About using 10X Visium data

[Pedramto89]

I am trying to use Visium data. However, it seems ICELLNET do not support Visium RDS data as it shows an error when I am trying to implement this code: average.clean= sc.data.cleaning(object = seurat, db = db, filter.perc = filter.perc, save_file = T, path="path/", force.file = F)

This is the error: "Error: Cannot find 'RNA' in this Seurat object"

In the RDS file saved from processed seurat object from visium, there are two data on assays: Spatial and SCT and there is no RNA. Do you have any solution?

[lmassenet-regad]

Dear Pedramto89, Thank you for raising this issue, we will correct the package in the coming days to be compatible with spatial transcriptomics Seurat object. In the mean time, you can adapt the sc.data.cleaning function below , by replacing 'RNA' assay by 'SCT' or 'Spatial' (line 3).

---

sc.data.cleaning <- function (object=object, db = db, filter.perc=NULL, save_file=T, path=NULL, force.file=FALSE) { if (!file.exists(paste0(path, "scRNAseq_statsInfo_for_ICELLNET.csv")) | force.file==T){ data=object[['SCT']]@ data
int = dplyr::intersect(as.matrix(rownames(data)), as.matrix(db[, 1:5]))

restrict only to gene restricted to matrix

data=data[which(rownames(data)%in%int),]
data.int=as.data.frame(expand.grid(rownames(data), unique(Idents(object)))) #create intermediate data frame
colnames(data.int)=c("Symbol","Cell_ID")
data.int$Perc_posCell=NA
data.int$Mean_exp=NA
#fill in the intermediate dataframe
print("Filling in intermediate table: percentage of expressing cell per cluster per gene, and mean of expression")
for (i in seq(1,dim(data.int)[1])){
  data.int[i,3:4]=Perc_exp_infos(object = object, gene=data.int[i,1], cell_id = data.int[i,2])
}
#Save matrix and histogram of Perc_pos_cell
if (save_file==TRUE){
  if (is.null(path)){
    path=getwd()
    print("Intermediate table were saved in the working directory as scRNAseq_statsInfo_for_ICELLNET.csv. Set path parameter to change the directory to save files.")
  }
  write.csv(data.int, file = paste0(path, "scRNAseq_statsInfo_for_ICELLNET.csv"))
  hist(data.int[,"Perc_posCell"])
  pdf(file = paste0(path, "scRNAseq_statsInfo_Perc_posCell_", Sys.Date(),".pdf"), width = 5, height = 5)
  hist(data.int[,"Perc_posCell"], 100)
  print("Intermediate table were saved as scRNAseq_statsInfo_for_ICELLNET.csv.")
  dev.off()
}

}else{ note( paste0("Following file used as intermediate statistics table: ", path, "scRNAseq_statsInfo_for_ICELLNET.csv. Use force.file=T to regenerate this file")) data.int=utils::read.csv(paste0(path,"scRNAseq_statsInfo_for_ICELLNET.csv" ), header = T) data.int=data.int[,-1] }

Filter expression above cell percentage value

if (!is.null(filter.perc)){ data.int=dplyr::filter(data.int, Perc_posCell > filter.perc/100) print("Filtering done") } average.cluster=reshape2::dcast(data.int[,-3], formula = Symbol~ Cell_ID, value.var = "Mean_exp", drop = T) rownames(average.cluster)=average.cluster$Symbol average.cluster[is.na(average.cluster)]<-0 return (average.cluster) }

[Pedramto89]

Thank you for your prompt response. So, regarding the scripts you sent, I have some questions especially about the parameters:

In data=seurat[['SCT']]@DaTa, the DaTa equal to what part of the seurat obj? My Seurat object in my scripts is "seurat" and I replaced the object within the scripts you sent with "seurat". But I don't know still what is DaTa.

This is the function I put:

sc.data.cleaning <- function (object=seurat, db = db, filter.perc=NULL, save_file=T, path=NULL, force.file=FALSE) { if (!file.exists(paste0(path, "scRNAseq_statsInfo_for_ICELLNET.csv")) | force.file==T){ data=seurat[['SCT']]@data int = dplyr::intersect(as.matrix(rownames(data)), as.matrix(db[, 1:5]))

restrict only to gene restricted to matrix

data=data[which(rownames(data)%in%int),]
data.int=as.data.frame(expand.grid(rownames(data), unique(Idents(seurat)))) #create intermediate data frame
colnames(data.int)=c("Symbol","Cell_ID")
data.int$Perc_posCell=NA
data.int$Mean_exp=NA
#fill in the intermediate dataframe
print("Filling in intermediate table: percentage of expressing cell per cluster per gene, and mean of expression")
for (i in seq(1,dim(data.int)[1])){
  data.int[i,3:4]=Perc_exp_infos(object = seurat, gene=data.int[i,1], cell_id = data.int[i,2])
}
#Save matrix and histogram of Perc_pos_cell
if (save_file==TRUE){
  if (is.null(path)){
    path=getwd()
    print("Intermediate table were saved in the working directory as scRNAseq_statsInfo_for_ICELLNET.csv. Set path parameter to change the directory to save files.")
  }
  write.csv(data.int, file = paste0(path, "scRNAseq_statsInfo_for_ICELLNET.csv"))
  hist(data.int[,"Perc_posCell"])
  pdf(file = paste0(path, "scRNAseq_statsInfo_Perc_posCell_", Sys.Date(),".pdf"), width = 5, height = 5)
  hist(data.int[,"Perc_posCell"], 100)
  print("Intermediate table were saved as scRNAseq_statsInfo_for_ICELLNET.csv.")
  dev.off()
}

}else{ note( paste0("Following file used as intermediate statistics table: ", path, "scRNAseq_statsInfo_for_ICELLNET.csv. Use force.file=T to regenerate this file")) data.int=utils::read.csv(paste0(path,"scRNAseq_statsInfo_for_ICELLNET.csv" ), header = T) data.int=data.int[,-1] }

Filter expression above cell percentage value

if (!is.null(filter.perc)){ data.int=dplyr::filter(data.int, Perc_posCell > filter.perc/100) print("Filtering done") } average.cluster=reshape2::dcast(data.int[,-3], formula = Symbol~ Cell_ID, value.var = "Mean_exp", drop = T) rownames(average.cluster)=average.cluster$Symbol average.cluster[is.na(average.cluster)]<-0 return (average.cluster) }

And I get this error: `> average.clean = sc.data.cleaning(object = seurat, db = db, filter.perc = filter.perc, save_file = T, path="path/", force.file = F)

[1] "Filling in intermediate table: percentage of expressing cell per cluster per gene, and mean of expression" Error: Cannot find 'RNA' in this Seurat object Called from: [[.Seurat(object, "RNA") Browse[1]> `

[lmassenet-regad]

Dear Pedramto89,

I upgraded the package in order to allow the use of SCT or Spatial assay (I would recommend SCT to use the normalised data) on ICELLNET. What you would need to do : 1- reinstall the package with the latest version 2- precise assay="SCT" in the sc.data.cleaning function, as following :

average.clean= sc.data.cleaning(object = seurat, assay="SCT", db = db, filter.perc = filter.perc, save_file = T, path="path/", force.file = F)

Let me know if you have other issues. Best,

[Pedramto89]

Thank you @lmassenet-regad It worked! Some to interpret the results, I am wondering what does the score and/or value mean on the heatmap? Also, on the dot plot, only ECM and growth factor is shown. Can we add more to this dot plot? Related to this, on the stacked bar chart, there are a limited couple of molecular families including "Notch family" and "ECM". How ca we add more? I mean is there a list of available families in the ICELLNET database to add on the bar chart?

[lmassenet-regad]

You can find these information on the vignette or in the publication. Briefly:

• The values in the heatmap correspond to the interaction score of a specific ligand/receptor interaction (min 0 when one gene is not present, and 100 if maximum gene expression value by ligand and receptor).
• The list of interaction families is available by doing table(db$Family)
• You can download / save the icellnet database locally on you computer (it is a simple table) and modify the classification/ add other classification as much as you want. The current classification has been done manually.