Differential Analysis Using R’s Edger Economics Statistics Project

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Differential Analysis Using R’s Edger
Introduction
The datasets
Two datasets were used in this analysis where the in-depth analysis was performed on both data sets independently, and the results compared. The first dataset was obtained from The Cancer Genome Atlas (TGCA). The first dataset consisted of Lung adenocarcinoma gene expressions. The mRNAseq preprocessor picked the “scaled estimate” value from Illumina HiSeq/GA2 mRNAseq level_3 (v2) dataset and made the mRNAseq matrix with log2 transformed for the downstream analysis. Preprocessing had already been done, but the raw data was available if necessary. The second dataset was from Bioconductor, a study on lung cancer gene expression. The data was initially published on Bioconductor in 2004 (Scharpf R, Zhong S, Parmigiani G (2019). lungExpression: ExpressionSets for Parmigiani et al., 2004 Clinical Cancer Research paper., R package version 0.24.0). The dataset called “lungExpression” was represented as an ExpressionSet and was already preprocessed.
After observing the two sets of data, a search was embarked on the best ways to integrate the two platforms usefully. However, the challenge lied in finding the most effective means to this end, a current research goal. The goal aims at unifying the two studies and reporting on how their respective findings compare post-integration. There is hope to strengthen the findings made using the two platforms. Such findings ought to ultimately help in understanding the relationship between the human genome and lung cancer. There exists a need to find the best way to get the dataset from Broad Institute into a usable format in R. Further, translation of these datasets in an integrate-able way for further analysis would be essential. Therefore, edgeR would be used to complete a differential expression analysis of the integrated data set to compare the prior results.
However, there would be a chance of an inability to integrate the two platforms because of the lack of information needed to do so. Hence, an attempt to get what would be required from the publishers of the data or another publication would be conducted. If it fails, an in-depth analysis would be performed on both data sets independently and the results compared against each other. RNA sequencing (RNA-seq) has become a very widely used technology for profiling gene expression. One of the most common aims of RNA-seq profiling is to identify genes or molecular pathways that are differentially expressed (DE) between two or more biological conditions. Conclusively, the goal of this analysis was to find out what genetic features are related to lung cancer (FireBrowse, firebrowse.org/? cohort=LUSC#).
Methodology
The paper demonstrated a computational workflow for the detection of differentially expressed genes and pathways from RNA-seq data by providing a complete analysis of an RNA-seq experiment profiling of genetic features related to lung cancer. The workflow used R software packages from the open-source Bioconductor project, and it covered all steps of the analysis pipeline, including alignment of reading sequences, data exploration, differential expression analysis, visuali...