README.md

In Facottons/DOAGDC: A Package to Download, Organize and Analyze Genomic Data Commons (GDC) Data

The DOAGDC package was developed to download, organize and analyze Genomic Data Commons (GDC) data. It uses several well-known R packages, as DESeq2, edgeR, mclust, and WGCNA. This is a free open-source software that implements academic research by the authors and co-workers. If you use it, please support the project by citing the appropriate journal article listed running the following command citation("DOAGDC").

Installation

# Checking for missing dependencies manually
if (!requireNamespace("BiocManager", quietly = TRUE))
    install.packages("BiocManager")
BiocManager::install(c("annotate", "AnnotationDbi", "BiocParallel", "clusterProfiler",
                    "DESeq2", "DO.db", "DOSE", "EBSeq", "edgeR", "gage",
                    "GO.db", "goseq", "IHW", "MineICA", "org.Hs.eg.db", "pathview",
                    "reactome.db", "ReactomePA", "SummarizedExperiment"))


# Get DOAGDC from GitHub:
install.packages("devtools") # if not already done
devtools::install_github("Facottons/DOAGDC")

Usage

There are some possible paths in DOAGDC analysis workflow:

The start point for all possible analyses is the download_gdc() function. As the name suggests, this function automatically download all selected data, and store it inside the appropriate folders. After this step, the download data can be concatenated into a single table using the concatenate_files() function. Then, choose which functions should be used in the next steps:

1. Groups Identification (like groups_identification_coxHR(), groups_identification_mclust(), or clinical_terms()).
2. Differential Expression Analysis (dea) (like dea_DESeq2(), dea_EBSeq(), and/or dea_edgeR()).
3. Visualization (like draw_heatmap(), or PCA_Analysis()).
4. Pathway enrichment analysis (like GOnto(), GSEA(), DO_React_enrich(), or KEEG_enrich()).

Example

This is a entire example that shows how to run a dea analysis, with groups generated by ‘Caspase-8-M-E’ expression:

library(DOAGDC)

# set the work dir path
DIR <- "/any/full/path/name"

# set the data type to use in groups identification step.
Data <- "protein"

# set the desired data base to be used ("gdc" or "legacy")
# for using GDC Legacy Archive
DB <- "legacy"

# set the tumor type
# for using 'Uterine Carcinosarcoma'
Tumor <- "UCS"


## step 1: Download
download_gdc(dataType = Data, tumor = Tumor, dataBase = DB,
    workDir = DIR)


## step 2: concatenate into a single table
# first normalized data to groups identification
concatenate_files(dataType = Data,
    Name = "Caspase-8-M-E",
    normalization = TRUE,
    tumorData = TRUE,
    dataBase = DB,
    workDir = DIR,
    tumor = Tumor)

It was created an object called UCS_LEGACY_protein_tumor_data, its name is based on your initial setups (i.e. tumor _ data-base _ data-type _tumor_data). This object prevents data of being messed up, keeping tumors, data bases, and data types in the correct environment. All downstream objects are going to be store inside this environment. From now on, you must use the name of this recent created object as value to the env function arguments.

# now, uses not normalized data, as required by the dea packages
# remember to use the object mentionate later
concatenate_files(dataType = "gene",
    normalization = FALSE,
    tumorData = TRUE,
    dataBase = "legacy",
    workDir = DIR,
    tumor = Tumor,
    env = UCS_LEGACY_protein_tumor_data)

## step 3: groups identification
# forcing three groups here ("G1", "G2", "G3")
groups_identification_mclust(dataType = Data,
    Name = "Caspase-8-M-E",
    tumor = Tumor,
    group.number = 3,
    dataBase = DB,
    workDir = DIR,
    env = UCS_LEGACY_protein_tumor_data)

## step 4: dea
# using edgeR 'exactTest' method
dea_edgeR(dataType = Data,
    Name = "Caspase-8-M-E",
    Method = "exactTest",
    workDir = DIR,
    env = UCS_LEGACY_protein_tumor_data)

## step 5: visualization
# PCA of 'G2_over_G1'
PCA_Analysis(Tool = "edgeR",
    dataType = Data,
    Name = "Caspase-8-M-E",
    pairName = "G2_over_G1",
    env = UCS_LEGACY_protein_tumor_data)

# Heat Map of 'G2_over_G1'
draw_heatmap(Tool = "edgeR",
    dataType = Data,
    Name = "Caspase-8-M-E",
    Method = "euclidean",
    pairName = "G2_over_G1",
    env = UCS_LEGACY_protein_tumor_data)

## step 6: Pathway enrichment analysis
# using only the 'Upregulated' genes
# powered by enrichGO (image below) and goseq
GOnto(condition = "Upregulated",
    Tool = "edgeR",
    env = UCS_LEGACY_protein_tumor_data)
}

More information

If you would like more information, please check the DOAGDC vignette or the DOAGDC Manual to learn even more.

Getting help

There are two main places to get help with DOAGDC:

1. The GitHub itself.

2. The Stack Overflow: It is a great source of programming questions and their answers. It is also an amazing place to get help with a helpful community. Before asking any questions, please read the how to ask guide.

Facottons/DOAGDC documentation built on April 7, 2020, 3:17 a.m.