Recently Published
headnbnuurdpseudotime_headnbnuclus15asroot
root.cells = WhichCells(headnbnu, idents = c('1', '5'))
headnbnuurdpseudotime_headnbnuwihiasroot
root.cells = WhichCells(headnbnuwi, slot = "scale.data", expression = `dd-Smed-v4-659-0-1` > 0)
headnbnuurdpseudotime_headnbnuwihiasroot
root.cells = WhichCells(headnbnuwi, slot = "scale.data", expression = `dd-Smed-v4-659-0-1` > 0)
headnbnuurdpseudotime_headnbnuwihiasroot
root.cells = WhichCells(headnbnuwi, slot = "scale.data", expression = `dd-Smed-v4-659-0-1` > 0)
headnbnuurdpseudotime_headnbnuwihiasroot
root.cells = WhichCells(headnbnuwi, slot = "scale.data", expression = `dd-Smed-v4-659-0-1` > 0)
headnbnuurdpseudotime_headnbnuwiclus4asroot
root.cells = WhichCells(headnbnuwi, idents = '4')
headnbnuurdpseudotime_headnbnuwiclus4asroot
root.cells = WhichCells(headnbnuwi, idents = '4')
headnbnuurdpseudotime_headnbnuwiclus4asroot
root.cells = WhichCells(headnbnuwi, idents = '4')
headnbnuurdpseudotime
root.cells = WhichCells(headnbnu, idents = c('1', '5'), downsample = 200)
headnbnuurdpseudotime
root.cells = WhichCells(headnbnu, idents = c('1', '5'), downsample = 200)
headnbnuurdpseudotime
root.cells = WhichCells(headnbnu, idents = c('1', '5'), downsample = 200)
headnbnuurdDM_parameter
headnbnuurd = calcDM(headnbnuurd, knn = 30, sigma.use = 1000)
plotDimArray(object = headnbnuurd, reduction.use = "dm", label = "Louvain-30", dims.to.plot = 1:18, plot.title="", outer.title="Diffusion Map Sigma1000 knn30", legend=F, alpha=0.3)
headnbep urdtree wi-1 20191219
alra cluster_downsample_for root allwi2negastip
headnbepurd.tree1 <- buildTree(headnbepurd.tree1, pseudotime = "pseudotime", tips.use=c("1", "2", "3"), divergence.method = "preference", cells.per.pseudotime.bin = 6, bins.per.pseudotime.window = 8, save.all.breakpoint.info = T, p.thresh=0.001)
tip.cells:68
tip.clusters: Louvain-30
priurd_diffusionmap_sigmanull
inspect diffuion map sigma
maybe a little too tight?
priurd, findVariableGenes
the first shows the relative library sizes and the gamma distribution fit to them. The second shows a histogram of each gene's CV ratio to the null for its mean expression level and the diffCV.cutoff threshold chosen. The third shows each gene's mean expression and CV, the determined null model (in pink), and whether the gene was selected as variable (green genes were variable).
nbepurd_findVariableGenes
nbepurd = findVariableGenes(nbepurd, cells.fit = NULL, set.object.var.genes = T,
diffCV.cutoff = 0.5, mean.min = 0.005, mean.max = 100, main.use = "",
do.plot = T)
nbepsubmature1 (cluster 4, 10) by section
DimPlot(nbepsubmature, group.by = "orig.ident")
urd pseudotime inseurat
nbepsub[["urd_pseudotime"]] = NA
class(nbepsub[["urd_pseudotime"]])
nbepsub@meta.data[rownames(nbepsuburd@pseudotime), "urd_pseudotime"] <- nbepsuburd@pseudotime$pseudotime
RidgePlot(nbepsub, features = "urd_pseudotime")
nbepsuburd pseudotime clusters dansity
plotDists(nbepsuburd, "pseudotime", category.label = "seurat_clusters")
nbepsuburd pseudotime
plotDim(nbepsuburd, "pseudotime")
cluster0_8.markers in nbepsubcluster8
cluster0_8.markers <- FindMarkers(nbepsub, ident.1 = 0, ident.2 = 8, min.pct = 0.25)
FeaturePlot(nbepsub, cells = WhichCells(nbepsub, idents = "8"), features = rownames(cluster8_0.markers)[1:5])
cluster0_8.markers in nbepsubcluster0
cluster0_8.markers <- FindMarkers(nbepsub, ident.1 = 0, ident.2 = 8, min.pct = 0.25)
FeaturePlot(nbepsub, cells = WhichCells(nbepsub, idents = "0"), features = rownames(cluster8_0.markers)[1:5])
nbepsublv0.8top5_cluster8_0.markers in principal
cluster8_0.markers <- FindMarkers(nbepsub, ident.1 = 8, ident.2 = 0, min.pct = 0.25)
rownames(cluster8_0.markers)[1:3]
FeaturePlot(pri, features = rownames(cluster8_0.markers)[1:5])
nbepsublouvainres0.8top10heatmap
nbepsub.markers <- FindAllMarkers(nbepsub, only.pos = TRUE, min.pct = 0.25, logfc.threshold = 0.25)
library(dplyr)
top10 <- nbepsub.markers %>% group_by(cluster) %>% top_n(n = 10, wt = avg_logFC)
DoHeatmap(nbepsub, features = top10$gene) + NoLegend()
urddminspect
nbepsuburd = calcDM(nbepsuburd)
plotDimArray(object = nbepsuburd, reduction.use = "dm", dims.to.plot = 1:18, label="seurat_clusters", plot.title="", outer.title="Diffusion Map Sigma null", legend=F, alpha=0.3)
nbepsub by section
DimPlot(nbepsub, group.by = "orig.ident")
nbepselect
Idents(nbep) = nbep[["seurat_clusters"]]
nbep = RenameIdents(nbep, '0' = 'drop', '1' = 'select', '2' = 'select', '3' = 'drop', '4' = 'drop',
'5' = 'select', '6' = 'select', '7' = 'select', '8' = 'drop', '9' = 'drop',
'10' = 'drop', '11' = 'select', '12' = 'drop', '13' = 'select','14' = 'drop',
'15' = 'drop', '16' = 'drop','17' = 'select', '18' = 'drop')
DimPlot(nbep)
nbeplv0.8top5_18 in principal
top5_18 = subset(nbep.markers %>% group_by(cluster) %>% top_n(n = 5, wt = avg_logFC), cluster == 18, select = gene)
FeaturePlot(pri, slot = "scale.data", features = pull(top5_18))
nbeplv0.8top5_18 in nbep
top5_18 = subset(nbep.markers %>% group_by(cluster) %>% top_n(n = 5, wt = avg_logFC), cluster == 18, select = gene)
FeaturePlot(nbep, slot = "scale.data", features = pull(top5_18))
nbeplv0.8top5_17 in principal
top5_17 = subset(nbep.markers %>% group_by(cluster) %>% top_n(n = 5, wt = avg_logFC), cluster == 17, select = gene)
FeaturePlot(pri, slot = "scale.data", features = pull(top5_17))
nbeplv0.8top5_17 in nbep
top5_17 = subset(nbep.markers %>% group_by(cluster) %>% top_n(n = 5, wt = avg_logFC), cluster == 17, select = gene)
FeaturePlot(nbep, slot = "scale.data", features = pull(top5_17))
nbeplv0.8top5_16 in principal
top5_16 = subset(nbep.markers %>% group_by(cluster) %>% top_n(n = 5, wt = avg_logFC), cluster == 16, select = gene)
FeaturePlot(pri, slot = "scale.data", features = pull(top5_16))
nbeplv0.8top5_16 in nbep
top5_16 = subset(nbep.markers %>% group_by(cluster) %>% top_n(n = 5, wt = avg_logFC), cluster == 16, select = gene)
FeaturePlot(nbep, slot = "scale.data", features = pull(top5_16))
nbeplv0.8top5_15 in principal
top5_15 = subset(nbep.markers %>% group_by(cluster) %>% top_n(n = 5, wt = avg_logFC), cluster == 15, select = gene)
FeaturePlot(pri, slot = "scale.data", features = pull(top5_15))
nbeplv0.8top5_15 in nbep
top5_15 = subset(nbep.markers %>% group_by(cluster) %>% top_n(n = 5, wt = avg_logFC), cluster == 15, select = gene)
FeaturePlot(nbep, slot = "scale.data", features = pull(top5_15))
nbeplv0.8top5_14 in principal
top5_14 = subset(nbep.markers %>% group_by(cluster) %>% top_n(n = 5, wt = avg_logFC), cluster == 14, select = gene)
FeaturePlot(pri, slot = "scale.data", features = pull(top5_14))
nbeplv0.8top5_14 in nbep
top5_14 = subset(nbep.markers %>% group_by(cluster) %>% top_n(n = 5, wt = avg_logFC), cluster == 14, select = gene)
FeaturePlot(nbep, slot = "scale.data", features = pull(top5_14))
nbeplv0.8top5_0 in principal
top5_13 = subset(nbep.markers %>% group_by(cluster) %>% top_n(n = 5, wt = avg_logFC), cluster == 13, select = gene)
FeaturePlot(pri, slot = "scale.data", features = pull(top5_13))
nbeplv0.8top5_13 in nbep
top5_13 = subset(nbep.markers %>% group_by(cluster) %>% top_n(n = 5, wt = avg_logFC), cluster == 13, select = gene)
FeaturePlot(nbep, slot = "scale.data", features = pull(top5_13))
nbeplv0.8top5_12 in principal
top5_12 = subset(nbep.markers %>% group_by(cluster) %>% top_n(n = 5, wt = avg_logFC), cluster == 12, select = gene)
FeaturePlot(pri, slot = "scale.data", features = pull(top5_12))
nbeplv0.8top5_12 in nbep
top5_12 = subset(nbep.markers %>% group_by(cluster) %>% top_n(n = 5, wt = avg_logFC), cluster == 12, select = gene)
FeaturePlot(nbep, slot = "scale.data", features = pull(top5_12))
nbeplv0.8top5_11 in principal
top5_11 = subset(nbep.markers %>% group_by(cluster) %>% top_n(n = 5, wt = avg_logFC), cluster == 11, select = gene)
FeaturePlot(pri, slot = "scale.data", features = pull(top5_11))
nbeplv0.8top5_11 in nbep
top5_11 = subset(nbep.markers %>% group_by(cluster) %>% top_n(n = 5, wt = avg_logFC), cluster == 11, select = gene)
FeaturePlot(nbep, slot = "scale.data", features = pull(top5_11))
nbeplv0.8top5_10 in principal
top5_10 = subset(nbep.markers %>% group_by(cluster) %>% top_n(n = 5, wt = avg_logFC), cluster == 10, select = gene)
FeaturePlot(pri, slot = "scale.data", features = pull(top5_10))
nbeplv0.8top5_10 in nbep
top5_10 = subset(nbep.markers %>% group_by(cluster) %>% top_n(n = 5, wt = avg_logFC), cluster == 10, select = gene)
FeaturePlot(nbep, slot = "scale.data", features = pull(top5_10))
nbeplv0.8top5_9 in principal
top5_9 = subset(nbep.markers %>% group_by(cluster) %>% top_n(n = 5, wt = avg_logFC), cluster == 9, select = gene)
FeaturePlot(pri, slot = "scale.data", features = pull(top5_9))
nbeplv0.8top5_9 in nbep
top5_9 = subset(nbep.markers %>% group_by(cluster) %>% top_n(n = 5, wt = avg_logFC), cluster == 9, select = gene)
FeaturePlot(nbep, slot = "scale.data", features = pull(top5_9))
nbeplv0.8top5_8 in principal
top5_8 = subset(nbep.markers %>% group_by(cluster) %>% top_n(n = 5, wt = avg_logFC), cluster == 8, select = gene)
FeaturePlot(pri, slot = "scale.data", features = pull(top5_8))
nbeplv0.8top5_8 in nbep
top5_8 = subset(nbep.markers %>% group_by(cluster) %>% top_n(n = 5, wt = avg_logFC), cluster == 8, select = gene)
FeaturePlot(nbep, slot = "scale.data", features = pull(top5_8))
nbeplv0.8top5_7 in principal
top5_7 = subset(nbep.markers %>% group_by(cluster) %>% top_n(n = 5, wt = avg_logFC), cluster == 7, select = gene)
FeaturePlot(pri, slot = "scale.data", features = pull(top5_7))
nbeplv0.8top5_7 in nbep
top5_7 = subset(nbep.markers %>% group_by(cluster) %>% top_n(n = 5, wt = avg_logFC), cluster == 7, select = gene)
FeaturePlot(nbep, slot = "scale.data", features = pull(top5_7))
nbeplv0.8top5_6 in principal
top5_6 = subset(nbep.markers %>% group_by(cluster) %>% top_n(n = 5, wt = avg_logFC), cluster == 6, select = gene)
FeaturePlot(pri, slot = "scale.data", features = pull(top5_6))
nbeplv0.8top5_6 in nbep
top5_6 = subset(nbep.markers %>% group_by(cluster) %>% top_n(n = 5, wt = avg_logFC), cluster == 6, select = gene)
FeaturePlot(nbep, slot = "scale.data", features = pull(top5_6))
nbeplv0.8top5_5 in principal
top5_5 = subset(nbep.markers %>% group_by(cluster) %>% top_n(n = 5, wt = avg_logFC), cluster == 5, select = gene)
FeaturePlot(pri, slot = "scale.data", features = pull(top5_5))
nbeplv0.8top5_5 in nbep
top5_5 = subset(nbep.markers %>% group_by(cluster) %>% top_n(n = 5, wt = avg_logFC), cluster == 5, select = gene)
FeaturePlot(nbep, slot = "scale.data", features = pull(top5_5))
nbeplv0.8top5_4 in principal
top5_4 = subset(nbep.markers %>% group_by(cluster) %>% top_n(n = 5, wt = avg_logFC), cluster == 4, select = gene)
FeaturePlot(pri, slot = "scale.data", features = pull(top5_4))
nbeplv0.8top5_4 in nbep
top5_4 = subset(nbep.markers %>% group_by(cluster) %>% top_n(n = 5, wt = avg_logFC), cluster == 4, select = gene)
FeaturePlot(nbep, slot = "scale.data", features = pull(top5_4))
nbeplv0.8top5_3 in principal
top5_3 = subset(nbep.markers %>% group_by(cluster) %>% top_n(n = 5, wt = avg_logFC), cluster == 3, select = gene)
FeaturePlot(pri, slot = "scale.data", features = pull(top5_3))
nbeplv0.8top5_3 in nbep
top5_3 = subset(nbep.markers %>% group_by(cluster) %>% top_n(n = 5, wt = avg_logFC), cluster == 3, select = gene)
FeaturePlot(nbep, slot = "scale.data", features = pull(top5_3))
nbeplv0.8top5_2 in principal
top5_2 = subset(nbep.markers %>% group_by(cluster) %>% top_n(n = 5, wt = avg_logFC), cluster == 2, select = gene)
FeaturePlot(pri, slot = "scale.data", features = pull(top5_2))
nbeplv0.8top5_2 in nbep
top5_2 = subset(nbep.markers %>% group_by(cluster) %>% top_n(n = 5, wt = avg_logFC), cluster == 2, select = gene)
FeaturePlot(nbep, slot = "scale.data", features = pull(top5_2))
nbeplv0.8top5_1 in principal
top5_1 = subset(nbep.markers %>% group_by(cluster) %>% top_n(n = 5, wt = avg_logFC), cluster == 1, select = gene)
FeaturePlot(pri, slot = "scale.data", features = pull(top5_1))
nbeplv0.8top5_1 in nbep
top5_1 = subset(nbep.markers %>% group_by(cluster) %>% top_n(n = 5, wt = avg_logFC), cluster == 1, select = gene)
FeaturePlot(nbep, slot = "scale.data", features = pull(top5_1))
nbeplv0.8top5_0 in nbep
top5_0 = subset(nbep.markers %>% group_by(cluster) %>% top_n(n = 5, wt = avg_logFC), cluster == 0, select = gene)
FeaturePlot(nbep, slot = "scale.data", features = pull(top5_0))
nbeplv0.8top5_0 in principal
top5_0 = subset(nbep.markers %>% group_by(cluster) %>% top_n(n = 5, wt = avg_logFC), cluster == 0, select = gene)
FeaturePlot(pri, slot = "scale.data", features = pull(top5_0))
pricipal tissue featureplot
wi-1, pc2, prog-1,
collagen, CTSL2(Cathepsin), MAT2A(Intestin),
VIT(Pharynx)
othertissuemkg epdssub
FeaturePlot(epdssub, features = c('dd-Smed-v4-907-0-1', 'dd-Smed-v4-636-0-1', 'dd-Smed-v4-1566-0-1', 'dd-Smed-v4-11968-0-1', 'dd-Smed-v4-659-0-1', 'dd-Smed-v4-2337-0-1', 'dd-Smed-v4-175-0-1'))
# MAT2A, APOB, pc2, chat, wi-1, collagen, CTSL2
epdermmal feature epdssub
FeaturePlot(epdssub, features = c('dd-Smed-v4-332-0-1', 'dd-Smed-v4-357-0-1', 'dd-Smed-v4-920-0-1', 'dd-Smed-v4-68-0-1', 'dd-Smed-v4-790-0-1', 'dd-Smed-v4-364-0-1', 'dd-Smed-v4-3065-0-1'))
# PROG-1, PFAM: SEA, agat-1, X1.C3.3, PRSS12, VIM, ifb
epdssub(stableclusers)
epdssub = SubsetData(epds, ident.use = c("5", "7", "9", "10", "11", "14", "15", "16", "17"))
DimPlot(epdssub)
epdssc3
DimPlot(epds, group.by = "sc3cluster")
epidermal downsample
epds = subset(ep, downsample = 100)
ep only epidermal feature plot
FeaturePlot(ep, features = c('dd-Smed-v4-332-0-1', 'dd-Smed-v4-357-0-1', 'dd-Smed-v4-920-0-1', 'dd-Smed-v4-68-0-1', 'dd-Smed-v4-790-0-1', 'dd-Smed-v4-364-0-1', 'dd-Smed-v4-3065-0-1'))
# PROG-1, PFAM: SEA, agat-1, X1.C3.3, PRSS12, VIM, ifb
epidermal lineage feature plot
PROG-1, PFAM: SEA, agat-1, X1.C3.3, PRSS12, VIM, ifb
FeaturePlot(epsctreg, features = c('dd-Smed-v4-332-0-1', 'dd-Smed-v4-357-0-1', 'dd-Smed-v4-920-0-1', 'dd-Smed-v4-68-0-1', 'dd-Smed-v4-790-0-1', 'dd-Smed-v4-364-0-1', 'dd-Smed-v4-3065-0-1'))
ep louvain res 0.5
epsctreg = FindClusters(epsctreg, algorithm = 1, random.seed = 19980219, verbose = TRUE, group.singletons = FALSE, resolution = 0.5)
epidermal cell cycle reg
epsctreg <- RunPCA(epsctreg, features = c(sfeatures, g2mfeatures), assay = "SCT",
npcs = 10, rev.pca = FALSE, weight.by.var = TRUE, verbose = TRUE,
ndims.print = 1:5, nfeatures.print = 10, reduction.name = "pca",
reduction.key = "PC_", seed.use = 19980219)
epidermal cell cycle reg split
epsctreg <- RunPCA(epsctreg, features = c(sfeatures, g2mfeatures), assay = "SCT",
npcs = 10, rev.pca = FALSE, weight.by.var = TRUE, verbose = TRUE,
ndims.print = 1:5, nfeatures.print = 10, reduction.name = "pca",
reduction.key = "PC_", seed.use = 19980219)
epidermal lineage tsne
prisctregpcannlouvainumaptsnerm = RenameIdents(prisctregpcannlouvainumaptsne,
'0' = 'other', '1' = 'epidermallineage', '2' = 'epidermallineage', '3' = 'other', '4' = 'other',
'5' = 'other', '6' = 'epidermallineage', '7' = 'other', '8' = 'other', '9' = 'other',
'10' = 'epidermallineage', '11' = 'other', '12' = 'other', '13' = 'epidermallineage','14' = 'other',
'15' = 'other', '16' = 'other','17' = 'other', '18' = 'other')
epidermal lineage umap
prisctregpcannlouvainumaptsnerm = RenameIdents(prisctregpcannlouvainumaptsne,
'0' = 'other', '1' = 'epidermallineage', '2' = 'epidermallineage', '3' = 'other', '4' = 'other',
'5' = 'other', '6' = 'epidermallineage', '7' = 'other', '8' = 'other', '9' = 'other',
'10' = 'epidermallineage', '11' = 'other', '12' = 'other', '13' = 'epidermallineage','14' = 'other',
'15' = 'other', '16' = 'other','17' = 'other', '18' = 'other')
DotPlot for potential epidermal progenetor
'dd-Smed-v4-8720-0-1', 'dd-Smed-v4-10988-0-1'
zfp1, dd10988
FeaturePlot epidermal
'dd-Smed-v4-332-0-1', 'dd-Smed-v4-357-0-1', 'dd-Smed-v4-920-0-1', 'dd-Smed-v4-68-0-1', 'dd-Smed-v4-790-0-1', 'dd-Smed-v4-364-0-1', 'dd-Smed-v4-3065-0-1'
PROG-1,PFAM: SEA, agat-1, X1.C3.3, PRSS12, VIM, ifb
principal feature neoblast
neoblast = c('dd-Smed-v4-648-0-1', 'dd-Smed-v4-659-0-1', 'dd-Smed-v4-1985-0-1', 'dd-Smed-v4-3777-0-1', 'dd-Smed-v4-10594-0-1',
'dd-Smed-v4-9821-0-1', 'dd-Smed-v4-1396-0-1', 'dd-Smed-v4-5651-0-1', 'dd-Smed-v4-5764-0-1', 'dd-Smed-v4-1651-0-1',
'dd-Smed-v4-2592-0-1')
# NA, wi-1, VASA-1, NA, NA,
# NA, ANP32A, NA, MCM5, RRM1
# bruli .....
DotPlot(prisctregpcannlouvainumaptsne, features = neoblast) + RotatedAxis()
principal feature epidermal
epidermal = c('dd-Smed-v4-69-0-1', 'dd-Smed-v4-61-0-1','dd-Smed-v4-213-0-1', 'dd-Smed-v4-332-0-1', 'dd-Smed-v4-363-0-1',
'dd-Smed-v4-3549-0-1', 'dd-Smed-v4-478-0-1', 'dd-Smed-v4-232-0-1', 'dd-Smed-v4-6912-0-1', 'dd-Smed-v4-3665-0-1',
'dd-Smed-v4-357-0-1', 'dd-Smed-v4-920-0-1', 'dd-Smed-v4-68-0-1', 'dd-Smed-v4-790-0-1', 'dd-Smed-v4-364-0-1', 'dd-Smed-v4-3065-0-1')
# NA, NA, NA, PROG-1, NA,
# NA, NA, ACTB, NUCB1, NA,
# PFAM: SEA, agat-1, X1.C3.3, PRSS12, VIM, ifb
DotPlot(prisctregpcannlouvainumaptsne, features = epidermal) + RotatedAxis()
prinsipal wi-1, prog-1, collagen, pc2, chat
'dd-Smed-v4-659-0-1', 'dd-Smed-v4-332-0-1', 'dd-Smed-v4-2337-0-1',
'dd-Smed-v4-1566-0-1', 'dd-Smed-v4-11968-0-1'
wi-1, prog-1, collagen, pc2, chat
heatmap of top10 feature gene downsample
DoHeatmap(subset(prisctregpcannlouvainumaptsne, downsample = 100), features = top10$gene) + NoLegend()
tSNE of louvain res 0.1
principal-sct-regsct-pca-findneighber-findcluster(groupsingleton = FALSE, algorithm = 1) res 0.1
runtsne, dim = 1:75
UMAP of louvain res 0.1
principal-sct-regsct-pca-findneighber-findcluster(groupsingleton = FALSE, algorithm = 1) res 0.1
runumap, dim = 1:75
UMAP of louvain res 0.8
principal-sct-regsct-pca-findneighber-findcluster(groupsingleton = FALSE, algorithm = 1) res 0.8
UMAP of leiden
principal-sct-regsct-pca-findneighber-findcluster(groupsingleton = FALSE, leiden)
elbow
percentage of variance explain by each pc
regccdif
split
regccdif
priseurat$CC.Difference <- priseurat$S.Score - priseurat$G2M.Score
prisctreg <- RunPCA(prisctreg, features = c(sfeatures, g2mfeatures), assay = "SCT",
npcs = 10, rev.pca = FALSE, weight.by.var = TRUE, verbose = TRUE,
ndims.print = 1:5, nfeatures.print = 10, reduction.name = "pca",
reduction.key = "PC_", seed.use = 19980219)
nonreg
split
nonreg
priseurat <- RunPCA(priseurat, features = c(sfeatures, g2mfeatures), assay = "SCT",
npcs = 10, rev.pca = FALSE, weight.by.var = TRUE, verbose = TRUE,
ndims.print = 1:5, nfeatures.print = 10, reduction.name = "pca",
reduction.key = "PC_", seed.use = 19980219)
FeaturePlot of anchor intergrated
pc2, chat, znf91
sct-scale.data
non-cellcycleregressed
FeaturePlot
epidermis
# wi1, zfp1, prog2, agat3, vim1, ifb, PRSS
facsgate
c(1, 2), reduction = "harmony", split.by = "ano_FACS_Gate"
harmony correctted pca
c(1, 2), reduction = "harmony"
UMAP
harmony
group.by.vars = c("dataset", "ano_Section")
assay.use="SCT"
Plot
harmony theta = 10
Plot
harmony theta = 6
Plot
harmony theta = 4
Plot
harmony theta = 0