pyVMO 0.0.5

A test python toolkit for variant site analysis

pyVMO

pyVMO (python Variant Memmap Object), a python toolkit to help you work with very large variational matrices.

Installation

pip install pyvmo

Usage

From GWAS

1. Compress and index your raw vcf files

bgzip your_raw.vcf
tabix -p vcf your_raw.vcf.gz

2. Convert the vcf file to vmo, which is a numpy based memmap file (memory map) that will help you read oversized matrices to get the job done in limited memory

shell

PyVMO converter -vcf2vmo your_raw.vcf.gz test.vmo

python

from pyvmo import VMO

vmo_path = "give_me_your_vmo_path"
raw_vcf_file = "your_raw.vcf.gz"

vmo = VMO(vmo_path)
vmo.store_vcf_to_vmo(raw_vcf_file)

3. Extraction of useful submatrices by sample listing, variant site quality control

shell

PyVMO extractor sample.id.list test.vmo filter.vmo

python

# extract by sample list
spl_idx_list = vmo.get_samples_index(sample_id_list)
spl_vmo_path = "give_me_your_sample_list_filtered_vmo_path"
spl_vmo = vmo.extract_sub_vmo(spl_vmo_path, spl_idx_list=spl_idx_list)

# extract by variant site quality control
var_idx_list = spl_vmo.site_filter(maf_thr=0.05, mis_thr=0.5, chunk_size=1000, n_jobs=20)
var_vmo_path = "give_me_your_variant_site_filtered_vmo_path"
var_vmo = spl_vmo.extract_sub_vmo(var_vmo_path, var_idx_list=var_idx_list)

4. Convert the vmo file into bimbam format

shell

PyVMO converter -vmo2bimbam filter.vmo filter.bimbam

python

bimbam_file = "give_me_your_bimbam_file_path"
var_vmo.to_bimbam(bimbam_file)

5. get genetic distance matrix

shell

PyVMO distance filter.vmo ibs.matrix

From Other practices

1. Get numpy array from vmo

m = var_vmo.get_matrix()

2. Get the sample list

sample_list = var_vmo.get_sample_info()

3. Get the variant information in a pandas dataframe

from toolbiox.lib.common.sqlite_command import pickle_load_obj, pickle_dump_obj

var_info_df = var_vmo.get_variant_info()

var_index = 1

chr_id = var_info_df.iloc[i]['CHROM']
pos = int(var_info_df.iloc[i]['POS'])
ref = var_info_df.iloc[i]['REF']
alt = pickle_load_obj(var_info_df.iloc[i]['ALT']) # alt is a list
qual = pickle_load_obj(var_info_df.iloc[i]['QUAL'])
filter = pickle_load_obj(var_info_df.iloc[i]['FILTER'])
info = pickle_load_obj(var_info_df.iloc[i]['INFO']) # info is a dict