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. inverting the cell selection, Random seed for downsampling. 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. You can set invert = TRUE, then it will exclude input cells. privacy statement. Which ability is most related to insanity: Wisdom, Charisma, Constitution, or Intelligence? to your account. Arguments Value Returns a randomly subsetted seurat object Examples crazyhottommy/scclusteval documentation built on Aug. 5, 2021, 3:20 p.m. 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. to your account. 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") Downsample each cell to a specified number of UMIs. rev2023.5.1.43405. Takes either a list of cells to use as a subset, or a parameter (for example, a gene), to subset on. Numeric [1,ncol(object)]. Also, please provide a reproducible example data for testing, dput (myData). Appreciate the detailed code you wrote. 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. This can be misleading. Can be used to downsample the data to a certain max per cell ident. To learn more, see our tips on writing great answers. Which language's style guidelines should be used when writing code that is supposed to be called from another language? We start by reading in the data. Downsample a seurat object, either globally or subset by a field Usage DownsampleSeurat(seuratObj, targetCells, subsetFields = NULL, seed = GetSeed()) Arguments. The code could only make sense if the data is a square, equal number of rows and columns. SubsetData(object, cells.use = NULL, subset.name = NULL, ident.use = NULL, max.cells.per.ident. Hi column name in object@meta.data, etc. I can figure out what it is by doing the following: meta_data = colnames (seurat_object@meta.data) [grepl ("DF.classification", colnames (seurat_object@meta.data))] Where meta_data = 'DF.classifications_0.25_0.03_252' and is a character class. The text was updated successfully, but these errors were encountered: I guess you can randomly sample your cells from that cluster using sample() (from the base in R). ctrl3 Micro 1000 cells Numeric [0,1]. Happy to hear that. I managed to reduce the vignette pbmc from the from 2700 to 600. I want to create a subset of a cell expressing certain genes only. identity class, high/low values for particular PCs, etc. 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. Connect and share knowledge within a single location that is structured and easy to search. I have a seurat object with 5 conditions and 9 cell types defined. use.imputed=TRUE), Run the code above in your browser using DataCamp Workspace, WhichCells: Identify cells matching certain criteria, WhichCells(object, ident = NULL, ident.remove = NULL, cells.use = NULL, Identify blue/translucent jelly-like animal on beach. Default is INF. This is called feature selection, and it has a major impact in the shape of the trajectory. to a point where your R doesn't crash, but that you loose the less cells), and then decreasing in the number of sampled cells and see if the results remain consistent and get recapitulated by lower number of cells. I keep running out of RAM with my current pipeline, Bar Graph of Expression Data from Seurat Object. 1) The downsampled percentage of cells in WT and KO is more over same compared to the actual % of cells in WT and KO 2) In each versions, I have highlighted the KO cells for cluster 1, 4, 5, 6 and 7 where the downsampled number is less than the WT cells. to your account. Subsets a Seurat object containing Spatial Transcriptomics data while making sure that the images and the spot coordinates are subsetted correctly. you may need to wrap feature names in backticks (``) if dashes For your last question, I suggest you read this bioRxiv paper. Example For the dispersion based methods in their default workflows, Seurat passes the cutoffs whereas Cell Ranger passes n_top_genes. To subscribe to this RSS feed, copy and paste this URL into your RSS reader. RDocumentation. It only takes a minute to sign up. Ubuntu won't accept my choice of password, Identify blue/translucent jelly-like animal on beach. I followed the example in #243, however this issue used a previous version of Seurat and the code didn't work as-is. What do hollow blue circles with a dot mean on the World Map? 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). If NULL, does not set a seed Value A vector of cell names See also FetchData Examples Have a question about this project? You signed in with another tab or window. It won't necessarily pick the expected number of cells . 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. However, if you did not compute FindClusters() yet, all your cells would show the information stored in object@meta.data$orig.ident in the object@ident slot. Why are players required to record the moves in World Championship Classical games? 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). What would be the best way to do it? New blog post from our CEO Prashanth: Community is the future of AI, Improving the copy in the close modal and post notices - 2023 edition, Subsetting of object existing of two samples, Set new Idents based on gene expression in Seurat and mix n match identities to compare using FindAllMarkers, What column and row naming requirements exist with Seurat (context: when loading SPLiT-Seq data), Subsetting a Seurat object based on colnames, How to manage memory contraints when analyzing a large number of gene count matrices? targetCells: The desired cell number to retain per unit of data. If there are insufficient cells to achieve the target min.group.size, only the available cells are retained. Seurat (version 2.3.4) Have a question about this project? This works for me, with the metadata column being called "group", and "endo" being one possible group there. In other words - is there a way to randomly subscluster my cells in an unsupervised manner? 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. Why does Acts not mention the deaths of Peter and Paul? Minimum number of cells to downsample to within sample.group. 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. accept.value = NULL, max.cells.per.ident = Inf, random.seed = 1, ). Thanks again for any help! clusters or whichever idents are chosen), and then for each of those groups calls sample if it contains more than the requested number of cells. I meant for you to try your original code for Dbh.pos, but alter Dbh.neg to, Still show the same problem: Dbh.pos <- Idents(my.data, WhichCells(my.data, expression = Dbh >0, slot = "data")) Error in CheckDots() : No named arguments passed Dbh.neg <- Idents(my.data, WhichCells(my.data, expression = Dbh == 0, slot = "data")) Error in CheckDots() : No named arguments passed, HmmmEasier to troubleshoot if you would post a, how to make a subset of cells expressing certain gene in seurat R, How a top-ranked engineering school reimagined CS curriculum (Ep. crash. For the new folks out there used to Satija lab vignettes, I'll just call large.obj pbmc, and downsampled.obj, pbmc.downsampled, and replace size determined by the number of columns in another object with an integer, 2999: pbmc.subsampled <- pbmc[, sample(colnames(pbmc), size =2999, replace=F)], Thank you Tim. By clicking Sign up for GitHub, you agree to our terms of service and However, to avoid cases where you might have different orig.ident stored in the object@meta.data 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. Short story about swapping bodies as a job; the person who hires the main character misuses his body. [: 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 The integration method that is available in the Seurat package utilizes the canonical correlation analysis (CCA). Try doing that, and see for yourself if the mean or the median remain the same. See Also. By clicking Post Your Answer, you agree to our terms of service, privacy policy and cookie policy. There are 33 cells under the identity. I want to subset from my original seurat object (BC3) meta.data based on orig.ident. ctrl1 Astro 1000 cells Thank you for the suggestion. I am pretty new to Seurat. This subset also has the same exact mean and median as my original object Im subsetting from. Already on GitHub? 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 { # } Sign up for a free GitHub account to open an issue and contact its maintainers and the community. 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? Logical expression indicating features/variables to keep, Extra parameters passed to WhichCells, such as slot, invert, or downsample. Already have an account? as.Seurat: Coerce to a 'Seurat' Object; as.sparse: Cast to Sparse; AttachDeps: . For instance, you might do something like this: You signed in with another tab or window. 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! Character. At the moment you are getting index from row comparison, then using that index to subset columns. You can however change the seed value and end up with a different dataset. Sign up for a free GitHub account to open an issue and contact its maintainers and the community. Well occasionally send you account related emails. Description Randomly subset (cells) seurat object by a rate Usage 1 RandomSubsetData (object, rate, random.subset.seed = NULL, .) 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. Default is INF. privacy statement. The text was updated successfully, but these errors were encountered: Hi, I think this is basically what you did, but I think this looks a little nicer. When do you use in the accusative case? These genes can then be used for dimensional reduction on the original data including all cells. Seurat:::subset.Seurat (pbmc_small,idents="BC0") An object of class Seurat 230 features across 36 samples within 1 assay Active assay: RNA (230 features, 20 variable features) 2 dimensional reductions calculated: pca, tsne Share Improve this answer Follow answered Jul 22, 2020 at 15:36 StupidWolf 1,658 1 6 21 Add a comment Your Answer They actually both fail due to syntax errors, yours included @williamsdrake . For example, Thanks for this, but I really want to understand more how the downsample function actualy works. exp1 Astro 1000 cells Already on GitHub? Returns a list of cells that match a particular set of criteria such as If anybody happens upon this in the future, there was a missing ')' in the above code. 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. SampleUMI(data, max.umi = 1000, upsample = FALSE, verbose = FALSE) Arguments data Matrix with the raw count data max.umi Number of UMIs to sample to upsample Upsamples all cells with fewer than max.umi verbose Any argument that can be retreived data.table vs dplyr: can one do something well the other can't or does poorly? 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 . Does it make sense to subsample as such even? But using a union of the variable genes might be even more robust. ctrl3 Astro 1000 cells If you are going to use idents like that, make sure that you have told the software what your default ident category is. This tutorial is meant to give a general overview of each step involved in analyzing a digital gene expression (DGE) matrix generated from a Parse Biosciences single cell whole transcription experiment. The steps in the Seurat integration workflow are outlined in the figure below: Hello All, Interpreting non-statistically significant results: Do we have "no evidence" or "insufficient evidence" to reject the null? Sign in subset_deg <- function(obj . Otherwise, if you'd like to have equal number of cells (optimally) per cluster in your final dataset after subsetting, then what you proposed would do the job. Thanks for the wonderful package. If the null hypothesis is never really true, is there a point to using a statistical test without a priori power analysis? Default is all identities. Find centralized, trusted content and collaborate around the technologies you use most. Learn more about Stack Overflow the company, and our products. Conditions: ctrl1, ctrl2, ctrl3, exp1, exp2 exp2 Astro 1000 cells. If the null hypothesis is never really true, is there a point to using a statistical test without a priori power analysis? Sign in This method expects "correspondences" or shared biological states among at least a subset of single cells across the groups. Creates a Seurat object containing only a subset of the cells in the original object. The final variable genes vector can be used for dimensional reduction. This is pretty much what Jean-Baptiste was pointing out. I would rather use the sample function directly. For the new folks out there used to Satija lab vignettes, I'll just call large.obj pbmc, and downsampled.obj, pbmc.downsampled, and replace size determined by the number of columns in another object with an integer, 2999: I was trying to do the same and is used your code. 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 . Inf; downsampling will happen after all other operations, including Folder's list view has different sized fonts in different folders. Downsample number of cells in Seurat object by specified factor. seuratObj: The seurat object. It first does all the selection and potential inversion of cells, and then this is the bit concerning downsampling: So indeed, it groups it into the identity classes (e.g. What pareameters are excluding these cells? privacy statement. This approach allows then to subset nicely, with more flexibility. Making statements based on opinion; back them up with references or personal experience. This is what worked for me: The text was updated successfully, but these errors were encountered: Thank you Tim. Is a downhill scooter lighter than a downhill MTB with same performance? I appreciate the lively discussion and great suggestions - @leonfodoulian I used your method and was able to do exactly what I wanted. Factor to downsample data by. Making statements based on opinion; back them up with references or personal experience. Thanks for contributing an answer to Stack Overflow! To learn more, see our tips on writing great answers. If a subsetField is provided, the string 'min' can also be . But this is something you can test by minimally subsetting your data (i.e. exp1 Micro 1000 cells What should I follow, if two altimeters show different altitudes? The first step is to select the genes Monocle will use as input for its machine learning approach. privacy statement. 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. For ex., 50k or 60k. Number of cells to subsample. If a subsetField is provided, the string 'min' can also be used, in which case, If provided, data will be grouped by these fields, and up to targetCells will be retained per group. Should I re-do this cinched PEX connection? This is what worked for me: downsampled.obj <- large.obj[, sample(colnames(large.obj), size = ncol(small.obj), replace=F))]. 351 2 15. Downsample a seurat object, either globally or subset by a field, The desired cell number to retain per unit of data. Downsample single cell data Downsample number of cells in Seurat object by specified factor downsampleSeurat( object , subsample.factor = 1 , subsample.n = NULL , sample.group = NULL , min.group.size = 500 , seed = 1023 , verbose = T ) Arguments Value Seurat Object Author Nicholas Mikolajewicz Parameter to subset on. It's a closed issue, but I stumbled across the same question as well, and went on to find the answer. DEG. However, for robustness issues, I would try to resample from obj1 several times using different seed values (which you can store for reproducibility), compute variable genes at each step as described above, and then get either the union or the intersection of those variable genes. If this new subset is not randomly sampled, then on what criteria is it sampled? However, when I try to do any of the following: seurat_object <- subset (seurat_object, subset = meta . Are there any canonical examples of the Prime Directive being broken that aren't shown on screen? Hi Leon, But it didnt work.. Subsetting from seurat object based on orig.ident? If specified, overides subsample.factor. 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. How to force Unity Editor/TestRunner to run at full speed when in background? I would like to randomly downsample each cell type for each condition. I would like to randomly downsample the larger object to have the same number of cells as the smaller object, however I am getting an error when trying to subset. SeuratCCA. Eg, the name of a gene, PC1, a However, you have to know that for reproducibility, a random seed is set (in this case random.seed = 1). Choose the flavor for identifying highly variable genes. I dont have much choice, its either that or my R crashes with so many cells. Was Aristarchus the first to propose heliocentrism? Examples Run this code # NOT . The number of column it is reduced ( so the object). Subset a Seurat object RDocumentation. Yes it does randomly sample (using the sample() function from base). Learn R. Search all packages and functions. downsampled.obj <- large.obj[, sample(colnames(large.obj), size = ncol(small.obj), replace=F))]. MathJax reference. Developed by Rahul Satija, Andrew Butler, Paul Hoffman, Tim Stuart. By clicking Accept all cookies, you agree Stack Exchange can store cookies on your device and disclose information in accordance with our Cookie Policy. Selecting cluster resolution using specificity criterion, Marker-based cell-type annotation using Miko Scoring, Gene program discovery using SSN analysis. A stupid suggestion, but did you try to give it as a string ? For more information on customizing the embed code, read Embedding Snippets. 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. 5 comments williamsdrake commented on Jun 4, 2020 edited Hi Seurat Team, Error in CellsByIdentities (object = object, cells = cells) : timoast closed this as completed on Jun 5, 2020 ShellyCoder mentioned this issue Identity classes to subset. . which, lets suppose, gives you 8 clusters), and would like to subset your dataset using the code you wrote, and assuming that all clusters are formed of at least 1000 cells, your final Seurat object will include 8000 cells. by default, throws an error, A predicate expression for feature/variable expression, @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. To subscribe to this RSS feed, copy and paste this URL into your RSS reader. Is it safe to publish research papers in cooperation with Russian academics? If NULL, does not set a seed. Bioinformatics Stack Exchange is a question and answer site for researchers, developers, students, teachers, and end users interested in bioinformatics. Cell types: Micro, Astro, Oligo, Endo, InN, ExN, Pericyte, OPC, NasN, ctrl1 Micro 1000 cells Why did US v. Assange skip the court of appeal? Numeric [1,ncol(object)]. Thank you. Site design / logo 2023 Stack Exchange Inc; user contributions licensed under CC BY-SA. Subsets a Seurat object containing Spatial Transcriptomics data while For this application, using SubsetData is fine, it seems from your answers. There are 2,700 single cells that were sequenced on the Illumina NextSeq 500. A package with high-level wrappers and pipelines for single-cell RNA-seq tools, Search the bimberlabinternal/CellMembrane package, bimberlabinternal/CellMembrane: A package with high-level wrappers and pipelines for single-cell RNA-seq tools, bimberlabinternal/CellMembrane documentation. Stack Exchange network consists of 181 Q&A communities including Stack Overflow, the largest, most trusted online community for developers to learn, share their knowledge, and build their careers. Heatmap of gene subset from microarray expression data in R. How to filter genes from seuratobject in slotname @data? Usage 1 2 3 The raw data can be found here. Asking for help, clarification, or responding to other answers. subset.name = NULL, accept.low = -Inf, accept.high = Inf, Great. So if you clustered your cells (e.g. So, I would like to merge the clusters together (using MergeSeurat option) and then recluster them to find overlap/distinctions between the clusters. What are the advantages of running a power tool on 240 V vs 120 V? If you make a dataframe containing the barcodes, conditions, and celltypes, you can sample 1000 cells within each condition/ celltype. Adding EV Charger (100A) in secondary panel (100A) fed off main (200A). Analysis and visualization of Spatial Transcriptomics data, Search the jbergenstrahle/STUtility package, jbergenstrahle/STUtility: Analysis and visualization of Spatial Transcriptomics data. Creates a Seurat object containing only a subset of the cells in the original object. If no cells are request, return a NULL; Returns a list of cells that match a particular set of criteria such as If I always end up with the same mean and median (UMI) then is it truly random sampling? which command here is leading to randomization ? Generating points along line with specifying the origin of point generation in QGIS. The best answers are voted up and rise to the top, Not the answer you're looking for? My question is Is this randomized ? They actually both fail due to syntax errors, yours included @williamsdrake . Error in CellsByIdentities(object = object, cells = cells) : max per cell ident. = 1000). Hi, I guess you can randomly sample your cells from that cluster using sample() (from the base in R). Is there a way to maybe pick a set number of cells (but randomly) from the larger cluster so that I am comparing a similar number of cells? Have a question about this project? Use MathJax to format equations. Well occasionally send you account related emails. Does it not? Can you still use Commanders Strike if the only attack available to forego is an attack against an ally? Why are players required to record the moves in World Championship Classical games? exp2 Micro 1000 cells Sign in What is the symbol (which looks similar to an equals sign) called? Downsample Seurat Description. 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 My analysis is helped by the fact that the larger cluster is very homogeneous - so, random sampling of ~1000 cells is still very representative. Connect and share knowledge within a single location that is structured and easy to search. By clicking Post Your Answer, you agree to our terms of service, privacy policy and cookie policy. Have a question about this project? Learn R. Search all packages and functions. Seurat has four tests for differential expression which can be set with the test.use parameter: ROC test ("roc"), t-test ("t"), LRT test based on zero-inflated data ("bimod", default), LRT test based on tobit-censoring models ("tobit") The ROC test returns the 'classification power' for any individual marker (ranging from 0 - random, to 1 - These genes can then be used for dimensional reduction on the original data including all cells. Already on GitHub? Step 1: choosing genes that define progress. expression: . however, when i use subset(), it returns with Error. making sure that the images and the spot coordinates are subsetted correctly. By clicking Sign up for GitHub, you agree to our terms of service and invert, or downsample. If anybody happens upon this in the future, there was a missing ')' in the above code. If no clustering was performed, and if the cells have the same orig.ident, only 1000 cells are sampled randomly independent of the clusters to which they will belong after computing FindClusters(). Subset of cell names. Setup the Seurat objects library ( Seurat) library ( SeuratData) library ( patchwork) library ( dplyr) library ( ggplot2) The dataset is available through our SeuratData package. **subset_deg **FindAllMarkers. Sign in Inferring a single-cell trajectory is a machine learning problem. If you use the default subset function there is a risk that images I actually did not need to randomly sample clusters but instead I wanted to randomly sample an object - for me my starting object after filtering. Most functions now take an assay parameter, but you can set a Default Assay to avoid repetitive statements. So if you repeat your subsetting several times with the same max.cells.per.ident, you will always end up having the same cells. However, one of the clusters has ~10-fold more number of cells than the other one. between numbers are present in the feature name, Maximum number of cells per identity class, default is