Human cancers are highly heterogeneous. However, due to the limit of technologies, the intercellular heterogeneity was not detectable genome-wide at single-cell level until recently. New technologies such as single-cell RNA-Seq and exome-Seq have revealed new insights and more profound complexity than what was previously thought. In collaboration with Dr. Weissman's group in the Genetics Department at Yale University, it is now feasible to perform multiple integrative assays from the same single tumor cell. However, rigorous computational methods are still lagging behind, in order to solve the computational challenge and determine true heterogeneity rather than noise. Here we propose a user-friendly bioinformatics platform that is optimized to integrative multiple types of single-cell NGS data, in particular, transcriptome, exome and CpG methylome data that are all obtained from the same cell. Specifically, in this study we will first construct and validate in parallel three new NGS bioinformatics pipelines to enable single-cell based RNA-Seq, exome- Seq, and CpG methylome data. We will then develop and validate an integration pipeline to analyze multiple types of high- throughput data, exemplified by the RNA-Seq, exome-Seq and CpG methylome single-cell data. To test the software suite, we will first obtain data sets from erythroleukemia cell line K562. We will then utilize this bioinformatics suite to investigate heterogeneity in Myeloid Leukemia patient samples provided by hematologist Dr. Stephanie Halene at Yale Stem Cell Center. Beyond deciphering tumor heterogeneity, this user-friendly bioinformatics platform is expected to be used widely by the single-cell sequencing community.

Public Health Relevance

The goal of this R01 proposal is to integrate multiple types of high-throughput data; in particular, the transcriptome, exome-sequencing and CpG methylome data generated from single leukemia cancer cells. The proposed project is designed to address the urgent need for user-friendly, integrative bioinformatics platform for single cell sequencing data. It is also aimed to understand the fundamental sources of tumor heterogeneity through data integration.

National Institute of Health (NIH)
National Library of Medicine (NLM)
Research Project (R01)
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Biomedical Library and Informatics Review Committee (BLR)
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Ye, Jane
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University of Michigan Ann Arbor
Biostatistics & Other Math Sci
Schools of Medicine
Ann Arbor
United States
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Alakwaa, Fadhl M; Chaudhary, Kumardeep; Garmire, Lana X (2018) Deep Learning Accurately Predicts Estrogen Receptor Status in Breast Cancer Metabolomics Data. J Proteome Res 17:337-347
Chaudhary, Kumardeep; Poirion, Olivier B; Lu, Liangqun et al. (2018) Deep Learning-Based Multi-Omics Integration Robustly Predicts Survival in Liver Cancer. Clin Cancer Res 24:1248-1259
Poirion, Olivier B; Chaudhary, Kumardeep; Garmire, Lana X (2018) Deep Learning data integration for better risk stratification models of bladder cancer. AMIA Jt Summits Transl Sci Proc 2017:197-206
Ching, Travers; Zhu, Xun; Garmire, Lana X (2018) Cox-nnet: An artificial neural network method for prognosis prediction of high-throughput omics data. PLoS Comput Biol 14:e1006076
Ching, Travers; Garmire, Lana X (2018) Pan-cancer analysis of expressed somatic nucleotide variants in long intergenic non-coding RNA. Pac Symp Biocomput 23:512-523
Chaudhary, Kumardeep; Poirion, Olivier B; Lu, Liangqun et al. (2018) Multimodal Meta-Analysis of 1,494 Hepatocellular Carcinoma Samples Reveals Significant Impact of Consensus Driver Genes on Phenotypes. Clin Cancer Res :
Poirion, Olivier; Zhu, Xun; Ching, Travers et al. (2018) Using single nucleotide variations in single-cell RNA-seq to identify subpopulations and genotype-phenotype linkage. Nat Commun 9:4892
Ortega, Michael A; Poirion, Olivier; Zhu, Xun et al. (2017) Using single-cell multiple omics approaches to resolve tumor heterogeneity. Clin Transl Med 6:46
Yang, Jennifer; Tanaka, Yoshiaki; Seay, Montrell et al. (2017) Single cell transcriptomics reveals unanticipated features of early hematopoietic precursors. Nucleic Acids Res 45:1281-1296
Garmire, Lana X; Gliske, Stephen; Nguyen, Quynh C et al. (2017) THE TRAINING OF NEXT GENERATION DATA SCIENTISTS IN BIOMEDICINE. Pac Symp Biocomput 22:640-645

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