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I believe when you say alignment, you mean aligning reads to a genome (sometimes to transcriptome) and count these to get count matrices. In the aforementioned paper, however, what is meant is "bringing different data sets to a level where they can be compared/integrated/...". Basically scRNA-seq data are heavily prone to batch effetcs and if you ...


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Modified from @devon-ryan's comments: The couple of samples I looked at have the sequence from read 1 embedded in their header. I1 isn't needed, they're already demultiplexed. You could feed those into STAR or similar tools, but processing them will be more of a pain since most tools expect the original unprocessed version of what they uploaded. Modified ...


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Assuming parentObj is the original seurat object, and tmp_subset is your subset, you can new_metadata <- parentObj@meta.data tmp_subset_annotation <- tmp_subset@meta.data # new_metadata original_annotation ATTCGGA-1 TCD4 AAATAAA-1 TCD4-naive ... # tmp_subset_annotation new_cellType ATTCGGA-1 TCD4-naive AAATAAA-1 TCD4-...


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Since you haven't shared any data or code so I will show it using the example given below, First you need to assign a specific cluster to the cells which are having another type like C_new. cells_df <- data.frame(row.names=paste0("Cell", 1:10, sep = ""),as.vector(paste0("C",1:10,sep=""))) colnames(cells_df) <- &...


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I've done sub-clustering a few times on my Seurat data sets. The approach I take is to subset the clusters that need to be clustered (i.e. using subset), carry out a clustering of only those cells, then transfer the subcluster labels back to the original dataset. Here's some rough code, which will need to be modified for your specific situation and code ...


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Increase the clustering resolution parameter to generate more (smaller) clusters, see FindClusters in the Seurat docs. Whether or not this will neatly, split your clusters into subclusters depends on your data, but normally one can easily separate CD4 and NK cells from PBMCs. See also the Clustree approach for determining the optimal resolution. ...


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