Analyses related to differential gene expression and functional annotation
library(devtools);
install_github("zhezhangsh/DEGandMore");
library(DEGandMore);
wget https://github.com/zhezhangsh/DEGandMore/raw/master/examples/gsea/GSEA_example.r
wget https://github.com/zhezhangsh/DEGandMore/raw/master/examples/gsea/GSEA_example.yaml
wget https://github.com/zhezhangsh/DEGandMore/raw/master/examples/gsea/GSEA_example.cls
wget https://github.com/zhezhangsh/DEGandMore/raw/master/examples/gsea/GSEA_example.gct
wget https://github.com/zhezhangsh/DEGandMore/raw/master/examples/gsea/gsea2-2.2.0.jar
wget https://github.com/zhezhangsh/DEGandMore/raw/master/examples/gsea/h.all.v5.0.symbols.gmt
wget https://github.com/zhezhangsh/DEGandMore/raw/master/examples/gsea/c2.cp.kegg.v5.0.symbols.gmt
wget https://github.com/zhezhangsh/DEGandMore/raw/master/examples/gsea/c2.cp.reactome.v5.0.symbols.gmt
wget https://github.com/zhezhangsh/DEGandMore/raw/master/examples/gsea/GENE_SYMBOL.chip
Rscript ./GSEA_example.r ./GSEA_example.yaml
# Alternatively, run the script as below if want to make the actual GSEA run later.
# The shell script will be save in ./RunGSEA.sh
Rscript ./GSEA_example.r ./GSEA_example.yaml norun
# Only the first time
library(devtools);
install_github("zhezhangsh/DEGandMore");
# For each run
library(DEGandMore);
GSEAviaJava('GSEA_example.yaml');
# Install and load the DEGandMore package
devtools::install_github("zhezhangsh/DEGandMore");
library(DEGandMore);
# Prepare the inputs following this example: https://github.com/zhezhangsh/DEGandMore/blob/master/examples/DeReport/inputs.rds?raw=true
# To load this example, download it to your working folder and call:
inputs<-readRDS('./inputs.rds');
# Create report by calling, where _inputs_ is the variable containing all the input data
CreateDeReport(inputs);
# This version currently requires a collection of gene sets, as in this example: https://github.com/zhezhangsh/DEGandMore/blob/master/examples/DeReport/default_set_human_5-1000.rds?raw=true
# To load this example, download it to your working folder and call:
geneset<-readRDS('./default_set_human_5-1000.rds');
the first column must be official gene symbol
expr: A data matrix includes processed gene expression data.
it is assumed that the data have been normalized and in linear scale (usually by log2-transformation)
indexes: Column indexes or column names of samples in expr, corresponding to the 2 sample groups. Each group must have more than 1 sample.
names: Names of two sample groups to be compared. By default, the first group is the control or reference.
genome: Name of the reference genome, in the form of "human", "hsa", or "hg38".
paired: Boolean indicates if the samples in the 2 groups are paired. Default is FALSE. If TRUE, the samples will be paired by their order.
penalty: Whether to give penalty to genes with high sample-sample variance within groups. No penalty if 0; highest possible penalty if 1.
homolog: A list of species to species mapping, that match each gene in anno to a gene in another species.
deg: Options of statistic test to identifiy differentially expressed genes (DEGs).
geneset: Location to file with all the gene sets to be tested for over-representative analsysis of DEGs.
output: Location to all output files
The gene clustering analysis can be used to identify gene subsets co-expressed across multiple sample groups. It can be run very easily with a single function call CreateClReport(fn.yaml).
library(DEGandMore);
CreateClReport(fn.yaml);
The function will download a couple of knitr templates from here and here and run the templates based on the information in the yaml file fn.yaml. This file defines all inputs of the analysis and can be downloaded from here. Please download this example to your local directory and edit it before making a run. Main elements of the yaml file include:
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