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jrc-combine
chgen
Commits
cf01fd7f
Commit
cf01fd7f
authored
2 years ago
by
Kateryna Nikulina
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script for analysis
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cf01fd7f
import
numpy
as
np
import
pandas
as
pd
import
glob
import
scipy.stats
as
stats
def
get_feature_pairs
(
df_with_grouped_intersections
,
name
,
bad
):
'''
Search for combinations of parameters with low or high intersections
Args:
df_with_grouped_intersections: a pandas dataframe, a table obtained from get_intersections function,
grouped by hospital case and parameters
'
combination
name: a string, a hospital case (e.g.
'
full df_1 in full df_2
'
)
bad: boolean, determines what parameters are needed - with low or high intersections
Return:
A pandas series with combinations of parameters
'''
data
=
df_with_grouped_intersections
[
df_with_grouped_intersections
[
'
Name
'
]
==
name
]
#find the lowest limit for normal values - below are outliers
q1
,
q3
=
df_with_grouped_intersections
[
df_with_grouped_intersections
[
"
Name
"
]
==
name
].
Intersection
.
quantile
([
0.25
,
0.75
])
low
=
q1
-
1.5
*
(
q3
-
q1
)
if
bad
:
#bad==true -> return bad pairs
df
=
df_with_grouped_intersections
[(
df_with_grouped_intersections
[
"
Name
"
]
==
name
)
&
(
df_with_grouped_intersections
[
'
Intersection
'
]
<
low
)]
else
:
#bad==false -> return good pairs
df
=
df_with_grouped_intersections
[(
df_with_grouped_intersections
[
"
Name
"
]
==
name
)
&
(
df_with_grouped_intersections
[
'
Intersection
'
]
>=
low
)]
return
df
.
Parameters
def
get_features_count_df
(
df_with_grouped_intersections
,
name
,
bad
):
'''
Counts occurences of a parameter
Args:
df_with_grouped_intersections: a pandas dataframe, a table obtained from get_intersections function,
grouped by hospital case and parameters
'
combination
name: a string, a hospital case (e.g.
'
full df_1 in full df_2
'
)
bad: boolean, determines what parameters are needed - with low or high intersections
Return:
df: a pandas dataframe, parameters and their count
'''
params
=
get_feature_pairs
(
df_with_grouped_intersections
,
name
,
bad
)
#clean string values, get only parameter names
param_list
=
[]
for
x
in
params
:
a
,
b
=
x
.
split
(
"
,
"
,
1
)
param_list
.
append
(
a
)
param_list
.
append
(
b
)
#count parameter appearance
dct
=
dict
(
zip
(
param_list
,[
param_list
.
count
(
i
)
for
i
in
param_list
]))
sorted_dct
=
dict
(
sorted
(
dct
.
items
(),
key
=
lambda
item
:
item
[
1
],
reverse
=
True
))
if
bad
:
df
=
pd
.
DataFrame
(
sorted_dct
.
items
(),
columns
=
[
'
Parameter
'
,
f
'
Count_in_bad_pairs
'
])
else
:
df
=
pd
.
DataFrame
(
sorted_dct
.
items
(),
columns
=
[
'
Parameter
'
,
f
'
Count_in_good_pairs
'
])
return
df
def
fishers_exact_test
(
df
,
name
,
param_count
):
'''
Perform enrichment analysis
Args:
df: a pandas dataframe, a table obtained from get_intersections function,
grouped by hospital case and parameters
'
combination
name: a string, a hospital case (e.g.
'
full df_1 in full df_2
'
)
param_count: int, total count of parameters common for both hospitals
Return:
df: a dataframe, with information about each parameter: count, p value, etc.
'''
param_count
=
param_count
-
1
#get bad pairs and their count
bad_pairs
=
get_feature_pairs
(
df_with_grouped_intersections
=
df
,
name
=
name
,
bad
=
True
)
df_bad
=
get_features_count_df
(
df_with_grouped_intersections
=
df
,
name
=
name
,
bad
=
True
)
#get good pairs and their count
good_pairs
=
get_feature_pairs
(
df_with_grouped_intersections
=
df
,
name
=
name
,
bad
=
False
)
df_good
=
get_features_count_df
(
df_with_grouped_intersections
=
df
,
name
=
name
,
bad
=
False
)
if
len
(
bad_pairs
)
!=
0
:
enrich_df
=
pd
.
merge
(
df_bad
,
df_good
,
how
=
'
outer
'
,
on
=
'
Parameter
'
).
fillna
(
0
)
#perform Fisher's exact test
odds_list
=
[]
pval_list
=
[]
for
i
in
enrich_df
.
index
:
v00
=
enrich_df
.
iloc
[
i
,[
1
,
2
]][
0
]
# count of a parameter presence in a bad list
v10
=
enrich_df
.
iloc
[
i
,[
1
,
2
]][
1
]
# count of a parameter presence in a good list
odds
,
pval
=
stats
.
fisher_exact
([[
v00
,(
len
(
bad_pairs
)
-
v00
)],[
v10
,(
len
(
good_pairs
)
-
v10
)]])
odds_list
.
append
(
odds
)
pval_list
.
append
(
pval
)
enrich_df
[
'
Name
'
]
=
name
enrich_df
[
"
Parameter_count
"
]
=
param_count
#parameter count
enrich_df
[
"
Ratio_in_bad_pairs
"
]
=
np
.
round
(
enrich_df
[
"
Count_in_bad_pairs
"
]
/
enrich_df
[
"
Parameter_count
"
],
2
)
enrich_df
[
"
odds_ratio
"
]
=
odds_list
enrich_df
[
'
p_value
'
]
=
pval_list
return
enrich_df
else
:
#there are no low intersections
enrich_df
=
pd
.
DataFrame
()
return
enrich_df
def
produce_summary_tables_ch
(
path_to_ch_outputs
,
dataset_labels
):
'''
Computes parameters with the lowest intersections, their count and the mean intersections for each datasets
Args:
path_to_ch_outputs: a string, a path to a folder containing the intersections of convex hull analysis
dataset_labels: a list of strings, names of the datasets used in convex hull analysis computations
Returns:
mean_coverage_df: dataframe, mean intersection for a dataset
bad_params_df: dataframe, names of parameters that cause the lowest intersections for a case
bad_params_df_count: dataframe, count of parameters with the lowest intersections
'''
all_df
=
pd
.
DataFrame
()
files
=
glob
.
glob
(
path_to_ch_outputs
)
for
file
in
files
:
ch_int
=
pd
.
read_csv
(
file
,
index_col
=
0
)
all_df
=
pd
.
concat
([
all_df
,
ch_int
])
all_df
.
Parameters
=
all_df
.
Parameters
.
str
.
replace
(
'
'
,
''
)
all_df
=
all_df
.
reset_index
()
all_df
=
pd
.
merge
(
all_df
,
all_df
.
Parameters
.
str
.
split
(
'
,
'
,
1
,
expand
=
True
),
left_index
=
True
,
right_index
=
True
)
### get labels from datasets
mean_coverage_df
=
pd
.
DataFrame
(
columns
=
dataset_labels
,
index
=
dataset_labels
)
bad_params_df_count
=
pd
.
DataFrame
(
columns
=
dataset_labels
,
index
=
dataset_labels
)
bad_params_df
=
pd
.
DataFrame
(
columns
=
dataset_labels
,
index
=
dataset_labels
)
### for each case of dataset intersection (dataset 1 in 2 or vice versa, etc..)
for
case
in
all_df
.
Name
.
unique
():
ch_int
=
all_df
[(
all_df
.
Name
==
case
)]
params
=
set
(
ch_int
[
1
].
unique
()).
union
(
set
(
ch_int
[
0
].
unique
()))
cover_list
=
[]
for
param
in
params
:
### take median intersection for each of parameters for a particular case of dataset intersection
cover_list
.
append
(
ch_int
[
ch_int
.
Parameters
.
str
.
contains
(
param
)].
Intersection
.
median
())
int_df
=
pd
.
DataFrame
(
cover_list
,
index
=
params
,
columns
=
[
"
Intersection
"
])
### here choose bad parameters based on criterium - outliers on the boxplot
chosen
=
int_df
[
int_df
.
Intersection
<
(
int_df
.
quantile
(
0.25
)
-
1.5
*
(
int_df
.
quantile
(
0.75
)
-
int_df
.
quantile
(
0.25
)))[
0
]]
### extract dataset names
indexes
=
case
.
replace
(
'
full
'
,
''
,
2
)
indexes
=
indexes
.
replace
(
'
sampled
'
,
''
).
replace
(
'
in
'
,
'
,
'
)
indexes
=
indexes
.
split
(
'
,
'
)
mean_coverage_df
.
loc
[
indexes
[
1
],
indexes
[
0
]]
=
int_df
.
Intersection
.
mean
()
bad_params_df_count
.
loc
[
indexes
[
1
],
indexes
[
0
]]
=
chosen
.
shape
[
0
]
bad_params_df
.
loc
[
indexes
[
1
],
indexes
[
0
]]
=
'
,
'
.
join
(
list
(
chosen
.
index
.
to_series
()))
bad_params_df_count
=
bad_params_df_count
.
astype
(
float
)
mean_coverage_df
=
mean_coverage_df
.
astype
(
float
)
return
mean_coverage_df
,
bad_params_df
,
bad_params_df_count
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