Novel multi-omics deconfounding variational autoencoders can obtain meaningful disease subtyping

TCGA pan-cancer mRNA and DNA data augmented with artificial confounders utilised in "Novel multi-omics deconfounding variational autoencoders can obtain meaningful disease subtyping" by Zuqi Li and Sonja Katz (manuscript in preparation).

The following data curation steps were carried out: 

Step 1. Download data from TCGA

R package `TCGAbiolinks`

2547 patients (after step 2) with 6 cancer types:

BRCA (731)

THCA (408)

BLCA (387)

LUSC (297)

HNSC (412)

KIRC (312)

mRNA expression profiles

DNAm expression profiles

Clinical data:

tumor stage: i, ia, ib, ii, iia, iib, iii, iiia, iiib, iiic, iv, iva, ivb, ivc, x

age at diagnosis

race: 'white', 'black or african amarican', 'asian', 'american indian or alaska native'

gender

Step 2. Removal criteria

Patients with

NA or 'not reported' clinical data

race 'american indian or alaska native'

tumor stage x

mRNA and DNAm probes with

0 variance across all included patients

not shared across all cancer types

with missing values

 Step 3. Encode clinical vairables and save datasets

mRNA dataset: 2547 patients x 58,456 mRNAs

DNAm dataset: 2547 patients x 232,088 DNAm

clinic dataset: 2547 patients x 6 variables    1. patient ID    2. tumor stage: 1, 1, 1, 2, 2, 2, 3, 3, 3, 3, 4, 4, 4, 4    3. age at diagnosis    4. race: asian(1), black or african amarican(2), white(3)    5. gender: female(0), male(1)    6. cancer type: BRCA(1), THCA(2), BLCA(3), LUSC(4), HNSC(5), KIRC(6)    

 Step 4. Pre-process the datasets

mRNA dataset: 'TCGA_mRNAs_processed.csv'

Take the 2000 mRNAs with highest variance

Rescale every feature to [0,1]

--> 2547 patients x 2000 mRNAs

DNAm dataset: 'TCGA_DNAm_processed.csv'

Take the 2000 DNAm with highest variance

Rescale every feature to [0,1]

--> 2547 patients x 2000 DNAm

clinic dataset: 'TCGA_clinic.csv'

Step 5. Simulate confounders (instructions can be found in Methods section of manuscript)

Linear confounder:

'TCGA_confounder_linear.csv' - linear confounding classes

'TCGA_DNAm_confounded_linear.csv' - linearly confounded DNAm data

'TCGA_mRNA2_confounded_linear.csv'  - linearly confounded mRNA data

Squared confounder

'TCGA_confounder.csv' - squared confounding classes

'TCGA_DNAm_confounded.csv' - squared confounded DNAm data

'TCGA_mRNA2_confounded.csv'  - squared confounded mRNA data

Categorical confounder 

'TCGA_confounder_categ2.csv' - categorical confounding classes

'TCGA_DNAm_confounded_categ2.csv' - categorically confounded DNAm data

'TCGA_mRNA2_confounded_categ2.csv'  - categorically  confounded mRNA data

Multiple confounders - combined effect (linear + squared + categorical)

'TCGA_confounder_multi.csv' - confounding classes for combined effect

'TCGA_DNAm_confounded_multi.csv' - DNAm data with combined effect

'TCGA_mRNA2_confounded_multi.csv'  - mRNA data with combined effect