seurat subset analysis seurat subset analysis

A value of 0.5 implies that the gene has no predictive . When we run SubsetData, we have (by default) not subsetted the raw.data slot as well, as this can be slow and usually unnecessary. FilterSlideSeq () Filter stray beads from Slide-seq puck. Find cells with highest scores for a given dimensional reduction technique, Find features with highest scores for a given dimensional reduction technique, TransferAnchorSet-class TransferAnchorSet, Update pre-V4 Assays generated with SCTransform in the Seurat to the new Renormalize raw data after merging the objects. The data we used is a 10k PBMC data getting from 10x Genomics website.. Right now it has 3 fields per celL: dataset ID, number of UMI reads detected per cell (nCount_RNA), and the number of expressed (detected) genes per same cell (nFeature_RNA). In Macosko et al, we implemented a resampling test inspired by the JackStraw procedure. [55] bit_4.0.4 rsvd_1.0.5 htmlwidgets_1.5.3 Not all of our trajectories are connected. GetImage() GetImage() GetImage(), GetTissueCoordinates() GetTissueCoordinates() GetTissueCoordinates(), IntegrationAnchorSet-class IntegrationAnchorSet, Radius() Radius() Radius(), RenameCells() RenameCells() RenameCells() RenameCells(), levels() `levels<-`(). It is very important to define the clusters correctly. As you will observe, the results often do not differ dramatically. Seurat (version 3.1.4) . To do this we sould go back to Seurat, subset by partition, then back to a CDS. By default we use 2000 most variable genes. Where does this (supposedly) Gibson quote come from? [139] expm_0.999-6 mgcv_1.8-36 grid_4.1.0 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! An alternative heuristic method generates an Elbow plot: a ranking of principle components based on the percentage of variance explained by each one (ElbowPlot() function). Interfacing Seurat with the R tidy universe | Bioinformatics | Oxford Takes either a list of cells to use as a subset, or a parameter (for example, a gene), to subset on. We start the analysis after two preliminary steps have been completed: 1) ambient RNA correction using soupX; 2) doublet detection using scrublet. We do this using a regular expression as in mito.genes <- grep(pattern = "^MT-". We can see that doublets dont often overlap with cell with low number of detected genes; at the same time, the latter often co-insides with high mitochondrial content. If so, how close was it? Insyno.combined@meta.data is there a column called sample? To do this we sould go back to Seurat, subset by partition, then back to a CDS. Automagically calculate a point size for ggplot2-based scatter plots, Determine text color based on background color, Plot the Barcode Distribution and Calculated Inflection Points, Move outliers towards center on dimension reduction plot, Color dimensional reduction plot by tree split, Combine ggplot2-based plots into a single plot, BlackAndWhite() BlueAndRed() CustomPalette() PurpleAndYellow(), DimPlot() PCAPlot() TSNEPlot() UMAPPlot(), Discrete colour palettes from the pals package, Visualize 'features' on a dimensional reduction plot, Boxplot of correlation of a variable (e.g. For details about stored CCA calculation parameters, see PrintCCAParams. Set of genes to use in CCA. In order to reveal subsets of genes coregulated only within a subset of patients SEURAT offers several biclustering algorithms. j, cells. rev2023.3.3.43278. [5] monocle3_1.0.0 SingleCellExperiment_1.14.1 Policy. random.seed = 1, seurat_object <- subset(seurat_object, subset = seurat_object@meta.data[[meta_data]] == 'Singlet'), the name in double brackets should be in quotes [["meta_data"]] and should exist as column-name in the meta.data data.frame (at least as I saw in my own seurat obj). How to notate a grace note at the start of a bar with lilypond? From earlier considerations, clusters 6 and 7 are probably lower quality cells that will disapper when we redo the clustering using the QC-filtered dataset. For example, we could regress out heterogeneity associated with (for example) cell cycle stage, or mitochondrial contamination. [124] raster_3.4-13 httpuv_1.6.2 R6_2.5.1 (default), then this list will be computed based on the next three How do you feel about the quality of the cells at this initial QC step? To create the seurat object, we will be extracting the filtered counts and metadata stored in our se_c SingleCellExperiment object created during quality control. Normalized data are stored in srat[['RNA']]@data of the RNA assay. covariate, Calculate the variance to mean ratio of logged values, Aggregate expression of multiple features into a single feature, Apply a ceiling and floor to all values in a matrix, Calculate the percentage of a vector above some threshold, Calculate the percentage of all counts that belong to a given set of features, Descriptions of data included with Seurat, Functions included for user convenience and to keep maintain backwards compatability, Functions re-exported from other packages, reexports AddMetaData as.Graph as.Neighbor as.Seurat as.sparse Assays Cells CellsByIdentities Command CreateAssayObject CreateDimReducObject CreateSeuratObject DefaultAssay DefaultAssay Distances Embeddings FetchData GetAssayData GetImage GetTissueCoordinates HVFInfo Idents Idents Images Index Index Indices IsGlobal JS JS Key Key Loadings Loadings LogSeuratCommand Misc Misc Neighbors Project Project Radius Reductions RenameCells RenameIdents ReorderIdent RowMergeSparseMatrices SetAssayData SetIdent SpatiallyVariableFeatures StashIdent Stdev SVFInfo Tool Tool UpdateSeuratObject VariableFeatures VariableFeatures WhichCells. Optimal resolution often increases for larger datasets. This results in significant memory and speed savings for Drop-seq/inDrop/10x data. privacy statement. seurat_object <- subset (seurat_object, subset = DF.classifications_0.25_0.03_252 == 'Singlet') #this approach works I would like to automate this process but the _0.25_0.03_252 of DF.classifications_0.25_0.03_252 is based on values that are calculated and will not be known in advance. To start the analysis, let's read in the SoupX -corrected matrices (see QC Chapter). Try updating the resolution parameter to generate more clusters (try 1e-5, 1e-3, 1e-1, and 0). While there is generally going to be a loss in power, the speed increases can be significant and the most highly differentially expressed features will likely still rise to the top. Disconnect between goals and daily tasksIs it me, or the industry? How Intuit democratizes AI development across teams through reusability. These will be further addressed below. After removing unwanted cells from the dataset, the next step is to normalize the data. The top principal components therefore represent a robust compression of the dataset. Differential expression can be done between two specific clusters, as well as between a cluster and all other cells. If, for example, the markers identified with cluster 1 suggest to you that cluster 1 represents the earliest developmental time point, you would likely root your pseudotime trajectory there. Is there a solution to add special characters from software and how to do it. We can now see much more defined clusters. Sign in An AUC value of 1 means that expression values for this gene alone can perfectly classify the two groupings (i.e. For T cells, the study identified various subsets, among which were regulatory T cells ( T regs), memory, MT-hi, activated, IL-17+, and PD-1+ T cells. If your mitochondrial genes are named differently, then you will need to adjust this pattern accordingly (e.g. Lets look at cluster sizes. Is there a single-word adjective for "having exceptionally strong moral principles"? Lets get reference datasets from celldex package. How do I subset a Seurat object using variable features? Intuitive way of visualizing how feature expression changes across different identity classes (clusters). Seurat allows you to easily explore QC metrics and filter cells based on any user-defined criteria. Since we have performed extensive QC with doublet and empty cell removal, we can now apply SCTransform normalization, that was shown to be beneficial for finding rare cell populations by improving signal/noise ratio. Since most values in an scRNA-seq matrix are 0, Seurat uses a sparse-matrix representation whenever possible. # hpca.ref <- celldex::HumanPrimaryCellAtlasData(), # dice.ref <- celldex::DatabaseImmuneCellExpressionData(), # hpca.main <- SingleR(test = sce,assay.type.test = 1,ref = hpca.ref,labels = hpca.ref$label.main), # hpca.fine <- SingleR(test = sce,assay.type.test = 1,ref = hpca.ref,labels = hpca.ref$label.fine), # dice.main <- SingleR(test = sce,assay.type.test = 1,ref = dice.ref,labels = dice.ref$label.main), # dice.fine <- SingleR(test = sce,assay.type.test = 1,ref = dice.ref,labels = dice.ref$label.fine), # srat@meta.data$hpca.main <- hpca.main$pruned.labels, # srat@meta.data$dice.main <- dice.main$pruned.labels, # srat@meta.data$hpca.fine <- hpca.fine$pruned.labels, # srat@meta.data$dice.fine <- dice.fine$pruned.labels. The plots above clearly show that high MT percentage strongly correlates with low UMI counts, and usually is interpreted as dead cells. To ensure our analysis was on high-quality cells . Is there a single-word adjective for "having exceptionally strong moral principles"? We can see theres a cluster of platelets located between clusters 6 and 14, that has not been identified. You can set both of these to 0, but with a dramatic increase in time - since this will test a large number of features that are unlikely to be highly discriminatory. Each with their own benefits and drawbacks: Identification of all markers for each cluster: this analysis compares each cluster against all others and outputs the genes that are differentially expressed/present. (palm-face-impact)@MariaKwhere were you 3 months ago?! Any other ideas how I would go about it? Acidity of alcohols and basicity of amines. [82] yaml_2.2.1 goftest_1.2-2 knitr_1.33 The Seurat alignment workflow takes as input a list of at least two scRNA-seq data sets, and briefly consists of the following steps ( Fig. Setting cells to a number plots the extreme cells on both ends of the spectrum, which dramatically speeds plotting for large datasets. Here, we analyze a dataset of 8,617 cord blood mononuclear cells (CBMCs), produced with CITE-seq, where we simultaneously measure the single cell transcriptomes alongside the expression of 11 surface proteins, whose levels are quantified with DNA-barcoded antibodies. Is it suspicious or odd to stand by the gate of a GA airport watching the planes? rescale. Seurat - Guided Clustering Tutorial Seurat - Satija Lab rev2023.3.3.43278. If I decide that batch correction is not required for my samples, could I subset cells from my original Seurat Object (after running Quality Control and clustering on it), set the assay to "RNA", and and run the standard SCTransform pipeline. [40] future.apply_1.8.1 abind_1.4-5 scales_1.1.1 Source: R/visualization.R. . In other words, is this workflow valid: SCT_not_integrated <- FindClusters(SCT_not_integrated) How do I subset a Seurat object using variable features? - Biostar: S The development branch however has some activity in the last year in preparation for Monocle3.1. A few QC metrics commonly used by the community include. Can be used to downsample the data to a certain Considering the popularity of the tidyverse ecosystem, which offers a large set of data display, query, manipulation, integration and visualization utilities, a great opportunity exists to interface the Seurat object with the tidyverse. Does anyone have an idea how I can automate the subset process? I have a Seurat object that I have run through doubletFinder. Reply to this email directly, view it on GitHub<. I checked the active.ident to make sure the identity has not shifted to any other column, but still I am getting the error? The best answers are voted up and rise to the top, Not the answer you're looking for? Seurat-package Seurat: Tools for Single Cell Genomics Description A toolkit for quality control, analysis, and exploration of single cell RNA sequencing data. Lets see if we have clusters defined by any of the technical differences. A vector of cells to keep. We therefore suggest these three approaches to consider. LAPACK: /Library/Frameworks/R.framework/Versions/4.1/Resources/lib/libRlapack.dylib . [43] pheatmap_1.0.12 DBI_1.1.1 miniUI_0.1.1.1 By providing the module-finding function with a list of possible resolutions, we are telling Louvain to perform the clustering at each resolution and select the result with the greatest modularity. Lets plot metadata only for cells that pass tentative QC: In order to do further analysis, we need to normalize the data to account for sequencing depth. Elapsed time: 0 seconds, Using existing Monocle 3 cluster membership and partitions, 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 cells = NULL, Spend a moment looking at the cell_data_set object and its slots (using slotNames) as well as cluster_cells. Let's plot the kernel density estimate for CD4 as follows. Now that we have loaded our data in seurat (using the CreateSeuratObject), we want to perform some initial QC on our cells. [100] e1071_1.7-8 spatstat.utils_2.2-0 tibble_3.1.3 20? [1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8 In reality, you would make the decision about where to root your trajectory based upon what you know about your experiment. PDF Seurat: Tools for Single Cell Genomics - Debian There are also differences in RNA content per cell type. Perform Canonical Correlation Analysis RunCCA Seurat - Satija Lab Cells within the graph-based clusters determined above should co-localize on these dimension reduction plots. Maximum modularity in 10 random starts: 0.7424 Is it known that BQP is not contained within NP? Can I make it faster? Again, these parameters should be adjusted according to your own data and observations. Have a question about this project? However, we can try automaic annotation with SingleR is workflow-agnostic (can be used with Seurat, SCE, etc). By default, Wilcoxon Rank Sum test is used. To do this, omit the features argument in the previous function call, i.e. Our approach was heavily inspired by recent manuscripts which applied graph-based clustering approaches to scRNA-seq data [SNN-Cliq, Xu and Su, Bioinformatics, 2015] and CyTOF data [PhenoGraph, Levine et al., Cell, 2015]. Next, we apply a linear transformation (scaling) that is a standard pre-processing step prior to dimensional reduction techniques like PCA. to your account. max per cell ident. This distinct subpopulation displays markers such as CD38 and CD59. I can figure out what it is by doing the following: locale: [4] sp_1.4-5 splines_4.1.0 listenv_0.8.0 Default is INF. [130] parallelly_1.27.0 codetools_0.2-18 gtools_3.9.2 [25] xfun_0.25 dplyr_1.0.7 crayon_1.4.1 Dendritic cell and NK aficionados may recognize that genes strongly associated with PCs 12 and 13 define rare immune subsets (i.e. In particular DimHeatmap() allows for easy exploration of the primary sources of heterogeneity in a dataset, and can be useful when trying to decide which PCs to include for further downstream analyses. Developed by Paul Hoffman, Satija Lab and Collaborators. Connect and share knowledge within a single location that is structured and easy to search. Functions related to the analysis of spatially-resolved single-cell data, Visualize clusters spatially and interactively, Visualize features spatially and interactively, Visualize spatial and clustering (dimensional reduction) data in a linked, FindMarkers: Gene expression markers of identity classes in Seurat Use of this site constitutes acceptance of our User Agreement and Privacy For greater detail on single cell RNA-Seq analysis, see the Introductory course materials here. But I especially don't get why this one did not work: This works for me, with the metadata column being called "group", and "endo" being one possible group there. Use regularized negative binomial regression to normalize UMI count data, Subset a Seurat Object based on the Barcode Distribution Inflection Points, Functions for testing differential gene (feature) expression, Gene expression markers for all identity classes, Finds markers that are conserved between the groups, Gene expression markers of identity classes, Prepare object to run differential expression on SCT assay with multiple models, Functions to reduce the dimensionality of datasets. Next step discovers the most variable features (genes) - these are usually most interesting for downstream analysis. [31] survival_3.2-12 zoo_1.8-9 glue_1.4.2 This may run very slowly. Furthermore, it is possible to apply all of the described algortihms to selected subsets (resulting cluster . Both cells and features are ordered according to their PCA scores. In fact, only clusters that belong to the same partition are connected by a trajectory. It is conventional to use more PCs with SCTransform; the exact number can be adjusted depending on your dataset. Seurat has specific functions for loading and working with drop-seq data. ident.use = NULL, Does a summoned creature play immediately after being summoned by a ready action? Seurat offers several non-linear dimensional reduction techniques, such as tSNE and UMAP, to visualize and explore these datasets. Differential expression allows us to define gene markers specific to each cluster. [.Seurat function - RDocumentation (i) It learns a shared gene correlation. Is it plausible for constructed languages to be used to affect thought and control or mold people towards desired outcomes? [70] labeling_0.4.2 rlang_0.4.11 reshape2_1.4.4 Lets try using fewer neighbors in the KNN graph, combined with Leiden algorithm (now default in scanpy) and slightly increased resolution: We already know that cluster 16 corresponds to platelets, and cluster 15 to dendritic 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 Already on GitHub? find Matrix::rBind and replace with rbind then save. Try setting do.clean=T when running SubsetData, this should fix the problem. Prepare an object list normalized with sctransform for integration. For example, the ROC test returns the classification power for any individual marker (ranging from 0 - random, to 1 - perfect). I'm hoping it's something as simple as doing this: I was playing around with it, but couldn't get it You just want a matrix of counts of the variable features? VlnPlot() (shows expression probability distributions across clusters), and FeaturePlot() (visualizes feature expression on a tSNE or PCA plot) are our most commonly used visualizations. [52] spatstat.core_2.3-0 spdep_1.1-8 proxy_0.4-26 [103] bslib_0.2.5.1 stringi_1.7.3 highr_0.9 Seurat can help you find markers that define clusters via differential expression. We identify significant PCs as those who have a strong enrichment of low p-value features. You can learn more about them on Tols webpage. SoupX output only has gene symbols available, so no additional options are needed. Functions for plotting data and adjusting. For mouse cell cycle genes you can use the solution detailed here. To give you experience with the analysis of single cell RNA sequencing (scRNA-seq) including performing quality control and identifying cell type subsets. Creates a Seurat object containing only a subset of the cells in the original object. [106] RSpectra_0.16-0 lattice_0.20-44 Matrix_1.3-4 To access the counts from our SingleCellExperiment, we can use the counts() function: [148] sf_1.0-2 shiny_1.6.0, # First split the sample by original identity, # perform standard preprocessing on each object. We can export this data to the Seurat object and visualize. Asking for help, clarification, or responding to other answers. Many thanks in advance. "../data/pbmc3k/filtered_gene_bc_matrices/hg19/". Lets add several more values useful in diagnostics of cell quality. For trajectory analysis, 'partitions' as well as 'clusters' are needed and so the Monocle cluster_cells function must also be performed. Single-cell analysis of olfactory neurogenesis and - Nature just "BC03" ? Mitochnondrial genes show certain dependency on cluster, being much lower in clusters 2 and 12. We've added a "Necessary cookies only" option to the cookie consent popup, 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? Default is the union of both the variable features sets present in both objects. FilterCells function - RDocumentation Seurat has several tests for differential expression which can be set with the test.use parameter (see our DE vignette for details). [73] later_1.3.0 pbmcapply_1.5.0 munsell_0.5.0 As another option to speed up these computations, max.cells.per.ident can be set. I keep running out of RAM with my current pipeline, Bar Graph of Expression Data from Seurat Object. Well occasionally send you account related emails. Monocles graph_test() function detects genes that vary over a trajectory. If FALSE, merge the data matrices also. Integrating single-cell transcriptomic data across different - Nature Other option is to get the cell names of that ident and then pass a vector of cell names. The first is more supervised, exploring PCs to determine relevant sources of heterogeneity, and could be used in conjunction with GSEA for example. In order to perform a k-means clustering, the user has to choose this from the available methods and provide the number of desired sample and gene clusters. Lets check the markers of smaller cell populations we have mentioned before - namely, platelets and dendritic cells. We can set the root to any one of our clusters by selecting the cells in that cluster to use as the root in the function order_cells. matrix. The number above each plot is a Pearson correlation coefficient. Seurat provides several useful ways of visualizing both cells and features that define the PCA, including VizDimReduction(), DimPlot(), and DimHeatmap(). We can also display the relationship between gene modules and monocle clusters as a heatmap. Though clearly a supervised analysis, we find this to be a valuable tool for exploring correlated feature sets. On 26 Jun 2018, at 21:14, Andrew Butler > wrote: In this tutorial, we will learn how to Read 10X sequencing data and change it into a seurat object, QC and selecting cells for further analysis, Normalizing the data, Identification . Traffic: 816 users visited in the last hour. But I especially don't get why this one did not work: If anyone can tell me why the latter did not function I would appreciate it. Similarly, we can define ribosomal proteins (their names begin with RPS or RPL), which often take substantial fraction of reads: Now, lets add the doublet annotation generated by scrublet to the Seurat object metadata. What does data in a count matrix look like? In a data set like this one, cells were not harvested in a time series, but may not have all been at the same developmental stage. We will define a window of a minimum of 200 detected genes per cell and a maximum of 2500 detected genes per cell. Seurat has a built-in list, cc.genes (older) and cc.genes.updated.2019 (newer), that defines genes involved in cell cycle. 8 Single cell RNA-seq analysis using Seurat ), # S3 method for Seurat Lets erase adj.matrix from memory to save RAM, and look at the Seurat object a bit closer. We encourage users to repeat downstream analyses with a different number of PCs (10, 15, or even 50!). [1] plyr_1.8.6 igraph_1.2.6 lazyeval_0.2.2 max.cells.per.ident = Inf, Seurat part 4 - Cell clustering - NGS Analysis My code is GPL licensed, can I issue a license to have my code be distributed in a specific MIT licensed project? 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 - Linear discriminant analysis on pooled CRISPR screen data. Using indicator constraint with two variables. Were only going to run the annotation against the Monaco Immune Database, but you can uncomment the two others to compare the automated annotations generated. The Read10X() function reads in the output of the cellranger pipeline from 10X, returning a unique molecular identified (UMI) count matrix. In this example, all three approaches yielded similar results, but we might have been justified in choosing anything between PC 7-12 as a cutoff. There are many tests that can be used to define markers, including a very fast and intuitive tf-idf. [28] RCurl_1.98-1.4 jsonlite_1.7.2 spatstat.data_2.1-0 By clicking Accept all cookies, you agree Stack Exchange can store cookies on your device and disclose information in accordance with our Cookie Policy. Use MathJax to format equations. Single-cell RNA-seq: Marker identification Chapter 3 Analysis Using Seurat | Fundamentals of scRNASeq Analysis What is the difference between nGenes and nUMIs? For clarity, in this previous line of code (and in future commands), we provide the default values for certain parameters in the function call. You signed in with another tab or window. First, lets set the active assay back to RNA, and re-do the normalization and scaling (since we removed a notable fraction of cells that failed QC): The following function allows to find markers for every cluster by comparing it to all remaining cells, while reporting only the positive ones. We can now do PCA, which is a common way of linear dimensionality reduction. It may make sense to then perform trajectory analysis on each partition separately. Whats the difference between "SubsetData" and "subset - GitHub In this case, we are plotting the top 20 markers (or all markers if less than 20) for each cluster. It has been downloaded in the course uppmax folder with subfolder: scrnaseq_course/data/PBMC_10x/pbmc3k_filtered_gene_bc_matrices.tar.gz # for anything calculated by the object, i.e. monocle3 uses a cell_data_set object, the as.cell_data_set function from SeuratWrappers can be used to convert a Seurat object to Monocle object. Lets remove the cells that did not pass QC and compare plots. What sort of strategies would a medieval military use against a fantasy giant? [121] bitops_1.0-7 irlba_2.3.3 Matrix.utils_0.9.8 For detailed dissection, it might be good to do differential expression between subclusters (see below). 'Seurat' aims to enable users to identify and interpret sources of heterogeneity from single cell transcriptomic measurements, and to integrate diverse types of single cell data.