Normalization and noise reduction for single cell RNA-seq experiments

Bioinformatics

Volumn 31, Issue 13, 2015, Pages 2225-2227

  Ding, Bo ^a   Zheng, Lina ^a   Zhu, Yun ^a   Li, Nan ^a   Jia, Haiyang ^a   Ai, Rizi ^a,b   Wildberg, Andre ^a   Wang, Wei ^a,c  

^a UNIVERSITY OF CALIFORNIA   (United States)

^b JILIN UNIVERSITY   (China)

^c UNIVERSITY OF CALIFORNIA   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

COMPUTER PROGRAM; GENE EXPRESSION PROFILING; HIGH THROUGHPUT SEQUENCING; HUMAN; PROCEDURES; SEQUENCE ANALYSIS; SIGNAL NOISE RATIO; SINGLE CELL ANALYSIS;

GENE EXPRESSION PROFILING; HIGH-THROUGHPUT NUCLEOTIDE SEQUENCING; HUMANS; SEQUENCE ANALYSIS, RNA; SIGNAL-TO-NOISE RATIO; SINGLE-CELL ANALYSIS; SOFTWARE;

EID: 84936803955     PISSN: 13674803     EISSN: 14602059     Source Type: Journal    
DOI: 10.1093/bioinformatics/btv122     Document Type: Article

References (6)

1. Brennecke,P. et al. (2013) Accounting for technical noise in single-cell RNA-seq experiments. Nat. Methods, 10, 1093-1095.
2. Bullard,J.H. et al. (2010) Evaluation of statistical methods for normalization and differential expression in mRNA-Seq experiments. BMC Bioinformatics, 11, 94.
3. Jaitin,D.A. et al. (2014) Massively parallel single-cell RNA-seq for markerfree decomposition of tissues into cell types. Science, 343, 776-779.
4. Robinson,M.D. and Oshlack,A. (2010) A scaling normalization method for differential expression analysis of RNA-seq data. Genome Biol., 11, R25.
5. Treutlein,B. et al. (2014) Reconstructing lineage hierarchies of the distal lung epithelium using single-cell RNA-seq. Nature, 509, 371-375.
6. Tu,Q. et al. (2012) Gene structure in the sea urchin Strongylocentrotus purpuratus based on transcriptome analysis. Genome Res., 22, 2079-2020.