add ClusterJudge, OPWeight, phenopath, scmap

git-svn-id: file:///home/git/hedgehog.fhcrc.org/bioconductor/trunk/madman/Rpacks/scmap@131231 bc3139a8-67e5-0310-9ffc-ced21a209358

DESCRIPTION

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+Package: scmap
+Type: Package
+Title: A tool for unsupervised projection of single cell RNA-seq data
+Version: 0.99.2
+Author: Vladimir Kiselev
+Maintainer: Vladimir Kiselev <[email protected]>
+Authors@R: c(person("Vladimir", "Kiselev", email =
+        "[email protected]", role=c("cre", "aut")),
+        person("Martin", "Hemberg", role=c("aut")))
+Description: Single-cell RNA-seq (scRNA-seq) is widely used to
+        investigate the composition of complex tissues since the
+        technology allows researchers to define cell-types using
+        unsupervised clustering of the transcriptome. However, due to
+        differences in experimental methods and computational analyses,
+        it is often challenging to directly compare the cells
+        identified in two different experiments. scmap is a method for
+        projecting cells from a scRNA-seq experiment on to the
+        cell-types identified in a different experiment.
+License: GPL-3
+Imports: Biobase, scater, dplyr, reshape2, matrixStats, proxy, utils,
+        googleVis, ggplot2, methods, stats, e1071, randomForest
+Depends: R(>= 3.4)
+Encoding: UTF-8
+LazyData: true
+RoxygenNote: 6.0.1
+Suggests: knitr, rmarkdown
+VignetteBuilder: knitr
+biocViews: SingleCell, Software, Classification, SupportVectorMachine,
+        RNASeq, Visualization, Transcriptomics, DataRepresentation,
+        Transcription, Sequencing, Preprocessing, GeneExpression,
+        DataImport
+NeedsCompilation: no
+URL: https://github.com/hemberg-lab/scmap
+BugReports: https://support.bioconductor.org/t/scmap/

NAMESPACE

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+# Generated by roxygen2: do not edit by hand
+
+export(createReference)
+export(getFeatures)
+export(getSankey)
+export(projectData)
+export(setFeatures)
+importClassesFrom(scater,SCESet)
+importFrom(Biobase,"fData<-")
+importFrom(Biobase,"pData<-")
+importFrom(Biobase,AnnotatedDataFrame)
+importFrom(Biobase,fData)
+importFrom(Biobase,pData)
+importFrom(dplyr,"%>%")
+importFrom(dplyr,group_by)
+importFrom(dplyr,summarise)
+importFrom(e1071,svm)
+importFrom(ggplot2,aes)
+importFrom(ggplot2,geom_abline)
+importFrom(ggplot2,geom_point)
+importFrom(ggplot2,ggplot)
+importFrom(ggplot2,labs)
+importFrom(ggplot2,scale_colour_manual)
+importFrom(ggplot2,theme_classic)
+importFrom(googleVis,gvisSankey)
+importFrom(matrixStats,colMaxs)
+importFrom(matrixStats,rowMaxs)
+importFrom(methods,is)
+importFrom(proxy,simil)
+importFrom(randomForest,randomForest)
+importFrom(reshape2,dcast)
+importFrom(reshape2,melt)
+importFrom(scater,calculateQCMetrics)
+importFrom(scater,get_exprs)
+importFrom(scater,newSCESet)
+importFrom(stats,cor)
+importFrom(stats,lm)
+importFrom(stats,median)
+importFrom(stats,predict)
+importFrom(utils,head)

R/AllGenerics.R

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+#' @export
+#' 
+#' @examples
+#' library(scater)
+#' pd <- AnnotatedDataFrame(ann)
+#' sceset <- newSCESet(fpkmData = yan, phenoData = pd, logExprsOffset = 1)
+#' sceset <- calculateQCMetrics(sceset)
+#' # use gene names as feature symbols
+#' fData(sceset)$feature_symbol <- featureNames(sceset)
+#' # remove features with duplicated names
+#' sceset <- sceset[!duplicated(fData(sceset)$feature_symbol), ]
+#' sceset <- getFeatures(sceset)
+#' 
+setGeneric("getFeatures", signature = "object", function(object, n_features = 500, suppress_plot = TRUE) {
+    standardGeneric("getFeatures")
+})
+
+#' @export
+#' 
+#' @examples
+#' library(scater)
+#' pd <- AnnotatedDataFrame(ann)
+#' sceset <- newSCESet(fpkmData = yan, phenoData = pd, logExprsOffset = 1)
+#' sceset <- calculateQCMetrics(sceset)
+#' # use gene names as feature symbols
+#' fData(sceset)$feature_symbol <- featureNames(sceset)
+#' # remove features with duplicated names
+#' sceset <- sceset[!duplicated(fData(sceset)$feature_symbol), ]
+#' sceset <- setFeatures(sceset, c('MMP2', 'ZHX3'))
+#' 
+setGeneric("setFeatures", signature = "object", function(object, features = NULL) {
+    standardGeneric("setFeatures")
+})
+
+#' @export
+#' 
+#' @examples
+#' library(scater)
+#' pd <- AnnotatedDataFrame(ann)
+#' sceset <- newSCESet(fpkmData = yan, phenoData = pd, logExprsOffset = 1)
+#' sceset <- calculateQCMetrics(sceset)
+#' # use gene names as feature symbols
+#' fData(sceset)$feature_symbol <- featureNames(sceset)
+#' # remove features with duplicated names
+#' sceset <- sceset[!duplicated(fData(sceset)$feature_symbol), ]
+#' sceset <- getFeatures(sceset)
+#' sceset <- projectData(projection = sceset, reference = sceset)
+#' 
+setGeneric("projectData", signature = "projection", function(projection = NULL, reference = NULL, cell_type_column = "cell_type1", 
+    method = "scmap", threshold = 0.7) {
+    standardGeneric("projectData")
+})
+
+#' @export
+#' 
+#' @examples
+#' library(scater)
+#' pd <- AnnotatedDataFrame(ann)
+#' sceset <- newSCESet(fpkmData = yan, phenoData = pd, logExprsOffset = 1)
+#' sceset <- calculateQCMetrics(sceset)
+#' # use gene names as feature symbols
+#' fData(sceset)$feature_symbol <- featureNames(sceset)
+#' # remove features with duplicated names
+#' sceset <- sceset[!duplicated(fData(sceset)$feature_symbol), ]
+#' sceset <- getFeatures(sceset)
+#' reference <- createReference(sceset[fData(sceset)$scmap_features, ])
+#' 
+setGeneric("createReference", signature = "reference", function(reference = NULL, cell_type_column = "cell_type1") {
+    standardGeneric("createReference")
+})
+