Experimental validation of methods for differential gene expression analysis and sample pooling in RNA-seq

BMC Genomics

Jane H. Christensen³  Qibin Li¹  Anders D. Børglum⁴  Ole Mors²  Mette Nyegaard³  Jia Ju¹  Francesco Lescai³  Ross Lazarus⁵  Per Qvist³  Anto P. Rajkumar³ 

[1]Beijing Genomics Institute, Shenzhen 518083, China

[2]Research Department P, Aarhus University Hospital, Risskov, Denmark

[3]Center for Integrative Sequencing, iSEQ, Aarhus University, Aarhus 8000, Denmark

[4]Translational Neuropsychiatry Unit, Aarhus University, Aarhus 8240, Denmark

[5]Computational Biology, Baker IDI heart and diabetes institute, Victoria 8008, Australia

关键词: Sensitivity and specificity;    Quantitative real-time polymerase chain reaction;    Predictive value of tests;    Next-generation RNA Sequencing;    Gene expression;   

Others  :  1221880
DOI  :  10.1186/s12864-015-1767-y

 received in 2014-09-01, accepted in 2015-07-10,  发布年份 2015

【 摘 要 】

Background

Massively parallel cDNA sequencing (RNA-seq) experiments are gradually superseding microarrays in quantitative gene expression profiling. However, many biologists are uncertain about the choice of differentially expressed gene (DEG) analysis methods and the validity of cost-saving sample pooling strategies for their RNA-seq experiments. Hence, we performed experimental validation of DEGs identified by Cuffdiff2, edgeR, DESeq2 and Two-stage Poisson Model (TSPM) in a RNA-seq experiment involving mice amygdalae micro-punches, using high-throughput qPCR on independent biological replicate samples. Moreover, we sequenced RNA-pools and compared their results with sequencing corresponding individual RNA samples.

Results

False-positivity rate of Cuffdiff2 and false-negativity rates of DESeq2 and TSPM were high. Among the four investigated DEG analysis methods, sensitivity and specificity of edgeR was relatively high. We documented the pooling bias and that the DEGs identified in pooled samples suffered low positive predictive values.

Conclusions

Our results highlighted the need for combined use of more sensitive DEG analysis methods and high-throughput validation of identified DEGs in future RNA-seq experiments. They indicated limited utility of sample pooling strategies for RNA-seq in similar setups and supported increasing the number of biological replicate samples.

【 授权许可】

   
2015 Rajkumar et al.

【 图 表 】

Fig. 1.

Fig. 2.

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