In dasroy/DELocal: Identifies differentially expressed genes with respect to other local genes

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DELocal

Citation:

Das Roy R, Hallikas O, Christensen MM, Renvoisé E, Jernvall J (2021) Chromosomal neighbourhoods allow identification of organ specific changes in gene expression. PLoS Comput Biol 17(9): e1008947. https://doi.org/10.1371/journal.pcbi.1008947

The goal of DELocal is to identify DE genes compared to their neighboring genes from the same chromosomal location.

neighbor
In the above figure it can be seen that Sostdc1 is differentially expressed in developing tooth tissues (E13 and E14). DELocal helps in identifying similar genes.

Installation

You can install the released version of DELocal with:

if (!requireNamespace("devtools")) {
  install.packages("devtools")
}
devtools::install_github("dasroy/delocal")

How to run

This is a basic example which shows you how to use DELocal:

First a SummarizedExperiment object will be configured with gene expression count matrix and gene location info.

Read the raw count values

library(DELocal)
count_matrix <- as.matrix(read.table(file = system.file("extdata", 
                                              "tooth_RNASeq_counts.txt", 
                                              package = "DELocal")))
colData <- data.frame(condition=gsub("\\..*",x=colnames(count_matrix),replacement = ""))

Getting gene chromosomal location

Example of required gene location information

gene_location <- read.table(file = system.file("extdata", 
                                              "gene_location.txt", 
                                              package = "DELocal"))
head(gene_location)

Example code to get gene location information like above

require(biomaRt)
gene_attributes<- c("ensembl_gene_id", "start_position", "chromosome_name")
ensembl_ms_mart <- useMart(biomart="ENSEMBL_MART_ENSEMBL",
                           dataset="mmusculus_gene_ensembl", host="www.ensembl.org")
gene_location_sample <- getBM(attributes=gene_attributes, mart=ensembl_ms_mart,
                       verbose = FALSE)
rownames(gene_location_sample) <- gene_location_sample$ensembl_gene_id

Integrating gene expression and location into a single object.

smrExpt <- SummarizedExperiment::SummarizedExperiment(assays=list(counts=count_matrix),
                                                      rowData = gene_location, 
                                                      colData=colData)
smrExpt

Final results

These may take long time to run the whole data therefore here we will analyse genes only from X chromosome.

contrast= c("condition","ME13","ME14")

require(dplyr)
x_genes <- SummarizedExperiment::rowData(smrExpt) %>% 
    as.data.frame() %>% 
    filter(chromosome_name=="X") %>% rownames() 

DELocal_result <- DELocal(pSmrExpt = smrExpt[x_genes,], #contrast = contrast,
                         nearest_neighbours = 5,pDesign = ~ condition,
                         pValue_cut = 0.05, pLogFold_cut = 0)

Dynamic neighbour

Here TAD domain boundaries will be used as dynamic boundaries

TADKB <- readRDS("../DELocal_manuscript/markdowns/Mouse_TAD_boundaries.rds")
gene_location_dynamicNeighbourhood <- TADKB %>% dplyr::select(ensembl_gene_id, start_position, chromosome_name,startTAD   ,endTAD) %>% unique()
rownames(gene_location_dynamicNeighbourhood) <- gene_location_dynamicNeighbourhood$ensembl_gene_id

# rename the columns as required by DELocal
colnames(gene_location_dynamicNeighbourhood)[4:5] <- c("neighbors_start","neighbors_end")

smrExpt_dynamicNeighbour <-
    SummarizedExperiment::SummarizedExperiment(
        assays = list(counts = count_matrix),
        rowData = gene_location_dynamicNeighbourhood[rownames(count_matrix), ],
        colData = colData
    )

one_genes <- SummarizedExperiment::rowData(smrExpt_dynamicNeighbour) %>% 
    as.data.frame() %>% 
    filter(chromosome_name=="1") %>% rownames() 

DELocal_result <- DELocal(smrExpt = smrExpt_dynamicNeighbour[one_genes,], contrast = contrast,
                         nearest_neighbours = 5,pDesign = ~ condition,
                         pValue_cut = 0.05, logFold_cut = 0)