Tree based method for cross-species data integration
speciesTree is initially designed for cross-species data analysis to identify homologous groups and evolutional patterns. The approach is used in BICCN cross species analysis to identify robust homologous groups in human, mouse and marmoset. The method can be extended to the other analysis as well, such as identifying roubust clusters in single and multi-modal datasets.
install.packages("devtools")
devtools::install_github("huqiwen0313/speciesTree")
library(speciesTree)- build robust homologous clusters across species using dynamic prunning example_data
library(speciesTree)
library(conos)
library(pagoda2)
# read example files
# gene expression profile
expression.matrix <- readRDS("./example_data/integrated_matrix_all_cells.RDS")
# mete cell clusters
meta_clusters <- readRDS("./example_data/all_cells_cluster_membership.RDS")
# read cell annotations
upperlevelinfo <- readRDS("./example_data/upperlevelinfo.rds")
species_annot <- sapply(strsplit(names(meta_clusters), "_"), `[`, 1)
names(species_annot) <- names(meta_clusters)
# read subsampled cell clusters
subsampledClusters <- readRDS("./example_data/subsampledClusters.rds")
# build tree
d <- cluster.matrix.expression.distances(t(expression.matrix), groups=meta_clusters, dist="cor",
useVariablegenes=FALSE, use.scaled.data=TRUE)
dendr <- hclust(as.dist(d), method='ward.D2')
dend <- as.dendrogram(dendr)
tree <- buildSpeciesTree(dend=dend, expMatrix=t(expression.matrix), subsampleClusters=subsampledClusters,
cls.groups=meta_clusters, upperlevelannot=upperlevelinfo, species=species_annot,
n.cores=10)
# prune tree
tree$dend <- TreeEntropy(tree$dend, entropy.cutoff = 2.9)
dend_pruned <- pruneTreeEntropy(tree$dend, cutoff=2.9)
# get homologous clusters
homo.clusters <- getClusters(dend_pruned, plotTree=TRUE)