README.md

In Kan-E/rnaseqviewer: Toolkit for automated, systematic, and integrated RNA-seq DEG analysis

rnaseqviewer

R package rnaseqviewer is a toolkit for automated, systematic, and integrated RNA-seq differential expression analysis. It has functions for DEG analysis of single dataset and intergrated DEG analysis of multiple DEG analysis. deseq2, edger and ebseq are functions for the DEG detection. Omics_overview, DEG_overview, and multiDEG_overview are functions for systematic visualization of clustering and DEG analysis. vennd, ORA, AutoExtraction and int_heatmap are functions for intergrated functional analysis. It also has two useful functions, kmeansClustering for k-means clustering analysis, and GeneSetConversion for gene symbol conversion from human to mouse.

Count matrix file format

Count matrix file format must be tab-separated text file(.txt). The replication number is represented by the underbar. Do not use it for anything else.

Examples

Installation

install.packages("devtools")
devtools::install_github("Kan-E/rnaseqviewer")

Usage

#Functions for DEG analysis
deseq2(Row_count_matrix,              #Row count data.txt (NOT normalized count data)
       method = "BH")                 #BH, Qvalue, or IHW 

edger(Row_count_matrix,               #Row count data.txt (NOT normalized count data)
       method = "BH")                 #BH, Qvalue, or IHW 

ebseq(Row_count_matrix)               #Row count data.txt (NOT normalized count data)

#Function for clustering analysis
Omics_overview(Count_matrix,          #normalized count data.txt
               heatmap = TRUE)        #In the case of FALSE, heatmap not shown                        

#Functions for visualization of DEG analysis
DEG_overview(Count_matrix,                            #normalized count data.txt
             DEG_result,                              #result data of EBseq (or DEseq2).txt
             Species = NULL,                          #human or mouse (for enrichment analysis)
             fdr = 0.05, fc = 2, basemean = 0)        #fdr ,fold change, and basemean threshold

multiDEG_overview(Normalized_count_matrix,            #normalized count data.txt
                  EBseq_result,                       #result data of EBseq.txt
                  EBseq_condmeans,                    #result data of EBseq.txt"
                  Species = NULL,                     #human or mouse (for enrichment analysis)
                  fdr = 0.05, fc = 2, basemeam = 0)   #fdr ,fold change, and basemean threshold

#Functions for integrated analysis
#venn diagram analysis 
vennd(gene_list_dir)                  #directory including gene list txt files (up to 7 files)

#Enrichment analysis
ORA(gene_list_dir,                    #directory including gene list txt files
    Species = "human",                #human or mouse 
    color = "qvalue")

#Boxplot and heatmap
AutoExtraction(Count_matrix,          #normalized count data.txt
               Gene_set_dir)          #directory including gene set txt files

#Integration of multiple count matrix files
int_heatmap(Count_matrix_dir,         #Directory including normalized count matrix txt files
            Gene_set,                 #gene set txt file
            pre_zscoring = T)         #option for zscoring before integration of data sets

#Other functions
kmeansClustering(Count_matrix,        #normalized count data.txt
                 Species = NULL,      #Species for enrichment analysis
                 km,                  #number of k-means clustering
                 km_repeats = 10000,  #number of k-means runs to get a consensus k-means clustering
                 basemean =0 )        #basemean threshold

GeneSetConversion(Gene_set_dir)       #directory including gene set txt files

Reference

EBSeq (for ebseq) - Ning Leng and Christina Kendziorski (2020). EBSeq: An R package for gene and isoform differential expression analysis of RNA-seq data. R package version 1.30.0.

DESeq2 (for deseq2) - Love, M.I., Huber, W., Anders, S. Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2 Genome Biology 15(12):550 (2014)

edgeR (for edger) - Robinson MD, McCarthy DJ and Smyth GK (2010). edgeR: a Bioconductor package for differential expression analysis of digital gene expression data. Bioinformatics 26, 139-140

IHW, Independent hypothesis weighting, and qvalue (for fdr control method of deseq2 and edger) - Nikolaos Ignatiadis, Bernd Klaus, Judith Zaugg and Wolfgang Huber (2016): Data-driven hypothesis weighting increases detection power in genome-scale multiple testing. Nature Methods 13:577, doi: 10.1038/nmeth.3885 - John D. Storey, Andrew J. Bass, Alan Dabney and David Robinson (2021). qvalue: Q-value estimation for false discovery rate control. R package version 2.26.0. http://github.com/jdstorey/qvalue

ggdendro (for dendrograms) - Andrie de Vries and Brian D. Ripley (2020). ggdendro: Create Dendrograms and Tree Diagrams Using 'ggplot2'. R package version 0.1.22. https://CRAN.R-project.org/package=ggdendro

clusterProfiler and DOSE (for enrichment analysis) - T Wu, E Hu, S Xu, M Chen, P Guo, Z Dai, T Feng, L Zhou, W Tang, L Zhan, X Fu, S Liu, X Bo, and G Yu. clusterProfiler 4.0: A universal enrichment tool for interpreting omics data. The Innovation. 2021, 2(3):100141 - Guangchuang Yu, Li-Gen Wang, Guang-Rong Yan, Qing-Yu He. DOSE: an R/Bioconductor package for Disease Ontology Semantic and Enrichment analysis. Bioinformatics 2015 31(4):608-609

AnnotationDbi, org.Hs.eg.db and org.Mm.eg.db (for genome wide annotation) - Hervé Pagès, Marc Carlson, Seth Falcon and Nianhua Li (2020). AnnotationDbi: Manipulation of SQLite-based annotations in Bioconductor. R package version 1.52.0. https://bioconductor.org/packages/AnnotationDbi - Marc Carlson (2020). org.Hs.eg.db: Genome wide annotation for Human. R package version 3.12.0. - Marc Carlson (2020). org.Mm.eg.db: Genome wide annotation for Mouse. R package version 3.12.0.

genefilter (for z-score normalization) - R. Gentleman, V. Carey, W. Huber and F. Hahne (2021). genefilter: methods for filtering genes from high-throughput experiments. R package version 1.72.1.

ComplexHeatmap (for heatmap and k-means clustering) - Gu, Z. (2016) Complex heatmaps reveal patterns and correlations in multidimensional genomic data. Bioinformatics.

ggplot2 and ggpubr (for boxplot and scater plot) - H. Wickham. ggplot2: Elegant Graphics for Data Analysis. Springer-Verlag New York, 2016. - Alboukadel Kassambara (2020). ggpubr: 'ggplot2' Based Publication Ready Plots. R package version 0.4.0. https://CRAN.R-project.org/package=ggpubr

venn (for venn diagram analysis) - Adrian Dusa (2021). venn: Draw Venn Diagrams. R package version 1.10. https://CRAN.R-project.org/package=venn

BioMart (for conversion of human gene symbol to mouse gene symbol) - Mapping identifiers for the integration of genomic datasets with the R/Bioconductor package biomaRt. Steffen Durinck, Paul T. Spellman, Ewan Birney and Wolfgang Huber, Nature Protocols 4, 1184-1191 (2009).

dplyr and tidyr (for data manipulation) - Hadley Wickham, Romain François, Lionel Henry and Kirill Müller (2021). dplyr: A Grammar of Data Manipulation. R package version 1.0.7. https://CRAN.R-project.org/package=dplyr - Hadley Wickham (2021). tidyr: Tidy Messy Data. R package version 1.1.3. https://CRAN.R-project.org/package=tidyr

Author

Kan Etoh [e-mail]

Kan-E/rnaseqviewer documentation built on May 30, 2022, 10:34 a.m.