<!-- README.md is generated from README.Rmd. Please edit that file -->
# 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>
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The goal of [DELocal](https://doi.org/10.1371/journal.pcbi.1008947) 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.
## Installation
You can install the released version of DELocal with:
``` r
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
``` r
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
``` r
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
``` r
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$ensembl_gene_id
```
### Integrating gene expression and location into a single object.
``` r
smrExpt <- SummarizedExperiment::SummarizedExperiment(assays=list(counts=count_matrix),
rowData = gene_location,
colData=colData)
smrExpt
#> class: SummarizedExperiment
#> dim: 52183 14
#> metadata(0):
#> assays(1): counts
#> rownames(52183): ENSMUSG00000000001 ENSMUSG00000000003 ...
#> ENSMUSG00000114967 ENSMUSG00000114968
#> rowData names(3): ensembl_gene_id start_position chromosome_name
#> colnames(14): ME14.E1M1R ME14.E2M1R ... ME13.E9M1R ME13.EXM1L
#> colData names(1): condition
```
## Final results
These may take long time to run the whole data therefore here we will
analyse genes only from X chromosome.
``` r
contrast= c("condition","ME13","ME14")
require(dplyr)
x_genes <- SummarizedExperiment::rowData(smrExpt) %>%
as.data.frame() %>%
filter(chromosome_name=="X") %>% rownames()
DELocal_result <- DELocal(pSmrExpt = smrExpt[x_genes,], #contrast = contrast,
nearest_neighbours = 5,pDesign = ~ condition,
pValue_cut = 0.05, pLogFold_cut = 0)
#> [1] "Default 1Mb neighborhood will be used"
```
### Dynamic neighbour
Here TAD domain boundaries will be used as dynamic boundaries
``` r
TADKB <- readRDS("../DELocal_manuscript/markdowns/Mouse_TAD_boundaries.rds")
gene_location_dynamicNeighbourhood <- TADKB %>% dplyr::select(ensembl_gene_id, start_position, chromosome_name,startTAD ,endTAD) %>% unique()
rownames(gene_location_dynamicNeighbourhood) <- gene_location_dynamicNeighbourhood$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)
```
