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Hi,
I’ve tried Neural Admixture on the UKBiobank, but running it in the unsupervised mode got a lot of structure in the White Europeans instead of the continental ancestries like in your paper. Do you know what could be the issue? Here is what I tried:
neural-admixture train --k 6 --data_path 500k_sampleQC_LDpruned_allchr.bed --save_dir sample500k/outputs --name sample500k --initialization kmeans --num_cpus 24
According to some experiments I've ran myself, the training relies on the PCA, which is by itself prone to biases if an unbalanced dataset is given as an input. I would guess that a solution for this would be to either downsample the european individuals or train on a reference dataset, and infer the UKBiobank afterwards. Would it make sense?
Yes, I will try now to train on a reference, it makes sense that the UKBiobank is just too unbalanced. Was just curious as it seems to have worked in the paper in an unsupervised way but couldn't figure out which parameters were used.
Hi,
I’ve tried Neural Admixture on the UKBiobank, but running it in the unsupervised mode got a lot of structure in the White Europeans instead of the continental ancestries like in your paper. Do you know what could be the issue? Here is what I tried:
neural-admixture train --k 6 --data_path 500k_sampleQC_LDpruned_allchr.bed --save_dir sample500k/outputs --name sample500k --initialization kmeans --num_cpus 24
neural-admixture train --k 6 --epochs 50 --batch_size 400 --learning_rate 1e-5 --data_path 500k_sampleQC_LDpruned_allchr.bed --save_dir sample500k/outputs --name sample500k_Test2 --initialization kmeans --num_cpus 24
The PCAs look fine.
Thanks for your help!
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