ERStruct 0.1.6

Determine number of principle components based on sequencing data

ERstruct - Official Python Implementation

A Python package for inferring the number of top informative PCs that capture population structure based on genotype information.

Requirements for Data File

Data files must be of .npy format. The data matrix must with 0,1,2 and/or NaN (for missing values) entries only, the rows represent individuals and columns represent markers. If there are more than one data files, the data matrix inside must with the same number of rows.

Dependencies

ERStruct depends on numpy, torch and joblib.

Installation

Users can install ERStruct by running the command below in command line:

pip install ERStruct

Import the module

from ERStruct import erstruct

Parameters

erstruct(n, path, rep, alpha, cpu_num=1, device_idx="cpu", varm=1, Kc=-1)

n (int) - total number of individuals in the study

path (str) - the path of data file(s)

filename (list) - the name of the data file(s)

rep (int) - number of simulation times for the null distribution

alpha (float) - significance level, can be either a scaler or a vector

Kc (int) - a coarse estimate of the top PCs number (set to -1 by default)

cpu_num (int) - optional, number of CPU cores to be used for parallel computing. (set to 1 by default)

device_idx (str) - device you are using, "cpu" pr "gpu". (set to "cpu" by default)

varm (int): - Allocated memory (in bytes) of GPUs for computing. When device_idx is set to "gpu", the varm parameter can be specified to increase the computational speed by allocating the required amount of memory (in bytes) to the GPU. (set to 2e+8 by default)

Examples

Run the code on CPUs:

test = erstruct(2504, './', ['test_chr21', 'test_chr22'], 5000, 1e-4, cpu_num=1, device_idx="cpu")
K = test.run()

Run the code on GPUs:

test = erstruct(2504, './', ['test_chr21', 'test_chr22'], 5000, 1e-4, device_idx="gpu", varm=12000000000)
K = test.run()

Example data files test_chr21.npy and test_chr22.npy can be found on the "sample_data" of ERStruct GitHub repository.

Other Details

Please refer to our paper

An Eigenvalue Ratio Approach to Inferring Population Structure from Whole Genome Sequencing Data.