update tutorial (ce8f6083) · Commits · github_fork / DAseq2

DESCRIPTION

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		Package: DAseq
		Type: Package
		Title: Detecting regions of differential abundance between scRNA-seq datasets
		Version: 1.0.1
		Version: 1.0.0
		Author: Jun Zhao <jun.zhao@yale.edu>
		Maintainer: Jun Zhao <jun.zhao@yale.edu>
		Description: DA-seq is a multiscale approach for detecting DA subpopulations.
		@@ -15,8 +15,12 @@ LazyData: true
		RoxygenNote: 6.1.1
		Imports:
		RANN,
		glmnet,
		caret,
		Seurat,
		e1071,
		reticulate,
		ggplot2,
		cowplot,
		reticulate,
		scales,
		tclust
		ggrepel

R/getDAcells.R

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		@@ -57,6 +57,11 @@ getDAcells <- function(
		}
		n.cells <- length(cell.labels)

		# default k-vector
		if(is.null(k.vector)){
		k.vector <- round(seq(n.cells0.0035, n.cells0.035, length.out = 10))
		}

		# get DA score vector for each cell
		cat("Calculating DA score vector.\n")
		X.knn.result <- daPerCell(

R/getDAregion.R

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		@@ -11,7 +11,7 @@
		#' @param prune.SNN parameter for Seurat function FindNeighbors(), default 1/15
		#' @param resolution parameter for Seurat function FindClusters(), default 0.05
		#' @param group.singletons parameter for Seurat function FindClusters(), default True
		#' @param min.cell integer, number of cells below which a DA region will be removed as outliers, default 10
		#' @param min.cell integer, number of cells below which a DA region will be removed as outliers, default 15
		#' @param do.plot a logical value to indicate whether to return ggplot objects showing the results, default True
		#' @param plot.embedding size N-by-2 matrix, 2D embedding for the cells
		#' @param size cell size to use in the plot, default 0.5
		@@ -34,15 +34,27 @@
		getDAregion <- function(
		X, da.cells,
		cell.labels, labels.1, labels.2,
		prune.SNN = 1/15, resolution = 0.05, group.singletons = F, min.cell = 10,
		prune.SNN = 1/15, resolution = 0.05, group.singletons = F, min.cell = 15,
		do.plot = T, plot.embedding = NULL, size = 0.5,
		...
		){
		if(!inherits(x = X, what = "matrix")){
		cat("Turning X to a matrix.\n")
		X <- as.matrix(X)
		}
		n.cells <- nrow(X)
		n.dims <- ncol(X)
		if(is.null(rownames(X))){
		rownames(X) <- paste("C",c(1:n.cells), sep = "")
		}
		# check label input
		if(!inherits(cell.labels, "character") \|
		!inherits(labels.1, "character") \| !inherits(labels.2, "character")){
		stop("Input parameters cell.labels, labels.1 and labels.2 must be character")
		}
		if(length(setdiff(cell.labels, c(labels.1, labels.2))) > 0){
		stop("Input parameter cell.labels contain labels not from labels.1 or labels.2")
		}
		seurat.version <- substr(packageVersion("Seurat"),1,1)
		if(seurat.version == "3"){
		X.S <- CreateSeuratObject(counts = t(X))

README.md

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		@@ -6,9 +6,7 @@ DA-seq is a method to detect cell subpopulations with differential abundance bet
		[This](https://github.com/KlugerLab/DAseq) repository contains the DA-seq package.

		## R Dependencies
		Required packages: RANN, tclust, ggplot2, cowplot, RColorBrewer, scales, reticulate

		Suggested package: Seurat
		Required packages: RANN, glmnet, caret, Seurat, e1071, reticulate, ggplot2, cowplot, scales, ggrepel

		## Python Dependencies
		Python 3 or above ([Miniconda](https://docs.conda.io/en/latest/miniconda.html) is recommended.)

data/X.melanoma.rda

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