swap MetaDE for metap, remove NMF dependency

DESCRIPTION

@@ -1,6 +1,6 @@
 Package: Seurat
-Version: 2.2.0
-Date: 2018-01-10
+Version: 2.2.1
+Date: 2018-02-14
 Title: Tools for Single Cell Genomics
 Description: A toolkit for quality control, analysis, and exploration of single cell RNA sequencing data. 'Seurat' aims to enable users to identify and interpret sources of heterogeneity from single cell transcriptomic measurements, and to integrate diverse types of single cell data. See Satija R, Farrell J, Gennert D, et al (2015) <doi:10.1038/nbt.3192>, Macosko E, Basu A, Satija R, et al (2015) <doi:10.1016/j.cell.2015.05.002>, and Butler A and Satija R (2017) <doi:10.1101/164889> for more details.
 Authors@R: c(
@@ -47,14 +47,14 @@ Imports:
     Rcpp,
     tclust,
     ranger,
-    NMF,
     dtw,
     SDMTools,
     plotly,
     diffusionMap,
     Hmisc,
     tidyr,
-    ggridges
+    ggridges,
+    metap
 LinkingTo: Rcpp, RcppEigen, RcppProgress
 License: GPL-3 | file LICENSE
 LazyData: true
@@ -95,5 +95,4 @@ Suggests:
     testthat,
     SummarizedExperiment,
     MAST,
-    DESeq2,
-    MetaDE
+    DESeq2

NAMESPACE

@@ -263,6 +263,7 @@ import(fpc)
 import(ggplot2)
 import(gridExtra)
 import(lars)
+import(metap)
 import(methods)
 import(parallel)
 import(pbapply)
@@ -277,7 +278,6 @@ importFrom(Matrix,colSums)
 importFrom(Matrix,readMM)
 importFrom(Matrix,rowSums)
 importFrom(Matrix,sparseMatrix)
-importFrom(NMF,aheatmap)
 importFrom(RColorBrewer,brewer.pal.info)
 importFrom(ROCR,performance)
 importFrom(ROCR,prediction)
@@ -306,6 +306,7 @@ importFrom(dplyr,top_n)
 importFrom(dplyr,ungroup)
 importFrom(dtw,dtw)
 importFrom(gdata,drop.levels)
+importFrom(gdata,interleave)
 importFrom(ggridges,geom_density_ridges)
 importFrom(ggridges,theme_ridges)
 importFrom(gplots,heatmap.2)
@@ -317,9 +318,11 @@ importFrom(graphics,abline)
 importFrom(graphics,axis)
 importFrom(graphics,contour)
 importFrom(graphics,hist)
+importFrom(graphics,lines)
 importFrom(graphics,locator)
 importFrom(graphics,mtext)
 importFrom(graphics,par)
+importFrom(graphics,plot)
 importFrom(graphics,points)
 importFrom(graphics,rect)
 importFrom(graphics,smoothScatter)
@@ -348,6 +351,7 @@ importFrom(stats,coef)
 importFrom(stats,complete.cases)
 importFrom(stats,cor)
 importFrom(stats,cov)
+importFrom(stats,density)
 importFrom(stats,dist)
 importFrom(stats,dnorm)
 importFrom(stats,glm)
@@ -366,6 +370,7 @@ importFrom(stats,poisson)
 importFrom(stats,predict)
 importFrom(stats,prop.test)
 importFrom(stats,qqplot)
+importFrom(stats,quantile)
 importFrom(stats,qunif)
 importFrom(stats,relevel)
 importFrom(stats,reorder)

NEWS.md

@@ -2,6 +2,11 @@
 All notable changes to Seurat will be documented in this file.
 The format is based on [Keep a Changelog](http://keepachangelog.com/en/1.0.0/)
 
+## [2.2.1] - 2018-02-14
+### Changed
+ - MetaDE replaced with metap for combining p-values (MetaDE was removed from CRAN)
+ - NMF heatmaps replaced (NMF to be archived by CRAN)
+
 ## [2.2.0] - 2018-01-10
 ### Added
  - Multiple alignment functionality with RunMultiCCA and AlignSubspace extended to multiple datasets

R/differential_expression.R

@@ -613,15 +613,16 @@ FindAllMarkersNode <- function(
 #' use all other cells for comparison.
 #' @param grouping.var grouping variable
 #' @param assay.type Type of assay to fetch data for (default is RNA)
-#' @param meta.method method for combining p-values. See MetaDE::MetaDE.pvalue
-#' for details.
+#' @param meta.method method for combining p-values. Should be a function from
+#' the metap package (NOTE: pass the function, not a string)
 #' @param \dots parameters to pass to FindMarkers
 #'
 #' @return Matrix containing a ranked list of putative conserved markers, and
 #' associated statistics (p-values within each group and a combined p-value
 #' (such as Fishers combined p-value or others from the MetaDE package),
 #' percentage of cells expressing the marker, average differences)
 #'
+#' @import metap
 #' @export
 #'
 #' @examples
@@ -632,19 +633,19 @@ FindAllMarkersNode <- function(
 #'   size = length(x = [email protected]),
 #'   replace = TRUE
 #' )
-#' FindConservedMarkers(pbmc_small, ident.1 = 1, ident.2 = 2, grouping.var = "groups")
+#' FindConservedMarkers(pbmc_small, ident.1 = 0, ident.2 = 1, grouping.var = "groups")
 #'
 FindConservedMarkers <- function(
   object,
   ident.1,
   ident.2 = NULL,
   grouping.var,
   assay.type = "RNA",
-  meta.method = "minP",
+  meta.method = minimump,
   ...
 ) {
-  if (!'MetaDE' %in% rownames(x = installed.packages())) {
-    stop("Please install MetaDE (note Bioconductor dependencies) - learn more at https://cran.r-project.org/web/packages/MetaDE/index.html")
+  if(class(meta.method) != "function") {
+    stop("meta.method should be a function from the metap package. Please see https://cran.r-project.org/web/packages/metap/metap.pdf for a detail description of the available functions.")
   }
   object.var <- FetchData(object = object, vars.all = grouping.var)
   object <- SetIdent(
@@ -716,12 +717,12 @@ FindConservedMarkers <- function(
     MARGIN = 1,
     FUN = max
   )
-  combined.pval <- MetaDE::MetaDE.pvalue(x = list(p = markers.combined[, pval.codes]),
-                                 meta.method = meta.method)$meta.analysis
-  combined.pval <- cbind(combined.pval$pval, combined.pval$FDR)
-  colnames(combined.pval) <- paste0(colnames(combined.pval), c(rep("_p_val", length(meta.method)), rep("_FDR", length(meta.method))))
-  markers.combined$combined_pval <- combined.pval
-  markers.combined <- markers.combined[order(markers.combined$combined_pval[,1]), ]
+  combined.pval <- data.frame(cp = apply(X = markers.combined[, pval.codes],
+                                         MARGIN = 1,
+                                         FUN = function(x) meta.method(x)$p))
+  colnames(combined.pval) <- paste0(as.character(formals()$meta.method), "_p_val")
+  markers.combined <- cbind(markers.combined, combined.pval)
+  markers.combined <- markers.combined[order(markers.combined[, paste0(as.character(formals()$meta.method), "_p_val")]), ]
   return(markers.combined)
 }
 

R/plotting_utilities.R

@@ -278,3 +278,7 @@ BlackAndWhite <- function(...) {
 PurpleAndYellow <- function(...) {
   return(CustomPalette(low = "magenta", high = "yellow", mid = "black", ...))
 }
+
+BlueAndRed <- function(...) {
+  return(CustomPalette(low = "#313695" , high = "#A50026", mid = "#FFFFBF", ...))
+}

R/spatial_internal.R

@@ -55,7 +55,6 @@ slimdmvnorm <- function (x, mean = rep(0, p), sigma = diag(p), log = FALSE) {
 ###############
 #Internal, not documented for now
 #
-#' @importFrom NMF aheatmap
 #' @importFrom utils write.table
 #
 CalcInsitu <- function(
@@ -129,13 +128,12 @@ CalcInsitu <- function(
     )
   }
   if (do.plot) {
-    aheatmap(
-      x = insilico.stain,
-      Rowv = NA,
-      Colv = NA,
-      color = col.use,
-      main = title.use
-    )
+    heatmap2NoKey(x = insilico.stain,
+                  Rowv = NA,
+                  Colv = NA,
+                  trace = "none",
+                  col = col.use,
+                  dimTitle = title.use)
   }
   if (do.return) {
     return(as.vector(x = insilico.stain))
@@ -172,8 +170,6 @@ map.cell.score <- function(gene, gene.value, insitu.bin, mu, sigma, alpha) {
 
 #Internal, not documented for now
 #
-#' @importFrom NMF aheatmap
-#
 MapCell <- function(
   object,
   cell.name,
@@ -246,14 +242,19 @@ MapCell <- function(
         )
       ), ]
     }
-    aheatmap(
-      x = matrix(data = total.prob, nrow=8, ncol=8),
-      Rowv = NA,
-      Colv = NA,
-      txt = txt.matrix,
-      color = BlackAndWhite()
+    heatmap2NoKey(x = matrix(data = total.prob, nrow=8, ncol=8),
+                  Rowv = NA,
+                  Colv = NA,
+                  col = BlackAndWhite(),
+                  trace = "none"
+    )
+    heatmap2NoKey(x = scale.probs,
+                  Rowv = NA,
+                  Colv = NA,
+                  col = BlueAndRed(),
+                  trace = "none",
+                  symbreaks = FALSE
     )
-    aheatmap(x = scale.probs, Rowv = NA, Colv = NA)
     ResetPar()
   }
   return(total.prob)

R/zfRenderSeurat.R

@@ -539,6 +539,7 @@ zf.insitu.ventral <- function(seuratObject, gene, label=TRUE, ...) {
 }
 
 #' @export
+#' @importFrom gdata interleave
 zf.insitu.side <- function(expressionMatrix, nonmirror = TRUE, mirror = TRUE) {
   # Determine geometry
   tierBins <- 30 # 1 bin per cell tier.