This document summarizes three papers on deep learning approaches for analyzing omics data: 1) A study that used denoising autoencoders to extract features from breast cancer gene expression data and found the features were linked to clinical characteristics and survival outcomes. 2) A study that used stacked denoising autoencoders to classify breast cancer samples and identify predictive genes related to diagnosis. 3) A study that integrated gene expression and miRNA data with autoencoders to identify survival subgroups in liver cancer patients, which were validated in additional cohorts and found to activate distinct pathways.

1. Deep Learning based
Multi-omics integration
A survey

2. Deep Learning in Bioinformatics
Min, Seonwoo, Byunghan Lee, and Sungroh Yoon. "Deep learning in bioinformatics." Briefings in Bioinformatics (2016)

3. Outline
• Summarize three related works on deep learning based
feature extraction / survival prediction on omics data
• Unsupervised feature construction and knowledge extraction from
genome-wide assays of breast cancer with denoising autoencoders
• A deep learning approach for cancer detection and relevant gene
identification
• Deep Learning based multi-omics integration robustly predicts
survival in liver cancer

4. Unsupervised feature construction and knowledge
extraction from genome-wide assays of breast cancer
with denoising autoencoders
Pacific Symposium on Biocomputing, 2015

5. Denoising Auto-Encoder (DAE)
• Build features that
reconstruct initial input data
from corrupted data
• Generate robust features
• Unsupervised learning
• Extract features in the non-
linear space

6. Data
• Two largest breast cancer dataset
• Train DAs and identify predictive features with METABRIC
dataset
• 2137 samples, 3000 2520 genes
• gene expression data from European Genomephenome Archive
• Evaluate with TCGA dataset independently
• 547 samples, 2520 genes

7. Features to clinical characteristics
• Genes are not linked to their neighbors
• Genes are linked by transcription
factors, pathway memberships
• Are constructed features linked to
clinical and molecular features of the
samples?
• Categorize tumor / normal samples
• Categorize ER+/- samples
• Categorize samples into molecular
subtypes(Luminal A/B, Basal-like, HER2-
enriched, Normal-like)

8. Features to clinical characteristics
• classifying tumor from
normal samples
• classifying ER + from ER -
samples
Robust performance across datasets

9. Features to transcription factor
• Breast cancer related transcription factors are linked to these high-
weight features (Node58)
• It contained genes that reflect activity of key ER-associated TFs
Most genes gave zero or low weight to a hidden node
High positive weightHigh negative weight

10. Features to patient survival
• Node whose activities best separated
two high / low survival groups
(Node5)
• Highly predictive of patient survival

11. Features to Biological pathways
• Pathways significantly associated with genes that consistently
gave high weights to a node
PID pathways enriched in Node5(5th feature)

12. Summary
• Unsupervised feature construction based on DAEs and
interpretation
• Apply to a breast cancer gene expression data
• Consistent results across different datasets
• In the future..
• Multiple layers of stacked DAEs
• Consistency across datasets will useful for data integration
• Limitations for large-scale data integration

13. A deep learning approach for cancer
detection and relevant gene identification
Pacific Symposium on Biocomputing, 2016

14. RNA-seq
samples
TCGA
Healthy
Cancer
Test Train
SDAE
features
DCGs
Model
Validation
weights
Overview
Supervised classification
(cancer detection)
Highly interactive genes
identification
1210 breast cancer samples

15. Stacked Denoising Auto-Encoder
• Extract functional features from high dimensional, noisy gene expression
profiles with reduced loss of information
• Select a layer has both low dimension and low validation error

16. Classification result
• Classify cancer samples from
healthy control samples
• Feature extraction
• SDAE
• Differentially expressed genes
(DIFFEXP)
• PCA
• KPCA (RBF kernel)
• Classification model
• SVM
• SVM (RBF kernel)
• single-layer ANN

17. Deeply connected genes
• Genes with the largest weights in W (the product of the
weight matrices for each layer) are the most strongly
connected to the extracted and highly predictive features
But lower performance than SDAE feateures
….

18. Summary
• SDAE to transform high-dimensional, noisy gene expression data
to a lower dimensional, meaningful representation
• Classify breast cancer samples from the healthy control samples
using new compact features
• Identify a set of highly interactive genes critical for the diagnosis
of breast cancer
• In the future..
• Need to improve the extraction of DCGs
• Limitation on the requirement for large data sets
• Identify cross-cancer biomarkers through the analysis of aggregated
heterogeneous cancer data

19. Deep Learning based multi-omics integration
robustly predicts survival in liver cancer
preprint, 2017

20. 360 tumor samples
15629 genes 365 miRNAs 19883 genes
100 features
37 features
high/poor survival

21. Why Autoencoders?
• Produce features linked to
clinical outcomes
• Analyze high-dimensional gene
expression data
• Integrate heterogeneous data
• Interpret the biological
functions (aggregate genes
sharing similar pathways)

22. Classification result
PCA

23. Classification result
Single-omics based
DL models

24. Validation in five cohorts
• Robustness of the model at predicting survival outcomes

25. Adding clinical information
• Age, Stage, Grade, Race, Risk factors (HBV, HCV, Alcohol, …)
• DL-based multi-omics model performs sufficiently well even
without clinical features

26. Functional analysis of the survival-subgroups
• KEGG pathway analysis to
pinpoint the pathways enriched
in two subtypes
• Two subtypes have different and
disjoint active pathways

27. Enriched pathway-gene analysis for upregulated genes
• S1 aggressive tumor
sub-group
• Enriched with cancer
related pathways

28. Enriched pathway-gene analysis for upregulated genes
• S2 less aggressive tumor
sub-group
• Activated metabolism
related pathways

29. Summary
• Contributions
• Identified two subtypes from the molecular level
• Consistent performance implying the reliability and robustness of
the model
• Sufficient performance without adding clinical features
• AE has much more efficiency to infer features linked to survival
• Validated in five additional cohorts
• Challenges
• The absence of cluster label information in original reports
• Lack of survival data in some cases

30. Conclusion
• Feature extraction with SDAE
• Robust to noisy datasets
• Extract meaningful features and reflect both linear and non-linear
relationships
• Consistent performance, good for multi-omics integration
• Multi-omics integration
• More sophisticated strategy to combine multiple features
• May incorporate pathways, handle overlapping genes

31. Thank you!
Q & A