DEGandMore

Analyses related to differential gene expression and functional annotation

Install and load library

library(devtools);
install_github("zhezhangsh/DEGandMore");
library(DEGandMore);

Run GSEA on MSigDB collections and summarize outputs

• Make sure both of Rscript and java are in your PATH
• This example run will save all input and output files into the current directory. Create a new directory and set it as the current directory if you want to
• Copy these input files into your current directory. These files are for the example run only. Prepare your own input file to make a customized run

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    

• Run the GSEA_example script on shell

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

• Or, run the example within your R console or RStudio

# Only the first time 
library(devtools);
install_github("zhezhangsh/DEGandMore");

# For each run
library(DEGandMore);
GSEAviaJava('GSEA_example.yaml');

• Open the index.html file to view results

Run pairwise comparison using the DeReport template

# 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 inputs is a list with the following elements:

• anno: Gene annotation information as a data.frame.

  • the row names are unique Entrez gene IDs
  • the first column must be official gene symbol
    • the last column must be full gene name

• expr: A data matrix includes processed gene expression data.

• each row corresponds to a gene and each column corresponds to a sample

  • rows must exactly match the rows of anno, with the same IDs and the same number of rows

• 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 set collection is a list with 2 elements:

• meta: metadata of the genesets. each row is a gene set. include the following columns
  • Source: where the gene sets were collected, such as BioSystems, MSigDB, etc.
  • Collection: a specific category of the gene sets, such as GO_MF, KEGG_pathway, etc.
  • Name: common name of the gene set
  • Size: total number of genes in the geneset
  • URL: link to the source of the gene set
• list: list of gene sets named by the row names of meta, each element corresponds to a row in meta and include unique Entrez IDs of genes belonging to the gene set

Run gene clustering analysis using the ClReport template

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:

• Preprocessed gene expression matrix, which is usually rescaled to make mean and SD of each gene equal to 0 and 1.0
• Text description of the project and the analysis methods (please edit for each project)
• A collection of predefined gene sets. Click here to download an example
• All parameters to run the analysis (see the example for details), such as
  • How to select significant genes for initiating clusters
  • How to cluster genes and select clusters
  • How to refine clusters
  • How to analyze individual clusters