Practice 3 : Differential expression analysis using EdgeR and DESeq2<\/h3>\n| Practice 3 will be performed in PIVOT via R Studio.<\/td>\n PIVOT: Platform for Interactive analysis and Visualization Of Transcriptomics data
\nQin Zhu, Junhyong Kim Lab, University of Pennsylvania
\nOct 26th, 2017<\/p>\n Intallation of PIVOT<\/h4>\nDependencies that needs to be manually installed.
\nYou may need to paste the following code line by line
\nand choose if previously installed packages should be updated (recommended).<\/p>\n install.packages("devtools") \nlibrary("devtools")\ninstall.packages("BiocManager")\nBiocManager::install("BiocUpgrade") \n#BiocManager::install("GO.db")\nBiocManager::install("HSMMSingleCell")\n#BiocManager::install("org.Mm.eg.db")\n#BiocManager::install("org.Hs.eg.db")\nBiocManager::install("DESeq2")\nBiocManager::install("SingleCellExperiment")\nBiocManager::install("scater")\nBiocManager::install("monocle")\nBiocManager::install("GenomeInfoDb")<\/code><\/pre>\nInstall PIVOT<\/h4>\ninstall_github("qinzhu\/PIVOT")\nBiocManager::install("BiocGenerics") # You need the latest BiocGenerics >=0.23.3<\/code><\/pre>\nLaunch PIVOT<\/h3>\nTo run PIVOT, in Rstudio console related to a web shiny interface, use command<\/p>\n library(PIVOT)\npivot()<\/code><\/pre>\nGo to your web browser.<\/p>\n
\nIntroduction<\/h3>\nAs you have seen, RNA sequencing (RNA-seq) is an experimental method for measuring RNA expression levels via high-throughput sequencing of small adapter-ligated fragments.
\nThe number of reads that map to a gene is the proxy for its expression level.
\nThere are many steps between receiving the raw reads from the sequencer and gaining biological insight.
\nIn this tutorial, we will start with a "Table of counts" and end with a "List of differentially expressed genes".<\/p>\n Step 1 : Data Input<\/h4>\nTo input expression matrix, select \u201cCounts Table\u201d as input file type. PIVOT expects the count matrix to have rows as genes and samples as columns.
\nGene names and sample names should be the first column and the first row, respectively.<\/p>\n \n- Go to file Tab.<\/li>\n
- Take the count file
gene_count_matrix.csv<\/code> generated previously.<\/li>\n- Import this file into Data input and then Input file type.<\/li>\n
- Add Use Tab separator to Skip Rows.<\/li>\n
- Check if yours data are imported in the rigth window.<\/li>\n<\/ul>\n
Step 2 : Input Design Information<\/h4>\nThe design infomation are used for sample point coloring and differential expression analysis. Users can input the entire sample meta sheet as
\ndesign information, or manually specify groups or batches for each sample.<\/p>\n \n- Go to DESIGN.<\/li>\n
- Go to Designed Table Upload. Upload
info.txt<\/code><\/li>\n- Verify that the header of the info file corresponds to the count file. <\/li>\n
- Choose the Separator : Space or the appropriate separator.<\/li>\n
- Verify on the Design Table Preview and submit design.<\/li>\n
- \n
Choose the Normalieation Method : <\/p>\n \n- for Edge R you can use
DESeq, Trimmed Mean of M-values TMM, or Upperquartile<\/code>.<\/li>\n- for DESeq you can use
DESeq, Modifed DESeq<\/code><\/li>\n<\/ul>\nIn order to have a quick view of your chosen data, look at the summary. <\/p>\n<\/li>\n<\/ul>\n step 3 : Feature Filtering<\/h4>\n\n- There are currently 3 types of feature filter in PIVOT: the expression filter, which filters based on various expression statistics; <\/li>\n
- You can choose the filter criteria. <\/li>\n<\/ul>\n
Step 4 : Select samples<\/h4>\n\n- Go To SAMPLE <\/li>\n
- Select your sample and condition,\n
|
![\"\"](\"https:\/\/southgreenplatform.github.io\/trainings\/\/images\/trainings-galaxy.png\")
\n<\/td>\n<\/tr>\n<\/table>\n![\"\"](\"https:\/\/southgreenplatform.github.io\/trainings\/\/images\/toggleLogo2.png\")
<\/p>\n