10x-scATAC-2019
10x-scATAC-2019 copied to clipboard
GreenleafLab
02
Making Seurat LSI Object... Binarizing matrix... Getting top 20000 features... Computing Term Frequency IDF... Computing SVD using irlba... Making Seurat Object... Error in CreateSeuratObject(mat, project = "scATAC", min.cells = 0, min.genes = 0) : unused argument (min.genes = 0)
Making Seurat LSI Object... Binarizing matrix... Getting top 20000 features... Computing Term Frequency IDF... Computing SVD using irlba... Making Seurat Object... Error in CreateAssayObject(counts = counts, min.cells = min.cells, min.features = min.features) : No feature names (rownames) names present in the input matrix
Sorry I am not familiar with Seurat V3, the error message seems to be focused on the fact that there are no rownames in the input counts object. Just add somewhere rownames whether it be simply
rownames(counts) <- paste0("n",seq_len(nrow(counts)))
@hjack123 I modify the origin code that use Seurat V3 to do LSI dimension reduction and find cluster
seuratLSI <- function(mat, nComponents = 50,
binarize = TRUE,
nFeatures = NULL, ...){
#TF IDF LSI adapted from flyATAC
cs <- Matrix::colSums(mat)
if(binarize){
message(paste0("Binarizing matrix..."))
mat@x[mat@x > 0] <- 1
}
if(!is.null(nFeatures)){
message(paste0("Getting top ", nFeatures, " features..."))
mat <- mat[head(order(Matrix::rowSums(mat),decreasing = TRUE),nFeatures),]
}
# create a seurat object
row.names(mat) <- seq(1, nrow(mat))
obj <- CreateSeuratObject(mat,
assay = "ATAC",
project='scATAC',
min.cells=0, min.features=0)
#Calc TF IDF, Calc SVD then LSI
obj <- RunLSI(object = obj, n = nComponents, scale.max = NULL)
return(obj)
}
addClusters <- function(obj,
minGroupSize = 50,
dims.use = seq_len(50),
initialResolution = 0.8,
n.start = 10, ...){
# get the SNN
obj<- FindNeighbors(obj, reduction = "lsi", dims = dims.use)
#First Iteration of Find Clusters
currentResolution <- initialResolution
obj <- FindClusters(obj, resolution = currentResolution)
res_name <- colnames([email protected])[grepl(currentResolution, colnames([email protected]))]
minSize <- min(table([email protected][[res_name]]))
nClust <- length(unique(paste0([email protected][[res_name]])))
message(sprintf("Current Resolution = %s, No of Clusters = %s, Minimum Cluster Size = %s", currentResolution, nClust, minSize))
if (is.null(minGroupSize)){
return(obj)
}
#If clusters are smaller than minimum group size
while(minSize <= minGroupSize){
res_name <- colnames([email protected])[grepl(currentResolution, colnames([email protected]))]
[email protected] <- [email protected][,-which(colnames([email protected])==res_name)]
currentResolution <- currentResolution*initialResolution
obj <- FindClusters(obj, resolution = currentResolution)
res_name <- colnames([email protected])[grepl(currentResolution, colnames([email protected]))]
minSize <- min(table([email protected][[res_name]]))
nClust <- length(unique(paste0([email protected][[res_name]])))
message(sprintf("Current Resolution = %s, No of Clusters = %s, Minimum Cluster Size = %s", currentResolution, nClust, minSize))
}
return(obj)
}
@hjack123 I modify the origin code that use Seurat V3 to do LSI dimension reduction and find cluster
seuratLSI <- function(mat, nComponents = 50, binarize = TRUE, nFeatures = NULL, ...){ #TF IDF LSI adapted from flyATAC cs <- Matrix::colSums(mat) if(binarize){ message(paste0("Binarizing matrix...")) mat@x[mat@x > 0] <- 1 } if(!is.null(nFeatures)){ message(paste0("Getting top ", nFeatures, " features...")) mat <- mat[head(order(Matrix::rowSums(mat),decreasing = TRUE),nFeatures),] } # create a seurat object row.names(mat) <- seq(1, nrow(mat)) obj <- CreateSeuratObject(mat, assay = "ATAC", project='scATAC', min.cells=0, min.features=0) #Calc TF IDF, Calc SVD then LSI obj <- RunLSI(object = obj, n = nComponents, scale.max = NULL) return(obj) }addClusters <- function(obj, minGroupSize = 50, dims.use = seq_len(50), initialResolution = 0.8, n.start = 10, ...){ # get the SNN obj<- FindNeighbors(obj, reduction = "lsi", dims = dims.use) #First Iteration of Find Clusters currentResolution <- initialResolution obj <- FindClusters(obj, resolution = currentResolution) res_name <- colnames([email protected])[grepl(currentResolution, colnames([email protected]))] minSize <- min(table([email protected][[res_name]])) nClust <- length(unique(paste0([email protected][[res_name]]))) message(sprintf("Current Resolution = %s, No of Clusters = %s, Minimum Cluster Size = %s", currentResolution, nClust, minSize)) if (is.null(minGroupSize)){ return(obj) } #If clusters are smaller than minimum group size while(minSize <= minGroupSize){ res_name <- colnames([email protected])[grepl(currentResolution, colnames([email protected]))] [email protected] <- [email protected][,-which(colnames([email protected])==res_name)] currentResolution <- currentResolution*initialResolution obj <- FindClusters(obj, resolution = currentResolution) res_name <- colnames([email protected])[grepl(currentResolution, colnames([email protected]))] minSize <- min(table([email protected][[res_name]])) nClust <- length(unique(paste0([email protected][[res_name]]))) message(sprintf("Current Resolution = %s, No of Clusters = %s, Minimum Cluster Size = %s", currentResolution, nClust, minSize)) } return(obj) }
Hi with your code I got the following msg, would you happen to have a solution? 1: RunLSI is being moved to Signac. Equivalent functionality can be achieved via the Signac::RunTFIDF and Signac::RunSVD functions; for more information on Signac, please see https://github.com/timoast/Signac 2: RunLSI is being moved to Signac. Equivalent functionality can be achieved via the Signac::RunTFIDF and Signac::RunSVD functions; for more information on Signac, please see https://github.com/timoast/Signac Error in LogNorm(data = tf.idf, display_progress = verbose, scale_factor = 10000) : No such slot: i.
thanks!
@hjack123 I got the msg too. It is beacuase the Seurat Group have develop a new package to process the scATAC-seq data, it is called Signac, you can ignore it.
I get the same warnings as @hjack123 , but the real issue is the error:
Error in LogNorm(data = tf.idf, display_progress = verbose, scale_factor = 10000) :
No such slot: i.
I was able to solve that by setting the variable features as the row.names before running RunLSI in the seuratLSI function.
VariableFeatures(obj) <- row.names(obj)
#TF IDF LSI adapted from flyATAC
cs <- Matrix::colSums(mat)
if(binarize){
message(paste0("Binarizing matrix..."))
mat@x[mat@x > 0] <- 1
}
if(!is.null(nFeatures)){
message(paste0("Getting top ", nFeatures, " features..."))
mat <- mat[head(order(Matrix::rowSums(mat),decreasing = TRUE),nFeatures),]
}
#create a seurat object
obj <- CreateSeuratObject(mat,
assay = "ATAC",
project='scATAC',
min.cells=0, min.features=0)
#Calc TF IDF, Calc SVD then LSI
VariableFeatures(obj) <- row.names(obj)
obj <- RunLSI(object = obj, n = nComponents, scale.max = NULL)
return(obj)
}
Thanks, @craiga02 for helping out with this issue! only a small correction. Add the row.names(mat) <- seq(1, nrow(mat)) before creating the Seurat object. It was before but is missing now somehow ;)
#create a seurat object
row.names(mat) <- seq(1, nrow(mat))
obj <- CreateSeuratObject(mat,
assay = "ATAC",
project='scATAC',
min.cells=0, min.features=0)
#Calc TF IDF, Calc SVD then LSI
VariableFeatures(obj) <- row.names(obj)
obj <- RunLSI(object = obj, n = nComponents, scale.max = NULL)
return(obj)
