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Enable leafcutter-ds to handle a group_file with no confounder columns #255

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Enable leafcutter-ds to handle a group_file with no confounder columns #255

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24 changes: 13 additions & 11 deletions leafcutter/differential_splicing/leafcutter_ds.py
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Original file line number Diff line number Diff line change
Expand Up @@ -50,6 +50,7 @@
meta = meta.rename(dict(zip([0, 1], ["sample", "group"])), axis = 1)

# Check if there are more than 2 columns in the metadata DataFrame
confounders = None
if len(meta.columns) > 2:
# Extract the confounders (columns 3 and onwards)
confounders = meta.iloc[:, 2:]
Expand All @@ -71,17 +72,18 @@
#remove samples with missing data (not in origial leafcutter as far as I can tell).
confounders = pd.DataFrame(confounders, index = meta['sample']).dropna()

all_samples = set(meta.loc[:,'sample'])
removed_samples = all_samples - set(confounders.index)
if len(removed_samples) != 0:
print('Samples removed due to missing values in covariates...')
print(','.join(list(removed_samples)))

# Encode the "group" column as numeric (0 and 1)
meta = meta[meta['sample'].isin(confounders.index)]

#if permute: numeric_x = np.random.permutation(numeric_x)
counts = counts[confounders.index]
# indented to here to fix confounders
all_samples = set(meta.loc[:,'sample'])
removed_samples = all_samples - set(confounders.index)
if len(removed_samples) != 0:
print('Samples removed due to missing values in covariates...')
print(','.join(list(removed_samples)))
# Encode the "group" column as numeric (0 and 1)
meta = meta[meta['sample'].isin(confounders.index)]
#if permute: numeric_x = np.random.permutation(numeric_x)
counts = counts[confounders.index]
else:
counts = counts[meta["sample"].tolist()]

scale_factor = 1.

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