ISS_cluster_spatial: Spatial clustering for ISS by Markov random field model.

In mashranga/MolDia: Single cell In-Situ sequencing (ISS) data analysis

Description

ISS spatial clustering by Markov random field with extended spatial parameters EM procedure. This function is incomplete and needs further development to work properly. See details.

Usage

ISS_cluster_spatial(data, neighbor.points = 4,
  spatial.info = "phisical")

Arguments

data	Input data in class MolDiaISS. Output of readISS.
neighbor.points	Number for neighbouring points. Default is 4.
spatial.info	Default is "phisical". Other value is "genetic".

Details

This function has build on the work on the article Pettit, J at el (2014). Current function "ISS_cluster_spatial" will only create 2 input for the actual algorithm veloped by Pettit, J at el (2014). After ruiing the function one has to run the actual algorithm for spatial clustering. See example for application.

References

Pettit, J., Tomer, R., Achim, K., Richardson, S., Azizi, L., & Marioni, J. (2014). Identifying Cell Types from Spatially Referenced Single-Cell Expression Datasets. PLoS Computational Biology, 10(9). doi:10.1371/journal.pcbi.1003824

Examples

## Reading data
ex_data <- readISS(file = system.file("extdata", "Hypocampus_left.csv", package="MolDia"),
                   cellid = "CellId", centX = "centroid_x", centY = "centroid_y")
myres   <- ISS_cluster_spatial(ex_data, neighbor.points = 4)

####################### Manual process to wun the algorithm 
## Set working dirctory
mwd <- "c:/MRF"

## Save required data in working directory 
main_data <- myres[[1]]
write.table(main_data, file = paste0(mwd,"/mdata.txt"), 
            col.names = FALSE, row.names = FALSE, quote = FALSE, sep = "\t")
neig_data <- myres[[2]]
write.table(neig_data, file = paste0(mwd,"/neig.txt"), 
            col.names = FALSE, row.names = FALSE, quote = FALSE,sep = "\t")

## For detail about the parameter and Download windows binary: https://github.com/jbogp/MRF_Platynereis_2014
## Save the windows binary in the current working directory. Make sure the exe file has the administrator privilege            
          
 win_exe <- paste0(mwd,"/EM.exe")
 
## Result folder name
 res_fold <- "myResultFolder"

## Parameter set up
 param1   <- paste0(mwd,"/mdata.txt")
 param2   <- paste0(mwd,"/neig.txt")
 param3   <- "rand"
 param4   <- 0
 param5   <- 20
 param6   <- paste0(mwd,"/",res_fold)
 param7   <- "myResultFile"
 param8   <- 20

## Run the function in windows CMD
 mycmd <- paste(win_exe, param1, param2, param3, param4, param5, param6, param7, param8)
 res   <- system(mycmd, intern = FALSE,invisible = FALSE)

## Read resulted cluster data data and convet it accordingly 
cell_clust <- read.csv(paste0(mwd,"/",res_fold,"/myResultFile.csv"), header = F)
cell_clust <- as.factor(cell_clust$V1)
names(cell_clust) <- rownames(ex_data@data)

## Insert cluster information into main data object
 ex_data@cluster <- cell_clust

## Map cluster data 
pp<- RCA_map(ex_data, what = "cluster"

## Plot Spatial clustering 
 ex_data <- ISS_preprocess(ex_data)
 ex_data <- ISS_tsne(ex_data)
 ex_data <- ISS_marker(ex_data)
 
 ISS_map(ex_data, what = "cell", main = "All cells")
 ISS_map(ex_data, what = "cluster", main = "Spatial cluster: phisical distance", label.topgene = NULL)
 ISS_map(ex_data, what = "tsne", main = "Spatial in tsne: phisical distance", label.topgene = 2)

mashranga/MolDia documentation built on May 26, 2019, 9:36 a.m.