automate marker detection (8709a9bb) · Commits · github_fork / DAseq2

DA-seq.R

+32 −2

Original line number	Diff line number	Diff line
		@@ -132,7 +132,7 @@ getDAregion <- function(
		X.region.plot <- plotCellLabel(
		X = plot.embedding[cell.idx,], label = as.factor(X.tclust),
		size = size, do.label = F, return.plot = T
		) + scale_color_manual(values = c("gray",hue_pal()(X.n.da)), breaks = c(1:X.n.da))
		) + scale_color_manual(values = c(rgb(255,255,255,max = 255,alpha = 0),hue_pal()(X.n.da)), breaks = c(1:X.n.da))
		} else {
		X.region.plot <- NULL
		}
		@@ -146,6 +146,36 @@ getDAregion <- function(



		## Step 3: detect genes that characterize DA regions from Step 2

		#' @param cell.idx result "da.cell.idx" from the output of function getDAcells
		#' @param da.region.label result "cluster.res" from the output of function getDAregion
		#' @param obj Seurat object that contain ALL cells in the analysis
		#' @param ... parameters for Seurat function FindMarkers()
		#'
		#' @return a list of matrices with markers for each DA region

		findMarkersForDAregion <- function(
		cell.idx, da.region.label, obj, ...
		){
		n.da <- length(unique(da.region.label)) - 1
		obj@meta.data$da <- 0
		obj@meta.data$da[cell.idx] <- da.region.label
		obj <- SetAllIdent(obj, id = "da")

		da.markers <- list()
		for(ii in 1:n.da){
		da.markers[[ii]] <- FindMarkers(obj, ident.1 = ii, ...)
		da.markers[[ii]]$pct.diff <- da.markers[[ii]]$pct.1 - da.markers[[ii]]$pct.2
		}

		return(da.markers)
		}





		##=======================================================##
		## Other functions

		@@ -292,7 +322,7 @@ plotCellLabel <- function(X, label, cell.col = NULL, size = 0.5, do.label = T, r
		# Plot cells with labels
		myggplot <- ggplot() + theme_cowplot() +
		geom_point(data = data.frame(Dim1 = X[,1], Dim2 = X[,2], Group = label, stringsAsFactors = F),
		aes(x = Dim1, y = Dim2, col = Group), size = size, alpha = 0.75) +
		aes(x = Dim1, y = Dim2, col = Group), size = size) +
		guides(colour = guide_legend(override.aes = list(size=3), title = NULL))

		# Change cell color

DA-seq_melanoma.R

+11 −18

Original line number	Diff line number	Diff line
		@@ -120,7 +120,9 @@ da_cells$da.cells.plot

		# cluster DA cells to get DA regions
		da_regions <- getDAregion(
		X = pca_embedding, cell.idx = da_cells$da.cell.idx, k = 5, alpha = 0.1,
		X = pca_embedding,
		cell.idx = da_cells$da.cell.idx,
		k = 5, alpha = 0.1,
		cell.labels = data_S@meta.data$lesion,
		labels.1 = labels_res,
		labels.2 = labels_nonres,
		@@ -130,23 +132,14 @@ da_regions$da.region.plot
		da_regions$DA.stat



		## Marker detection

		# set ident in Seurat
		n.da <- length(unique(da_regions$cluster.res)) - 1
		data_S@meta.data$da <- 0
		data_S@meta.data$da[da_cells$da.cell.idx] <- da_regions$cluster.res
		data_S <- SetAllIdent(data_S, id = "da")
		TSNEPlot(data_S, pt.size = 0.5)


		# identify markers for each DA region
		da.markers <- list()
		for(i in 1:n.da){
		da.markers[[i]] <- FindMarkers(data_S, ident.1 = i, only.pos = T, min.pct = 0.1, min.diff.pct = 0.09)
		da.markers[[i]]$pct.diff <- da.markers[[i]]$pct.1 - da.markers[[i]]$pct.2
		}
		# Marker detection for DA regions
		da_markers <- findMarkersForDAregion(
		cell.idx = da_cells$da.cell.idx,
		da.region.label = da_regions$cluster.res,
		obj = data_S,
		only.pos = T, min.pct = 0.1, min.diff.pct = 0.09
		)
		str(da_markers)


		# plot some markers

README.md

+9 −0

Original line number	Diff line number	Diff line
		@@ -17,6 +17,8 @@ DA-seq can be used as follows:

		Let X be a N-by-p matrix of the PCA embeddings of merged scRNA-seq datasets A (A1 and A2) and B (B1 and B2); X.label be a vector of N specifying the original of each cell ('A1', 'A2', 'B1', or 'B2'); X.2d be the 2D embedding of the cells.

		For marker detection of each DA region, let SeuratObj be a Seurat V2.3.0 ([tutorial](https://satijalab.org/seurat/v2.4/pbmc3k_tutorial.html)) object containing the merged datasets (N cells), after proper preprocessing steps (normalization, scaling, etc.).

		~~~~
		X.da.cells <- getDAcells(
		X = X,
		@@ -36,6 +38,13 @@ X.da.regions <- getDAregion(
		labels.2 = c("B1","B2"),
		plot.embedding = X.2d
		)

		X.da.markers <- findMarkersForDAregion(
		cell.idx = X.da.cells$da.cell.idx,
		da.region.label = X.da.regions$cluster.res,
		obj = SeuratObj,
		only.pos = T, min.pct = 0.1, min.diff.pct = 0.09
		)
		~~~~

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