Gunady, M. K. | Search bioRxiv

bioRxiv2019

scGAIN: Single Cell RNA-seq Data Imputation using Generative Adversarial Networks

Single cell RNA sequencing (scRNA-seq) provides a rich view into the heterogeneity underlying a cell population. However single-cell data are usually noisy and very sparse due to the presence of dropout genes. In this work we propose an approach to impute missing gene expressions in single cell data using generative adversarial networks (GANs). By learning an approximate distribution of the data, our approach, scGAIN, can impute dropouts in simulated and real single cell data. The work in this paper discusses how to adopt GAIN training model into the domain of imputing single cell data. Experiments show that scGAIN gives competitive results compared to the state-of-the-art approaches while showing superiority in various aspects in simulation and real data. Imputation by scGAIN successfully recovers the underlying clustering of different subpopulations, provides sharp estimates around true mean expressions and increase the correspondence with matched bulk RNAseq experiments.

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bioRxiv2018

Fast and interpretable alternative splicing and differential gene-level expression analysis using transcriptome segmentation with Yanagi

IntroductionAnalysis of differential alternative splicing from RNA-seq data is complicated by the fact that many RNA-seq reads map to multiple transcripts, besides, the annotated transcripts are often a small subset of the possible transcripts of a gene. Here we describe Yanagi, a tool for segmenting transcriptome to create a library of maximal L-disjoint segments from a complete transcriptome annotation. That segment library preserves all transcriptome substrings of length L and transcripts structural relationships while eliminating unnecessary sequence duplications.\n\nContributionsIn this paper, we formalize the concept of transcriptome segmentation and propose an efficient algorithm for generating segment libraries based on a length parameter dependent on specific RNA-Seq library construction. The resulting segment sequences can be used with pseudo-alignment tools to quantify expression at the segment level. We characterize the segment libraries for the reference transcriptomes of Drosophila melanogaster and Homo sapiens and provide gene-level visualization of the segments for better interpretability. Then we demonstrate the use of segments-level quantification into gene expression and alternative splicing analysis. The notion of transcript segmentation as introduced here and implemented in Yanagi opens the door for the application of lightweight, ultra-fast pseudo-alignment algorithms in a wide variety of RNA-seq analyses.\n\nConclusionUsing segment library rather than the standard transcriptome succeeds in significantly reducing ambigious alignments where reads are multimapped to several sequences in the reference. That allowed avoiding the quantification step required by standard kmer-based pipelines for gene expression analysis. Moreover, using segment counts as statistics for alternative splicing analysis enables achieving comparable performance to counting-based approaches (e.g. rMATS) while rather using fast and lighthweight pseudo alignment.

bioinformatics↗