Closed Prathyusha-konda closed 10 months ago
Hi, I have an integrated seurat object from 6 datasets from which I want to subset specific clusters and recluster using liger. I would like to know if there is a recommended workflow for this.
Should I split the integrated seurat and rerun the entire pipeline? (from this tutorial - https://htmlpreview.github.io/?https://github.com/satijalab/seurat.wrappers/blob/master/docs/liger.html)
Does it matter if the seurat object was originally integrated with liger or logNormalize or SCT?
Thanks in advance for your suggestions!
ifnb <- NormalizeData(ifnb) ifnb <- FindVariableFeatures(ifnb) ifnb <- ScaleData(ifnb, split.by = "stim", do.center = FALSE) ifnb <- RunOptimizeALS(ifnb, k = 20, lambda = 5, split.by = "stim") ifnb <- RunQuantileNorm(ifnb, split.by = "stim") ifnb <- FindNeighbors(ifnb, reduction = "iNMF", dims = 1:20) ifnb <- FindClusters(ifnb, resolution = 0.55) ifnb <- RunUMAP(ifnb, dims = 1:ncol(ifnb[["iNMF"]]), reduction = "iNMF")
Hi, we would suggest subsetting the cells in the clusters of interest and running the entire LIGER pipeline (start with the count data).
Hi, I have an integrated seurat object from 6 datasets from which I want to subset specific clusters and recluster using liger. I would like to know if there is a recommended workflow for this.
Should I split the integrated seurat and rerun the entire pipeline? (from this tutorial - https://htmlpreview.github.io/?https://github.com/satijalab/seurat.wrappers/blob/master/docs/liger.html)
Does it matter if the seurat object was originally integrated with liger or logNormalize or SCT?
Thanks in advance for your suggestions!
ifnb <- NormalizeData(ifnb) ifnb <- FindVariableFeatures(ifnb) ifnb <- ScaleData(ifnb, split.by = "stim", do.center = FALSE) ifnb <- RunOptimizeALS(ifnb, k = 20, lambda = 5, split.by = "stim") ifnb <- RunQuantileNorm(ifnb, split.by = "stim") ifnb <- FindNeighbors(ifnb, reduction = "iNMF", dims = 1:20) ifnb <- FindClusters(ifnb, resolution = 0.55) ifnb <- RunUMAP(ifnb, dims = 1:ncol(ifnb[["iNMF"]]), reduction = "iNMF")