nih-cfde · s-canchi · Feb 24, 2021 · Jan 29, 2021 · Jan 29, 2021 · Jan 29, 2021
diff --git a/docs/Bioinformatics-Skills/.pages b/docs/Bioinformatics-Skills/.pages
@@ -9,3 +9,4 @@ nav:
   - GWAS in the Cloud: GWAS-in-the-cloud
   - Snakemake Workflow Management: Snakemake
   - Simulate_Illumina_Reads.md
+  - RNAseq-on-Cavatica
diff --git a/docs/Bioinformatics-Skills/RNAseq-on-Cavatica/.pages b/docs/Bioinformatics-Skills/RNAseq-on-Cavatica/.pages
@@ -0,0 +1,11 @@
+nav:
+ - rna_seq_1.md
+ - rna_seq_2.md
+ - rna_seq_3.md
+ - rna_seq_4.md
+ - rna_seq_5.md
+ - rna_seq_6.md
+ - rna_seq_7.md
+ - rna_seq_8.md
+
+
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diff --git a/docs/Bioinformatics-Skills/RNAseq-on-Cavatica/rna-seq-supporting-docs/.Rapp.history b/docs/Bioinformatics-Skills/RNAseq-on-Cavatica/rna-seq-supporting-docs/.Rapp.history
diff --git a/docs/Bioinformatics-Skills/RNAseq-on-Cavatica/rna-seq-supporting-docs/Cancer_DGE_Analysis.R b/docs/Bioinformatics-Skills/RNAseq-on-Cavatica/rna-seq-supporting-docs/Cancer_DGE_Analysis.R
@@ -0,0 +1,172 @@
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+  # Author: Saranya Canchi
+  # Filename: Cancer_DGE_Analysis.R
+  # Purpose: R script for differential gene expression between 
+  #          pediatric cancer types using DESeq2 
+  # Version: 1.0
+  # Date: 01/22/2021
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+# Install libraries####
+
+if (!requireNamespace("BiocManager", quietly = TRUE))
+  install.packages("BiocManager")
+BiocManager::install(c("tximport",
+                       "regionReport",
+                       "org.Hs.eg.db",
+                       "pcaExplorer",
+                       "BiocStyle"), 
+                        update=FALSE,
+                        ask=FALSE)
+
+# Load libraries####
+
+library(GenomicFeatures)
+library(DESeq2)
+library(tximport)
+library(org.Hs.eg.db)
+library(pcaExplorer)
+library(regionReport)
+library(ggplot2)
+library(knitr)
+
+# Generate transcript and gene name table for gene level summary####
+## Using GenomicFeatures pkg to read in the reference GTF files - following steps from DESeq2 app
+
+data_dir <- "/sbgenomics/project-files"
+txdb <- makeTxDbFromGFF(file= file.path(data_dir,"Homo_sapiens.GRCh38.84.gtf"))
+k <- keys(txdb, keytype = "TXNAME")
+
+## HGNC gene name not available in list of filters. Using Ensemble gene name for one to one mapping. 
+
+tx2gene <- select(txdb, k, "GENEID", "TXNAME") 
+head(tx2gene)
+
+# Read the phenotype data####
+
+pheno_data <- read.csv(file.path(data_dir,"phenotype_filtered.csv"))
+str(pheno_data)
+
+## Converting covariates of interest to factors
+### Setting Ependymoma as reference factor level for histology variable
+
+pheno_data$histology <- factor(pheno_data$histology, 
+                               levels=c("Ependymoma", "Medulloblastoma"))
+pheno_data$tumor_location <- factor(pheno_data$tumor_location)
+pheno_data$diagnosis_age_range <- factor(pheno_data$diagnosis_age_range)
+
+# Generate gene level summary using tximport####
+
+head(list.files(data_dir))
+files <- file.path(data_dir, pheno_data$name)
+names(files) <- pheno_data$sample_id
+head(files)
+txi_sum <- tximport(files,
+                    type="kallisto",
+                    tx2gene=tx2gene,
+                    ignoreTxVersion = TRUE)
+names(txi_sum)
+head(txi_sum$counts)
+
+# DESeq2 import and analysis####
+
+dds_cancer <- DESeqDataSetFromTximport(txi_sum,
+                                       colData = pheno_data,
+                                       design = ~ diagnosis_age_range + tumor_location + histology)
+head(rownames(dds_cancer))
+
+## Generating additional gene info to add to dds object
+
+cancer_gene_map <- mapIds(org.Hs.eg.db,
+                          keys=rownames(dds_cancer),
+                          column="SYMBOL",
+                          keytype="ENSEMBL",
+                          multiVals="first")
+cancer_gene_map <- stack(cancer_gene_map)
+head(cancer_gene_map)
+colnames(cancer_gene_map)=c("gene_symbol","ensembl_gene_id")
+
+### Check row for row line up and add gene symbol column
+
+all(rownames(dds_cancer) == cancer_gene_map$ensembl_gene_id)
+mcols(dds_cancer) <- cbind(mcols(dds_cancer), cancer_gene_map$gene_symbol)
+colnames(rowData(dds_cancer))[1]="gene_symbol"
+
+# PCA interactive analysis - pcaExplorer####
+## Select Try again in the popup window to open in a new browser tab.
+
+pcaExplorer(dds=dds_cancer,
+            annotation=cancer_gene_map)
+
+## Alternatively use Variance stabilization transformation to generate PCA plot
+
+output_dir <- "/sbgenomics/output-files"
+vsd_cancer <- vst(dds_cancer, blind=FALSE)
+
+### Using histology and tumor_location variables
+
+png(filename=file.path(output_dir,"PCA_histology_tumor-location.png"))
+plotPCA(vsd_cancer, intgroup=c("histology","tumor_location"))
+dev.off()
+
+### Using histology and diagnosis_age_range variables
+
+png(filename=file.path(output_dir,"PCA_histology_diagnosis-age.png"))
+plotPCA(vsd_cancer, intgroup=c("histology","diagnosis_age_range"))
+dev.off()
+
+### Note: The PCA plots in the report generated by the DESeq2 app use rlog transformation. 
+# We can create the same plot using the following code:
+#rld_cancer <- rlog(dds_cancer, blind=FALSE)
+#plotPCA(rld_cancer, intgroup=c("histology"))
+
+
+# DGE analysis#### 
+
+dds_cancer <- DESeq(dds_cancer, 
+                    fit="parametric", 
+                    test='Wald', 
+                    betaPrior=TRUE, 
+                    parallel=TRUE)
+
+res_cancer <- results(dds_cancer,
+                      contrast=c("histology","Medulloblastoma","Ependymoma"),
+                      alpha=0.05)
+summary(res_cancer)
+resultsNames(dds_cancer)
+
+plotMA(res_cancer,
+       ylim=range(res_cancer$log2FoldChange, na.rm=TRUE))
+
+## Write the results to a table
+
+res_cancer_order <- res_cancer[order(res_cancer$pvalue),]
+write.csv(as.data.frame(res_cancer_order),
+          file=file.path(output_dir,"Cancer_DESeq2_DGE_results.csv"))
+
+## Write normalized counts to a file
+
+norm_counts <- counts(dds_cancer,normalized=TRUE)
+write.table(norm_counts, 
+            file=file.path(output_dir,"Cancer_DESeq2_normalized_counts.txt"),
+            sep="\t",
+            quote=FALSE,
+            col.names = NA) 
+
+# Create a report with all the visualization from the DESeq2 vignette####
+
+dir.create(file.path(output_dir,"DESeq2-Report"), 
+           showWarnings = FALSE, 
+           recursive = TRUE)
+
+report <- DESeq2Report(dds = dds_cancer, 
+                       project = 'Pediatric Cancer DGE Analysis with DESeq2', 
+                       intgroup = c('histology','diagnosis_age_range'),
+                       res = res_cancer,   
+                       outdir = file.path(output_dir,"DESeq2-Report"),
+                       output = 'Cancer-DESeq2-Report', 
+                       theme = theme_bw())
+
+save.image(file=file.path(output_dir,paste0("Cancer_DGE_",Sys.Date(), ".env.Rdata")))
+
+
diff --git a/...ormatics-Skills/RNAseq-on-Cavatica/rna-seq-supporting-docs/Cancer_DGE_Analysis_Automate.R b/...ormatics-Skills/RNAseq-on-Cavatica/rna-seq-supporting-docs/Cancer_DGE_Analysis_Automate.R
@@ -0,0 +1,110 @@
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+  # Author: Saranya Canchi
+  # Filename: Cancer_DGE_Analysis_Automate.R
+  # Purpose: R script for differential gene expression between 
+  #          pediatric cancer types using DESeq2 
+  # Version: 1.0
+  # Date: 01/22/2021
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+# Install libraries####
+
+if (!requireNamespace("BiocManager", quietly = TRUE))
+  install.packages("BiocManager")
+BiocManager::install(c("tximport",
+                       "regionReport",
+                       "BiocStyle"), 
+                       update=FALSE,
+                       ask=FALSE)
+
+# Load libraries####
+
+library(GenomicFeatures)
+library(DESeq2)
+library(tximport)
+library(regionReport)
+library(ggplot2)
+library(knitr)
+
+# Generate transcript and gene name table for gene level summary####
+## Using GenomicFeatures pkg to read in the reference GTF files - following steps from DESeq2 app
+
+data_dir <- "/sbgenomics/project-files"
+txdb <- makeTxDbFromGFF(file= file.path(data_dir,"Homo_sapiens.GRCh38.84.gtf"))
+k <- keys(txdb, keytype = "TXNAME")
+
+## HGNC gene name not available in list of filters. Using Ensemble gene name for one to one mapping. 
+
+tx2gene <- select(txdb, k, "GENEID", "TXNAME") 
+
+# Read the phenotype data####
+
+pheno_data <- read.csv(file.path(data_dir,"phenotype_filtered.csv"))
+
+## Converting covariates of interest to factors
+### Setting Ependymoma as reference factor level for histology variable
+
+pheno_data$histology <- factor(pheno_data$histology, levels=c("Ependymoma", "Medulloblastoma"))
+pheno_data$tumor_location <- factor(pheno_data$tumor_location)
+pheno_data$diagnosis_age_range <- factor(pheno_data$diagnosis_age_range)
+
+# Generate gene level summary using tximport####
+
+head(list.files(data_dir))
+files <- file.path(data_dir, pheno_data$name)
+names(files) <- pheno_data$sample_id
+txi_sum <- tximport(files,
+                    type="kallisto",
+                    tx2gene=tx2gene,
+                    ignoreTxVersion = TRUE)
+
+# DESeq2 import and analysis####
+
+dds_cancer <- DESeqDataSetFromTximport(txi_sum,
+                                       colData = pheno_data,
+                                       design = ~ diagnosis_age_range + tumor_location + histology)
+
+# DGE analysis#### 
+
+dds_cancer <- DESeq(dds_cancer, 
+                    fit="parametric", 
+                    test='Wald', 
+                    betaPrior=TRUE, 
+                    parallel=TRUE)
+
+res_cancer <- results(dds_cancer,
+                      contrast=c("histology","Medulloblastoma","Ependymoma"),
+                      alpha=0.05)
+
+## Write the results to a table
+
+output_dir <- "/sbgenomics/output-files"
+res_cancer_order <- res_cancer[order(res_cancer$pvalue),]
+write.csv(as.data.frame(res_cancer_order),
+          file=file.path(output_dir,"Cancer_DESeq2_DGE_results.csv"))
+
+## Write normalized counts to a file
+
+norm_counts <- counts(dds_cancer,normalized=TRUE)
+write.table(norm_counts, 
+            file=file.path(output_dir,"Cancer_DESeq2_normalized_counts.txt"),
+            sep="\t",
+            quote=FALSE,
+            col.names = NA) 
+
+# Create a report with all the visualization from the DESeq2 vignette####
+
+dir.create(file.path(output_dir,"DESeq2-Report"), 
+           showWarnings = FALSE, 
+           recursive = TRUE)
+
+report <- DESeq2Report(dds = dds_cancer, 
+                       project = 'Pediatric Cancer DGE Analysis with DESeq2', 
+                       intgroup = c('histology','diagnosis_age_range'),
+                       res = res_cancer,   
+                       outdir = file.path(output_dir,"DESeq2-Report"),
+                       output = 'Cancer-DESeq2-Report', 
+                       theme = theme_bw())
+
+save.image(file=file.path(output_dir,paste0("Cancer_DGE_",Sys.Date(), ".env.Rdata")))
+
diff --git a/docs/Bioinformatics-Skills/RNAseq-on-Cavatica/rna_seq_1.md b/docs/Bioinformatics-Skills/RNAseq-on-Cavatica/rna_seq_1.md
@@ -0,0 +1,60 @@
+---
+layout: page
+title: RNAseq Tutorial Overview
+---
+
+Differential Gene Expression Analysis on Cavatica Cloud Platform
+====================================================================
+
+**RNA sequencing (RNAseq)** is a high throughput technique that provides qualitative and quantitative information about RNA biology including transcriptome-wide expression quantification, discovery of novel genes and gene isoforms, and differential expression.
+
+The goal of this tutorial is to enable you to: </br>
+
+1. create virtual cancer cohorts using the NIH Common Fund-supported Gabriella Miller Kids First Data portal (KF Portal). </br>
+2. analyze the differential gene expression (DGE) on Cavatica, an integrated cloud based platform.
+
+You will learn two different approaches for DGE analysis using open access human cancer data on Cavatica: **(a)** using a public workflow app and **(b)** running code from an analysis script on an instance with RStudio computational environment.
+
+**Table of contents**
+
+| Est. Time| Lesson Name | Description|
+| ---|--------|--------|
+| 10 mins |[An Introduction to RNAseq](./rna_seq_2.md)| Background about RNAseq
+| 20 mins |[Selecting Kids First Cancer Cohort](./rna_seq_3.md)| Select Kids First open access cancer RNAseq files and push to Cavatica  |
+| 20 mins |[Cavatica - View, Filter, Tag and Download](./rna_seq_4.md) | Filter imported data, tag and download relevant metadata from Cavatica |
+| 20 mins |[Setup DESeq2 Public App](./rna_seq_5.md)| Setting up the workflow app based on DESeq2 on Cavatica |
+| 15 mins |[Phenotype File and Upload to Cavatica](./rna_seq_6.md) | Reformat metadata file and upload it to Cavatica |
+| 50 mins |[Analysis with DESeq2 Public App](./rna_seq_7.md) | Run the DESeq2 app with appropriate inputs and computational settings |
+| 60 mins |[Analysis using Data Cruncher](./rna_seq_8.md) | Analysis on an instance in the RStudio environment |
+
+!!! note "Learning Objectives"
+    * learn to build virtual cohorts on KF portal
+    * learn to navigate project folder and perform file operations on Cavatica
+    * learn to upload and download data from Cavatica
+    * learn to search, copy, and edit public workflow apps on Cavatica
+    * learn to perform differential gene expression (DGE) analysis using DESeq2 app
+    * learn to setup analysis environment and execute code for DGE analysis
+
+=== "Prerequisites"
+    * Setup: Integrated login accounts on Kid's First Data Portal & Cavatica - Follow our lessons on account setup and connecting the two accounts.
+           - [Setup Kids First account](../Kids-First/Portal-Setup-And-Permissions/KF_3_KF_Registration.md)
+           - [Register for Cavatica](../Kids-First/Portal-Setup-And-Permissions/KF_4_Cavatica_Registration.md)
+           - [Connect KF and Cavatica](../Kids-First/Portal-Setup-And-Permissions/KF_5_ConnectingAccounts.md)
+
+    !!! note "Login Credentials"
+
+        You do not need **eRA Commons ID** to do the lesson!
+
+    * Background: Knowledge of biology and rudimentary genetics.
+    * Technology: Basic knowledge of R and command line. Familiarity with RStudio is useful.
+    * Financial: Pilot funds ($100) are provided to every user on Cavatica with linked KF accounts.
+    * Time: Initial account setup may take hours to a day for verification. Setup of eRA Commons ID may take days and is institute dependent.
+=== "Est.Cost"
+    - DESeq2 app < $1.00
+    - Analysis with R < $1.00      
+=== "Tutorial Resources"
+    - [Kids First Data Portal](https://kidsfirstdrc.org)
+    - [Cavatica Documentation](https://docs.cavatica.org/docs/getting-started)
+    - [Playlist of video tutorials explaining concepts used in RNAseq analysis](https://www.youtube.com/playlist?list=PLblh5JKOoLUJo2Q6xK4tZElbIvAACEykp)
+    - [DESeq2 vignette](https://bioconductor.org/packages/release/bioc/vignettes/DESeq2/inst/doc/DESeq2.html#how-do-i-use-vst-or-rlog-data-for-differential-testing)
+    - [tximport](https://bioconductor.org/packages/release/bioc/vignettes/tximport/inst/doc/tximport.html)