raw count matrix (tumor vs normal)

Hello,

I am trying to apply your tool, SCEVAN, to an integrated seurat object. I am trying to compare two types of tumors, and both are found in this seurat object (SeuratObject_TNBC.rds). I would need to use another seurat object to obtain the normal cells matrix as reference (SeuratObject_NormEpi.rds). I am not sure on how I can combine the two together for SCEVAN.

In the code below, I filtered the TNBC.rds object to two samples to compare different tumor types. I wanted to know how I could compare the two matrices. Would I add the normal counts matrix to both? Would I include all 3 in a list of matrices?

Here is the paper that explains the data in more detail (x).

Any help would be appreciated,

#libraries
#!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!
#install_github("miccec/yaGST") #required for scevan
library(yaGST)
#install.packages("devtools")
library(devtools)
#install_github("AntonioDeFalco/SCEVAN")
library(SCEVAN)
#install.packages("seurat")
library(Seurat)
#install.packages("dplyr")
library(dplyr)
#!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!

#read in the object
#!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!
tnbc.epi <- readRDS("/home/mahad/data/SeuratObject_TNBC.rds") #tumor obj
tnorm <- readRDS("/home/mahad/data/SeuratObject_NormTotal.rds") # all norm obj
epi <- readRDS("/home/mahad/data/SeuratObject_NormEpi.rds") # norm epi obj

#updating the object if it won't open
tnbc.epi = UpdateSeuratObject(object = tnbc.epi)

#rename clusters
tnbc.epi <- RenameIdents(tnbc.epi,
                         '0' = "epithelial",
                         '2' = "epithelial-cycling")

#subset to only epithelial cells
tnbc.epi <- subset(x = tnbc.epi, idents = c("epithelial", "epithelial-cycling"))

#rename clusters
tnorm <- RenameIdents(tnorm,
                      '5' = "epithelial")

#subset to only epithelial cells
tnorm <- subset(x = tnorm, idents = "epithelial")

#save object
#saveRDS(object = tnbc.epi, "~/BRCA/BRCA-data/tnbc.epi.rds")
#!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!

#extract count matrix 
#!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!
#subset the data to a specific sample (B1)
tnbc.epi.B1.0554 <- subset(tnbc.epi, subset = group == "TN_B1_0554")

#subset the data to a specific sample (TN)
tnbc.epi.TN.0126 <- subset(tnbc.epi, subset = group == "TN_0126")

#subset the data to a specific sample (epi-normal)
epi.1105 <- subset(epi, subset = group == "N_1105_epi")

#generate matrix
B1.MTX <- as.matrix(tnbc.epi.B1.0554@assays[["RNA"]]@data)
TN.MTX <- as.matrix(tnbc.epi.TN.0126@assays[["RNA"]]@data)
EP.MTX <- as.matrix(epi.1105@assays[["RNA"]]@data)

#add the normal cells to tumors cells as a matrix 
TN.MTX <- bind_rows(as.data.frame(TN.MTX), as.data.frame(EP.MTX))
B1.MTX <- bind_rows(as.data.frame(B1.MTX), as.data.frame(EP.MTX))

#remove NAs
B1.MTX[is.na(B1.MTX)] = 0
TN.MTX[is.na(TN.MTX)] = 0
EP.MTX[is.na(TN.MTX)] = 0

#join B1 and TN matrices
listCountMtx <- list(b1 = B1.MTX, tn = TN.MTX)
#!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!

#run pipeline on multiple samples
#!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!
results <- SCEVAN::multiSampleComparisonClonalCN(
  listCountMtx,#cnt mtx with gns on rows (Gn Symbl or nsmbl ID) & cells on columns.
  analysisName = "all", #analysisName : Name of the analysis (optional)
  organism = "human" ,
  #sample =
  par_cores = 5 #par_cores : Number of cores (default 20)
)
#!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!

AntonioDeFalco / SCEVAN

raw count matrix (tumor vs normal) #77

sample =