Can be used to downsample the data to a certain exp1 Astro 1000 cells 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. If a subsetField is provided, the string 'min' can also be . So if you want to sample randomly 1000 cells, independent of the clusters to which those cells belong, you can simply provide a vector of cell names to the cells.use argument. However, one of the clusters has ~10-fold more number of cells than the other one. Have a question about this project? ctrl2 Astro 1000 cells You can see the code that is actually called as such: SeuratObject:::subset.Seurat, which in turn calls SeuratObject:::WhichCells.Seurat (as @yuhanH mentioned). Other option is to get the cell names of that ident and then pass a vector of cell names. Returns a list of cells that match a particular set of criteria such as Examples ## Not run: # Subset using meta data to keep spots with more than 1000 unique genes se.subset <- SubsetSTData(se, expression = nFeature_RNA >= 1000) # Subset by a . Hi, I guess you can randomly sample your cells from that cluster using sample() (from the base in R). These genes can then be used for dimensional reduction on the original data including all cells. DoHeatmap ( subset (pbmc3k.final, downsample = 100), features = features, size = 3) New additions to FeaturePlot FeaturePlot (pbmc3k.final, features = "MS4A1") FeaturePlot (pbmc3k.final, features = "MS4A1", min.cutoff = 1, max.cutoff = 3) FeaturePlot (pbmc3k.final, features = c ("MS4A1", "PTPRCAP"), min.cutoff = "q10", max.cutoff = "q90") The best answers are voted up and rise to the top, Not the answer you're looking for? Sign up for a free GitHub account to open an issue and contact its maintainers and the community. Downsample Seurat Description. Using the same logic as @StupidWolf, I am getting the gene expression, then make a dataframe with two columns, and this information is directly added on the Seurat object. With Seurat, you can easily switch between different assays at the single cell level (such as ADT counts from CITE-seq, or integrated/batch-corrected data). Here, the GEX = pbmc_small, for exemple. rev2023.5.1.43405. By clicking Accept all cookies, you agree Stack Exchange can store cookies on your device and disclose information in accordance with our Cookie Policy. subset: bool (default: False) Inplace subset to highly-variable genes if True otherwise merely indicate highly variable genes. Here we present an example analysis of 65k peripheral blood mononuclear blood cells (PBMCs) using the R package Seurat. SeuratCCA. Meta data grouping variable in which min.group.size will be enforced. making sure that the images and the spot coordinates are subsetted correctly. RDocumentation. Sign in Related question: "SubsetData" cannot be directly used to randomly sample 1000 cells (let's say) from a larger object? The text was updated successfully, but these errors were encountered: This is more of a general R question than a question directly related to Seurat, but i will try to give you an idea. You can however change the seed value and end up with a different dataset. Have a question about this project? Already on GitHub? # Subset Seurat object based on identity class, also see ?SubsetData subset (x = pbmc, idents = "B cells") subset (x = pbmc, idents = c ("CD4 T cells", "CD8 T cells"), invert = TRUE) subset (x = pbmc, subset = MS4A1 > 3) subset (x = pbmc, subset = MS4A1 > 3 & PC1 > 5) subset (x = pbmc, subset = MS4A1 > 3, idents = "B cells") subset (x = pbmc, 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-: library (Seurat) CD14_expression = GetAssayData (object = pbmc_small, assay = "RNA", slot = "data") ["CD14",] This vector contains the counts for CD14 and also the names of the cells: head (CD14_expression,30 . ctrl3 Micro 1000 cells accept.value = NULL, max.cells.per.ident = Inf, random.seed = 1, ). # install dataset InstallData ("ifnb") Error in CellsByIdentities(object = object, cells = cells) : However, when I try to do any of the following: seurat_object <- subset (seurat_object, subset = meta . - zx8754. downsampled.obj <- large.obj[, sample(colnames(large.obj), size = ncol(small.obj), replace=F))]. 351 2 15. downsample Maximum number of cells per identity class, default is Inf; downsampling will happen after all other operations, including inverting the cell selection seed Random seed for downsampling. Any argument that can be retreived Site design / logo 2023 Stack Exchange Inc; user contributions licensed under CC BY-SA. privacy statement. subset.name = NULL, accept.low = -Inf, accept.high = Inf, By clicking Sign up for GitHub, you agree to our terms of service and What is the symbol (which looks similar to an equals sign) called? 4 comments chrismahony commented on May 19, 2020 Collaborator yuhanH closed this as completed on May 22, 2020 evanbiederstedt mentioned this issue on Dec 23, 2021 Downsample from each cluster kharchenkolab/conos#115 Is a downhill scooter lighter than a downhill MTB with same performance? Connect and share knowledge within a single location that is structured and easy to search. Downsample each cell to a specified number of UMIs. We start by reading in the data. For instance, you might do something like this: You signed in with another tab or window. Setup the Seurat Object For this tutorial, we will be analyzing the a dataset of Peripheral Blood Mononuclear Cells (PBMC) freely available from 10X Genomics. using FetchData, Low cutoff for the parameter (default is -Inf), High cutoff for the parameter (default is Inf), Returns all cells with the subset name equal to this value. A stupid suggestion, but did you try to give it as a string ? identity class, high/low values for particular PCs, etc. Conditions: ctrl1, ctrl2, ctrl3, exp1, exp2 Image of minimal degree representation of quasisimple group unique up to conjugacy, Folder's list view has different sized fonts in different folders. Happy to hear that. Numeric [1,ncol(object)]. There are 33 cells under the identity. For this application, using SubsetData is fine, it seems from your answers. If I always end up with the same mean and median (UMI) then is it truly random sampling? 1. If I verify the subsetted object, it does have the nr of cells I asked for in max.cells.per.ident (only one ident in one starting object). So, it's just a random selection. Sign up for a free GitHub account to open an issue and contact its maintainers and the community. Sign in @del2007: What you showed as an example allows you to sample randomly a maximum of 1000 cells from each cluster who's information is stored in object@ident. What are the advantages of running a power tool on 240 V vs 120 V? If no cells are request, return a NULL; Character. rev2023.5.1.43405. If you use the default subset function there is a risk that images By clicking Sign up for GitHub, you agree to our terms of service and If NULL, does not set a seed. If ident.use = NULL, then Seurat looks at your actual object@ident (see Seurat::WhichCells, l.6). Inf; downsampling will happen after all other operations, including Additional arguments to be passed to FetchData (for example, Hi If you are going to use idents like that, make sure that you have told the software what your default ident category is. Generating points along line with specifying the origin of point generation in QGIS. Why are players required to record the moves in World Championship Classical games? If this new subset is not randomly sampled, then on what criteria is it sampled? however, when i use subset(), it returns with Error. To use subset on a Seurat object, (see ?subset.Seurat) , you have to provide: What you have should work, but try calling the actual function (in case there are packages that clash): Thanks for contributing an answer to Bioinformatics Stack Exchange! Eg, the name of a gene, PC1, a Therefore I wanted to confirm: does the SubsetData blindly randomly sample? So, I would like to merge the clusters together (using MergeSeurat option) and then recluster them to find overlap/distinctions between the clusters. This method expects "correspondences" or shared biological states among at least a subset of single cells across the groups. You signed in with another tab or window. by default, throws an error, A predicate expression for feature/variable expression, Number of cells to subsample. You can then create a vector of cells including the sampled cells and the remaining cells, then subset your Seurat object using SubsetData() and compute the variable genes on this new Seurat object. ctrl1 Astro 1000 cells Setup the Seurat objects library ( Seurat) library ( SeuratData) library ( patchwork) library ( dplyr) library ( ggplot2) The dataset is available through our SeuratData package. Description Randomly subset (cells) seurat object by a rate Usage 1 RandomSubsetData (object, rate, random.subset.seed = NULL, .) Default is NULL. Takes either a list of cells to use as a subset, or a parameter (for example, a gene), to subset on. Sign in Takes either a list of cells to use as a subset, or a parameter (for example, a gene), to subset on. Well occasionally send you account related emails. random.seed Random seed for downsampling Value Returns a Seurat object containing only the relevant subset of cells Examples Run this code # NOT RUN { pbmc1 <- SubsetData (object = pbmc_small, cells = colnames (x = pbmc_small) [1:40]) pbmc1 # } # NOT RUN { # } Why does Acts not mention the deaths of Peter and Paul? It's a closed issue, but I stumbled across the same question as well, and went on to find the answer. By clicking Sign up for GitHub, you agree to our terms of service and Identify blue/translucent jelly-like animal on beach. I want to create a subset of a cell expressing certain genes only. **subset_deg **FindAllMarkers. The slice_sample() function in the dplyr package is useful here. privacy statement. Try doing that, and see for yourself if the mean or the median remain the same. Interpreting non-statistically significant results: Do we have "no evidence" or "insufficient evidence" to reject the null? Numeric [0,1]. Well occasionally send you account related emails. This approach allows then to subset nicely, with more flexibility. If specified, overides subsample.factor. Making statements based on opinion; back them up with references or personal experience. I ma just worried it is just picking the first 600 and not randomizing, https://www.rdocumentation.org/packages/base/versions/3.6.2/topics/sample. Thanks for the wonderful package. 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 . How are engines numbered on Starship and Super Heavy? For the dispersion based methods in their default workflows, Seurat passes the cutoffs whereas Cell Ranger passes n_top_genes. Creates a Seurat object containing only a subset of the cells in the original object. Seurat: Error in FetchData.Seurat(object = object, vars = unique(x = expr.char[vars.use]), : None of the requested variables were found: Ubiquitous regulation of highly specific marker genes. max per cell ident. There are 2,700 single cells that were sequenced on the Illumina NextSeq 500. The first step is to select the genes Monocle will use as input for its machine learning approach. ctrl2 Micro 1000 cells = 1000). This works for me, with the metadata column being called "group", and "endo" being one possible group there. This is what worked for me: downsampled.obj <- large.obj[, sample(colnames(large.obj), size = ncol(small.obj), replace=F))]. 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. Find centralized, trusted content and collaborate around the technologies you use most. I keep running out of RAM with my current pipeline, Bar Graph of Expression Data from Seurat Object. Again, Id like to confirm that it randomly samples! which command here is leading to randomization ? Learn more about Stack Overflow the company, and our products. My question is Is this randomized ? What should I follow, if two altimeters show different altitudes? Can be used to downsample the data to a certain max per cell ident. I can figure out what it is by doing the following: meta_data = colnames ([email protected]) [grepl ("DF.classification", colnames ([email protected]))] Where meta_data = 'DF.classifications_0.25_0.03_252' and is a character class.