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put the reference single-cell and real bulk together
X_full = pd.concat([X_concat, real_bulk_df])
Y_full = pd.concat([Y_concat, Y_concat.iloc[range(15)]]) ## stop gap for now, we just add random cell type labels
meta_df = pd.concat([meta_concat, real_bulk_meta_df])
The text was updated successfully, but these errors were encountered:
code redundancy in cell 26
read in the bulk data
real_bulk_df = pd.DataFrame(adata.X, columns=adata.var_names, index=adata.obs.index)
real_bulk_meta_df = adata.obs
select genes that are in both
intersect_genes = np.intersect1d(gene_df, real_bulk_df.columns)
X_concat = X_concat[intersect_genes]
real_bulk_df = real_bulk_df[intersect_genes]
get the bulk metadata formatted
real_bulk_meta_df = real_bulk_meta_df[["sample_id", "stim"]]
real_bulk_meta_df["isTraining"] = "Train"
real_bulk_meta_df["cell_prop_type"] = "realistic"
real_bulk_meta_df["samp_type"] = "bulk"
real_bulk_meta_df['sample_id'] = real_bulk_meta_df['sample_id'].astype(str)
put the reference single-cell and real bulk together
X_full = pd.concat([X_concat, real_bulk_df])
Y_full = pd.concat([Y_concat, Y_concat.iloc[range(15)]]) ## stop gap for now, we just add random cell type labels
meta_df = pd.concat([meta_concat, real_bulk_meta_df])
The text was updated successfully, but these errors were encountered: