merge dev

.Rbuildignore

@@ -10,4 +10,5 @@ CHANGELOG.md
 ^vignettes\figure$
 docker
 CONTRIBUTING.md
-data-raw
+data-raw
+^\.circleci$

.circleci/config.yml

@@ -0,0 +1,20 @@
+version: 2
+jobs:
+  build:
+    docker:
+      - image: rocker/verse:4.0.3
+    environment:
+            _R_CHECK_FORCE_SUGGESTS_: false
+    steps:
+      - checkout
+      - run:
+          name: Install package dependencies
+          command: R -e "devtools::install_deps(dep = TRUE)"
+      - run:
+          name: Build package
+          command: R CMD build .
+      - run:
+          name: Check package
+          command: R CMD check --no-manual *tar.gz
+      - store_test_results:
+          path: tmp/tests

.gitignore

@@ -3,3 +3,4 @@
 .RData
 .Ruserdata
 conos*.tar.gz
+conos*Rcheck

CHANGELOG.md

@@ -1,5 +1,13 @@
 ## Upcoming
 
+## [1.4.4] - 2021-11-08
+
+### Changed
+- Added `fail.on.error=TRUE` in some plapplys
+- Re-added `getGeneExpression()` methods for Seurat (lost in merge 552408f)
+- Switched to CircleCI
+
+
 ## [1.4.3] - 2021-02-08
 
 ### Changed

DESCRIPTION

@@ -1,6 +1,6 @@
 Package: conos
 Title: Clustering on Network of Samples
-Version: 1.4.3
+Version: 1.4.4
 Authors@R: c(person("Viktor","Petukhov", email="[email protected]", role="aut"), person("Nikolas","Barkas", email="[email protected]", role="aut"), person("Peter", "Kharchenko", email = "[email protected]", role = "aut"), person("Weiliang", "Qiu", email = "[email protected]", role = c("ctb")), person("Evan", "Biederstedt", email="[email protected]", role=c("aut", "cre")))
 Description: Wires together large collections of single-cell RNA-seq datasets, which allows for both the identification of recurrent cell clusters and the propagation of information between datasets in multi-sample or atlas-scale collections. 'Conos' focuses on the uniform mapping of homologous cell types across heterogeneous sample collections. For instance, users could investigate a collection of dozens of peripheral blood samples from cancer patients combined with dozens of controls, which perhaps includes samples of a related tissue such as lymph nodes. This package interacts with data available through the 'conosPanel' package, which is available in a 'drat' repository. To access this data package, see the instructions at <https://github.com/kharchenkolab/conos>. The size of the 'conosPanel' package is approximately 12 MB.
 License: GPL-3
@@ -30,11 +30,11 @@ Imports:
     reshape2,
     rlang,
     Rtsne,
-    sccore (> 0.1.2),
+    sccore (>= 1.0.0),
     stats,
     tools,
     utils
-RoxygenNote: 7.1.1
+RoxygenNote: 7.1.2
 Suggests:
     AnnotationDbi,
     BiocParallel,

R/access_wrappers.R

@@ -215,6 +215,29 @@ getGeneExpression.default <- function(sample, gene) {
   return(stats::setNames(rep(NA, ncol(count.matrix)), colnames(count.matrix)))
 }
 
+#' @rdname getGeneExpression
+setMethod("getGeneExpression", signature("Seurat"), function(sample, gene) {
+  checkSeuratV3()
+  ## https://satijalab.org/seurat/essential_commands.html
+  if (gene %in% rownames(Seurat::GetAssayData(object = sample))){
+    ## rownames(data) are gene names
+    return(Seurat::GetAssayData(object = sample)[gene, ])
+  }
+
+  return(stats::setNames(rep(NA, ncol(Seurat::GetAssayData(object = sample))), colnames(Seurat::GetAssayData(object = sample)))) 
+})
+
+#' @rdname getGeneExpression
+setMethod("getGeneExpression", signature("seurat"), function(sample, gene) {
+  ## https://satijalab.org/seurat/essential_commands.html
+  if (gene %in% rownames(sample@data)){
+    ## rownames(data) are gene names
+    return(sample@data[gene, ])
+  }
+
+  return(stats::setNames(rep(NA, ncol(sample@data)), colnames(sample@data))) 
+})
+
 
 #' Access raw count matrix from sample
 #' 

R/conclass.R

@@ -154,7 +154,7 @@ Conos <- R6::R6Class("Conos", lock_objects=FALSE,
     #' @return joint graph to be used for downstream analysis
     #' @examples
     #' con <- Conos$new(small_panel.preprocessed, n.cores=1)
-    #' con$buildGraph(k=10, k.self=5, space='PCA', ncomps=10, n.odgenes=20, matching.method='mNN', 
+    #' con$buildGraph(k=10, k.self=5, space='PCA', ncomps=10, n.odgenes=20, matching.method='mNN',
     #'     metric='angular', score.component.variance=TRUE, verbose=TRUE)
     #'
     #'
@@ -381,7 +381,7 @@ Conos <- R6::R6Class("Conos", lock_objects=FALSE,
       groups %<>% as.factor() %>% droplevels()
       # TODO: add Seurat
       '%ni%' <- Negate('%in%')
-      if ('Pagoda2' %ni% class(self$samples[[1]])){
+      if ('Pagoda2' %ni% class(self$samples[[1]])) {
         stop("Only Pagoda2 objects are supported for marker genes")
       }
 
@@ -396,7 +396,8 @@ Conos <- R6::R6Class("Conos", lock_objects=FALSE,
 
         de.genes %<>% lapply(function(x) if ((length(x) > 0) && (nrow(x) > 0)) subset(x, complete.cases(x)) else x)
         de.genes %<>% names() %>% setNames(., .) %>%
-          sccore::plapply(function(n) appendSpecificityMetricsToDE(de.genes[[n]], groups.clean, n, p2.counts=cm.merged, append.auc=append.auc), progress=verbose, n.cores=self$n.cores)
+          sccore::plapply(function(n) appendSpecificityMetricsToDE(de.genes[[n]], groups.clean, n, p2.counts=cm.merged, append.auc=append.auc),
+                          progress=verbose, n.cores=self$n.cores, fail.on.error=TRUE)
       }
 
       if (verbose) message("All done!")
@@ -418,7 +419,7 @@ Conos <- R6::R6Class("Conos", lock_objects=FALSE,
     #' @return invisible list containing identified communities (groups) and the full community detection result (result); The results are stored in $clusters$name slot in the conos object. Each such slot contains an object with elements: $results which stores the raw output of the community detection method, and $groups which is a factor on cells describing the resulting clustering. The later can be used, for instance, in plotting: con$plotGraph(groups=con$clusters$leiden$groups). If test.stability==TRUE, then the result object will also contain a $stability slot.
     #' @examples
     #' con <- Conos$new(small_panel.preprocessed, n.cores=1)
-    #' con$buildGraph(k=10, k.self=5, space='PCA', ncomps=10, n.odgenes=20, matching.method='mNN', 
+    #' con$buildGraph(k=10, k.self=5, space='PCA', ncomps=10, n.odgenes=20, matching.method='mNN',
     #'     metric='angular', score.component.variance=TRUE, verbose=TRUE)
     #' con$findCommunities(method = igraph::walktrap.community, steps=5)
     #'
@@ -851,7 +852,7 @@ Conos <- R6::R6Class("Conos", lock_objects=FALSE,
     #' @param count.matrix Alternative gene count matrix to correct (rows: genes, columns: cells; has to be dense matrix). Default: joint count matrix for all datasets.
     #' @param normalize boolean Whether to normalize values (default=TRUE)
     #' @return smoothed expression of the input genes
-    #' 
+    #'
     correctGenes=function(genes=NULL, n.od.genes=500, fading=10.0, fading.const=0.5, max.iters=15, tol=5e-3, name='diffusion', verbose=TRUE, count.matrix=NULL, normalize=TRUE) {
       edges <- igraph::as_edgelist(self$graph)
       edge.weights <- igraph::edge.attributes(self$graph)$weight
@@ -1065,7 +1066,7 @@ Conos <- R6::R6Class("Conos", lock_objects=FALSE,
       if(any(is.na(mi))) { # some pairs are missing
         if(verbose) message('running ',sum(is.na(mi)),' additional ',space,' space pairs ')
         xl2 <- sccore::plapply(which(is.na(mi)), function(i) {
-          if(space=='CPCA') {
+          if (space=='CPCA') {
             xcp <- quickCPCA(self$samples[sn.pairs[,i]],data.type=data.type,ncomps=ncomps,n.odgenes=n.odgenes,verbose=FALSE,var.scale=var.scale, score.component.variance=score.component.variance)
           } else if(space=='JNMF') {
             xcp <- quickJNMF(self$samples[sn.pairs[,i]],data.type=data.type,n.comps=ncomps,n.odgenes=n.odgenes,var.scale=var.scale,verbose=FALSE,max.iter=3e3)
@@ -1078,7 +1079,7 @@ Conos <- R6::R6Class("Conos", lock_objects=FALSE,
           }
           if(verbose) cat(".")
           xcp
-        }, n.cores=self$n.cores, mc.preschedule=(space=='PCA'), progress=FALSE)
+        }, n.cores=self$n.cores, mc.preschedule=(space=='PCA'), progress=FALSE, fail.on.error=TRUE)
 
         names(xl2) <- apply(sn.pairs[,which(is.na(mi)),drop=FALSE],2,paste,collapse='.vs.')
         xl2 <- xl2[!unlist(lapply(xl2,is.null))]

README.md

@@ -1,4 +1,4 @@
-[![Build Status](https://travis-ci.com/kharchenkolab/conos.svg?branch=master)](https://travis-ci.com/github/kharchenkolab/conos)
+[![<kharchenkolab>](https://circleci.com/gh/kharchenkolab/conos.svg?style=svg)](https://app.circleci.com/pipelines/github/kharchenkolab/conos)
 [![CRAN status](https://www.r-pkg.org/badges/version/conos)](https://cran.r-project.org/package=conos)
 [![CRAN downloads](https://cranlogs.r-pkg.org/badges/conos)](https://cran.r-project.org/package=conos)
 
@@ -238,5 +238,5 @@ The R package can be cited as:
 ```
 Viktor Petukhov, Nikolas Barkas, Peter Kharchenko, and Evan
 Biederstedt (2021). conos: Clustering on Network of Samples. R
-package version 1.4.3.
+package version 1.4.4.
 ```

src/RcppExports.cpp

@@ -8,6 +8,11 @@
 
 using namespace Rcpp;
 
+#ifdef RCPP_USE_GLOBAL_ROSTREAM
+Rcpp::Rostream<true>&  Rcpp::Rcout = Rcpp::Rcpp_cout_get();
+Rcpp::Rostream<false>& Rcpp::Rcerr = Rcpp::Rcpp_cerr_get();
+#endif
+
 // RjnmfC
 arma::field<arma::mat> RjnmfC(arma::mat Xs, arma::mat Xu, int k, double alpha, double lambda, double epsilon, int maxiter, bool verbose);
 RcppExport SEXP _conos_RjnmfC(SEXP XsSEXP, SEXP XuSEXP, SEXP kSEXP, SEXP alphaSEXP, SEXP lambdaSEXP, SEXP epsilonSEXP, SEXP maxiterSEXP, SEXP verboseSEXP) {