knitr::opts_chunk$set( collapse = TRUE, comment = "#>" )
Reconstructing ordered ontogenic trajectories provides methods for:
The main goal of roots is to infer plausible developmental journeys guided by the user.
Can be installed through CRAN or GitHub
install.packages("roots")
library(devtools) install_github("wjawaid/roots")
Here I take the mouse adult haematopoietic data from Nestorowa et al.. Data is downloaded and processed using the goggles() function as below.
library(roots) ## Load data blood <- read.table("http://blood.stemcells.cam.ac.uk/data/norm_counts_nestorowa_data.txt", sep = " ") cellNames <- read.table("http://blood.stemcells.cam.ac.uk/data/cell_names_nestorowa_data.txt", sep = " ", stringsAsFactors = FALSE)[,1] rownames(blood) <- gsub("LT\\.", "LT-", cellNames) geneNames <- read.table("http://blood.stemcells.cam.ac.uk/data/gene_names_nestorowa_data.txt", sep = " ", stringsAsFactors = FALSE)[,1] colnames(blood) <- geneNames blood <- as.matrix(blood) rm(cellNames, geneNames) ## Load metadata meta <- read.csv("http://blood.stemcells.cam.ac.uk/data/wj_out_jd.csv") colnames(meta) <- c("cellType", "index", "name") rownames(meta) <- meta$name meta$col <- bglab::ggCol(meta$cellType) nmeta <- data.frame(col=rep("#00000011", nrow(blood)), stringsAsFactors = FALSE, row.names = rownames(blood)) nmeta[rownames(meta),"col"] <- meta$col leg <- data.frame(cell=as.character(unique(meta$cellType)), col=as.character(unique(meta$col)), stringsAsFactors = FALSE) legOrd <- c(5, 8, 6, 7, 1, 4, 2, 3) ## Analyse xx <- goggles(blood) ## Plot plot(xx$l, pch=16, col = nmeta[rownames(xx$l), "col"]) legend("topright", legend = leg$cell[legOrd], fill=leg$col[legOrd], inset=0.02)
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