| import os |
| import numpy as np |
| import pandas as pd |
| import PATH |
| import utils |
| from Bio import SeqIO |
| from scipy import sparse |
|
|
|
|
| pj = lambda x: os.path.join(utils.data_dir,x) |
|
|
|
|
| |
| fa = SeqIO.parse(pj('gencode_v17_5utr_15bpcds.fa'),'fasta') |
| tx_seq=dict() |
| for seq_record in fa: |
| tx=seq_record.id |
| seq=seq_record.seq |
| if len(seq)<30: |
| continue |
| if "ATG" not in seq: |
| continue |
| if tx not in tx_seq or len(seq)>len(tx_seq[tx]): |
| tx_seq[tx]=seq |
|
|
| |
| RP_data_path = {} |
| RP_data_path['muscle'] = pj('df_counts_and_len.TE_sorted.Muscle.with_annot.txt') |
| RP_data_path['PC3'] = pj('df_counts_and_len.TE_sorted.pc3.with_annot.txt') |
| RP_data_path['293T'] = pj('df_counts_and_len.TE_sorted.HEK_Andrev2015.with_annot.txt') |
|
|
| |
| |
| feat_mat = pd.read_csv("util/Zhang_et_al_features.csv",index_col=0) |
| feat_mat.insert(0, 'seq', [tx_seq[x].upper().__str__() for x in feat_mat.index.values]) |
|
|
|
|
| processed_dict = {} |
| for cell_line, csv_path in RP_data_path.items(): |
|
|
| assert os.path.exists(csv_path), f"The raw data file for {cell_line} does not exist!" |
| |
| print(f"processing {cell_line}...\n") |
| RP_raw_data = pd.read_table(csv_path, sep=' ') |
| RP_raw_data.loc[:,'T_id'] = RP_raw_data.index.values |
| |
| RP_raw_data = RP_raw_data.query('rpkm_rnaseq >5 & rpkm_riboseq > 0.1') |
| RP_raw_data['log_te'] = np.log(RP_raw_data.te.values) |
|
|
| RP_feat_merge = RP_raw_data.merge(feat_mat,left_on=['T_id'],right_index=True,suffixes=["",""]) |
| RP_feat_merge.sort_values('rpkm_rnaseq', ascending=False, inplace=True) |
| |
| |
| RP_raw_dedup = RP_feat_merge.drop_duplicates(RP_feat_merge.columns[17:], keep='first') |
| RP_raw_dedup['utr'] = RP_raw_dedup['seq'].apply(lambda x: x[-216:-16]) |
| RP_raw_dedup['utr_len'] = RP_raw_dedup.utr.apply(len) |
| RP_raw_dedup.query('`utr_len`>30') |
| RP_raw_dedup = RP_raw_dedup.drop_duplicates(['utr'], keep='first') |
| |
| |
| processed_dict[cell_line] = RP_raw_dedup |
| processed_dict[cell_line].to_csv(pj(f"RP_{cell_line}_MTL_transfer.csv")) |
|
|
| print("The preprocssing for RP tasks is Finished !!") |
| print(f"The files are saved to {utils.data_dir}") |
