1.4.3_collect_geuvadis_preds

Author

Saideep Gona

Published

November 22, 2023

Collect predictions into expression table

1.4.2_predict_for_linearization.qmd was run in order to generate predictions from all the GEUVADIS inputs. These predictions need to be combined and formatted for use in the predixcan pipeline in order to perform the linearization.

This will be done here. First lets join the individual columns together

Code 
import os,sys
import h5py

mode = "Flu"

with open("/beagle3/haky/users/saideep/github_repos/Daily-Blog-Sai/posts/2023-11-16-linearization/individuals.txt", "r") as inds_f:
    inds = inds_f.read().split()

enformer_geuvadis_pred_folder = "/beagle3/haky/data/Geuvadis_TSS_enformer_prediction"

with h5py.File(os.path.join(enformer_geuvadis_pred_folder, inds[0]+".h5"), "r") as f:
    genes_dsets = list(f.keys())
    genes_dsets.sort()


paste_com = [
    "paste",
    "-d'\t'"
]

inds_include = []
for ind in inds:
    if os.path.exists(os.path.join("/beagle3/haky/users/saideep/projects/aracena_modeling/linearization",f"{ind}_preds_RNAseq_{mode}_from_HDF5_mean_log_revised.txt")):
        paste_com.append(os.path.join("/beagle3/haky/users/saideep/projects/aracena_modeling/linearization",f"{ind}_preds_RNAseq_{mode}_from_HDF5_mean_log_revised.txt"))
        inds_include.append(ind)

paste_com.append("|")
paste_com.append("tail")
paste_com.append("-n")
paste_com.append("+2")

paste_com.append(">")
paste_com.append(os.path.join("/beagle3/haky/users/saideep/projects/aracena_modeling/linearization",f"combined_expression_preds_RNAseq_{mode}_from_HDF5_mean_log_revised.txt"))

print(" ".join(paste_com))

os.system(" ".join(paste_com))
paste -d'   ' /beagle3/haky/users/saideep/projects/aracena_modeling/linearization/HG00096_preds_RNAseq_Flu_from_HDF5_mean_log_revised.txt /beagle3/haky/users/saideep/projects/aracena_modeling/linearization/HG00097_preds_RNAseq_Flu_from_HDF5_mean_log_revised.txt /beagle3/haky/users/saideep/projects/aracena_modeling/linearization/HG00099_preds_RNAseq_Flu_from_HDF5_mean_log_revised.txt /beagle3/haky/users/saideep/projects/aracena_modeling/linearization/HG00100_preds_RNAseq_Flu_from_HDF5_mean_log_revised.txt /beagle3/haky/users/saideep/projects/aracena_modeling/linearization/HG00101_preds_RNAseq_Flu_from_HDF5_mean_log_revised.txt /beagle3/haky/users/saideep/projects/aracena_modeling/linearization/HG00102_preds_RNAseq_Flu_from_HDF5_mean_log_revised.txt /beagle3/haky/users/saideep/projects/aracena_modeling/linearization/HG00103_preds_RNAseq_Flu_from_HDF5_mean_log_revised.txt /beagle3/haky/users/saideep/projects/aracena_modeling/linearization/HG00104_preds_RNAseq_Flu_from_HDF5_mean_log_revised.txt /beagle3/haky/users/saideep/projects/aracena_modeling/linearization/HG00105_preds_RNAseq_Flu_from_HDF5_mean_log_revised.txt /beagle3/haky/users/saideep/projects/aracena_modeling/linearization/HG00106_preds_RNAseq_Flu_from_HDF5_mean_log_revised.txt /beagle3/haky/users/saideep/projects/aracena_modeling/linearization/HG00108_preds_RNAseq_Flu_from_HDF5_mean_log_revised.txt /beagle3/haky/users/saideep/projects/aracena_modeling/linearization/HG00109_preds_RNAseq_Flu_from_HDF5_mean_log_revised.txt /beagle3/haky/users/saideep/projects/aracena_modeling/linearization/HG00110_preds_RNAseq_Flu_from_HDF5_mean_log_revised.txt /beagle3/haky/users/saideep/projects/aracena_modeling/linearization/HG00111_preds_RNAseq_Flu_from_HDF5_mean_log_revised.txt /beagle3/haky/users/saideep/projects/aracena_modeling/linearization/HG00112_preds_RNAseq_Flu_from_HDF5_mean_log_revised.txt 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/beagle3/haky/users/saideep/projects/aracena_modeling/linearization/NA20828_preds_RNAseq_Flu_from_HDF5_mean_log_revised.txt | tail -n +2 > /beagle3/haky/users/saideep/projects/aracena_modeling/linearization/combined_expression_preds_RNAseq_Flu_from_HDF5_mean_log_revised.txt
0
Code 
import pandas as pd


combined_expression_table = pd.read_csv(os.path.join("/beagle3/haky/users/saideep/projects/aracena_modeling/linearization",f"combined_expression_preds_RNAseq_{mode}_from_HDF5_mean_log_revised.txt"), delimiter="\t", header=None)

combined_expression_table.columns = inds_include
Code 
# Create mapping of gene regions to ensemble gene ids
gene_region_mapping = {}
c=0
with open("/beagle3/haky/users/saideep/github_repos/Daily-Blog-Sai/posts/2023-11-16-linearization/canonical_TSS_full_metadata.txt", "r") as genes_f:
    for line in genes_f:
        if c==0:
            c+=1
            continue
        tss_region = line.strip().split(",")[9].split("_")
        tss_plus_one = "_".join([tss_region[0],str(int(tss_region[1])-1),str(int(tss_region[2])-1)])
        gene_region_mapping[tss_plus_one] = line.split(",")[0]

# Filter gene list for genes not in the gene annotation file
include_gene = []
gene_ids = []
for gene in genes_dsets:
    cur_gene_region = gene.rstrip("_predictions")
    if cur_gene_region not in gene_region_mapping:
        print(cur_gene_region)
        include_gene.append(False)
    else:
        gene_ids.append(gene_region_mapping[cur_gene_region])
        include_gene.append(True)

combined_expression_table_genes_in_anno = combined_expression_table[include_gene]
combined_expression_table_genes_in_anno.index = gene_ids
Code 
combined_expression_table_genes_in_anno.to_csv(os.path.join("/beagle3/haky/users/saideep/projects/aracena_modeling/linearization",f"combined_expression_labeled_preds_RNAseq_{mode}_from_HDF5_mean_log_revised.csv"), index=True, index_label="NAME")
combined_expression_table_genes_in_anno
HG00096 HG00097 HG00099 HG00100 HG00101 HG00102 HG00103 HG00104 HG00105 HG00106 ... NA20810 NA20811 NA20812 NA20813 NA20814 NA20815 NA20816 NA20819 NA20826 NA20828
ENSG00000107554 6.176113 6.177222 6.176221 6.173289 6.173616 6.169876 6.176611 6.179381 6.178418 6.175015 ... 6.173263 6.160520 6.173274 6.162813 6.175375 6.159120 6.174253 6.161088 6.180325 6.175465
ENSG00000120054 6.382760 6.381888 6.383716 6.386971 6.383029 6.380874 6.388599 6.383591 6.380599 6.384809 ... 6.383235 6.380087 6.380182 6.379715 6.385091 6.379951 6.382305 6.377090 6.381080 6.380957
ENSG00000107566 6.092461 6.087451 6.097249 6.088495 6.089581 6.092341 6.090414 6.096685 6.085389 6.099591 ... 6.090416 6.089707 6.088715 6.087218 6.096573 6.115430 6.089532 6.088303 6.087579 6.088502
ENSG00000213341 6.120293 6.118823 6.128206 6.117380 6.120965 6.122586 6.120394 6.129291 6.123256 6.130027 ... 6.118963 6.121393 6.119843 6.120786 6.125049 6.125867 6.119252 6.114763 6.120289 6.120682
ENSG00000095485 6.167021 6.172599 6.165887 6.166842 6.167097 6.167953 6.181055 6.172719 6.180451 6.164558 ... 6.169954 6.163896 6.180362 6.173347 6.163775 6.165043 6.166249 6.176004 6.173405 6.165270
... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ...
ENSG00000147202 6.450532 6.450532 6.450532 6.450532 6.450532 6.450532 6.450532 6.450532 6.450532 6.450532 ... 6.450532 6.450532 6.450532 6.450532 6.450532 6.450532 6.450532 6.450532 6.450532 6.450532
ENSG00000204086 5.634237 5.634237 5.634237 5.634237 5.634237 5.634237 5.634237 5.634237 5.634237 5.634237 ... 5.634237 5.634237 5.634237 5.634237 5.634237 5.634237 5.634237 5.634237 5.634237 5.634237
ENSG00000101850 5.896976 5.896976 5.896976 5.896976 5.896976 5.896976 5.896976 5.896976 5.896976 5.896976 ... 5.896976 5.896976 5.896976 5.896976 5.896976 5.896976 5.896976 5.896976 5.896976 5.896976
ENSG00000146950 5.793148 5.793148 5.793148 5.793148 5.793148 5.793148 5.793148 5.793148 5.793148 5.793148 ... 5.793148 5.793148 5.793148 5.793148 5.793148 5.793148 5.793148 5.793148 5.793148 5.793148
ENSG00000234469 5.910339 5.910339 5.910339 5.910339 5.910339 5.910339 5.910339 5.910339 5.910339 5.910339 ... 5.910339 5.910339 5.910339 5.910339 5.910339 5.910339 5.910339 5.910339 5.910339 5.910339

19563 rows × 448 columns