title: WemIQ: an accurate and robust isoform quantification method for RNA-seq data.
abstract: BACKGROUND
  The deconvolution of isoform expression from RNA-seq remains challenging because of non-uniform read sampling and subtle differences among isoforms.
  
  
  RESULTS
  We present a weighted-log-likelihood expectation maximization method on isoform quantification (WemIQ). WemIQ integrates an effective bias removal with a weighted expectation maximization (EM) algorithm to distribute reads among isoforms efficiently. The weight represents the oversampling or undersampling of sequence reads and is estimated through a generalized Poisson model without any presumption on the bias sources and formats. WemIQ significantly improves the quantification of isoform and gene expression as well as the derived exon inclusion rates. It provides robust expression estimates across different laboratories and protocols, which is valuable for the integrative analysis of RNA-seq. For the recent single-cell RNA-seq data, WemIQ also provides the opportunity to distinguish bias heterogeneity from true biological heterogeneity and uncovers smaller cell-to-cell expression variability.
authors.affiliation: Ming Hsieh Department of Electrical Engineering, University of Southern California, Los Angeles, CA 90089, USA and Molecular and Computational Biology, Department of Biological Sciences, University of Southern California, Los Angeles, CA 90089, USA.
authors.forename: Jing
authors.initials: J
authors.lastname: Zhang
authors.affiliation: Ming Hsieh Department of Electrical Engineering, University of Southern California, Los Angeles, CA 90089, USA and Molecular and Computational Biology, Department of Biological Sciences, University of Southern California, Los Angeles, CA 90089, USA.
authors.forename: C-C Jay
authors.initials: CC
authors.lastname: Kuo
authors.affiliation: Ming Hsieh Department of Electrical Engineering, University of Southern California, Los Angeles, CA 90089, USA and Molecular and Computational Biology, Department of Biological Sciences, University of Southern California, Los Angeles, CA 90089, USA.
authors.forename: Liang
authors.initials: L
authors.lastname: Chen
mesh_terms: D000465:Algorithms; D005091:Exons / Q000235:genetics*; D020869:Gene Expression Profiling / Q000379:methods*; D006801:Humans; D008962:Models, Theoretical; D020033:Protein Isoforms; D012313:RNA / Q000235:genetics*; D017423:Sequence Analysis, RNA / Q000379:methods*; D012984:Software
chemical_list: D020033:Protein Isoforms; D012313:RNA