seurat subset analysis

Staging Ground Beta 1 Recap, and Reviewers needed for Beta 2, R: subsetting data frame by both certain column names (as a variable) and field values. To learn more, see our tips on writing great answers. Seurat allows you to easily explore QC metrics and filter cells based on any user-defined criteria. In the example below, we visualize gene and molecule counts, plot their relationship, and exclude cells with a clear outlier number of genes detected as potential multiplets. This is done using gene.column option; default is 2, which is gene symbol. Now I am wondering, how do I extract a data frame or matrix of this Seurat object with the built in function or would I have to do it in a "homemade"-R-way? Browse other questions tagged, Start here for a quick overview of the site, Detailed answers to any questions you might have, Discuss the workings and policies of this site. RunCCA(object1, object2, .) Using Seurat with multi-modal data; Analysis, visualization, and integration of spatial datasets with Seurat; Data Integration; Introduction to scRNA-seq integration; Mapping and annotating query datasets; . 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. For this tutorial, we will be analyzing the a dataset of Peripheral Blood Mononuclear Cells (PBMC) freely available from 10X Genomics. Search all packages and functions. (i) It learns a shared gene correlation. (default), then this list will be computed based on the next three 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 . Extra parameters passed to WhichCells , such as slot, invert, or downsample. SubsetData is a relic from the Seurat v2.X days; it's been updated to work on the Seurat v3 object, but was done in a rather crude way.SubsetData will be marked as defunct in a future release of Seurat.. subset was built with the Seurat v3 object in mind, and will be pushed as the preferred way to subset a Seurat object. To start the analysis, lets read in the SoupX-corrected matrices (see QC Chapter). # for anything calculated by the object, i.e. integrated.sub <-subset (as.Seurat (cds, assay = NULL), monocle3_partitions == 1) cds <-as.cell_data_set (integrated . max.cells.per.ident = Inf, The third is a heuristic that is commonly used, and can be calculated instantly. 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. 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. 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? Acidity of alcohols and basicity of amines. Seurat has a built-in list, cc.genes (older) and cc.genes.updated.2019 (newer), that defines genes involved in cell cycle. however, when i use subset(), it returns with Error. Batch split images vertically in half, sequentially numbering the output files. We therefore suggest these three approaches to consider. renormalize. Use MathJax to format equations. This has to be done after normalization and scaling. 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 will also correct for % MT genes and cell cycle scores using vars.to.regress variables; our previous exploration has shown that neither cell cycle score nor MT percentage change very dramatically between clusters, so we will not remove biological signal, but only some unwanted variation. This can in some cases cause problems downstream, but setting do.clean=T does a full subset. What is the difference between nGenes and nUMIs? For detailed dissection, it might be good to do differential expression between subclusters (see below). rev2023.3.3.43278. A very comprehensive tutorial can be found on the Trapnell lab website. GetImage() GetImage() GetImage(), GetTissueCoordinates() GetTissueCoordinates() GetTissueCoordinates(), IntegrationAnchorSet-class IntegrationAnchorSet, Radius() Radius() Radius(), RenameCells() RenameCells() RenameCells() RenameCells(), levels() `levels<-`(). [142] rpart_4.1-15 coda_0.19-4 class_7.3-19 These match our expectations (and each other) reasonably well. [15] BiocGenerics_0.38.0 myseurat@meta.data[which(myseurat@meta.data$celltype=="AT1")[1],]. accept.value = NULL, Is there a single-word adjective for "having exceptionally strong moral principles"? original object. We start the analysis after two preliminary steps have been completed: 1) ambient RNA correction using soupX; 2) doublet detection using scrublet. assay = NULL, Seurat allows you to easily explore QC metrics and filter cells based on any user-defined criteria. 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). Seurat has specific functions for loading and working with drop-seq data. [145] tidyr_1.1.3 rmarkdown_2.10 Rtsne_0.15 # Identify the 10 most highly variable genes, # plot variable features with and without labels, # Examine and visualize PCA results a few different ways, # NOTE: This process can take a long time for big datasets, comment out for expediency. In other words, is this workflow valid: SCT_not_integrated <- FindClusters(SCT_not_integrated) By definition it is influenced by how clusters are defined, so its important to find the correct resolution of your clustering before defining the markers. The FindClusters() function implements this procedure, and contains a resolution parameter that sets the granularity of the downstream clustering, with increased values leading to a greater number of clusters. If not, an easy modification to the workflow above would be to add something like the following before RunCCA: 'Seurat' aims to enable users to identify and interpret sources of heterogeneity from single cell transcrip-tomic measurements, and to integrate diverse types of single cell data. The contents in this chapter are adapted from Seurat - Guided Clustering Tutorial with little modification. How do I subset a Seurat object using variable features? In order to reveal subsets of genes coregulated only within a subset of patients SEURAT offers several biclustering algorithms. Cheers. [46] Rcpp_1.0.7 spData_0.3.10 viridisLite_0.4.0 Note that SCT is the active assay now. rev2023.3.3.43278. Seurat (version 3.1.4) . Returns a Seurat object containing only the relevant subset of cells, Run the code above in your browser using DataCamp Workspace, SubsetData: Return a subset of the Seurat object, pbmc1 <- SubsetData(object = pbmc_small, cells = colnames(x = pbmc_small)[. [1] plyr_1.8.6 igraph_1.2.6 lazyeval_0.2.2 [13] matrixStats_0.60.0 Biobase_2.52.0 To do this we sould go back to Seurat, subset by partition, then back to a CDS. We can see theres a cluster of platelets located between clusters 6 and 14, that has not been identified. Why did Ukraine abstain from the UNHRC vote on China? data, Visualize features in dimensional reduction space interactively, Label clusters on a ggplot2-based scatter plot, SeuratTheme() CenterTitle() DarkTheme() FontSize() NoAxes() NoLegend() NoGrid() SeuratAxes() SpatialTheme() RestoreLegend() RotatedAxis() BoldTitle() WhiteBackground(), Get the intensity and/or luminance of a color, Function related to tree-based analysis of identity classes, Phylogenetic Analysis of Identity Classes, Useful functions to help with a variety of tasks, Calculate module scores for feature expression programs in single cells, Aggregated feature expression by identity class, Averaged feature expression by identity class. [58] httr_1.4.2 RColorBrewer_1.1-2 ellipsis_0.3.2 subcell@meta.data[1,]. Importantly, the distance metric which drives the clustering analysis (based on previously identified PCs) remains the same. SCTAssay class, as.Seurat() as.Seurat(), Convert objects to SingleCellExperiment objects, as.sparse() as.data.frame(), Functions for preprocessing single-cell data, Calculate the Barcode Distribution Inflection, Calculate pearson residuals of features not in the scale.data, Demultiplex samples based on data from cell 'hashing', Load a 10x Genomics Visium Spatial Experiment into a Seurat object, Demultiplex samples based on classification method from MULTI-seq (McGinnis et al., bioRxiv 2018), Load in data from remote or local mtx files. ), # S3 method for Seurat This is where comparing many databases, as well as using individual markers from literature, would all be very valuable. active@meta.data$sample <- "active" Does anyone have an idea how I can automate the subset process? However, this isnt required and the same behavior can be achieved with: We next calculate a subset of features that exhibit high cell-to-cell variation in the dataset (i.e, they are highly expressed in some cells, and lowly expressed in others). The data from all 4 samples was combined in R v.3.5.2 using the Seurat package v.3.0.0 and an aggregate Seurat object was generated 21,22. [130] parallelly_1.27.0 codetools_0.2-18 gtools_3.9.2 Making statements based on opinion; back them up with references or personal experience. VlnPlot() (shows expression probability distributions across clusters), and FeaturePlot() (visualizes feature expression on a tSNE or PCA plot) are our most commonly used visualizations. locale: All cells that cannot be reached from a trajectory with our selected root will be gray, which represents infinite pseudotime. How can this new ban on drag possibly be considered constitutional? Seurat offers several non-linear dimensional reduction techniques, such as tSNE and UMAP, to visualize and explore these datasets. Why did Ukraine abstain from the UNHRC vote on China? Lets see if we have clusters defined by any of the technical differences. matrix. Matrix products: default [94] grr_0.9.5 R.oo_1.24.0 hdf5r_1.3.3 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 - features. It is conventional to use more PCs with SCTransform; the exact number can be adjusted depending on your dataset. Perform Canonical Correlation Analysis RunCCA Seurat Perform Canonical Correlation Analysis Source: R/generics.R, R/dimensional_reduction.R Runs a canonical correlation analysis using a diagonal implementation of CCA. Mitochnondrial genes show certain dependency on cluster, being much lower in clusters 2 and 12. Adjust the number of cores as needed. Thanks for contributing an answer to Stack Overflow! We identify significant PCs as those who have a strong enrichment of low p-value features. Scaling is an essential step in the Seurat workflow, but only on genes that will be used as input to PCA. Try setting do.clean=T when running SubsetData, this should fix the problem. [1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8 By clicking Post Your Answer, you agree to our terms of service, privacy policy and cookie policy. [52] spatstat.core_2.3-0 spdep_1.1-8 proxy_0.4-26 [22] spatstat.sparse_2.0-0 colorspace_2.0-2 ggrepel_0.9.1 How can this new ban on drag possibly be considered constitutional? The ScaleData() function: This step takes too long! I subsetted my original object, choosing clusters 1,2 & 4 from both samples to create a new seurat object for each sample which I will merged and re-run clustersing for comparison with clustering of my macrophage only sample. Lets look at cluster sizes. [79] evaluate_0.14 stringr_1.4.0 fastmap_1.1.0 Seurat-package Seurat: Tools for Single Cell Genomics Description A toolkit for quality control, analysis, and exploration of single cell RNA sequencing data. Functions for plotting data and adjusting. 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. Identifying the true dimensionality of a dataset can be challenging/uncertain for the user. 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. Modules will only be calculated for genes that vary as a function of pseudotime. Motivation: Seurat is one of the most popular software suites for the analysis of single-cell RNA sequencing data. There are also differences in RNA content per cell type. However, our approach to partitioning the cellular distance matrix into clusters has dramatically improved. This results in significant memory and speed savings for Drop-seq/inDrop/10x data. Increasing clustering resolution in FindClusters to 2 would help separate the platelet cluster (try it! . How can I remove unwanted sources of variation, as in Seurat v2? :) Thank you. The Read10X() function reads in the output of the cellranger pipeline from 10X, returning a unique molecular identified (UMI) count matrix. I think this is basically what you did, but I think this looks a little nicer. These will be used in downstream analysis, like PCA. Traffic: 816 users visited in the last hour. The raw data can be found here. However, these groups are so rare, they are difficult to distinguish from background noise for a dataset of this size without prior knowledge. Not only does it work better, but it also follow's the standard R object . For greater detail on single cell RNA-Seq analysis, see the Introductory course materials here. The JackStrawPlot() function provides a visualization tool for comparing the distribution of p-values for each PC with a uniform distribution (dashed line). : Next we perform PCA on the scaled data. DimPlot uses UMAP by default, with Seurat clusters as identity: In order to control for clustering resolution and other possible artifacts, we will take a close look at two minor cell populations: 1) dendritic cells (DCs), 2) platelets, aka thrombocytes. Note that there are two cell type assignments, label.main and label.fine. Again, these parameters should be adjusted according to your own data and observations. I can figure out what it is by doing the following: Where meta_data = 'DF.classifications_0.25_0.03_252' and is a character class. If some clusters lack any notable markers, adjust the clustering. We can look at the expression of some of these genes overlaid on the trajectory plot. Lets add the annotations to the Seurat object metadata so we can use them: Finally, lets visualize the fine-grained annotations. Any argument that can be retreived Both vignettes can be found in this repository. Prepare an object list normalized with sctransform for integration. How to notate a grace note at the start of a bar with lilypond? Explore what the pseudotime analysis looks like with the root in different clusters. We also filter cells based on the percentage of mitochondrial genes present. To do this, omit the features argument in the previous function call, i.e. Functions related to the mixscape algorithm, DE and EnrichR pathway visualization barplot, Differential expression heatmap for mixscape. By clicking Accept all cookies, you agree Stack Exchange can store cookies on your device and disclose information in accordance with our Cookie Policy. You signed in with another tab or window. Note: In order to detect mitochondrial genes, we need to tell Seurat how to distinguish these genes. Visualize spatial clustering and expression data. [11] S4Vectors_0.30.0 MatrixGenerics_1.4.2 [7] scattermore_0.7 ggplot2_3.3.5 digest_0.6.27 Low-quality cells or empty droplets will often have very few genes, Cell doublets or multiplets may exhibit an aberrantly high gene count, Similarly, the total number of molecules detected within a cell (correlates strongly with unique genes), The percentage of reads that map to the mitochondrial genome, Low-quality / dying cells often exhibit extensive mitochondrial contamination, We calculate mitochondrial QC metrics with the, We use the set of all genes starting with, The number of unique genes and total molecules are automatically calculated during, You can find them stored in the object meta data, We filter cells that have unique feature counts over 2,500 or less than 200, We filter cells that have >5% mitochondrial counts, Shifts the expression of each gene, so that the mean expression across cells is 0, Scales the expression of each gene, so that the variance across cells is 1, This step gives equal weight in downstream analyses, so that highly-expressed genes do not dominate. i, features. By clicking Accept all cookies, you agree Stack Exchange can store cookies on your device and disclose information in accordance with our Cookie Policy. FeaturePlot (pbmc, "CD4") Briefly, these methods embed cells in a graph structure - for example a K-nearest neighbor (KNN) graph, with edges drawn between cells with similar feature expression patterns, and then attempt to partition this graph into highly interconnected quasi-cliques or communities. Renormalize raw data after merging the objects. Here the pseudotime trajectory is rooted in cluster 5. Bulk update symbol size units from mm to map units in rule-based symbology. But I especially don't get why this one did not work: [1] stats4 parallel stats graphics grDevices utils datasets For trajectory analysis, partitions as well as clusters are needed and so the Monocle cluster_cells function must also be performed. Functions for interacting with a Seurat object, Cells() Cells() Cells() Cells(), Get a vector of cell names associated with an image (or set of images). The steps below encompass the standard pre-processing workflow for scRNA-seq data in Seurat. accept.value = NULL, Connect and share knowledge within a single location that is structured and easy to search. Is it known that BQP is not contained within NP? Our filtered dataset now contains 8824 cells - so approximately 12% of cells were removed for various reasons. [133] boot_1.3-28 MASS_7.3-54 assertthat_0.2.1 Biclustering is the simultaneous clustering of rows and columns of a data matrix. # Lets examine a few genes in the first thirty cells, # The [[ operator can add columns to object metadata. MathJax reference. Next, we apply a linear transformation (scaling) that is a standard pre-processing step prior to dimensional reduction techniques like PCA. subset.AnchorSet.Rd. Determine statistical significance of PCA scores. Because Seurat is now the most widely used package for single cell data analysis we will want to use Monocle with Seurat. Higher resolution leads to more clusters (default is 0.8). We also suggest exploring RidgePlot(), CellScatter(), and DotPlot() as additional methods to view your dataset. The size of the dot encodes the percentage of cells within a class, while the color encodes the AverageExpression level across all cells within a class (blue is high). Creates a Seurat object containing only a subset of the cells in the original object. [76] tools_4.1.0 generics_0.1.0 ggridges_0.5.3 Cheers The main function from Nebulosa is the plot_density. Default is the union of both the variable features sets present in both objects. Policy. Lets erase adj.matrix from memory to save RAM, and look at the Seurat object a bit closer. The first step in trajectory analysis is the learn_graph() function. You may have an issue with this function in newer version of R an rBind Error. other attached packages: 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? Try updating the resolution parameter to generate more clusters (try 1e-5, 1e-3, 1e-1, and 0). 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. Can you help me with this? For example, the ROC test returns the classification power for any individual marker (ranging from 0 - random, to 1 - perfect). Cells within the graph-based clusters determined above should co-localize on these dimension reduction plots. The top principal components therefore represent a robust compression of the dataset. 4 Visualize data with Nebulosa. [43] pheatmap_1.0.12 DBI_1.1.1 miniUI_0.1.1.1 However, how many components should we choose to include? Note that you can change many plot parameters using ggplot2 features - passing them with & operator. Eg, the name of a gene, PC_1, a To cluster the cells, we next apply modularity optimization techniques such as the Louvain algorithm (default) or SLM [SLM, Blondel et al., Journal of Statistical Mechanics], to iteratively group cells together, with the goal of optimizing the standard modularity function. It has been downloaded in the course uppmax folder with subfolder: scrnaseq_course/data/PBMC_10x/pbmc3k_filtered_gene_bc_matrices.tar.gz 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. Creates a Seurat object containing only a subset of the cells in the original object. low.threshold = -Inf, To overcome the extensive technical noise in any single feature for scRNA-seq data, Seurat clusters cells based on their PCA scores, with each PC essentially representing a metafeature that combines information across a correlated feature set. Furthermore, it is possible to apply all of the described algortihms to selected subsets (resulting cluster . 28 27 27 17, R version 4.1.0 (2021-05-18) Identity class can be seen in srat@active.ident, or using Idents() function. [136] leidenbase_0.1.3 sctransform_0.3.2 GenomeInfoDbData_1.2.6 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. This heatmap displays the association of each gene module with each cell type. To subscribe to this RSS feed, copy and paste this URL into your RSS reader. More, # approximate techniques such as those implemented in ElbowPlot() can be used to reduce, # Look at cluster IDs of the first 5 cells, # If you haven't installed UMAP, you can do so via reticulate::py_install(packages =, # note that you can set `label = TRUE` or use the LabelClusters function to help label, # find all markers distinguishing cluster 5 from clusters 0 and 3, # find markers for every cluster compared to all remaining cells, report only the positive, Analysis, visualization, and integration of spatial datasets with Seurat, Fast integration using reciprocal PCA (RPCA), Integrating scRNA-seq and scATAC-seq data, Demultiplexing with hashtag oligos (HTOs), Interoperability between single-cell object formats, [SNN-Cliq, Xu and Su, Bioinformatics, 2015]. The grouping.var needs to refer to a meta.data column that distinguishes which of the two groups each cell belongs to that you're trying to align. The data we used is a 10k PBMC data getting from 10x Genomics website.. Policy. 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). 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. cluster3.seurat.obj <- CreateSeuratObject(counts = cluster3.raw.data, project = "cluster3", min.cells = 3, min.features = 200) cluster3.seurat.obj <- NormalizeData . It is recommended to do differential expression on the RNA assay, and not the SCTransform. Lets take a quick glance at the markers. Why do small African island nations perform better than African continental nations, considering democracy and human development? The palettes used in this exercise were developed by Paul Tol. rescale. The second implements a statistical test based on a random null model, but is time-consuming for large datasets, and may not return a clear PC cutoff. [88] RANN_2.6.1 pbapply_1.4-3 future_1.21.0 For example, the count matrix is stored in pbmc[["RNA"]]@counts. By clicking Sign up for GitHub, you agree to our terms of service and

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