Entering edit mode
3.2 years ago
elengss
•
0
Dear all,
I am trying to follow the WNN vignette here
After the steps below, I would like to annotate my clusters, hence I need to know the markers which best represent each cluster.
pbmc <- FindMultiModalNeighbors(pbmc, reduction.list = list("pca", "lsi"), dims.list = list(1:50, 2:50))
pbmc <- RunUMAP(pbmc, nn.name = "weighted.nn", reduction.name = "wnn.umap", reduction.key = "wnnUMAP_")
pbmc <- FindClusters(pbmc, graph.name = "wsnn", algorithm = 3, verbose = FALSE)
So I go on to do
allmarkers <- FindAllMarkers(pbmc)
However, I am unsure how to interpret the findings. Below is the allmarkers object, which is calculated from ATAC assay counts, since the last default assay was set to "ATAC" with DefaultAssay(pbmc) <- "ATAC". Is there any way to find the genes or chromatin peaks most associated with the clusters coming out from the WNN graph that combines both RNAseq and ATACseq?
Thanks in advance.
> head(allmarkers)
p_val avg_log2FC pct.1 pct.2 p_val_adj cluster
chr1-186828497-186829311 1.242405e-06 -0.4548612 0.025 0.162 0.1287653 0
chr1-42462733-42463616 6.747624e-06 -0.3321175 0.130 0.302 0.6993372 0
chr19-11505637-11505933 1.287234e-05 0.3955261 0.365 0.210 1.0000000 0
chr3-112858127-112859021 1.368274e-05 -0.4490370 0.065 0.202 1.0000000 0
chr9-129482422-129483279 1.468715e-05 0.4140303 0.300 0.154 1.0000000 0
chr7-18221372-18222277 1.476420e-05 -0.5840315 0.025 0.135 1.0000000 0
gene
chr1-186828497-186829311 chr1-186828497-186829311
chr1-42462733-42463616 chr1-42462733-42463616
chr19-11505637-11505933 chr19-11505637-11505933
chr3-112858127-112859021 chr3-112858127-112859021
chr9-129482422-129483279 chr9-129482422-129483279
chr7-18221372-18222277 chr7-18221372-18222277