WebDimPlot(pbmc, reduction = "pca") In particular DimHeatmap () allows for easy exploration of the primary sources of heterogeneity in a dataset, and can be useful when trying to decide which PCs to include for further downstream analyses. Both cells and features are ordered according to their PCA scores. WebNov 26, 2024 · gc1.1 <- FindNeighbors (gc1.1, dims = 1:40) gc1.1 <- FindClusters (gc1.1, resolution = 0) gc1.1 <- RunUMAP (gc1.1, dims = 1:40) DimPlot (gc1.1, reduction = …
单细胞数据挖掘(5,6)-聚类、筛选marker基因及拼图(生信技能树视 …
Web原则上,我们可以使用不同的方法计算细胞和细胞簇之间的相似性。同样,也可以使用不同的归一化策略。在simspec包中,我们基于在给定的基因列表(默认是高度变化的基因)中使用Spearman相关性(默认)或Pearson相关性作为相似性的度量。同时,提供了两种不同的归一化 … Web## Default S3 method: FindNeighbors ( object, query = NULL, distance.matrix = FALSE, k.param = 20, return.neighbor = FALSE, compute.SNN = !return.neighbor, prune.SNN = 1/15, nn.method = "annoy", n.trees = 50, annoy.metric = "euclidean", nn.eps = 0, verbose = TRUE, force.recalc = FALSE, l2.norm = FALSE, cache.index = FALSE, index = NULL, ... … tagged classics over the edge
findNeighbors function - RDocumentation
WebAug 13, 2024 · This is accomplished by providing reduction = "harmony" to FindNeighbors (). so <- FindNeighbors( so, reduction = "harmony", dims = 1:20) so <- FindClusters( so, resolution = 0.5) Harmony is also highly tunable. Additional co-variates can be included and the strength of alignment can be tuned for each co-variate using the theta argument. WebAug 8, 2024 · Introduction A few years ago I came across this paper by Michael W. Dorrity and Lauren M. Saunders et. al. who used dimensionality reduction (DR) techniques to infer protein complexes and pathways from a dataset of 1,484 single gene deletions in the yeast genome. They used a DR algorithm called Uniform Manifold Approximation and … WebJan 2, 2024 · I tried different clustering resolutions on it using: sunion <- ScaleData (sunion, verbose = FALSE) sunion <- RunPCA (sunion, npcs = 50, verbose = FALSE) sunion <- RunUMAP (sunion, reduction = "pca", dims = 1:50) sunion <- FindNeighbors (sunion, reduction = "pca", dims = 1:50) sunion <- FindClusters (sunion, resolution = 0.5) tagged connection