Weird clustering results

[Larrycpan]

I performed analysis as follows without reported error:

proj_PC_1 <- addIterativeLSI(ArchRProj = proj_PC_1, useMatrix = "TileMatrix", name = "IterativeLSI", 
  iterations = 4, clusterParams = list(resolution = c(0.6), sampleCells = 12000, n.start = 10), 
  varFeatures = 15000, dimsToUse = 1:30, seed = 1234, threads = 40, force=T)
proj_PC_1 <- addHarmony(ArchRProj = proj_PC_1, reducedDims = "IterativeLSI", name = "Harmony", groupBy = "Sample", force=T)
proj_PC_1 <- addClusters(input = proj_PC_1, reducedDims = "IterativeLSI", method = "Seurat", name = "Clusters", resolution = 0.6, force = T)
proj_PC_1 <- addUMAP(ArchRProj = proj_PC_1, reducedDims = "IterativeLSI", name = "UMAP",
                     nNeighbors = 40, minDist = 0.4, metric = "euclidean", threads = 40, force=T)

p1 <- plotEmbedding(ArchRProj = proj_PC_1, colorBy = "cellColData", name = "Sample", embedding = "UMAP")
p2 <- plotEmbedding(ArchRProj = proj_PC_1, colorBy = "cellColData", name = "Clusters", embedding = "UMAP")
plotPDF(p1, p2, name = "UMAP-Sample-Clusters.pdf", ArchRProj = proj_PC_1, addDOC = F, width = 5, height = 5)

proj_PC_1 <- addUMAP(ArchRProj = proj_PC_1, reducedDims = "Harmony", name = "UMAPHarmony", nNeighbors = 40, minDist = 0.4, metric = "euclidean", threads = 40, force=T)

p3 <- plotEmbedding(ArchRProj = proj_PC_1, colorBy = "cellColData", name = "Sample", embedding = "UMAPHarmony")
p4 <- plotEmbedding(ArchRProj = proj_PC_1, colorBy = "cellColData", name = "Clusters", embedding = "UMAPHarmony")
plotPDF(p3, p4, name = "UMAP-Harmony-Sample-Clusters.pdf", ArchRProj = proj_PC_1, addDOC = FALSE, width = 5, height = 5)

But I got the results of mixed clusters, how can I fix it?

[rcorces]

Hi @KeWXiong! Thanks for using ArchR! Please make sure that your post belongs in the Issues section. Only bugs and error reports belong in the Issues section. Usage questions and feature requests should be posted in the Discussions section, not in Issues.
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[rcorces]

Unless I'm mis-reading your code, the problem is here:

proj_PC_1 <- addClusters(input = proj_PC_1, reducedDims = "IterativeLSI", method = "Seurat", name = "Clusters", resolution = 0.6, force = T)

You are adding clusters based on the IterativeLSI reducedDims and then plotting the UMAP based on the Harmony reducedDims. You should add clusters based on Harmony instead.

[Larrycpan]

Unless I'm mis-reading your code, the problem is here:

proj_PC_1 <- addClusters(input = proj_PC_1, reducedDims = "IterativeLSI", method = "Seurat", name = "Clusters", resolution = 0.6, force = T)

You are adding clusters based on the IterativeLSI reducedDims and then plotting the UMAP based on the Harmony reducedDims. You should add clusters based on Harmony instead.

Thanks, it looks much better!

[Larrycpan]

@rcorces @jgranja24 Thanks for helping. Further, I use another dataset for the analysis, but the results are not consistent with previously published studies that points are relatively separate. Instead, my result seems a little strange. I have changed the parameters of dimensionality reduction and no big changes happen. How can I deal with it?

proj <- addIterativeLSI(ArchRProj = proj, useMatrix = "TileMatrix", name = "IterativeLSI", 
                           iterations = 2, clusterParams = list(resolution = seq(0.2), sampleCells = 10000, n.start = 10), 
                           varFeatures = 15000, dimsToUse = 1:30, threads = 40, seed = 1234, force = T)
proj <- addHarmony(ArchRProj = proj, reducedDims = "IterativeLSI", name = "Harmony", groupBy = "Sample", force = T)
proj <- addClusters(input = proj, reducedDims = "Harmony", method = "Seurat", name = "Clusters", force = T, resolution = 0.2)
proj <- addUMAP(ArchRProj = proj, reducedDims = "Harmony", name = "UMAPHarmony", nNeighbors = 30, minDist = 0.4, metric = "cosine", force = T)

p1 <- plotEmbedding(ArchRProj = proj, colorBy = "cellColData", name = "Sample", embedding = "UMAPHarmony")
p2 <- plotEmbedding(ArchRProj = proj, colorBy = "cellColData", name = "Clusters", embedding = "UMAPHarmony")

[rcorces]

Sorry - We cannot provide help with individual analyses and do not provide recommendations beyond what is in the manual and function parameter definitions.