Description
Mass Cytometry S4 Class Structure Pipeline for Images.
Description
Containerizes cytometry data and allows for S4 class structure to extend slots related to cell morphology, spatial coordinates, phenotype network information, and unique cellular labeling.
README.md
imcExperiment data container
Containerizing IMC data into the SummarizedExperiment class, this container inherits packages from FlowSOM and diffcyt to compute clusters and test for differential abundance or state heterogeneity. Creating a flowSet is cumbersome, so we can stream-line into a summarized experiment into a quick and fast way to detect changes in cell populations.
library(CATALYST)
library(diffcyt)
library(imcExperiment)
data(imcdata)
head(rownames(imcdata))
imcData<-imcdata
# for plot scatter to work need to set the rowData feature in a specific way.
channel<-sapply(strsplit(rownames(imcData),"_"),function(x) x[3])
channel[34:35]<-c("Ir1911","Ir1931")
marker<-sapply(strsplit(rownames(imcData),"_"),function(x) x[2])
rowData(imcData)<-DataFrame(channel_name=channel,marker_name=marker)
rownames(imcData)<-marker
plotScatter(imcData,rownames(imcData)[17:18],assay='counts')
# convert to flowSet
## the warning has to do with duplicated Iridium channels.
(fsimc <- sce2fcs(imcData, split_by = "ROIID"))
## now we have a flowSet.
pData(fsimc)
fsApply(fsimc,nrow)
dim(exprs(fsimc[[1]]))
exprs(fsimc[[1]])[1:5,1:5]
## set up the metadata files.
head(marker_info)
exper_info<-data.frame(group_id=colData(imcData)$Treatment[match(pData(fsimc)$name,
colData(imcData)$ROIID)],
patient_id=colData(imcData)$Patient.Number[match(pData(fsimc)$name,
colData(imcData)$ROIID)],
sample_id=pData(fsimc)$name)
## create design
design<-createDesignMatrix(
exper_info,cols_design=c("group_id","patient_id"))
##set up contrast
contrast<-createContrast(c(0,1,0))
nrow(contrast)==ncol(design)
data.frame(parameters=colnames(design),contrast)
## flowSet to DiffCyt
out_DA<-diffcyt(
d_input=fsimc,
experiment_info=exper_info,
marker_info=marker_info,
design=design,
contrast=contrast,
analysis_type = "DA",
seed_clustering = 123
)
topTable(out_DA,format_vals = TRUE)
out_DS<-diffcyt(
d_input=fsimc,
experiment_info=exper_info,
marker_info=marker_info,
design=design,
contrast=contrast,
analysis_type='DS',
seed_clustering = 123,
plot=FALSE)
topTable(out_DS,format_vals = TRUE)