But before downsampling, if you see KO cells are higher compared to WT cells. Inf; downsampling will happen after all other operations, including Why did US v. Assange skip the court of appeal? For more information on customizing the embed code, read Embedding Snippets. You can subset from the counts matrix, below I use pbmc_small dataset from the package, and I get cells that are CD14+ and CD14-: This vector contains the counts for CD14 and also the names of the cells: Getting the ids can be done using which : A bit dumb, but I guess this is one way to check whether it works: I am using this code to actually add the information directly on the meta.data. exp1 Micro 1000 cells Boolean algebra of the lattice of subspaces of a vector space? Sign up for a free GitHub account to open an issue and contact its maintainers and the community. It's a closed issue, but I stumbled across the same question as well, and went on to find the answer. This is due to having ~100k cells in my starting object so I randomly sampled 60k or 50k with the SubsetData as I mentioned to use for the downstream analysis. This approach allows then to subset nicely, with more flexibility. This subset also has the same exact mean and median as my original object Im subsetting from. making sure that the images and the spot coordinates are subsetted correctly. crash. For this application, using SubsetData is fine, it seems from your answers. It only takes a minute to sign up. I think this is basically what you did, but I think this looks a little nicer. max per cell ident. Subsets a Seurat object containing Spatial Transcriptomics data while making sure that the images and the spot coordinates are subsetted correctly. Again, Id like to confirm that it randomly samples! Already on GitHub? which command here is leading to randomization ? These genes can then be used for dimensional reduction on the original data including all cells. 351 2 15. 565), Improving the copy in the close modal and post notices - 2023 edition, New blog post from our CEO Prashanth: Community is the future of AI. I want to subset from my original seurat object (BC3) meta.data based on orig.ident. If a subsetField is provided, the string 'min' can also be . The first step is to select the genes Monocle will use as input for its machine learning approach. Learn R. Search all packages and functions. My analysis is helped by the fact that the larger cluster is very homogeneous - so, random sampling of ~1000 cells is still very representative. Browse other questions tagged, Where developers & technologists share private knowledge with coworkers, Reach developers & technologists worldwide, I try this and show another error: Dbh.pos <- Idents(my.data, WhichCells(my.data, expression = Dbh == >0, slot = "data")) Error: unexpected '>' in "Dbh.pos <- Idents(my.data, WhichCells(my.data, expression = Dbh == >", Looks like you altered Dbh.pos? Thanks again for any help! Here is the slightly modified code I tried with the error: The error after the last line is: By clicking Sign up for GitHub, you agree to our terms of service and What pareameters are excluding these cells? privacy statement. This works for me, with the metadata column being called "group", and "endo" being one possible group there. Setup the Seurat objects library ( Seurat) library ( SeuratData) library ( patchwork) library ( dplyr) library ( ggplot2) The dataset is available through our SeuratData package. Error in CellsByIdentities(object = object, cells = cells) : Connect and share knowledge within a single location that is structured and easy to search. Seurat part 4 - Cell clustering - NGS Analysis Any argument that can be retreived SeuratDEG 2022-06-01 - just "BC03" ? Have a question about this project? 1 comment bari89 commented on Nov 18, 2021 mhkowalski closed this as completed on Nov 19, 2021 Sign up for free to join this conversation on GitHub . However, to avoid cases where you might have different orig.ident stored in the [email protected] slot, which happened in my case, I suggest you create a new column where you have the same identity for all your cells, and set the identity of all your cells to that identity. However, you have to know that for reproducibility, a random seed is set (in this case random.seed = 1). Other option is to get the cell names of that ident and then pass a vector of cell names. This is what worked for me: Includes an option to upsample cells below specified UMI as well. however, when i use subset(), it returns with Error. DEG. [: Simple subsetter for Seurat objects [ [: Metadata and associated object accessor dim (Seurat): Number of cells and features for the active assay dimnames (Seurat): The cell and feature names for the active assay head (Seurat): Get the first rows of cell-level metadata merge (Seurat): Merge two or more Seurat objects together **subset_deg **FindAllMarkers. Also, please provide a reproducible example data for testing, dput (myData). Identify blue/translucent jelly-like animal on beach. Of course, your case does not exactly match theirs, since they have ~1.3M cells and, therefore, more chance to maximally enrich in rare cell types, and the tissues you're studying might be very different. Returns a list of cells that match a particular set of criteria such as identity class, high/low values for particular PCs, ect.. However, when I try to do any of the following: seurat_object <- subset (seurat_object, subset = meta . Downsampling Seurat Object Issue #5312 satijalab/seurat GitHub