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.
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.
You can install the released version of DELocal with:
if (!requireNamespace("devtools")) {
install.packages("devtools")
}
devtools::install_github("dasroy/delocal")
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 valueslibrary(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)
#> ensembl_gene_id start_position chromosome_name
#> ENSMUSG00000000001 ENSMUSG00000000001 108107280 3
#> ENSMUSG00000000003 ENSMUSG00000000003 77837901 X
#> ENSMUSG00000000028 ENSMUSG00000000028 18780447 16
#> ENSMUSG00000000031 ENSMUSG00000000031 142575529 7
#> ENSMUSG00000000037 ENSMUSG00000000037 161082525 X
#> ENSMUSG00000000049 ENSMUSG00000000049 108343354 11 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$ensembl_gene_id Integrating gene expression and location into a single object.
smrExpt <- SummarizedExperiment::SummarizedExperiment(assays=list(counts=count_matrix),
rowData = gene_location,
colData=colData)
smrExpt
#> class: SummarizedExperiment
#> dim: 52183 14
#> metadata(0):
#> assays(1): counts
#> rownames(52183): ENSMUSG00000000001 ENSMUSG00000000003 ...
#> ENSMUSG00000114967 ENSMUSG00000114968
#> rowData names(3): ensembl_gene_id start_position chromosome_name
#> colnames(14): ME14.E1M1R ME14.E2M1R ... ME13.E9M1R ME13.EXM1L
#> colData names(1): condition
These may take long time to run the whole data therefore here we will analyse genes only from X chromosome.
contrast= c("condition","ME13","ME14")
require(dplyr)
x_genes <- SummarizedExperiment::rowData(smrExpt) %>%
as.data.frame() %>%
filter(chromosome_name=="X") %>% rownames()
DELocal_result <- DELocal(pSmrExpt = smrExpt[x_genes,], #contrast = contrast,
nearest_neighbours = 5,pDesign = ~ condition,
pValue_cut = 0.05, pLogFold_cut = 0)
#> [1] "Default 1Mb neighborhood will be used"
Here TAD domain boundaries will be used as dynamic boundaries
TADKB <- readRDS("../DELocal_manuscript/markdowns/Mouse_TAD_boundaries.rds")
gene_location_dynamicNeighbourhood <- TADKB %>% dplyr::select(ensembl_gene_id, start_position, chromosome_name,startTAD ,endTAD) %>% unique()
rownames(gene_location_dynamicNeighbourhood) <- gene_location_dynamicNeighbourhood$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)
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