README.md

Reconstructing Ordered Ontogenic Trajectories

Wajid Jawaid 2017-09-11

CRAN_Status_Badge Project Status: Active - The project has reached a stable, usable state and is being actively developed. CRAN mirror downloads

Reconstructing ordered ontogenic trajectories provides methods for:

  1. Identifying informative genes (crude),
  2. Finding a sparse graph structure between closely related cells by removing spurious edges and
  3. Traversing the graph in a biologiclal informed way using a Directed Non-markovian Monte-Carlo method.

The main goal of roots is to infer plausible developmental journeys guided by the user.

Installation

Can be installed through CRAN or GitHub

CRAN

install.packages("roots")

Bleeding edge

library(devtools)
install_github("wjawaid/roots")

Example

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)

Output from goggle() function



wjawaid/roots documentation built on Sept. 12, 2017, 9:51 a.m.