Genomics

Dataset Information

37

Single cell RNA-seq data of human hESCs to evaluate SCnorm: robust normalization of single-cell rna-seq data


ABSTRACT: Normalization of RNA-sequencing data is essential for accurate downstream inference, but the assumptions upon which most methods are based do not hold in the single-cell setting. Consequently, applying existing normalization methods to single-cell RNA-seq data introduces artifacts that bias downstream analyses. To address this, we introduce SCnorm for accurate and efficient normalization of scRNA-seq data. Overall design: Total 183 single cells (92 H1 cells, 91 H9 cells), sequenced twice, were used to evaluate SCnorm in normalizing single cell RNA-seq experiments. Total 48 bulk H1 samples were used to compare bulk and single cell properties. For single-cell RNA-seq, the identical single-cell indexed and fragmented cDNA were pooled at 96 cells per lane or at 24 cells per lane to test the effects of sequencing depth, resulting in approximately 1 million and 4 million mapped reads per cell in the two pooling groups, respectively.

INSTRUMENT(S): Illumina HiSeq 2500 (Homo sapiens)

SUBMITTER: Rhonda Bacher  

PROVIDER: GSE85917 | GEO | 2017-04-05

SECONDARY ACCESSION(S): PRJNA339754

REPOSITORIES: GEO

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Publications

SCnorm: robust normalization of single-cell RNA-seq data.

Bacher Rhonda R   Chu Li-Fang LF   Leng Ning N   Gasch Audrey P AP   Thomson James A JA   Stewart Ron M RM   Newton Michael M   Kendziorski Christina C  

Nature methods 20170417 6


The normalization of RNA-seq data is essential for accurate downstream inference, but the assumptions upon which most normalization methods are based are not applicable in the single-cell setting. Consequently, applying existing normalization methods to single-cell RNA-seq data introduces artifacts that bias downstream analyses. To address this, we introduce SCnorm for accurate and efficient normalization of single-cell RNA-seq data. ...[more]

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