Skip to content

2. ugtm for ancestry clustering

Jump to bottom
Héléna A. Gaspar edited this page May 13, 2018 · 1 revision

Ancestry clustering

This tutorial will show you how to do ancestry clustering using GTM (our uGTM implementation) or t-SNE (sklearn implementation).

First step: download files

You can download files for ancestry classification using 1000 genomes Phase 3 data from here (http://lovingscience.com/ancestries/), which are already formatted for this software. In this tutorial, we will use the following files:

  • recoded_1000G.noadmixed.mat: 20 populations from 1000 Genomes Project (excluding MXL, ACB, ASW, ITU, STU, GIH)
  • recoded_1000G.raw.noadmixed.lbls3_3: the corresponding ancestry labels (GBR and CEU were merged to obtain one category for Northern/Western European ancestry)
  • recoded_1000G.raw.noadmixed.lbls3: the corresponding ancestry labels (GBR and CEU were not merged)
  • recoded_1000G.raw.noadmixed.ids: the corresponding individual IDs

You can find out how these files were created by clicking here.

Second step: visualization:

To build a map with parameters [k,m,l,s] = [16,4,0.1,0.3] and 10 principal components, run the following command:

python runGTM.py --model GTM --data recoded_1000G.noadmixed.mat --labels recoded_1000G.raw.noadmixed.lbls3 --labeltype discrete --out outputname --pca --n_components 10 --regularization 0.1 --rbf_width_factor 0.3 --missing --missing_strategy median --random_state 8 --ids recoded_1000G.raw.noadmixed.ids

It should be noted that our genotype file has missing values that we are handling with the --missing and --missing strategy options. You should obtain a pdf and an html file. The html file looks like this: 1000G_GTM_20populations.html

To build a t-SNE map, run:

python runGTM.py --model GTM --data recoded_1000G.noadmixed.mat --labels recoded_1000G.raw.noadmixed.lbls3 --labeltype discrete --out outputname --pca --n_components 10 --missing --missing_strategy median --random_state 8 --ids recoded_1000G.raw.noadmixed.ids

Click here to access the t-SNE map: 1000G_t-SNE_20populations.html

Third step: evaluation of classification performances in a crossvalidation experiment, compare GTM and linear SVM:

python runGTM.py --model GTM --data recoded_1000G.noadmixed.mat --labels recoded_1000G.raw.noadmixed.lbls3 --labeltype discrete --out outputname --pca --n_components 10 --missing --missing_strategy median --random_state 8 --crossvalidate

This will give you per-class reports. Default class priors are equiprobable (cf. --prior option).

Fourth step: train on provided data and project a test set onto the map:

python runGTM.py --model GTM --data recoded_1000G.noadmixed.mat --test recoded_1000G_MXL.mat --labels recoded_1000G.raw.noadmixed.lbls3 --labeltype discrete --out outputname --pca --n_components 10 --missing --missing_strategy median --random_state 8

This will give you:

  • predictions for individuals (output_indiv_predictions.csv)
  • posterior probabilities for each ancestry (output_indiv_probabilities.csv)
  • posterior probabilities for the whole test set (output_group_probabilities.csv)
  • a map with projected test set colored in black.

The projection for MXL population (Mexicans) can be visualized here: 1000G_GTM_projection_MXL.html

Clone this wiki locally