filtered_combined_matrix_samples = filtered_combined_matrix.dropna(axis=1, thresh=thresh, how="any")
// "Reset" zero values that were set to NA in RNA-seq samples (i.e., make these zero again) in combined_matrix
combined_matrix_zero = filtered_combined_matrix_samples.fillna(value=0)
// Transpose combined_matrix; transposed_matrix
transposed_matrix = combined_matrix_zero.transpose()
After Change
transposed_matrix = combined_matrix_zero.transpose()
// Remove -inf and inf
transposed_matrix = transposed_matrix.replace([np.inf, -np.inf], np.nan)
// Perform imputation of missing values with IterativeSVD (rank=10) on the transposed_matrix; imputed_matrix
imputed_matrix = IterativeSVD(rank=10).fit_transform(transposed_matrix)
// Untranspose imputed_matrix (genes are now rows, samples are now columns)
untransposed_imputed_matrix = imputed_matrix.transpose()
// Convert back to Pandas
untransposed_imputed_matrix_df = pd.DataFrame.from_records(untransposed_imputed_matrix)
untransposed_imputed_matrix_df.index = combined_matrix_zero.index
untransposed_imputed_matrix_df.columns = combined_matrix_zero.columns
// Quantile normalize imputed_matrix where genes are rows and samples are columns
// XXX: Refactor QN target acquisition and application before doing this
