ERStruct 0.2.1

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

1. Data files must be of numpy array .npy format. Users can convert VCF (variant call format) file in to numpy array via vcfnp package： https://pypi.org/project/vcfnp/, and convert bgen file in to numpy array via bgen-reader package： https://pypi.org/project/bgen-reader/.
2. The data matrix must with 0,1,2 and/or NaN (for missing values) entries only. Noting that our package imputes all the missing data (NaN) by 0. Users may perform other types of imputations beforehand.
3. 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

Parameters

erstruct(n, path, rep, alpha, cpu_num=1, device_idx="cpu", varm=2e8, Kc=-1)

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

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

rep (int) - number of simulation times for the null distribution (set to 5000 by default). We recommend to use rep between 2/alpha and 5/alpha.

alpha (float) - significance level, can be either a scaler or a vector (set to 1e-3 by default)

Kc (int) - a coarse estimate of the top PCs number (set to -1 by default, denoting Kc = floor(n/10) when the algorithm running)

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

Import ERStruct algorithm

from ERStruct import erstruct

Download sample dataset (the dataset consists of chromosome 21 and chromosome 22 information for 500 individuals obtained from sequencing data of the 1000 Genomes Project.):

from ERStruct import download_sample
download_sample()

Run ERStruct algorithm on sample dataset with CPUs:

test = erstruct(500, ['chr21.npy', 'chr22.npy'], 1000, 5e-3, cpu_num=1, device_idx="cpu")
K = test.run()

Run ERStruct algorithm on sample dataset with GPUs:

test = erstruct(500, ['chr21.npy', 'chr22.npy'], 1000, 5e-3, device_idx="gpu", varm=2e8)
K = test.run()

Other Details

Please refer to our paper

ERStruct: A Python Package for Inferring the Number of Top Principal Components from Whole Genome Sequencing Data

For details of the ERStruct algorithm:

ERStruct: An Eigenvalue Ratio Approach to Inferring Population Structure from Sequencing Data

If you have any question, please contact the email [e-mail].