Poster abstracts

Poster number 20 submitted by Pearlly Yan

CLEAR: Coverage-based Limiting-cell Experiment Analysis for RNA-seq

Pearlly Yan (Department of Internal Medicine, The Ohio State University), Michael G Sovic, Xi Chen (OSUCCC - Genomics Shared Resources, The Ohio State University), Chi-Ling Chiang, Eileen Hu (Department of Internal Medicine, The Ohio State University), Jiyeon Denninger, Elizabeth Kirby (Department of Psychology, The Ohio State University), Logan A Walker, Ralf Bundschuh (Department of Physics, The Ohio State University), John C Byrd, Natarajan Muthusamy (Department of Internal Medicine, The Ohio State University)

Abstract:
Direct cDNA preamplification protocols developed for single-cell RNA-seq have enabled transcriptome profiling of small clinical samples and rare cells without sample pooling and RNA extraction. Currently, there is no algorithm to distinguish noisy transcripts from robust transcripts in limiting-cell RNA-seq (lcRNA-seq) data to permit their removal from downstream analyses. Herein, we present CLEAR, a workflow that identifies reliably quantifiable transcripts in lcRNA-seq data for differentially expressed gene (DEG) analysis. Libraries at three input amounts from FACS-derived CD5+ and CD5- cells from a single chronic lymphocytic leukemia patient were used to develop CLEAR. When using CLEAR transcripts vs. using all transcripts, downstream analyses reveal more shared transcripts across different input RNA amounts, and improved Principal Component Analysis separation and more DEGs between cell types. As proof-of-principle, CLEAR was applied to an in-house lcRNA-seq dataset and two publicly available datasets. CLEAR can be used in large clinical studies by imputing signal dropouts.

References:
Bhargava et al. (2014). Technical Variations in Low-Input RNA-Seq Methodologies. Scientific Reports, 3678.
Feng et al. (2015). mRIN for Direct Assessment of Genome-Wide and Gene-Specific mRNA Integrity from Large-Scale RNA-Sequencing Data. Nature Communications, 7816.
Ilicic et al. (2016). Classification of low quality cells from single-cell RNA-seq data. Genome Biology, 17(29).
Lun et al. (2016). Pooling across cells to normalize single-cell RNA sequencing data with many zero counts. Genome Biology, 17(75).

Keywords: rare cells, pre-filtering, differential gene expression analysis