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First draft of subgroup normalization #39

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6b9ae22
First draft of subgroup normalizaiton
bethac07 May 17, 2023
176204c
expand subgroup option
ErinWeisbart Jun 12, 2023
1482416
Merge branch 'master' into normalization_group
bethac07 May 20, 2024
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6 changes: 6 additions & 0 deletions config_template.yml
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Original file line number Diff line number Diff line change
Expand Up @@ -28,13 +28,17 @@ normalize:
features: infer
mad_robustize_fudge_factor: 0
image_features: true
subgroups: false
subgroup_col: <Column to subdivide on>
min_cells: 1
normalize_negcon:
perform: true
method: mad_robustize
features: infer
mad_robustize_fudge_factor: 0
image_features: true
subgroups: false
subgroup_col: <Column to subdivide on>
min_cells: 1
feature_select:
perform: true
Expand All @@ -47,6 +51,7 @@ feature_select:
- correlation_threshold
- drop_na_columns
- blocklist
subgroups: false
min_cells: 1
feature_select_negcon:
perform: true
Expand All @@ -59,6 +64,7 @@ feature_select_negcon:
- correlation_threshold
- drop_na_columns
- blocklist
subgroups: false
min_cells: 1
quality_control:
perform: true
Expand Down
184 changes: 178 additions & 6 deletions profiles/profile.py
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Original file line number Diff line number Diff line change
Expand Up @@ -18,6 +18,7 @@
from pycytominer.cyto_utils.cells import SingleCells
from pycytominer.cyto_utils import (
get_default_compartments,
output,
write_gct,
)
from pycytominer import (
Expand Down Expand Up @@ -187,7 +188,28 @@ def pipeline_normalize(self, batch, plate, steps, samples, suffix=None):
normalization_features = cyto_utils.infer_cp_features(
pd.read_csv(annotate_output_file), compartments=self.compartments
)

if "subgroups" in normalize_steps.keys() and normalize_steps["subgroups"]:
profile_df = pd.read_csv(annotate_output_file)
normed_df = (
profile_df
.groupby(normalize_steps["subgroup_col"], group_keys=False)
.apply(
lambda x:normalize(
profiles=x,
features=normalization_features,
image_features=image_features,
samples=samples,
method=normalization_method,
float_format=self.pipeline_options["float_format"],
mad_robustize_epsilon=fudge_factor,
)
)
)
output(
normed_df,
output_filename=pathlib.PurePath(output_dir, f"{plate}_subgroup_normalized.csv.gz"),
compression_options=self.pipeline_options["compression"]
)
normalize(
profiles=annotate_output_file,
features=normalization_features,
Expand All @@ -211,9 +233,11 @@ def pipeline_feature_select(self, steps, suffix=None,min_cells=1):
image_features = feature_select_steps["image_features"]

all_plates_df = pd.DataFrame()
sub_all_plates_df = pd.DataFrame()

for batch in self.profile_config:
batch_df = pd.DataFrame()
sub_batch_df = pd.DataFrame()
for plate in self.profile_config[batch]:
output_dir = pathlib.PurePath(".", pipeline_output, batch, plate)
if suffix:
Expand All @@ -222,7 +246,14 @@ def pipeline_feature_select(self, steps, suffix=None,min_cells=1):
)
feature_select_output_file_plate = pathlib.PurePath(
output_dir,
f"{plate}_normalized_feature_select_{suffix}_plate.csv.gz",
f"{plate}_subgroup_normalized_feature_select_{suffix}_plate.csv.gz",
)
subgroup_normalize_output_file = pathlib.PurePath(
output_dir, f"{plate}_normalized_{suffix}.csv.gz"
)
subgroup_feature_select_output_file_plate = pathlib.PurePath(
output_dir,
f"{plate}_subgroup_normalized_feature_select_{suffix}_plate.csv.gz",
)
else:
normalize_output_file = pathlib.PurePath(
Expand All @@ -231,6 +262,12 @@ def pipeline_feature_select(self, steps, suffix=None,min_cells=1):
feature_select_output_file_plate = pathlib.PurePath(
output_dir, f"{plate}_normalized_feature_select_plate.csv.gz"
)
subgroup_normalize_output_file = pathlib.PurePath(
output_dir, f"{plate}_subgroup_normalized.csv.gz"
)
subgroup_feature_select_output_file_plate = pathlib.PurePath(
output_dir, f"{plate}_subgroup_normalized_feature_select_plate.csv.gz"
)
if feature_select_features == "infer" and self.noncanonical:
feature_select_features = cyto_utils.infer_cp_features(
pd.read_csv(normalize_output_file),
Expand All @@ -242,6 +279,12 @@ def pipeline_feature_select(self, steps, suffix=None,min_cells=1):
.assign(Metadata_batch=batch)
.astype({'Metadata_Plate': str})
)
if "subgroups" in feature_select_steps.keys() and feature_select_steps["subgroups"]:
sub_df = (
pd.read_csv(subgroup_normalize_output_file)
.assign(Metadata_batch=batch)
.astype({'Metadata_Plate': str})
)

if level == "plate":
df = df.drop(columns=["Metadata_batch"])
Expand All @@ -259,11 +302,25 @@ def pipeline_feature_select(self, steps, suffix=None,min_cells=1):
float_format=self.pipeline_options["float_format"],
samples=fs_samples,
)
if "subgroups" in feature_select_steps.keys() and feature_select_steps["subgroups"]:
sub_df = sub_df.drop(columns=["Metadata_batch"])
feature_select(
profiles=sub_df,
features=feature_select_features,
image_features=image_features,
operation=feature_select_operations,
output_file=subgroup_feature_select_output_file_plate,
compression_options=self.pipeline_options["compression"],
float_format=self.pipeline_options["float_format"],
)
elif level == "batch":
batch_df = concat_dataframes(batch_df, df, image_features)
if "subgroups" in feature_select_steps.keys() and feature_select_steps["subgroups"]:
sub_batch_df = concat_dataframes(sub_batch_df, sub_df, image_features)
elif level == "all":
all_plates_df = concat_dataframes(all_plates_df, df, image_features)

if "subgroups" in feature_select_steps.keys() and feature_select_steps["subgroups"]:
sub_all_plates_df = concat_dataframes(sub_all_plates_df, sub_df, image_features)
if level == "batch":
if min_cells == 1:
fs_samples = "all"
Expand All @@ -276,32 +333,55 @@ def pipeline_feature_select(self, steps, suffix=None,min_cells=1):
operation=feature_select_operations,
samples=fs_samples,
)
if "subgroups" in feature_select_steps.keys() and feature_select_steps["subgroups"]:
sub_fs_df = feature_select(
profiles=sub_batch_df,
features=feature_select_features,
image_features=image_features,
operation=feature_select_operations,
)
for plate in self.profile_config[batch]:
output_dir = pathlib.PurePath(".", pipeline_output, batch, plate)
if suffix:
feature_select_output_file_batch = pathlib.PurePath(
output_dir,
f"{plate}_normalized_feature_select_{suffix}_batch.csv.gz",
)
sub_feature_select_output_file_batch = pathlib.PurePath(
output_dir,
f"{plate}_subgroup_normalized_feature_select_{suffix}_batch.csv.gz",
)
else:
feature_select_output_file_batch = pathlib.PurePath(
output_dir,
f"{plate}_normalized_feature_select_batch.csv.gz",
)
sub_feature_select_output_file_batch = pathlib.PurePath(
output_dir,
f"{plate}_subgroup_normalized_feature_select_batch.csv.gz",
)
if feature_select_features == "infer" and self.noncanonical:
feature_select_features = cyto_utils.infer_cp_features(
batch_df, compartments=self.compartments
)

df = fs_df.query("Metadata_Plate==@plate").reset_index(drop=True)
df = df.drop(columns=["Metadata_batch"])

df = df.drop(columns=["Metadata_batch"])
cyto_utils.output(
output_filename=feature_select_output_file_batch,
df=df,
compression_options=self.pipeline_options["compression"],
float_format=self.pipeline_options["float_format"],
)
if "subgroups" in feature_select_steps.keys() and feature_select_steps["subgroups"]:
sub_df = sub_fs_df.query("Metadata_Plate==@plate").reset_index(drop=True)
sub_df = sub_df.drop(columns=["Metadata_batch"])
cyto_utils.output(
output_filename=sub_feature_select_output_file_batch,
df=sub_df,
compression_options=self.pipeline_options["compression"],
float_format=self.pipeline_options["float_format"],
)

if gct:
create_gct_directories(batch)
Expand All @@ -318,6 +398,18 @@ def pipeline_feature_select(self, steps, suffix=None,min_cells=1):
batch,
f"{batch}_normalized_feature_select_{suffix}_batch.gct",
)
sub_stacked_file = pathlib.PurePath(
".",
"gct",
batch,
f"{batch}_subgroup_normalized_feature_select_{suffix}_batch.csv.gz",
)
sub_gct_file = pathlib.PurePath(
".",
"gct",
batch,
f"{batch}_subgroup_normalized_feature_select_{suffix}_batch.gct",
)
else:
stacked_file = pathlib.PurePath(
".",
Expand All @@ -331,13 +423,33 @@ def pipeline_feature_select(self, steps, suffix=None,min_cells=1):
batch,
f"{batch}_normalized_feature_select_batch.gct",
)
sub_stacked_file = pathlib.PurePath(
".",
"gct",
batch,
f"{batch}_subgroup_normalized_feature_select_batch.csv.gz",
)
sub_gct_file = pathlib.PurePath(
".",
"gct",
batch,
f"{batch}_subgroup_normalized_feature_select_batch.gct",
)
cyto_utils.output(
output_filename=stacked_file,
df=fs_df,
compression_options=self.pipeline_options["compression"],
float_format=self.pipeline_options["float_format"],
)
write_gct(profiles=fs_df, output_file=gct_file)
if "subgroups" in feature_select_steps.keys() and feature_select_steps["subgroups"]:
cyto_utils.output(
output_filename=sub_stacked_file,
df=sub_fs_df,
compression_options=self.pipeline_options["compression"],
float_format=self.pipeline_options["float_format"],
)
write_gct(profiles=sub_fs_df, output_file=sub_gct_file)

if level == "all":
if min_cells == 1:
Expand All @@ -351,21 +463,39 @@ def pipeline_feature_select(self, steps, suffix=None,min_cells=1):
operation=feature_select_operations,
samples=fs_samples,
)
if "subgroups" in feature_select_steps.keys() and feature_select_steps["subgroups"]:
sub_fs_df = feature_select(
profiles=sub_all_plates_df,
features=feature_select_features,
image_features=image_features,
operation=feature_select_operations,
)
for batch in self.profile_config:
fs_batch_df = fs_df.loc[fs_df.Metadata_batch == batch].reset_index(
drop=True
)
if "subgroups" in feature_select_steps.keys() and feature_select_steps["subgroups"]:
sub_fs_batch_df = sub_fs_df.loc[sub_fs_df.Metadata_batch == batch].reset_index(
drop=True
)
for plate in self.profile_config[batch]:
output_dir = pathlib.PurePath(".", pipeline_output, batch, plate)
if suffix:
feature_select_output_file_all = pathlib.PurePath(
output_dir,
f"{plate}_normalized_feature_select_{suffix}_all.csv.gz",
)
sub_feature_select_output_file_all = pathlib.PurePath(
output_dir,
f"{plate}_subgroup_normalized_feature_select_{suffix}_all.csv.gz",
)
else:
feature_select_output_file_all = pathlib.PurePath(
output_dir, f"{plate}_normalized_feature_select_all.csv.gz"
)
sub_feature_select_output_file_all = pathlib.PurePath(
output_dir, f"{plate}_subgroup_normalized_feature_select_all.csv.gz"
)
if feature_select_features == "infer" and self.noncanonical:
feature_select_features = cyto_utils.infer_cp_features(
all_plates_df, compartments=self.compartments
Expand All @@ -383,6 +513,17 @@ def pipeline_feature_select(self, steps, suffix=None,min_cells=1):
compression_options=self.pipeline_options["compression"],
float_format=self.pipeline_options["float_format"],
)
if "subgroups" in feature_select_steps.keys() and feature_select_steps["subgroups"]:
sub_df = sub_fs_batch_df.query("Metadata_Plate==@plate").reset_index(
drop=True
)
sub_df = sub_df.drop(columns=["Metadata_batch"])
cyto_utils.output(
output_filename=sub_feature_select_output_file_all,
df=sub_df,
compression_options=self.pipeline_options["compression"],
float_format=self.pipeline_options["float_format"],
)

if gct:
create_gct_directories(batch)
Expand All @@ -399,6 +540,18 @@ def pipeline_feature_select(self, steps, suffix=None,min_cells=1):
batch,
f"{batch}_normalized_feature_select_{suffix}_all.gct",
)
sub_stacked_file = pathlib.PurePath(
".",
"gct",
batch,
f"{batch}_subgroup_normalized_feature_select_{suffix}_all.csv.gz",
)
sub_gct_file = pathlib.PurePath(
".",
"gct",
batch,
f"{batch}_subgroup_normalized_feature_select_{suffix}_all.gct",
)
else:
stacked_file = pathlib.PurePath(
".",
Expand All @@ -412,14 +565,33 @@ def pipeline_feature_select(self, steps, suffix=None,min_cells=1):
batch,
f"{batch}_normalized_feature_select_all.gct",
)
sub_stacked_file = pathlib.PurePath(
".",
"gct",
batch,
f"{batch}_subgroup_normalized_feature_select_all.csv.gz",
)
sub_gct_file = pathlib.PurePath(
".",
"gct",
batch,
f"{batch}_subgroup_normalized_feature_select_all.gct",
)
cyto_utils.output(
output_filename=stacked_file,
df=fs_batch_df,
compression_options=self.pipeline_options["compression"],
float_format=self.pipeline_options["float_format"],
)
write_gct(profiles=fs_batch_df, output_file=gct_file)

if "subgroups" in feature_select_steps.keys() and feature_select_steps["subgroups"]:
cyto_utils.output(
output_filename=sub_stacked_file,
df=sub_fs_batch_df,
compression_options=self.pipeline_options["compression"],
float_format=self.pipeline_options["float_format"],
)
write_gct(profiles=sub_fs_batch_df, output_file=sub_gct_file)
def pipeline_quality_control(self, operations):
pipeline_output = self.pipeline["output_dir"]

Expand Down