Hello,
I downloaded a single cell dataset from GEO (147424). The samples are gunzipped comma separated txt files (rows = genes, columns = cells) . Read in the files with read.table:
S1_LES <- read.table("./GSE147424_RAW/uncompressed_txt_counts/S1_LS/GSM4430459_MS.sample1.clean.data.txt", header = T, sep = ",", dec = ".", row.names = 1)
...
Created the seurat objects:
S1_LES_SO <- CreateSeuratObject(counts = S1_LES, min.cells = 3, min.features = 200)
...
started to do integration. When i ran this:
sample_SO_names <- c("S1_LES_SO", "S2_LES_SO", "S3_NL_SO", "S4_HC_SO", "S5_LES_SO", "S6_HC_SO", "S7_LES_SO", "S8_HC_SO", "S9_HC_SO", "S10_HC_SO", "S11_NL_SO", "S12_HC_SO", "S13_HC_SO", "S14_NL_SO", "S15_NL_SO", "S16_NL_SO", "S17_HC_SO")
seuratObj_list1 <- mget(sample_SO_names)
performLogNorm <- function(seuratObj){
seuratObj <- NormalizeData(object = seuratObj)
seuratObj <- FindVariableFeatures(seuratObj, selection.method = "vst", nfeatures = 2000)
return (seuratObj)
}
sample_list_qc <- mapply(FUN = performLogNorm, seuratObj = seurat_list1, SIMPLIFY = FALSE)
I got an error:
Probably it has to do with the data being just one file. Usually i just give the folder path for the Read10X function and it's good to go.
While reading in with read.table:
Thank you for any help.
1 answer
Meantime managed to solve this. The problem was that i created the seurat object from the dataset matrices directly that was downloaded. So when it showed that "Counts matrix provided is not sparse" i had to convert this into a sparse matrix. First into a matrix because it was read in as a dataframe and then into a sparse matrix with the "Matrix" package:
S1_LES <- as.matrix(S1_LES)
S1_LES <- Matrix(S1_LES, sparse = TRUE)
After this it went through integration without any problem.
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