@@ -2,7 +2,7 @@

 ^.*\.Rproj$

 ^\.Rproj\.user$

 ^\.github$

-^FEDUP-Ex.R$

+^fedup-Ex.R$

 ^data-raw$

 ^\.travis\.yml$

 ^README\.Rmd$

@@ -1,4 +1,4 @@

-Package: FEDUP

+Package: fedup

 Title: Fisher's Test for Enrichment and Depletion of User-Defined Pathways

 Version: 0.99.0

 Date: 2021-02-17

@@ -45,5 +45,5 @@ Encoding: UTF-8

 Language: en-US

 License: MIT + file LICENSE

 VignetteBuilder: knitr

-URL: https://github.com/rosscm/FEDUP

+URL: https://github.com/rosscm/fedup

 RoxygenNote: 7.1.1

@@ -5,9 +5,9 @@

 #'

 #' Raw data location

 #' system.file("extdata", "Human_Reactome_November_17_2020_symbol.gmt",

-#'     package="FEDUP")

+#'     package = "fedup")

 #' Script to prepare data

-#' system.file("data-raw", "pathwaysGMT.R", package="FEDUP")

+#' system.file("data-raw", "pathwaysGMT.R", package ="fedup")

 #'

 #' @format a named list of 1437 vectors

 "pathwaysGMT"

@@ -15,10 +15,10 @@

 #' Example list of yeast SAFE terms obtained from a XLSX file.

 #'

 #' Raw data location

-#' system.file("extdata", "SAFE_terms.xlsx", package="FEDUP")

+#' system.file("extdata", "SAFE_terms.xlsx", package = "fedup")

 #'

 #' Script to prepare data

-#' system.file("data-raw", "pathwaysXLSX.R", package="FEDUP")

+#' system.file("data-raw", "pathwaysXLSX.R", package = "fedup")

 #'

 #' @format a named list of 30 vectors

 "pathwaysXLSX"

@@ -26,10 +26,10 @@

 #' Example list of yeast SAFE terms obtained from a TXT file.

 #'

 #' Raw data location

-#' system.file("extdata", "SAFE_terms.txt", package="FEDUP")

+#' system.file("extdata", "SAFE_terms.txt", package = "fedup")

 #'

 #' Script to prepare data

-#' system.file("data-raw", "pathwaysTXT.R", package="FEDUP")

+#' system.file("data-raw", "pathwaysTXT.R", package = "fedup")

 #'

 #' @format a named list of 30 vectors

 "pathwaysTXT"

@@ -37,7 +37,7 @@

 #' Example vector of human genes to use as test set for enrichment.

 #'

 #' Script to prepare data

-#' system.file("data-raw", "genes.R", package="FEDUP")

+#' system.file("data-raw", "genes.R", package = "fedup")

 #'

 #' @format a character vector with 190 elements (gene IDs)

 "testGene"

@@ -45,7 +45,7 @@

 #' Example vector of human genes to use as background set for enrichment.

 #'

 #' Script to generate data

-#' system.file("data-raw", "genes.R", package="FEDUP")

+#' system.file("data-raw", "genes.R", package = "fedup")

 #'

 #' @format a character vector with 10208 elements (gene IDs)

 "backgroundGene"

@@ -118,6 +118,6 @@ runFedup <- function(testGene, backgroundGene, pathways) {

     res <- res[order(res$pvalue), ]

     res$qvalue <- p.adjust(res$pvalue, method = "BH")

-    message("You did it! FEDUP ran successfully, feeling pretty good huh?")

+    message("You did it! fedup ran successfully, feeling pretty good huh?")

     return(res)

 }

@@ -1,6 +1,6 @@

 #' Writes an enrichment dataset file for use in Cytoscape EnrichmentMap.

 #'

-#' @param df (data.frame) table with FEDUP enrichment results.

+#' @param df (data.frame) table with fedup enrichment results.

 #'  (see runFedup() for column descriptions)

 #' @param resultsFile (char) name of output results file.

 #' @return table of gene enrichment and depletion results formatted as a

@@ -32,14 +32,14 @@ writeFemap <- function(df, resultsFile) {

         select("pathway", "description", "pvalue", "qvalue", "status")

     fwrite(df_em, resultsFile, sep = "\t", col.names = TRUE, quote = FALSE)

-    message("Wrote out Cytoscape-formatted FEDUP results file to ", resultsFile)

+    message("Wrote out Cytoscape-formatted fedup results file to ", resultsFile)

 }

 #' Draws a network representation of overlaps among enriched and depleted

 #' pathways using EnrichmentMap (EM) in Cytoscape.

 #'

 #' @param gmtFile (char) path to GMT file (must be an absolute path).

-#' @param resultsFile (char) path to file with FEDUP results

+#' @param resultsFile (char) path to file with fedup results

 #'  (must be an absolute path).

 #' @param pvalue (numeric) pvalue cutoff. Pathways with a higher pvalue

 #'  will not be included in the EM (value between 0 and 1; default 1).

@@ -59,14 +59,14 @@ writeFemap <- function(df, resultsFile) {

 #' gmtFile <- tempfile("pathwaysGMT", fileext = ".gmt")

 #' fedupRes <- runFedup(testGene, backgroundGene, pathwaysGMT)

 #' resultsFile <- tempfile("fedupRes", fileext = ".txt")

-#' netFile <- tempfile("FEDUP_EM", fileext = ".png")

+#' netFile <- tempfile("fedup_EM", fileext = ".png")

 #' writePathways(pathwaysGMT, gmtFile)

 #' writeFemap(fedupRes, resultsFile)

 #' plotFemap(

 #'     gmtFile = gmtFile,

 #'     resultsFile = resultsFile,

 #'     qvalue = 0.05,

-#'     netName = "FEDUP_EM",

+#'     netName = "fedup_EM",

 #'     netFile = netFile

 #' )

 #' }

@@ -16,12 +16,12 @@

 #' @examples

 #' pathways <- readPathways(

 #'     system.file("extdata", "Human_Reactome_November_17_2020_symbol.gmt",

-#'         package = "FEDUP"

+#'         package = "fedup"

 #'     ),

 #'     minGene = 10, maxGene = 500

 #' )

 #' pathways <- readPathways(

-#'     system.file("extdata", "SAFE_terms.xlsx", package = "FEDUP"),

+#'     system.file("extdata", "SAFE_terms.xlsx", package = "fedup"),

 #'     header = TRUE, pathCol = "Enriched.GO.names", geneCol = "Gene.ID"

 #' )

 #' @importFrom openxlsx read.xlsx

@@ -1,6 +1,6 @@

 #' Visualizes pathway enrichment and depletion using ggplot.

 #'

-#' @param df (data.frame) table with FEDUP enrichment results to plot.

+#' @param df (data.frame) table with fedup enrichment results to plot.

 #' @param xVar (char) x-axis variable (must be a column value in \code{df}).

 #' @param yVar (char) y-axis variable (must be a column value in \code{df}).

 #' @param xLab (char) x-axis label (default \code{xVar} value).

@@ -11,15 +11,15 @@ knitr::opts_chunk$set(

 )

 ```

-# FEDUP

+# fedup

-[![Build Status](https://travis-ci.com/rosscm/FEDUP.svg?token=GNK3AGqE8dtKVRC56zpJ&branch=main)](https://travis-ci.com/rosscm/FEDUP)

-![R-CMD-check](https://github.com/rosscm/FEDUP/workflows/R-CMD-check/badge.svg)

-![R-CMD-check-bioc](https://github.com/rosscm/FEDUP/workflows/R-CMD-check-bioc/badge.svg)

-![test-coverage](https://github.com/rosscm/FEDUP/workflows/test-coverage/badge.svg)

-[![codecov](https://codecov.io/gh/rosscm/FEDUP/branch/main/graph/badge.svg?token=AVOAV1ILVL)](https://codecov.io/gh/rosscm/FEDUP)

+[![Build Status](https://travis-ci.com/rosscm/fedup.svg?token=GNK3AGqE8dtKVRC56zpJ&branch=main)](https://travis-ci.com/rosscm/fedup)

+![R-CMD-check](https://github.com/rosscm/fedup/workflows/R-CMD-check/badge.svg)

+![R-CMD-check-bioc](https://github.com/rosscm/fedup/workflows/R-CMD-check-bioc/badge.svg)

+![test-coverage](https://github.com/rosscm/fedup/workflows/test-coverage/badge.svg)

+[![codecov](https://codecov.io/gh/rosscm/fedup/branch/main/graph/badge.svg?token=AVOAV1ILVL)](https://codecov.io/gh/rosscm/fedup)

-`FEDUP` is an R package that tests for enrichment and depletion of user-defined

+`fedup` is an R package that tests for enrichment and depletion of user-defined

 pathways using a Fisher's exact test. The method is designed for versatile

 pathway annotation formats (eg. gmt, txt, xlsx) to allow the user to run

 pathway analysis on custom annotations. This package is also

@@ -38,10 +38,17 @@ that enhances the interpretability of the results.

 ## Installation

-Install `FEDUP` via devtools:

+Install `fedup` via devtools:

 ```{r, message = FALSE}

-devtools::install_github("rosscm/FEDUP", quiet = TRUE)

+devtools::install_github("rosscm/fedup", quiet = TRUE)

+```

+

+Load package:

+

+```{r}

+#library(fedup)

+load_all()

 ```

 # Running the package

@@ -56,7 +63,6 @@ to see strong **enrichment** for pathways related to muscle contraction and,

 **depletion** for pathways *not* associated with muscle contraction. Let's see!

 ```{r}

-library(FEDUP)

 data(testGene)

 data(backgroundGene)

 data(pathwaysGMT)

@@ -94,7 +100,7 @@ muscle contraction, such as `OLFACTORY SIGNALING PATHWAY` and

 Plot enriched and depleted pathways (qvalue < 0.05) in the form of a dot plot

 via the `plotDotPlot` function:

-```{r, FEDUP_dotplot, fig.width = 9, fig.height = 9.5}

+```{r, fedup_dotplot, fig.width = 9, fig.height = 9.5}

 fedupPlot <- fedupRes[which(fedupRes$qvalue < 0.05),]

 fedupPlot$log10qvalue <- -log10(fedupPlot$qvalue + 1e-10) # -log10(qvalue)

 fedupPlot$pathway <- gsub("\\%.*", "", fedupPlot$pathway) # clean names

@@ -146,21 +152,21 @@ writePathways(pathwaysGMT, gmtFile)

 Cytoscape is open right? If so, run these lines and let the `plotFemap`

 magic happen:

-```{r, FEDUP_EM}

-netFile <- tempfile("FEDUP_EM", fileext = ".png")

+```{r, fedup_EM}

+netFile <- tempfile("fedup_EM", fileext = ".png")

 plotFemap(

   gmtFile = gmtFile,

   resultsFile = resultsFile,

   qvalue = 0.05,

-  netName = "FEDUP_EM",

+  netName = "fedup_EM",

   netFile = netFile

 )

 ```

-![](man/figures/FEDUP_EM-1.png)

+![](man/figures/fedup_EM-1.png)

 After some manual rearrangement of the annotated pathway clusters, this is the

-resulting EnrichmentMap we get from our `FEDUP` results. Much better!

+resulting EnrichmentMap we get from our `fedup` results. Much better!

 This has effectively summarized the 76 pathways from our dot plot into 14 unique

 biological themes (including 4 unclustered pathways). We can now see clear

@@ -173,7 +179,7 @@ Try this out yourself! Hopefully it’s the only fedup you achieve

 # Versioning

 For the versions available, see the [tags on this

-repo](https://github.com/rosscm/FEDUP/tags).

+repo](https://github.com/rosscm/fedup/tags).

 # Shoutouts

@@ -1,22 +1,25 @@

+---

+output: github_document

+---

-# FEDUP

-[![Build

-Status](https://travis-ci.com/rosscm/FEDUP.svg?token=GNK3AGqE8dtKVRC56zpJ&branch=main)](https://travis-ci.com/rosscm/FEDUP)

-![R-CMD-check](https://github.com/rosscm/FEDUP/workflows/R-CMD-check/badge.svg)

-![R-CMD-check-bioc](https://github.com/rosscm/FEDUP/workflows/R-CMD-check-bioc/badge.svg)

-![test-coverage](https://github.com/rosscm/FEDUP/workflows/test-coverage/badge.svg)

-[![codecov](https://codecov.io/gh/rosscm/FEDUP/branch/main/graph/badge.svg?token=AVOAV1ILVL)](https://codecov.io/gh/rosscm/FEDUP)

-`FEDUP` is an R package that tests for enrichment and depletion of

-user-defined pathways using a Fisher’s exact test. The method is

-designed for versatile pathway annotation formats (eg. gmt, txt, xlsx)

-to allow the user to run pathway analysis on custom annotations. This

-package is also integrated with Cytoscape to provide network-based

-pathway visualization that enhances the interpretability of the results.

+# fedup

-# Getting started

+[![Build Status](https://travis-ci.com/rosscm/fedup.svg?token=GNK3AGqE8dtKVRC56zpJ&branch=main)](https://travis-ci.com/rosscm/fedup)

+![R-CMD-check](https://github.com/rosscm/fedup/workflows/R-CMD-check/badge.svg)

+![R-CMD-check-bioc](https://github.com/rosscm/fedup/workflows/R-CMD-check-bioc/badge.svg)

+![test-coverage](https://github.com/rosscm/fedup/workflows/test-coverage/badge.svg)

+[![codecov](https://codecov.io/gh/rosscm/fedup/branch/main/graph/badge.svg?token=AVOAV1ILVL)](https://codecov.io/gh/rosscm/fedup)

+

+`fedup` is an R package that tests for enrichment and depletion of user-defined

+pathways using a Fisher's exact test. The method is designed for versatile

+pathway annotation formats (eg. gmt, txt, xlsx) to allow the user to run

+pathway analysis on custom annotations. This package is also

+integrated with Cytoscape to provide network-based pathway visualization

+that enhances the interpretability of the results.

+# Getting started

 ## System prerequisites

 **R version** ≥ 4.1  

@@ -28,27 +31,35 @@ pathway visualization that enhances the interpretability of the results.

 ## Installation

-Install `FEDUP` via devtools:

+Install `fedup` via devtools:

-``` r

-devtools::install_github("rosscm/FEDUP", quiet = TRUE)

+

+```r

+devtools::install_github("rosscm/fedup", quiet = TRUE)

 ```

-# Running the package

+Load package:

+

+```r

+#library(fedup)

+load_all()

+#> Loading fedup

+```

+

+# Running the package

 ## Sample input

 Load test genes (`testGene`), background genes (`backgroudGene`), and

 pathways (`pathwaysGMT`):

-Note, the sample `testGene` object only consists of genes from the

-pathway `MUSCLE CONTRACTION%REACTOME DATABASE ID RELEASE 74%397014`. So

-we would expect to see strong **enrichment** for pathways related to

-muscle contraction and, **depletion** for pathways *not* associated with

-muscle contraction. Let’s see!

+Note, the sample `testGene` object only consists of genes from the pathway

+`MUSCLE CONTRACTION%REACTOME DATABASE ID RELEASE 74%397014`. So we would expect

+to see strong **enrichment** for pathways related to muscle contraction and,

+**depletion** for pathways *not* associated with muscle contraction. Let's see!

-``` r

-library(FEDUP)

+

+```r

 data(testGene)

 data(backgroundGene)

 data(pathwaysGMT)

@@ -56,7 +67,8 @@ data(pathwaysGMT)

 Take a look at the data structure:

-``` r

+

+```r

 str(testGene)

 #>  chr [1:190] "NKX2-5" "SCN4A" "ITGB5" "SCN4B" "PAK2" "GATA4" "AKAP9" ...

 str(backgroundGene)

@@ -73,18 +85,20 @@ str(head(pathwaysGMT))

 Now use `runFedup` on sample data:

-``` r

+

+```r

 fedupRes <- runFedup(testGene, backgroundGene, pathwaysGMT)

 #> Data input:

 #>  => 190 test genes

 #>  => 10208 background genes

 #>  => 1437 pathawys

-#> You did it! FEDUP ran successfully, feeling pretty good huh?

+#> You did it! fedup ran successfully, feeling pretty good huh?

 ```

 View output results table sorted by pvalue:

-``` r

+

+```r

 print(head(fedupRes[which(fedupRes$status == "Enriched"),]))

 #>                                                             pathway size

 #> 1:        MUSCLE CONTRACTION%REACTOME DATABASE ID RELEASE 74%397014  190

@@ -131,19 +145,19 @@ print(head(fedupRes[which(fedupRes$status == "Depleted"),]))

 #> 6: 0.17258203

 ```

-Here we see the strongest enrichment for the `MUSCLE CONTRACTION`

-pathway. Since our test set of genes are exclusively from this pathway,

-this is totally expected. We also see significant enrichment for other

-muscle contraction pathways, including `CARDIAC CONDUCTION` and

-`SMOOTH MUSCLE CONTRACTION`. Conversely, we see significant depletion

-for functions not associated with muscle contraction, such as

-`OLFACTORY SIGNALING PATHWAY` and

+Here we see the strongest enrichment for the `MUSCLE CONTRACTION` pathway.

+Since our test set of genes are exclusively from this pathway, this is totally

+expected. We also see significant enrichment for other muscle contraction

+pathways, including `CARDIAC CONDUCTION` and `SMOOTH MUSCLE CONTRACTION`.

+Conversely, we see significant depletion for functions not associated with

+muscle contraction, such as `OLFACTORY SIGNALING PATHWAY` and

 `AMINO ACID AND DERIVATIVE METABOLISM`. Nice!

-Plot enriched and depleted pathways (qvalue < 0.05) in the form of a

-dot plot via the `plotDotPlot` function:

+Plot enriched and depleted pathways (qvalue < 0.05) in the form of a dot plot

+via the `plotDotPlot` function:

-``` r

+

+```r

 fedupPlot <- fedupRes[which(fedupRes$qvalue < 0.05),]

 fedupPlot$log10qvalue <- -log10(fedupPlot$qvalue + 1e-10) # -log10(qvalue)

 fedupPlot$pathway <- gsub("\\%.*", "", fedupPlot$pathway) # clean names

@@ -165,12 +179,11 @@ p <- p + # facet by status to separate enriched and depleted pathways

 print(p)

 ```

-<img src="man/figures/FEDUP_dotplot-1.png" width="100%" />

+<img src="man/figures/fedup_dotplot-1.png" title="plot of chunk fedup_dotplot" alt="plot of chunk fedup_dotplot" width="100%" />

-Look at all those chick… enrichments! This is a bit overwhelming, isn’t

-it? How do we interpret these 76 seemingly redundant pathways in a way

-that doesn’t hurt our tired brains even more? Oh I know, let’s use

-EnrichmentMap!

+Look at all those chick... enrichments! This is a bit overwhelming, isn't it?

+How do we interpret these 76 seemingly redundant pathways in a way that doesn't

+hurt our tired brains even more? Oh I know, let's use EnrichmentMap!

 First, make sure to have

 [Cytoscape](https://cytoscape.org/download.html) downloaded and and open

@@ -178,36 +191,38 @@ on your computer. You’ll also need to install the

 [EnrichmentMap](http://apps.cytoscape.org/apps/enrichmentmap) and

 [AutoAnnotate](http://apps.cytoscape.org/apps/autoannotate) apps.

-Then format results for compatibility with EnrichmentMap with

-`writeFemap`:

+Then format results for compatibility with EnrichmentMap with `writeFemap`:

+

-``` r

+```r

 resultsFile <- tempfile("fedupRes", fileext = ".txt")

 writeFemap(fedupRes, resultsFile)

-#> Wrote out Cytoscape-formatted FEDUP results file to /var/folders/mh/_0z2r5zj3k75yhtgm6l7xy3m0000gn/T//RtmpmEZRQI/fedupRes18391308b962e.txt

+#> Wrote out Cytoscape-formatted fedup results file to /var/folders/mh/_0z2r5zj3k75yhtgm6l7xy3m0000gn/T//RtmpKkUyBJ/fedupRes11041452f8f8f.txt

 ```

 Prepare a pathway annotation file (gmt format) from the pathway list you

-passed to `runFedup` using the `writePathways` function (you don’t need

-to run this function if your pathway annotations are already in gmt

-format, but it doesn’t hurt to make sure):

+passed to `runFedup` using the `writePathways` function (you don't need to run

+this function if your pathway annotations are already in gmt format, but it

+doesn't hurt to make sure):

-``` r

+

+```r

 gmtFile <- tempfile("pathwaysGMT", fileext = ".gmt")

 writePathways(pathwaysGMT, gmtFile)

-#> Wrote out pathway gmt file to /var/folders/mh/_0z2r5zj3k75yhtgm6l7xy3m0000gn/T//RtmpmEZRQI/pathwaysGMT1839144457fcd.gmt

+#> Wrote out pathway gmt file to /var/folders/mh/_0z2r5zj3k75yhtgm6l7xy3m0000gn/T//RtmpKkUyBJ/pathwaysGMT1104162f048f0.gmt

 ```

 Cytoscape is open right? If so, run these lines and let the `plotFemap`

 magic happen:

-``` r

-netFile <- tempfile("FEDUP_EM", fileext = ".png")

+

+```r

+netFile <- tempfile("fedup_EM", fileext = ".png")

 plotFemap(

   gmtFile = gmtFile,

   resultsFile = resultsFile,

   qvalue = 0.05,

-  netName = "FEDUP_EM",

+  netName = "fedup_EM",

   netFile = netFile

 )

 #> You are connected to Cytoscape!

@@ -215,22 +230,20 @@ plotFemap(

 #> Setting network chart data

 #> Annotating the network using AutoAnnotate

 #> Applying a force-directed network layout

-#> Drawing out network to /var/folders/mh/_0z2r5zj3k75yhtgm6l7xy3m0000gn/T//RtmpmEZRQI/FEDUP_EM1839132bab598.png

+#> Drawing out network to /var/folders/mh/_0z2r5zj3k75yhtgm6l7xy3m0000gn/T//RtmpKkUyBJ/fedup_EM110411a5eaa57.png

 #>                                                                                    file 

-#> "/var/folders/mh/_0z2r5zj3k75yhtgm6l7xy3m0000gn/T/RtmpmEZRQI/FEDUP_EM1839132bab598.png"

+#> "/var/folders/mh/_0z2r5zj3k75yhtgm6l7xy3m0000gn/T/RtmpKkUyBJ/fedup_EM110411a5eaa57.png"

 ```

-![](man/figures/FEDUP_EM-1.png)

+![](man/figures/fedup_EM-1.png)

-After some manual rearrangement of the annotated pathway clusters, this

-is the resulting EnrichmentMap we get from our `FEDUP` results. Much

-better!

+After some manual rearrangement of the annotated pathway clusters, this is the

+resulting EnrichmentMap we get from our `fedup` results. Much better!

-This has effectively summarized the 76 pathways from our dot plot into

-14 unique biological themes (including 4 unclustered pathways). We can

-now see clear themes in the data pertaining to muscle contraction, such

-as `NMDA receptor function`, `calcium homeostasis`, and

-`ATPase transport`.

+This has effectively summarized the 76 pathways from our dot plot into 14 unique

+biological themes (including 4 unclustered pathways). We can now see clear

+themes in the data pertaining to muscle contraction, such as `NMDA receptor

+function`, `calcium homeostasis`, and `ATPase transport`.

 Try this out yourself! Hopefully it’s the only fedup you achieve

 :grimacing:

@@ -238,7 +251,7 @@ Try this out yourself! Hopefully it’s the only fedup you achieve

 # Versioning

 For the versions available, see the [tags on this

-repo](https://github.com/rosscm/FEDUP/tags).

+repo](https://github.com/rosscm/fedup/tags).

 # Shoutouts

@@ -1,5 +1,5 @@

 ## code to prepare `testGene` and `backgroundGene` datasets goes here

-pathwayFile <- system.file("extdata", "Human_Reactome_November_17_2020_symbol.gmt", package = "FEDUP")

+pathwayFile <- system.file("extdata", "Human_Reactome_November_17_2020_symbol.gmt", package = "fedup")

 pathwaysGMT <- readPathways(pathwayFile, minGene = 10, maxGene = 500)

 testGene <- pathwaysGMT[[grep("397014", names(pathwaysGMT))]] # Reactome muscle contraction pathway

@@ -1,5 +1,5 @@

 ## code to prepare `pathwaysGMT` dataset goes here

-pathwayFile <- system.file("extdata", "Human_Reactome_November_17_2020_symbol.gmt", package = "FEDUP")

+pathwayFile <- system.file("extdata", "Human_Reactome_November_17_2020_symbol.gmt", package = "fedup")

 pathwaysGMT <- readPathways(pathwayFile, minGene = 10, maxGene = 500)

 names(pathwaysGMT) <- stringi::stri_trans_general(names(pathwaysGMT), "latin-ascii")

 usethis::use_data(pathwaysGMT, compress = "xz", version = 2, overwrite = TRUE)

@@ -1,7 +1,7 @@

 ## code to prepare `pathwaysXLSX` dataset goes here

 library(tibble)

-pathwayFile <- system.file("extdata", "SAFE_terms.txt", package = "FEDUP")

+pathwayFile <- system.file("extdata", "SAFE_terms.txt", package = "fedup")

 pathwaysTXT <- readPathways(

     pathwayFile,

     header = TRUE,

@@ -2,7 +2,7 @@

 library(openxlsx)

 library(tibble)

-pathwayFile <- system.file("extdata", "SAFE_terms.xlsx", package = "FEDUP")

+pathwayFile <- system.file("extdata", "SAFE_terms.xlsx", package = "fedup")

 pathwaysXLSX <- readPathways(

     pathwayFile,

     header = TRUE,

@@ -12,6 +12,6 @@ backgroundGene

 }

 \description{

 Script to generate data

-system.file("data-raw", "genes.R", package="FEDUP")

+system.file("data-raw", "genes.R", package = "fedup")

 }

 \keyword{datasets}

Binary files a/man/figures/FEDUP_dotplot-1.png and b/man/figures/FEDUP_dotplot-1.png differ

@@ -17,8 +17,8 @@ Raw GMT file is available from

 \details{

 Raw data location

 system.file("extdata", "Human_Reactome_November_17_2020_symbol.gmt",

-    package="FEDUP")

+    package = "fedup")

 Script to prepare data

-system.file("data-raw", "pathwaysGMT.R", package="FEDUP")

+system.file("data-raw", "pathwaysGMT.R", package ="fedup")

 }

 \keyword{datasets}

@@ -12,10 +12,10 @@ pathwaysTXT

 }

 \description{

 Raw data location

-system.file("extdata", "SAFE_terms.txt", package="FEDUP")

+system.file("extdata", "SAFE_terms.txt", package = "fedup")

 }

 \details{

 Script to prepare data

-system.file("data-raw", "pathwaysTXT.R", package="FEDUP")

+system.file("data-raw", "pathwaysTXT.R", package = "fedup")

 }

 \keyword{datasets}

@@ -12,10 +12,10 @@ pathwaysXLSX

 }

 \description{

 Raw data location

-system.file("extdata", "SAFE_terms.xlsx", package="FEDUP")

+system.file("extdata", "SAFE_terms.xlsx", package = "fedup")

 }

 \details{

 Script to prepare data

-system.file("data-raw", "pathwaysXLSX.R", package="FEDUP")

+system.file("data-raw", "pathwaysXLSX.R", package = "fedup")

 }

 \keyword{datasets}

@@ -19,7 +19,7 @@ plotDotPlot(

 )

 }

 \arguments{

-\item{df}{(data.frame) table with FEDUP enrichment results to plot.}

+\item{df}{(data.frame) table with fedup enrichment results to plot.}

 \item{xVar}{(char) x-axis variable (must be a column value in \code{df}).}

@@ -17,7 +17,7 @@ plotFemap(

 \arguments{

 \item{gmtFile}{(char) path to GMT file (must be an absolute path).}

-\item{resultsFile}{(char) path to file with FEDUP results

+\item{resultsFile}{(char) path to file with fedup results

 (must be an absolute path).}

 \item{pvalue}{(numeric) pvalue cutoff. Pathways with a higher pvalue

@@ -48,14 +48,14 @@ data(pathwaysGMT)

 gmtFile <- tempfile("pathwaysGMT", fileext = ".gmt")

 fedupRes <- runFedup(testGene, backgroundGene, pathwaysGMT)

 resultsFile <- tempfile("fedupRes", fileext = ".txt")

-netFile <- tempfile("FEDUP_EM", fileext = ".png")

+netFile <- tempfile("fedup_EM", fileext = ".png")

 writePathways(pathwaysGMT, gmtFile)

 writeFemap(fedupRes, resultsFile)

 plotFemap(

     gmtFile = gmtFile,

     resultsFile = resultsFile,

     qvalue = 0.05,

-    netName = "FEDUP_EM",

+    netName = "fedup_EM",

     netFile = netFile

 )

 }

@@ -42,12 +42,12 @@ Currently supports the following file format: gmt, txt, xlsx.

 \examples{

 pathways <- readPathways(

     system.file("extdata", "Human_Reactome_November_17_2020_symbol.gmt",

-        package = "FEDUP"

+        package = "fedup"

     ),

     minGene = 10, maxGene = 500

 )

 pathways <- readPathways(

-    system.file("extdata", "SAFE_terms.xlsx", package = "FEDUP"),

+    system.file("extdata", "SAFE_terms.xlsx", package = "fedup"),

     header = TRUE, pathCol = "Enriched.GO.names", geneCol = "Gene.ID"

 )

 }

@@ -12,6 +12,6 @@ testGene

 }

 \description{

 Script to prepare data

-system.file("data-raw", "genes.R", package="FEDUP")

+system.file("data-raw", "genes.R", package = "fedup")

 }

 \keyword{datasets}

@@ -7,7 +7,7 @@

 writeFemap(df, resultsFile)

 }

 \arguments{

-\item{df}{(data.frame) table with FEDUP enrichment results.

+\item{df}{(data.frame) table with fedup enrichment results.

 (see runFedup() for column descriptions)}

 \item{resultsFile}{(char) name of output results file.}

@@ -1,4 +1,4 @@

 library(testthat)

-library(FEDUP)

+library(fedup)

-test_check("FEDUP")

+test_check("fedup")

@@ -1,6 +1,6 @@

 context("Enrichment analysis")

-test_that("Test that FEDUP stops without proper inputs", {

+test_that("Test that fedup stops without proper inputs", {

     data(testGene)

     data(backgroundGene)

     data(pathwaysGMT)

@@ -13,7 +13,7 @@ test_that("Test that FEDUP stops without proper inputs", {

     expect_error(runFedup(backgroundGene, testGene, pathwaysGMT))

 })

-test_that("Test that FEDUP analysis works", {

+test_that("Test that fedup analysis works", {

     data(testGene)

     data(backgroundGene)

     data(pathwaysGMT)

@@ -4,7 +4,7 @@ test_that("Test that readPathways stops without proper inputs", {

     expect_error(readPathways("test.123.xls"))

     expect_error(readPathways("test.gmt.123"))

-    pathwayFile <- system.file("extdata", "SAFE_terms.xlsx", package = "FEDUP")

+    pathwayFile <- system.file("extdata", "SAFE_terms.xlsx", package = "fedup")

     expect_error(readPathways(

         pathwayFile,

         header = TRUE,

@@ -23,7 +23,7 @@ test_that("Test that readPathways stops without proper inputs", {

 })

 test_that("Test that readPathways works with GMT input", {

-    pathwayFile <- system.file("extdata", "Human_Reactome_November_17_2020_symbol.gmt", package = "FEDUP")

+    pathwayFile <- system.file("extdata", "Human_Reactome_November_17_2020_symbol.gmt", package = "fedup")

     s <- c("gmt", "txt", "xlsx")

     f <- sub(".*\\.", "", pathwayFile)

     expect_true(f %in% s)

@@ -35,7 +35,7 @@ test_that("Test that readPathways works with GMT input", {

 })

 test_that("Test that readPathways works with XLSX input", {

-    pathwayFile <- system.file("extdata", "SAFE_terms.xlsx", package = "FEDUP")

+    pathwayFile <- system.file("extdata", "SAFE_terms.xlsx", package = "fedup")

     s <- c("gmt", "txt", "xlsx")

     f <- sub(".*\\.", "", pathwayFile)

     expect_true(f %in% s)

@@ -50,7 +50,7 @@ test_that("Test that readPathways works with XLSX input", {

 })

 test_that("Test that readPathways works with TXT input", {

-    pathwayFile <- system.file("extdata", "SAFE_terms.txt", package = "FEDUP")

+    pathwayFile <- system.file("extdata", "SAFE_terms.txt", package = "fedup")

     s <- c("gmt", "txt", "xlsx")

     f <- sub(".*\\.", "", pathwayFile)

     expect_true(f %in% s)

@@ -1,5 +1,5 @@

 ---

-title: "Introduction to FEDUP"

+title: "Introduction to fedup"

 author: Catherine Ross

 output:

     rmdformats::html_clean:

@@ -8,7 +8,7 @@ output:

         thumbnails: false

         lightbox: true

 vignette: >

-    %\VignetteIndexEntry{Introduction to FEDUP}

+    %\VignetteIndexEntry{Introduction to fedup}

     %\VignetteEngine{knitr::rmarkdown}

     %\VignetteEncoding{UTF-8}

 ---

@@ -22,7 +22,7 @@ knitr::opts_chunk$set(

 )

 ```

-FEDUP is an R package that tests for enrichment and depletion of user-defined

+This is an R package that tests for enrichment and depletion of user-defined

 pathways using a Fisher's exact test. The method is designed for versatile

 pathway annotation formats (eg. gmt, txt, xlsx) to allow the user to run

 pathway analysis on custom annotations. This package is also

@@ -41,10 +41,17 @@ that enhances the interpretability of the results.

 ## Installation

-Install `FEDUP` via devtools:

+Install `fedup` via devtools:

 ```{r}

-devtools::install_github("rosscm/FEDUP", quiet = TRUE)

+devtools::install_github("rosscm/fedup", quiet = TRUE)

+```

+

+Load package:

+

+```{r}

+#library(fedup)

+devtools::load_all()

 ```

 # Running the package

@@ -59,7 +66,7 @@ to see strong **enrichment** for pathways related to muscle contraction and,

 **depletion** for pathways *not* associated with muscle contraction. Let's see!

 ```{r, message = FALSE}

-library(FEDUP)

+library(fedup)

 data(testGene)

 data(backgroundGene)

 data(pathwaysGMT)

@@ -97,7 +104,7 @@ muscle contraction, such as `OLFACTORY SIGNALING PATHWAY` and

 Plot enriched and depleted pathways (qvalue < 0.05) in the form of a dot plot

 via the `plotDotPlot` function:

-```{r, FEDUP_dotplot, fig.width = 9, fig.height = 9.5}

+```{r, fedup_dotplot, fig.width = 9, fig.height = 9.5}

 fedupPlot <- fedupRes[which(fedupRes$qvalue < 0.05),]

 fedupPlot$log10qvalue <- -log10(fedupPlot$qvalue + 1e-10) # -log10(qvalue)

 fedupPlot$pathway <- gsub("\\%.*", "", fedupPlot$pathway) # clean names

@@ -149,20 +156,20 @@ Cytoscape is open right? If so, run these lines and let the `plotFemap`

 magic happen:

 ```{r}

-#netFile <- tempfile("FEDUP_EM", fileext = ".png")

+#netFile <- tempfile("fedup_EM", fileext = ".png")

 #plotFemap(

 #  gmtFile = gmtFile,

 #  resultsFile = resultsFile,

 #  qvalue = 0.05,

-#  netName = "FEDUP_EM",

+#  netName = "fedup_EM",

 #  netFile = netFile

 #)

 ```

-![](figures/FEDUP_EM-1.png)

+![](figures/fedup_EM-1.png)

 After some manual rearrangement of the annotated pathway clusters, this is the

-resulting EnrichmentMap we get from our `FEDUP` results. Much better!

+resulting EnrichmentMap we get from our `fedup` results. Much better!

 This has effectively summarized the 76 pathways from our dot plot into 14 unique

 biological themes (including 4 unclustered pathways). We can now see clear

@@ -172,10 +179,10 @@ function`, `calcium homeostasis`, and `ATPase transport`.

 ## Manual input

-Supply `FEDUP` with your own custom pathway annotations, eg. from an excel file:

+Supply `fedup` with your own custom pathway annotations, eg. from an excel file:

 ```{r}

-pathwayFile <- system.file("extdata", "SAFE_terms.xlsx", package = "FEDUP")

+pathwayFile <- system.file("extdata", "SAFE_terms.xlsx", package = "fedup")

 ```

 Take a look at the raw data structure:

@@ -218,7 +225,7 @@ print(head(fedupRes[which(fedupRes$status == "Depleted"),]))

 Run through `plotDotPlot` to visualize:

-```{r, FEDUP_dotplot_manual, fig.width = 5, fig.height = 3.5}

+```{r, fedup_dotplot_manual, fig.width = 5, fig.height = 3.5}

 fedupPlot <- fedupRes[which(fedupRes$qvalue < 0.05),]

 fedupPlot$log10qvalue <- -log10(fedupPlot$qvalue + 1e-10) # log10(qvalue)

 fedupPlot$pathway <- gsub("\\%.*", "", fedupPlot$pathway) # clean names

@@ -243,4 +250,4 @@ print(p)

 # Versioning

 For the versions available, see the

-[tags on this repo](https://github.com/rosscm/FEDUP/tags).

+[tags on this repo](https://github.com/rosscm/fedup/tags).