Accelerating Gene Set Enrichment Analysis on CUDA-Enabled GPUs - - PowerPoint PPT Presentation

Accelerating Gene Set Enrichment Analysis on CUDA-Enabled GPUs

Bertil Schmidt Christian Hundt

Contents

• Gene Set Enrichment Analysis (GSEA)

– Background – Algorithmic details

• cudaGSEA
• Performance evaluation

GSEA and Bioinformatics

• High throughput technologies generate large-scale

gene expression data sets

– RNA-Seq – Microarrays

• GSEA uses annotated gene sets to mine a given gene

expression matrix

– MSigDB contains over 10K signatures each containing around 100 gene identifiers on average

• Typical GSEA study:

– identify metabolic pathways that are differentially changed in human type-2 diabetes

Gene Set Enrichment Analysis

• Reveals correlation between gene

sets and diseases using gene expression data

• State-of-the-art tool with over

10,000 citations

• Written in (multi-threaded) Java
• Highly time consuming

– analyzing 20,639 genes measured in 200 patients with 4,725 pathways and 1M permutations takes around 1 week with GSEA 2.2.2 software on a CPU

• We present

– GSEA parallelization on a GPU using CUDA (cudaGSEA) – cudaGSEA around two orders-of- magnitude faster than BroadGSEA

GSEA Algorithm – Gene Ranking

• Gene expression matrix D obtained from RNA-Seq or Microarray experiments
• For each gene i and patient j with associated (binary) phenotype C expression value

D[i,j] is stored

• Diseases driven by complex gene interactions  simply reporting top-ranked genes

produce many false positives

• Domain experts provides set of genes that might possibly explain observed

phenotypes

GSEA Algorithm – Enrichment score

• Enrichment score (ES) measure correlation between given gene set S and

calculated gene ranking g(i)

– Report maximum deviation of a running sum (k) – Sum increases if we hit a member of S and decreases otherwise

• How significant is ES = 0.857?  p-value calculation using permutation testing

GSEA Algorithm – Permuation testing

GSEA Algorithm – Permuation testing

GSEA Algorithm

|ES|

• |ES|
• Histogram of 1,000,000 enrichment scores gained by permuting

patient phenotypes

• Estimate p-value by counting events in both tails
• Why so many permutations?

– When testing 1,000 gene sets at significance level p<0.001 we need more than 1,000,000 samples to reject null hypothesis at 1,000p < 0.001 (Bonferroni correction)

CUDA Parallelization

Transpose D to ensure coalesced memory accesses

CUDA Parallelization

CUDA Parallelization

CUDA Implementation Details

• Support for single-precision and double-precision
• Resulting matrix of enrichment scores (#gene sets x

#permutations) can be large

– e.g. 5K x 1M x 8B = 40GB

• p-value estimation, Family-wise error rate (FWER),

normalized enrichment score (NES) computation can be accomplished on the GPU with (sum/max) reduction kernels without the need for storing this matrix

• False discovery rate (FDR) computation this matrix is

transferred to the CPU for post-processing

cudaGSEA Features

• Reading data sets directly in Broad Institute-compatible file

formats

• Supporting several local deviation measures

– Mean-based measures (difference/quotient/log-quotient of means) – Mean and standard deviation-based measures (signal to noise- ratio, t-tests, one/two-pass estimation) – Numerically stable summation schemes for local measures and ES (Kahan etc.)

• Package for the R framework and standalone application
• Multi-threaded CPU version in C++ using OpenMP

• GSE19429 dataset

– collapsed to 20,639 gene symbols; 200 patients (183 cases + 17 controls)

• Hallmark: 50 gene sets

– MSigDB 5.1 smallest gene set collection

• GeForce Titan X (single precison) / Tesla K40c (double precision, ECC off), CUDA 7.5
• 10 core Xeon E5-2660v3@2.60GHz, 20 Threads, Ubuntu 14.04, gcc 4.8.4, 64-bit OpenJDK
• BroadGSEA v.2.2.2

Performance Evaluation

Performance Evaluation

• GSE19429 dataset

– collapsed to 20,639 gene symbols; 200 patients (183 cases + 17 controls)

• C2: 4726 gene sets

– MSigDB 5.1 largest gene set collection

• GeForce Titan X (single precison) / Tesla K40c (double precision, ECC off), CUDA 7.5
• 10 core Xeon E5-2660v3@2.60GHz, 20 Threads, Ubuntu 14.04, gcc 4.8.4, 64-bit OpenJDK
• BroadGSEA v.2.2.2

Conclusion

• High-throughput technologies establish the need for

scalable bioinformatics tools that can process large- scale gene expression data sets

• CUDA is a suitable technology to address this need
• cudaGSEA on one GPU achieves around two orders-of-

magnitude speedup versus BroadGSEA on a CPU

– analyzing 20,639 genes measured in 200 patients with 4,726 pathways and 1M permutations takes around 1 week with GSEA 2.2.2 on a Xeon E5-2660v3 CPU while less than 1 hour on a GeForce Titan X

• Source code available at:

– https://github.com/gravitino/cudaGSEA

• Group Website:

– https://www.hpc.informatik.uni-mainz.de/

Accelerating Gene Set Enrichment Analysis on CUDA-Enabled GPUs

Bertil Schmidt, Christian Hundt Institute of Computer Science Johannes Gutenberg University Mainz {bertil.schmidt, hundt}@uni-mainz.de

Thank you!