Feature/plotly

DESCRIPTION

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   tinytest (>= 1.2.3),
   ttdo (>= 0.0.6),
   UpSetR
-RoxygenNote: 7.1.1
+RoxygenNote: 7.1.2

OmicNavigatorExample-main-testing/.gitattributes

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+# linguist overrides
+# https://github.com/github/linguist/blob/master/docs/overrides.md
+# Ignore generated HTML report file
+results/report.html linguist-generated=true

OmicNavigatorExample-main-testing/.gitignore

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+.Rproj.user
+.Rhistory
+.RData
+.Ruserdata
+*.tar.gz

OmicNavigatorExample-main-testing/OmicNavigatorExample-main.Rproj

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+Version: 1.0
+
+RestoreWorkspace: Default
+SaveWorkspace: Default
+AlwaysSaveHistory: Default
+
+EnableCodeIndexing: Yes
+UseSpacesForTab: Yes
+NumSpacesForTab: 2
+Encoding: UTF-8
+
+RnwWeave: Sweave
+LaTeX: pdfLaTeX

OmicNavigatorExample-main-testing/OmicNavigatorExample.Rproj

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+Version: 1.0
+
+RestoreWorkspace: Default
+SaveWorkspace: Default
+AlwaysSaveHistory: Default
+
+EnableCodeIndexing: Yes
+UseSpacesForTab: Yes
+NumSpacesForTab: 2
+Encoding: UTF-8
+
+RnwWeave: Sweave
+LaTeX: pdfLaTeX
+
+AutoAppendNewline: Yes
+StripTrailingWhitespace: Yes

OmicNavigatorExample-main-testing/README.md

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+# How to create an OmicNavigator study
+
+This repository contains an example of how to use the [OmicNavigator R
+package][on-rpkg] to convert an omic analysis into a study package to be
+explored with the [OmicNavigator app][on-app]. For more details, please see the
+User's Guide attached to the [latest release][latest].
+
+[on-rpkg]: https://github.com/abbvie-external/OmicNavigator
+[on-app]: https://github.com/abbvie-external/OmicNavigatorWebApp
+[latest]: https://github.com/abbvie-external/OmicNavigator/releases/latest
+
+**Files:**
+
+* [`setup.R`](./setup.R) - Installs the required R packages for the example
+
+* [`data/`](./data/) - The input RNA-seq counts and MSigDB annotations
+
+* [`analyze.Rmd`](./analyze.Rmd) - Performs a differential expression and enrichment
+analysis of the RNA-seq experiment in [`data/`](./data/). It uses limma+voom,
+but OmicNavigator is agnostic to how you perform your analysis. If you prefer,
+you could use Python or a GUI to perform the analysis, as long as you export the
+results. Also, the HTML produced by this Rmd is included as an external report
+in the final OmicNavigator study package (note: including a report file is optional).
+
+* [`results/`](./results/) - The analysis results exported by
+[`analyze.Rmd`](./analyze.Rmd)
+
+* [`build.R`](./build.R) - Builds the OmicNavigator study package from the
+results files that [`analyze.Rmd`](./analyze.Rmd) exported to
+[`results/`](./results/). Installs the study package and starts the web app.
+
+## Run the code
+
+Follow the steps below to install the dependencies, perform the analysis, and
+create the OmicNavigator study package. Note that the analysis results are
+already available in `results/`, so if you want you can skip running
+`analysis.Rmd` and go straight to running `build.R`.
+
+1. Install R package dependencies
+
+    ```
+    source("setup.R", local = new.env())
+    ```
+
+1. Perform the differential expression analysis. This reads the input files in
+`data/` and exports the output files to `results/`
+
+    ```
+    library(rmarkdown)
+    render("analyze.Rmd", output_file = "results/report.html", envir = new.env())
+    ```
+
+1. Create and install the OmicNavigator study package. This reads the analysis
+results files in `results/`, converts them to an OmicNavigator study package,
+installs the package, and starts the app.
+
+    ```
+    source("build.R")
+    ```
+
+## Acknowledgements
+
+The example limma+voom code was adapted from the Bioconductor workflow
+[RNAseq123](https://bioconductor.org/packages/release/workflows/html/RNAseq123.html).
+
+If you use the code, please cite:
+
+> Law CW, Alhamdoosh M, Su S et al. RNA-seq analysis is easy as 1-2-3
+> with limma, Glimma and edgeR [version 2; referees: 3 approved].
+> F1000Research 2016, 5:1408 (doi: 10.12688/f1000research.9005.2)
+
+If you use the data, please cite:
+
+> Sheridan JM, Ritchie ME, Best SA, et al.: A pooled shRNA screen for
+> regulators of primary mammary stem and progenitor cells identifies
+> roles for Asap1 and Prox1. BMC Cancer. 2015; 15(1): 221.

OmicNavigatorExample-main-testing/analyze.Rmd

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+---
+title: "Analyze RNA-seq experiment with limma+voom"
+output:
+  html_document:
+    toc: true
+---
+
+## Introduction
+
+This example limma+voom code was adapted from the Bioconductor workflow
+[RNAseq123](https://bioconductor.org/packages/release/workflows/html/RNAseq123.html)
+to demonstrate how to use [OmicNavigator](https://github.com/abbvie-external/OmicNavigator). More information about this code and the data required to execute it can be found at [OmicNavigatorExample](https://github.com/abbvie-external/OmicNavigatorExample). 
+
+Below is the description of the experiment from the [RNAseq123
+vignette][vignette]:
+
+[vignette]: https://bioconductor.org/packages/release/workflows/vignettes/RNAseq123/inst/doc/limmaWorkflow.html
+
+> The experiment analysed in this workflow is from Sheridan et al. (2015)
+> (Sheridan et al. 2015) and consists of three cell populations (basal, luminal
+> progenitor (LP) and mature luminal (ML)) sorted from the mammary glands of
+> female virgin mice, each profiled in triplicate. RNA samples were sequenced
+> across three batches on an Illumina HiSeq 2000 to obtain 100 base-pair
+> single-end reads. The analysis outlined in this article assumes that reads
+> obtained from an RNA-seq experiment have been aligned to an appropriate
+> reference genome and summarised into counts associated with gene-specific
+> regions. In this instance, reads were aligned to the mouse reference genome
+> (mm10) using the R based pipeline available in the Rsubread package
+> (specifically the align function (Liao, Smyth, and Shi 2013) followed by
+> featureCounts (Liao, Smyth, and Shi 2014) for gene-level summarisation based on
+> the in-built mm10 RefSeq-based annotation).
+
+The results of this analysis were subsequently converted into an OmicNavigator study with [build.R](https://github.com/abbvie-external/OmicNavigatorExample/blob/main/build.R). 
+
+## Prepare data
+
+Load required packages
+
+```{r packages, message=FALSE}
+library("limma")
+library("edgeR")
+library("Mus.musculus")
+```
+
+Import RNA-seq counts
+
+```{r data}
+files <- Sys.glob("data/*txt")
+x <- readDGE(files, columns = c(1, 3))
+```
+
+Organize sample data
+
+```{r samples}
+samplenames <- make.names(basename(colnames(x)))
+colnames(x) <- samplenames
+x$samples <- cbind(samplenames, x$samples)
+group <- as.factor(c("LP", "ML", "Basal", "Basal", "ML", "LP",
+                     "Basal", "ML", "LP"))
+x$samples$group <- group
+lane <- as.factor(rep(c("L004", "L006", "L008"), c(3, 4, 2)))
+x$samples$lane <- lane
+```
+
+Organize feature data
+
+```{r features}
+geneid <- rownames(x)
+genes <- OrganismDbi::select(
+  Mus.musculus,
+  keys = geneid,
+  columns = c("SYMBOL", "TXCHROM"),
+  keytype = "ENTREZID"
+)
+genes <- genes[!duplicated(genes$ENTREZID), ]
+x$genes <- genes
+colnames(x$genes) <- c("entrez", "symbol", "chrom")
+```
+
+Organize metaFeature data
+
+```{r metaFeatures}
+metaFeatures <- OrganismDbi::select(
+  Mus.musculus,
+  keys = x$genes$entrez,
+  columns = c("ENSEMBL", "ENSEMBLTRANS", "ENSEMBLPROT"),
+  keytype = "ENTREZID"
+)
+colnames(metaFeatures)[1] <- "entrez"
+```
+
+Filter and normalize counts
+
+```{r normalize}
+cpm <- cpm(x)
+keep.exprs <- rowSums(cpm > 1) >= 3
+x <- x[keep.exprs, , keep.lib.sizes = FALSE]
+x <- calcNormFactors(x, method = "TMM")
+```
+
+## Differential expression analysis
+
+Specify linear model and contrasts to test
+
+```{r model}
+design <- model.matrix(~0 + group + lane, data = x$samples)
+colnames(design) <- sub("(group|lane)", "", colnames(design))
+contrastsMatrix <- makeContrasts(BasalvsLP = Basal - LP,
+                                 BasalvsML = Basal - ML,
+                                 LPvsML = LP - ML,
+                                 levels = colnames(design))
+```
+
+Fit the model
+
+```{r fit}
+v <- voom(x, design)
+vfit <- lmFit(v, design)
+vfit <- contrasts.fit(vfit, contrasts = contrastsMatrix)
+efit <- eBayes(vfit)
+```
+
+## Enrichment analysis
+
+Load Mm.h, which contains the MSigDB Hallmark gene sets for the enrichment
+analysis
+
+```{r msigdb}
+load("data/mouse_H_v5p2.rdata")
+idx <- ids2indices(Mm.H, id = rownames(v))
+```
+
+Calculate enrichment for each of the 3 tests
+
+```{r enrichment}
+enrichedBasalvsLP <- camera(v, idx, design, contrast = contrastsMatrix[, 1])
+enrichedBasalvsML <- camera(v, idx, design, contrast = contrastsMatrix[, 2])
+enrichedLPvsML <- camera(v, idx, design, contrast = contrastsMatrix[, 3])
+```
+
+## Export results
+
+```{r export}
+dir.create("results", showWarnings = FALSE)
+export <- function(x, file, row.names = FALSE, ...) {
+  write.table(x, file = file, quote = FALSE, sep = "\t", row.names = row.names, ...)
+}
+
+export(x$samples, "results/samples.txt")
+export(x$genes, "results/features.txt")
+export(metaFeatures, "results/metaFeatures.txt")
+
+# Convert counts to log-counts-per-million
+assays <- as.data.frame(cpm(x, log = TRUE))
+export(assays, "results/assays.txt", row.names = TRUE)
+
+# Test results
+create_results_table <- function(fit, coef) {
+  topTable(fit, coef = coef, number = Inf, sort.by = "p")[, -2:-3]
+}
+export(create_results_table(efit, "BasalvsLP"), "results/BasalvsLP.txt")
+export(create_results_table(efit, "BasalvsML"), "results/BasalvsML.txt")
+export(create_results_table(efit, "LPvsML"), "results/LPvsML.txt")
+
+# Enrichments
+export(enrichedBasalvsLP, "results/enrichedBasalvsLP.txt", row.names = TRUE)
+export(enrichedBasalvsML, "results/enrichedBasalvsML.txt", row.names = TRUE)
+export(enrichedLPvsML, "results/enrichedLPvsML.txt", row.names = TRUE)
+```
+
+## Citations
+
+> Law CW, Alhamdoosh M, Su S et al. RNA-seq analysis is easy as 1-2-3
+> with limma, Glimma and edgeR [version 2; referees: 3 approved].
+> F1000Research 2016, 5:1408 (doi: 10.12688/f1000research.9005.2)
+
+> Sheridan JM, Ritchie ME, Best SA, et al.: A pooled shRNA screen for
+> regulators of primary mammary stem and progenitor cells identifies
+> roles for Asap1 and Prox1. BMC Cancer. 2015; 15(1): 221.