DOI: 10.5061/dryad.66t1g1k0h
title: Two-step mixed model approach to analyzing differential alternative RNA splicing: Datasets and R scripts for analysis of alternative splicing

description: Changes in gene expression can correlate with poor disease outcomes in two
 ways: through changes in relative transcript levels or through alternative
 RNA splicing leading to changes in relative abundance of individual
 transcript isoforms. The objective of this research is to develop new
 statistical methods in detecting and analyzing both differentially
 expressed and spliced isoforms, which appropriately account for the
 dependence between isoforms and multiple testing corrections for the
 multi-dimensional structure of at both the gene- and isoform- level. We
 developed a linear mixed effects model-based approach for analyzing the
 complex alternative RNA splicing regulation patterns detected by
 whole-transcriptome RNA-sequencing technologies. This approach thoroughly
 characterizes and differentiates three types of genes related to
 alternative RNA splicing events with distinct differential
 expression/splicing patterns. We applied the concept of appropriately
 controlling for the gene-level overall false discovery rate (OFDR) in this
 multi-dimensional alternative RNA splicing analysis utilizing a two-step
 hierarchical hypothesis testing framework. In the initial screening test
 we identify genes that have differentially expressed or spliced isoforms;
 in the subsequent confirmatory testing stage we examine only the isoforms
 for genes that have passed the screening tests. Comparisons with other
 methods through application to a whole transcriptome RNA-Seq study of
 adenoid cystic carcinoma and extensive simulation studies have
 demonstrated the advantages and improved performances of our method. Our
 proposed method appropriately controls the gene-level OFDR, maintains
 statistical power, and is flexible to incorporate advanced experimental
 designs.
description: The dataset was collected through whole-transcriptome
 RNA-Sequencing technologies. The processing method was described in the
 manuscript.
description: Tools.R: An R script file that includes all the
 functions for performing the analysis with the method proposed in our
 manuscript. Tutorial.html: A step-by-step tutorial
 illustrating how to perform the analyses that we have proposed in our
 manuscript using the functions included in Tools.R.
 data4demo.RData: The R data set used in the above tutorial. It
 consists of a subset of 400 genes from the RNA-Sequencing study of adenoid
 cystic carcinoma (ACC) reported in our manuscript with the genes selected
 for ease of computation and demonstration. The data set contains isoform
 abundances of the 400 genes from 14 ACC patients. Eight of the patients
 are free of cancer, while the rest of the six are not during the follow-up
 time.  ACC.html: This is an html documentation to
 explain the detailed steps and R codes for the application of our proposed
 method to an RNA-sequencing study of adenoid cystic carcinoma
 (ACC). acc.RData: R Data set used in the above
 analysis of the RNA-Sequencing Study of Adenoid Cystic Carcinoma
 (ACC). The data set contains isoform abundances of the 2820 genes from 14
 ACC patients. Eight of the patients are free of cancer, while the rest of
 the six are not during the follow-up time. AML.html:
 This is an html documentation to explain the detailed steps and R codes
 for the application of our proposed method to an RNA-sequencing study of
 Pediatric Acute Myeloid Leukemia (AML). AML_isoform_outcome.RData: R Data
 set used in the above analysis of the RNA-Sequencing Study of Pediatric
 Acute Myeloid Leukemia (ACC).