'
,
'
UNIVARIATE_MIN_PVAL
'
,
'
UNIVARIATE_MIN_QVAL
'
]])
=======
pvalue
=
-
np
.
log10
(
df
.
JASS_PVAL
)
pvalue_univ
=
-
np
.
log10
(
df
.
UNIVARIATE_MIN_PVAL
)
pvalue_univ
=
-
np
.
log10
(
df
.
UNIVARIATE_MIN_PVAL
)
qvalue_univ
=
-
np
.
log10
(
df
.
UNIVARIATE_MIN_QVAL
)
# compute_expected pvalue
# compute_expected pvalue
pvalue
.
sort_values
().
values
QQ_pval
=
pd
.
DataFrame
(
index
=-
np
.
log10
(
np
.
arange
(
1
,
pvalue
.
shape
[
0
]
+
1
)
/
(
pvalue
.
shape
[
0
]
+
2
)),
{
"
JASS pvalue
"
:
pvalue
.
sort_values
(),
QQ_pval
=
pd
.
DataFrame
({
"
JASS p-value
"
:
pvalue
.
sort_values
().
values
,
"
UNIV pvalue
"
:
pvalue_univ
.
sort_values
()})
"
Univariate p-value
"
:
pvalue_univ
.
sort_values
().
values
,
"
Univariate q-value
"
:
qvalue_univ
.
sort_values
().
values
,
})
QQ_pval
=
QQ_pval
.
iloc
[::
20
,].
dropna
()
exp_val
=
np
.
flip
(
-
np
.
log10
(
QQ_pval
.
index
.
values
/
QQ_pval
.
index
.
max
()))
exp_val
[
-
1
]
=
QQ_pval
.
max
().
max
()
QQ_pval
.
index
=
exp_val
QQ_pval
=
QQ_pval
.
iloc
[:
-
1
,]
pval_median
=
df
.
JASS_PVAL
.
median
()
pval_median
=
df
.
JASS_PVAL
.
median
()
pval_median_univ
=
df
.
UNIVARIATE_MIN_PVAL
.
median
()
pval_median_univ
=
df
.
UNIVARIATE_MIN_PVAL
.
median
()
pval_median_quniv
=
df
.
UNIVARIATE_MIN_QVAL
.
median
()
print
(
"
median pval
"
)
print
(
"
median pval
"
)
print
(
pval_median
)
print
(
pval_median
)
lambda_value_jass
=
chi
.
sf
(
pval_median
)
/
chi
.
sf
(
0.5
)
lambda_value_jass
=
chi2
.
sf
(
pval_median
,
df
=
1
)
/
chi2
.
sf
(
0.5
,
df
=
1
)
lambda_value_univ
=
chi
.
sf
(
pval_median_univ
)
/
chi
.
sf
(
0.5
)
lambda_value_jass
lambda_value_univ
=
chi2
.
sf
(
pval_median_univ
,
df
=
1
)
/
chi2
.
sf
(
0.5
,
df
=
1
)
lambda_value_quniv
=
chi2
.
sf
(
pval_median_quniv
,
df
=
1
)
/
chi2
.
sf
(
0.5
,
df
=
1
)
p
=
sns
.
lineplot
(
data
=
QQ_pval
)
p
=
sns
.
lineplot
(
data
=
QQ_pval
)
p
.
set
(
"
QQ plot
"
)
alim
=
exp_val
[
-
2
]
p
.
set_xlabel
(
"
Expected p-values
"
,
fontsize
=
20
)
plt
.
plot
([
0
,
alim
],[
0
,
alim
],
c
=
"
red
"
,
linewidth
=
0.5
)
p
.
set_ylabel
(
"
Observed p-values
"
,
fontsize
=
20
)
p
.
set_title
(
"
QQ plot
\n
λ JASS = {:.2f}
\n
λ univariate p-values = {:.2f} λ univariate q-values = {:.2f}
"
.
format
(
lambda_value_jass
,
lambda_value_univ
,
lambda_value_quniv
),
fontsize
=
11
)
p
.
set_xlabel
(
"
Expected -log10(p-values)
"
,
fontsize
=
13
)
p
.
set_ylabel
(
"
Observed -log10(p-values)
"
,
fontsize
=
13
)
plt
.
savefig
(
qq_plot_path
)
plt
.
savefig
(
qq_plot_path
)
>>>>>>>
jass
/
models
/
plots
.
py
plt
.
clf
()
print
(
"
------ QQ plot -----
"
)
print
(
"
------ QQ plot -----
"
)
def
create_qq_plot_by_GWAS
(
init_file_path
:
str
,
qq_plot_folder
:
str
):
def
create_qq_plot_by_GWAS
(
init_file_path
:
str
,
qq_plot_folder
:
str
):
df
=
read_hdf
(
init_file_path
,
"
SumStatTab
"
,
where
=
"
Region < {0}
"
.
format
(
2
))
df
=
read_hdf
(
init_file_path
,
"
SumStatTab
"
,
where
=
"
Region < {0}
"
.
format
(
2
))
uni_var
=
[
i
for
i
in
df
.
columns
if
i
[:
2
]
==
"
z_
"
]
uni_var
=
[
i
for
i
in
df
.
columns
if
i
[:
2
]
==
"
z_
"
]
...
...
This diff is collapsed.
Click to expand it.
requirements.txt
+
2
−
1
View file @
eda41353
...
@@ -8,6 +8,7 @@ pandas
...
@@ -8,6 +8,7 @@ pandas
tables
tables
scipy
scipy
matplotlib
matplotlib
seaborn
celery
celery
pydantic
pydantic
fastapi
fastapi
...
...
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