Using broadSeq to analyze RNA-seq data

1 Introduction

To analyze RNA-seq count data, there are several ways/methods for each steps like

1. Transforming/scaling of the count data,

2. QC by clustering the samples using PCA, hierarchical clustering or multidimensional scaling

3. Most importantly identification of differentially expressed.

For each of these steps, there are different packages or tools whose input and output formats are very different. Therefore it is very difficult to use all these features from different packages in a study.

Figure 1: Input and output data structures of different methods to idetify differentially expressed genes

The broadSeq package simplifies the process of including many Bioconductor packages for RNA-seq data and evaluating their performance.