timoast
commented
6 months ago These are different software implementations, different implementations of the visualisation, and potentially different set of cells. We don't expect the output to be identical, please reopen this issue if you think there is a bug in the software and can provide a reproducible example
I did scATACseq using BM LSK cells from mice. TSS enrichment score likes fine - the peak is at around 10 in cellranger report. But TSS enrichment score is bad - the peak is around 2. I saw they used different name. Cellranger used relative enrichment in the report. Signac used mean TSS enrichment score. Does they used different calculation methods? Do I have some problem in code?
metadata <- read.csv( file = "./ND-A2/singlecell.csv", header = TRUE, row.names = 1 ) row.names(counts)[1:10]
chrom_assay <- CreateChromatinAssay( counts = counts, sep = c(":", "-"), genome = "mm10",#mm10 is for C57 and in v75 fragments = './ND-A2/fragments.tsv.gz', min.cells = 1#clude features detected in at least this many cells. Will subset the counts matrix as well. To reintroduce excluded features, create a new object with a lower cutoff. )
NDLSK <- CreateSeuratObject( counts = chrom_assay, assay = "peaks", project = 'LSK_ND',# we will know which data when we merge different data meta.data = metadata )
extract gene annotations from EnsDb
annotations <- GetGRangesFromEnsDb(ensdb = EnsDb.Mmusculus.v79)
change to UCSC style since the data was mapped to mm10
seqlevels(annotations) <- paste0('chr', seqlevels(annotations)) genome(annotations) <- "mm10"
add the gene information to the object
Annotation(NDLSK) <- annotations
Computing QC Metrics
NDLSK <- NucleosomeSignal(object = NDLSK)
NDLSK$nucleosome_group <- ifelse(NDLSK$nucleosome_signal > 2, 'NS > 2', 'NS < 2') FragmentHistogram(object = NDLSK, group.by = 'nucleosome_group', region = 'chr1-1-10000000') ggsave( filename = "./NDLSK single sample/1.2.1 before QC data quiclity FragmentHistogram.pdf", width = 8, height = 4 ) FragmentHistogram(object = NDLSK, region = 'chr1-1-10000000')
NDLSK <- TSSEnrichment(NDLSK, fast = FALSE) NDLSK$high.tss <- ifelse(NDLSK$TSS.enrichment > 1, 'High', 'Low') TSSPlot(NDLSK, group.by = 'high.tss') + NoLegend()
TSSPlot(NDLSK) + NoLegend()
cell ranger:
signac: