Fused inverse-normal (FIN) method for meta analysis

Fused inverse-normal method for meta-analysis of RNA-seq data.

This repository contains an implementation of fused inverse-normal (FIN) method for meta-analysis of multiple but related RNA sequencing (RNA-seq) studies. The current code demonstrates the method for combination of p-values from three different real glioblastoma RNA-seq studies and can be adapted to any number of RNA-seq gene expression studies.

Published article

Prasad B, Li X. Fused inverse-normal method for integrated differential expression analysis of RNA-seq data. 2021 (submitted), preprint: https://arxiv.org/abs/2106.06865

Citation

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Prerequisites:

• Results from per-study differential analysis for RNA-seq studies. Popular methods such as DESeq and edgeR can be used for this step. These results should at least contain the raw p-values and log_2(FC) (logFC) from the individual differential analaysis.
• Assessment of the underlying assumption that p-values for all genes obtained from per-study differential analysis are uniformly distributed under the null hypothesis. Usually this assumption is not satisfied in case of RNA-seq data but filtering of weakly expressed genes using the method described in Rau et al. (2014)[1] or using the counts per million criteria described in Raithel et al. (2016)[2] circumvents this difficulty to a significant extent.
• R version 3.6.0 or above.

References

1. Rau A, Marot G, Jaffrézic F. Differential meta-analysis of RNA-seq data from multiple studies. BMC bioinformatics. 2014 Dec 1;15(1):91. DOI
2. Raithel S, Johnson L, Galliart M, Brown S, Shelton J, Herndon N, Bello NM. Inferential considerations for low-count RNA-seq transcripts: a case study on the dominant prairie grass Andropogon gerardii. BMC genomics. 2016 Dec 1;17(1):140. DOI