I have 2-dimensional data (Column-Cell1,Cell2.., Row-Gene1,Gene2..) in which I want to delete rows with 99% zeroes and with the resultant matrix drop columns with 99% zeroes in them. I have written the following code to do the same, however since the matrix is very large, it is taking a long time to run. Is there a better way to approach this issue?
import pandas as pd
import numpy as np
def read_in(matrix_file):
matrix_df=pd.read_csv(matrix_file,index_col=0)
return(matrix_df)
def genes_less_exp(matrix_df):
num_columns=matrix_df.shape[1]
for index, row in matrix_df.iterrows():
zero_els=np.count_nonzero(row.values==0)
gene_per_zero=(float(zero_els)/float(num_columns))*100
if gene_per_zero >= 99:
matrix_df.drop([index],axis=0,inplace=True)
return(matrix_df)
def cells_less_exp(matrix_df):
num_rows=matrix_df.shape[0]
for label,content in matrix_df.iteritems():
zero_els=np.count_nonzero(content.values==0)
cells_per_zero=(float(zero_els)/float(num_rows))*100
if cells_per_zero >= 99:
matrix_df.drop(label,axis=1,inplace=True)
return(matrix_df)
if __name__ == "__main__":
matrix_df=read_in("Data/big-matrix.csv")
print("original:"+str(matrix_df.shape))
filtered_genes=genes_less_exp(matrix_df)
print("filtered_genes:"+str(filtered_genes.shape))
filtered_cells=cells_less_exp(filtered_genes)
print("filtered_cells:"+str(filtered_cells.shape))
filtered_cells.to_csv("abi.99.percent.filtered.csv", sep=',')
Its easier if you reframe your question to "keep those with less than 99% 0s".
def drop_almost_zero(df, percentage):
row_cut_off = int(percentage/100*len(df.columns))
df = df[(df==0).sum(axis='columns') <= row_cut_off]
column_cut_off = int(percentage/100*len(df))
b = (df == 0).sum(axis='rows')
df = df[ b[ b <= column_cut_off].index.values ]
return df
#test
size = 50
percentage = 90
rows = size//2
columns = size
a = np.random.choice(2, size=(rows, columns), p=[(1-0.1), 0.1])
df = pd.DataFrame(a, columns=[f'c{i}' for i in range(size)])
df = drop_almost_zero(df,percentage)
assert (df == 0).sum(axis='rows').max() <= percentage/100*rows
assert (df == 0).sum(axis='columns').max() <= percentage/100*columns
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