Proper documentations

Linking each entry

R/alliance.R

@@ -4,7 +4,6 @@
 #' @examples
 #' geneList <- c("DDX41","PNKP","ERCC1","IRF3","XRCC1")
 #' \dontrun{alliance(geneList)}
-
 alliance <- function (geneList,
     alliancePath="GENE-DESCRIPTION-TSV_HUMAN.tsv",
     keyType="SYMBOL",

R/biotextgraph.R

@@ -1,6 +1,8 @@
 #' biotextgraph
 #' 
-#' wrapper for functions refseq, pubmed, enzyme, and bugsigdb
+#' @description wrapper for functions refseq, pubmed, enzyme, and bugsigdb
+#' @details The function calls the various types of databases (refseq, pubmed, ...)
+#' for summarizing the textual data.
 #' 
 #' @param target "pubmed", "bugsigdb", "refseq", "ec"
 #' @param argList passed to each function
@@ -152,7 +154,9 @@ setMethod("plot",
 
 #' plotNet
 #' 
-#' Plot the network changing the visualization parameters
+#' @description Plot the network stored in the biotext object with changing the visualization parameters.
+#' @details The function accepts the already calculated biotext object and outputs the visualization based on
+#' the specified parameters.
 #' 
 #' @rdname plotnet
 #' @param x biotextgraph object
@@ -183,6 +187,11 @@ setMethod("plot",
 #' @param scaleRange scale for label and node size in the network.
 #' @param autoScale scale the label and node size automatically for the large network.
 #' @export
+#' @examples
+#' library(ggraph)
+#' geneList <- c("DDX41","PNKP","ERCC1","IRF3","XRCC1")
+#' test <- refseq(geneList)
+#' plotNet(test, asis=TRUE)
 #' @return biotext object with network visualization changed
 setGeneric("plotNet",
     function(x, layout="nicely", edgeLink=TRUE,
@@ -291,8 +300,9 @@ setMethod("plotNet", "biotext",
 
 #' plotWC
 #' 
-#' plot the wordcloud changing the visualization parameters
-#' 
+#' @description Plot the wordcloud changing the visualization parameters.
+#' @details The function accepts the already calculated biotext object and outputs the visualization based on
+#' the specified parameters.
 #' @rdname plotwc
 #' @param x biotext object
 #' @param tagPalette tag palette when `tag` is TRUE. It is also used for dependency network
@@ -310,6 +320,10 @@ setMethod("plotNet", "biotext",
 #' @param asis plot the original network (default to FALSE)
 #' @param fontFamily font family to use, default to "sans".
 #' @export
+#' @examples
+#' geneList <- c("DDX41","PNKP","ERCC1","IRF3","XRCC1")
+#' test <- refseq(geneList, plotType="wc")
+#' plotWC(test, asis=TRUE)
 #' @return wordcloud visualization
 setGeneric("plotWC",
     function(x, tagPalette=NULL, madeUpper=c("dna","rna"),
@@ -453,7 +467,8 @@ setMethod("plotWC", "biotext",
 
 #' getSlot
 #' 
-#' get the slot value from biotext object
+#' @description get the slot value from biotext object
+#' @details get the slot value from biotext object
 #' 
 #' @param x biotext object
 #' @param slot slot name
@@ -464,10 +479,10 @@ setMethod("plotWC", "biotext",
 #' @return attribute value
 setGeneric("getSlot",
     function(x, slot) standardGeneric("getSlot"))
-
 #' getSlot
 #' 
-#' get the slot value from biotext object
+#' @description get the slot value from biotext object
+#' @details get the slot value from biotext object
 #' 
 #' @param x biotext object
 #' @param slot slot name
@@ -482,7 +497,10 @@ setMethod("getSlot", "biotext",
 
 #' plotORA
 #' 
-#' plot volcano-plot like plot for ORA results
+#' @description plot volcano-plot like plot for ORA results
+#' @details Plot the volcano-plot like plot for the ORA results
+#' using the biotext object. The ORA should be performed beforehand
+#' by specifying ora option to TRUE.
 #' 
 #' @param x biotext object
 #' @param thresh hline to draw in plot

R/compareWordNet.R

@@ -1,6 +1,9 @@
 #' compareWordNet
 #' 
-#' compare two gene clusters based on words 
+#' @description Compare multiple networks based on words 
+#' @details The function accepts list (named) of biotext object, and 
+#' plot the merged network highlighting the intersection of the network
+#' and identified clusters.
 #' 
 #' @param listOfNets list consisting results of wc* functions (plotType="network")
 #' @param titles title to be shown on plot
@@ -264,7 +267,9 @@ compareWordNet <- function(listOfNets, titles=NULL,
 
 #' plotDynamic
 #' 
-#' list network of words using graphlayouts::layout_as_dynamic
+#' @description List network of words using graphlayouts::layout_as_dynamic
+#' @details The function accepts the list of biotext object storing inferred networks.
+#' The networks are aligned by the specific layout and plotted.
 #' 
 #' @param listOfNets list consisting results of wc* functions (plotType="network")
 #' @param concat "union" or "intersection"

R/enzyme.R

@@ -1,9 +1,11 @@
 #' enzyme
 #' 
-#' Query the Enzyme Comission number and obtain description,
+#' @description Text mining the enzyme information.
+#' 
+#' @details Query the Enzyme Comission number and obtain description,
 #' and search pubmed for these enzymes and make word cloud and
-#' correlation network. Need "enzyme.dat" from ExPASy
-#' (https://enzyme.expasy.org/).
+#' correlation network. Need to specify the path to "enzyme.dat"
+#' downloaded from from ExPASy (https://enzyme.expasy.org/).
 #' 
 #' @param file file downloaded from expasy
 #' @param ecnum candidate ecnum, like those obtained from eggNOG-mapper
@@ -22,8 +24,7 @@
 #' file <- "enzyme.dat"
 #' \dontrun{enzyme(file, ecnum="1.2.1.1")}
 #' @export
-#' 
-
+#' @seealso generalf
 enzyme <- function(file, ecnum, onlyTerm=FALSE, onlyDf=FALSE, target="abstract",
                  taxec=FALSE, taxFile=NULL, candTax=NULL, argList=list(),
                  apiKey=NULL) {

R/exportWCNetwork.R

@@ -1,10 +1,10 @@
 #' exportWCNetwork
 #' 
-#' export the wordcloud network provided the gene cluster network
-#' Node size will be the number of genes in the cluster
-#' By default use cola layout, and see below for the parameters
-#' https://github.com/cytoscape/cytoscape.js-cola
-#' 
+#' @description Export wordcloud networks to Cytoscape.js 
+#' @details The function exports the wordcloud network provided the gene cluster network.
+#' Node size will be the number of genes in the cluster.
+#' By default use cola layout, and see below for the parameters of the layout.
+#' (https://github.com/cytoscape/cytoscape.js-cola) 
 #' If "strength" attribute is in the edge of igraph object,
 #' the parameter is used to size the edge width.
 #' 

R/external.R

@@ -1,7 +1,8 @@
 
 #' refseqWGCNA
 #' 
-#' Return the list of biotext class object per cluster for the blockwise module results in WGCNA
+#' @description Text mining WGCNA results
+#' @details Return the list of biotext class object per cluster for the blockwise module results in WGCNA
 #' 
 #' @param wgcna results of blockwiseModules()
 #' @param keyType key type of gene
@@ -26,7 +27,8 @@ refseqWGCNA <- function(wgcna, keyType="ENSEMBL", argList=list()) {
 
 #' refseqDESeq2
 #' 
-#' Return the biotext class object by specified filter in DESeq2 results object
+#' @description Text mining DESeq2 results
+#' @details Return the biotext class object by specified filter in DESeq2 results object
 #' 
 #' @param res results of DESeq2::results()
 #' @param condition filtering condition

R/geom_sc_wordcloud.R

@@ -1,8 +1,10 @@
 
 #' obtainMarkersWC
 #' 
-#' obtain wordclouds of cluster markers identified in Seurat
-#' 
+#' @description obtain wordclouds of cluster markers identified in Seurat
+#' @details Using the results of marker gene identification such as `FindAllMarkers` from Seurat,
+#' Recursively summarize the textual information of markers and output the wordclouds.
+#' @rdname obtainMarkersWC
 #' @param markers marker data frame
 #' @param cols list of colors
 #' @param wcArgs arguments for ggwordcloud
@@ -61,7 +63,11 @@ obtainMarkersWC <- function(markers,
 
 
 #' obtainMarkersWCScran
-#' make gene wordcloud from scran::findMarkers() results
+#' 
+#' @description Make gene wordcloud from scran::findMarkers() results
+#' @details using the results of marker gene identification such as `findMarkers` from scran,
+#' Recursively summarize the textual information of markers and output the wordclouds.
+#' @rdname obtainMarkersWCScran
 #' @param markers marker list
 #' @param cols list of colors
 #' @param wcArgs arguments for ggwordcloud
@@ -127,7 +133,9 @@ obtainMarkersWCScran <- function(markers,
 }
 
 #' ggplot_add.geom_sc_wordcloud
-#' use ggplot_add to populate single-cell plot with textual information
+#' @description Use ggplot_add to populate single-cell plot with textual information
+#' @details Use layered approach to add wordclouds to the dimension reduction plots
+#' from single cell transcriptomics data. Use with `ggsc`.
 #' @param object An object to add to the plot
 #' @param plot The ggplot object to add object to
 #' @param object_name The name of the object to add
@@ -290,6 +298,9 @@ ggplot_add.geom_sc_wordcloud <- function(object, plot, object_name) {
 }
 
 #' geom_sc_wordcloud
+#' @description Use ggplot_add to populate single-cell plot with textual information
+#' @details Use layered approach to add wordclouds to the dimension reduction plots
+#' from single cell transcriptomics data. Use with `ggsc`.
 #' @param markers FindAllMarkers() results
 #' @param show_markers candidate clusters to be appear in plot, default to NULL,
 #' which means all the clusters are plotted

R/getWordsOnDendro.R

@@ -1,7 +1,9 @@
 #' plotEigengeneNetworksWithWords
 #' 
-#' plot an eigengene dendrogram with word annotation
-#' 
+#' @description Plot an eigengene dendrogram with word annotation
+#' @details The function accepts the module eigengene (ME) data.frame and
+#' named vector of genes with ME information and returns the dendrogram with
+#' textual information. The input is the typical output of WGCNA.
 #' 
 #' @param MEs module eigengene data (data.frame of row as sample and col as gene cluster IDs)
 #' @param colors named vector of cluster
@@ -236,7 +238,10 @@ plotEigengeneNetworksWithWords <- function (MEs, colors, nboot=100,
 
 #' getWordsOnDendro
 #' 
-#' Get grobs to plot on the dendrogram with the position information
+#' @description Get grobs to plot on the dendrogram with the position information
+#' @details The function accepts the dendrogram and named vector of genes with associated clusters
+#' and returns the list of plots with the positional information. Used internally in
+#' `plotEigengeneNetworksWithWords`.
 #' 
 #' @param dhc dendrogram
 #' @param geneVec gene-named vector of node names in dendrogram
@@ -505,8 +510,9 @@ getWordsOnDendro <- function(dhc, geneVec, geneNumLimit=1000,
 
 #' returnPyramid
 #' 
-#' Return pyramid plots
-#' 
+#' @description Return pyramid plots
+#' @details Returns the pyramid plots of text frequencies between clusters.
+#' Used internally in getWordsOnDendro.
 #' 
 #' @param L genes in the cluster
 #' @param R genes in the other cluster

R/manual.R

@@ -1,6 +1,6 @@
 #' manual
 #' 
-#' Produce networks using manual input
+#' Produce networks using manual input.
 #' 
 #' @rdname generalf
 #' @examples

R/pubmed.R

@@ -1,6 +1,7 @@
 #' pubmed
 #' 
 #' make word cloud or correlation network from PubMed
+#' 
 #' @rdname generalf
 #' @export
 #' @examples \dontrun{pubmed("DDX41")}

R/refseq.R

@@ -1,6 +1,9 @@
 #' refseq, alliance, pubmed, manual, bugsigdb
 #' 
-#' Text mining RefSeq description, PubMed, BugSigDB and the other manually curated data
+#' @description Text mining RefSeq description, PubMed, BugSigDB and the other manually curated data.
+#' @details The main functions of the `biotextgraph` package. The functions accepts
+#' a character vector of biological entities and returns the summarized statistics and visualization
+#' contained in `biotext` object.
 #' 
 #' @param geneList gene ID list
 #' @param queries query ID list

R/sc.R

@@ -1,8 +1,9 @@
 #' TextMarkers
 #' 
-#' Take results of Seurat::FindAllMarkers as input,
-#' and plot wordcloud or network for all the clusters.
-#' 
+#' @description Take results of `Seurat::FindAllMarkers` as input and plot wordcloud or network for all the clusters.
+#' @details Using the results of marker gene identification such as `FindAllMarkers` from Seurat,
+#' Recursively summarize the textual information of markers and output the visualizations.
+#' @seealso obtainMarkersWC
 #' @param df result of FindAllMarkers()
 #' @param keyType keytype
 #' @param type wc or network
@@ -96,10 +97,12 @@ TextMarkers <- function(df, keyType="SYMBOL",type="wc", genePlot=TRUE,
 
 
 #' TextMarkersScran
+#' @description Make gene wordcloud from scran::findMarkers() results
 #' 
-#' Take results of scran::findMarkers as input,
-#' and plot wordcloud or network for all the clusters.
+#' @details using the results of marker gene identification such as `findMarkers` from scran,
+#' Recursively summarize the textual information of markers and output the visualizations.
 #' 
+#' @seealso obtainMarkersWCScran
 #' @param res result of findMarkers()
 #' @param keyType keytype
 #' @param type wc or network
@@ -216,6 +219,11 @@ TextMarkersScran <- function(res,
 
 #' plotReducedDimWithTexts
 #' 
+#' @description Directly output the dimension reduction plot with textual information.
+#' @details The function accepts the SingleCellExperiment object and marker gene information
+#' and output the reduced dimension plot with the textual information.
+#' @rdname plotReducedDimWithTexts
+#' @seealso DimPlotWithTexts
 #' @param sce sce object
 #' @param marker.info results of findMarkers()
 #' @param colour_by colorize by this label
@@ -416,7 +424,11 @@ plotReducedDimWithTexts <- function(sce, marker.info,
 
 
 #' DimPlotWithTexts
-#' 
+#' @description Directly output the dimension reduction plot with textual information.
+#' @details The function accepts the Seurat object and marker gene information
+#' and output the reduced dimension plot with the textual information.
+#' @seealso plotReducedDimWithTexts
+#' @rdname DimPlotWithTexts
 #' @param seu Seurat object
 #' @param markers results of FindAllMarkers()
 #' @param label plot label or not