Differential expression analysis of RNA sequencing data compared by different statistical analysis
2014
Batool, A.
In past eras microarrays was the most imperative and commonly used methodology for phenotypic deviation in biology. Now the recent methodology which is known as RNA Sequencing developed. In present study we studied the identification of differential expression (DE) genes in RNA sequencing (RNA-seq) data. Sequencing assays provide digital measures of sequence abundance, i.e., read counts. The experiment was performed on planarian animals. The three experimental groups: control (RNAi), Ihx1/5-1(RNAi) and pitx (RNAi) were chosen for experiment. We used edgeR which is easily accessible within R framework, to identify the differential expression in planarian data. Systematic bias is inherent in genomic experiments data; TMM normalization method were applied to remove technical sources of variability. Negative binomial distribution based models was used for RNA-seq data. GLM likelihood ratio test was applied to identify the up-regulated and down-regulated genes. For the adjustment of p-value false discovery rate was used. On the basis of false discovery rate the final decision about up-regulated and down-regulated genes was done.
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