Seurat spatial

Seurat spatial. One 10X Genomics Visium dataset will be analyzed with Seurat in this tutorial, and you may explore other dataset sources from various sequencing technologies, and other computational toolkits listed in this (non-exhaustive Oct 31, 2023 · We demonstrate these methods using a publicly available ~12,000 human PBMC ‘multiome’ dataset from 10x Genomics. When coords is a data. the PC 1 scores - "PC_1") fov. 0. size bug when rasterization is set to true Seurat object. # Dimensional reduction plot DimPlot (object = pbmc, reduction = "pca") # Dimensional reduction plot, with cells colored by a quantitative feature Defaults to UMAP if Sep 4, 2023 · The spatial transcriptomics data obtained with either standard Visium, RRST or SMA were processed and analyzed using R (v 4. The method currently supports five integration methods. ck. mtx, genes. The analysis module provides end-to-end analysis by implementing a wide range of algorithms for characterizing tissue composition, spatial expression Oct 24, 2022 · STELLAR (spatial cell learning) is a geometric deep learning model that works with spatially resolved single-cell datasets to both assign cell types in unannotated datasets based on a reference Jul 16, 2020 · Here, we present STUtility, an R package that conveniently enables the user to perform these tasks. Mapping scRNA-seq data onto CITE-seq references vignette. We demonstrate the use of WNN analysis Methods defined on the SpatialImage class. Name of normalization method used Oct 2, 2023 · Introduction. cluster. To use, simply make a ggplot2-based scatter plot (such as DimPlot() or FeaturePlot()) and pass the resulting plot to HoverLocator() # Include additional data to Mar 1, 2023 · Among the existing clustering methods employed in spatial domain identification, k-means, Louvain’s method 6, and Seurat 7 utilize only gene expression data to cluster spots into different Nov 10, 2021 · UC Berkeley Center for Computational Biology (CCB) Skills Seminar Nov 10, 2021. An object of class scalefactors; see scalefactors for more information. If you use Seurat in your research, please considering The vignettes below demonstrate three scalable analyses in Seurat v5: Unsupervised clustering analysis of a large dataset (1. tsv (or features. Downstream analysis (i. We confirmed Seurat's accuracy using Mar 25, 2024 · Existing Seurat workflows for clustering, visualization, and downstream analysis have been updated to support both Visium and Visium HD data. Recently, several methods have been developed for spatial transcriptomics to overcome this limitation. normalization. Mar 30, 2023. Visualization in Seurat. frame with spatially-resolved molecule information or a Molecules object. The Seurat tool has a function called "Read10X()" that will automatically take a directory containing the matrices output from Cell Ranger and input them into the R environment so you don't have to worry about doing this manually. Name of new integrated dimensional reduction. Name of associated assay. Spatial transcriptomic data with the Visium platform is in many ways similar to scRNAseq data. 2 parameters. Dimensional reduction, visualization, and clustering. Integration workflow: Seurat v5 introduces a streamlined integration and data transfer workflows that performs integration in low-dimensional space, and improves speed and memory efficiency. Number of neighbors to consider for each cell. colors. cell. Method for normalization. A data. If you have multiple counts matrices, you can also create a Seurat object that is If return. All reactions. Nov 21, 2022 · Seurat is an R toolkit for single-cell genomic data analysis and provides methods for dimensionality reduction and clustering of spatial transcriptomics data. Seurat is available on CRAN for all platforms. Vector of cells to plot (default is all cells) overlap. This is then natural-log transformed using log1p. Both for 10X/Slideseq (which do have the nice constructors) and also for generic spatial data for other modalities like FISH) A toolkit for quality control, analysis, and exploration of single cell RNA sequencing data. Independent preprocessing and dimensional reduction of each modality individually. 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. Visualizing ‘pseudo-bulk’ coverage tracks. Mar 6, 2023 · Spatial Seurat. For the purposes of this vignette, we treat the datasets as originating from two different experiments and integrate them together. For example, in this data set of the mouse brain, the gene Hpca is a strong hippocampus marker and Ttr is a . Mar 30, 2023 · How to construct a spatial object in Seurat. May 11, 2024 · The updated Seurat spatial framework has the option to treat cells as individual points, or also to visualize cell boundaries (segmentations). orig. We first load one spatial transcriptomics dataset into Seurat, and then explore the Seurat object a bit for single-cell data storage and manipulation. A number of older tutorials can be found at: The scanpy_usage repository. ctrl A three-dimensional array with PNG image data, see readPNG for more details. 3), the single-cell genomics toolkit Seurat and the spatial About Seurat. We cells. A vector or named vector can be given in order to load several data directories. data', the 'counts' slot is left empty, the 'data' slot is filled with NA, and 'scale. Can be useful when analyses require comparisons between human and mouse gene names for example. assay. coordinates. Number of clusters to return based on the niche assay Dec 18, 2023 · e To evaluate the spatial relevance of a gene set, we perform two kernel density estimations on the two-dimensional spatial map with each cell having equal weights or using the pathway scores as Oct 31, 2023 · Intro: Seurat v4 Reference Mapping. Name of output clusters. The software supports the following features: Calculating single-cell QC metrics. This update improves speed and memory consumption, the stability of Oct 31, 2023 · QC and selecting cells for further analysis. niches. Cell classifications to count in spatial neighborhood. If you use Seurat in your research, please considering Learn how to load a 10x Genomics Visium Spatial Experiment into a Seurat object, a popular R package for single-cell analysis. slice. a gene name - "MS4A1") A column name from meta. Features can come from: An Assay feature (e. We will use a Visium spatial transcriptomics dataset of the human lymphnode, which is publicly available from the 10x genomics website: link. 5M immune cells from healthy and COVID donors. Keep axes and panel background. As I didn't see any function doing that I put together a little function to help me convert my data. During the webinar, viewers will: Learn about the flexible and scalable infrastructure that enables the routine analysis of millions of cells on a laptop computer We also recommend installing these additional packages, which are used in our vignettes, and enhance the functionality of Seurat: Signac: analysis of single-cell chromatin data. name. Only keep spots that have been determined to be over tissue. By default, Seurat ignores cell segmentations and treats each cell as a point ('centroids'). To use, simply make a ggplot2-based scatter plot (such as DimPlot() or FeaturePlot()) and pass the resulting plot to HoverLocator() # Include additional data to Oct 31, 2023 · The workflow consists of three steps. factor. rpca) that aims to co-embed shared cell types across batches: The bulk of Seurat’s differential expression features can be accessed through the FindMarkers() function. spot. We note that Visium HD data is generated from spatially patterned olignocleotides labeled in 2um x 2um bins. molecules. The IntegrateLayers function, described in our vignette, will then align shared cell types across these layers. FOV object to gather cell positions from. frame, Centroids, or Segmentation, name to store coordinates as. method = "LogNormalize", the integrated data is returned to the data slot and can be treated as log-normalized, corrected data. All plotting functions will return a ggplot2 plot by default, allowing easy customization with ggplot2. PNG file to read in. Preprocessing an scRNA-seq dataset includes removing low quality cells, reducing the many dimensions of data that make it difficult to work with, working to define clusters, and ultimately finding some biological meaning and insights! Mar 7, 2022 · Next, we compared the performance of STRIDE with other published cell-type deconvolution tools, including methods developed for spatial transcriptomics, such as SPOTlight , NMFreg , Seurat CCA , RCTD and cell2location , as well as the ones for bulk RNA-seq, such as CIBERSORTx and EPIC . Learning cell-specific modality ‘weights’, and constructing a WNN graph that integrates the modalities. I first tried to use aggregated matrix with spaceranger aggr data_dir<-"Seurat\\\\Aggr" A1_10X_Spatial<-L Seurat: Spatial Transcriptomics; Seurat v3. Choose the scale factor ("lowres"/"hires") to apply in order to matchthe plot with the specified `image` - defaults to "lowres" group. tsv files provided by 10X. key. A Seurat object. Spatial coordinates Arguments passed to other methods. visium_sge() downloads the dataset from 10x Genomics and returns an AnnData object that contains counts, images and spatial coordinates. Colors to use for the color bar. See Satija R, Farrell J, Gennert D, et al (2015) doi:10. “ LogNormalize ”: Feature counts for each cell are divided by the total counts for that cell and multiplied by the scale. SeuratData: automatically load datasets pre-packaged as Seurat objects. Name of Assay in the Seurat object. mito") A column name from a DimReduc object corresponding to the cell embedding values (e. . By default, Seurat performs differential expression (DE) testing based on the non-parametric Wilcoxon rank sum test. Source: R/visualization. ). ) of the WNN graph. To easily tell which original object any particular cell came from, you can set the add. 2官网; Seurat 新版教程：分析空间转录组数据; Seurat4. dir. Is creating such a function in the works? Oct 31, 2023 · Seurat v5 enables streamlined integrative analysis using the IntegrateLayers function. 3192 , Macosko E, Basu A, Satija R, et al (2015) doi:10. If return. RNA staining methods assay only a small Mar 16, 2024 · Seurat-objects containing data derived from spatial experiments (method = 'spatial'): If you specify argument method as 'spatial' transformSeuratToSpata() assumes that the provided seurat-object contains a SpatialImage-object in slot @images from which it will extract the coordinates and the histology image. List of features to check expression levels against, defaults to rownames(x = object) nbin. It contains UMI counts for 5-20 cells instead of single cells, but is still quite sparse in the same way as scRNAseq data is, but with the additional information about spatial location in the tissue. scale. Names of layers in assay. . bioinformatics, . scale. While many of the methods are conserved (both procedures begin by identifying anchors), there are two important distinctions between data transfer and integration: In data transfer, Seurat does not correct or modify the query expression data. Hi All, I'm currently trying to merge multiple spatial data generated with spaceranger count. disp. Inspired by methods in Goltsev et al, Cell 2018 and He et al, NBT 2022, we consider the ‘local neighborhood’ for each cell Seurat object. STUtility builds on the Seurat framework and uses familiar APIs and well-proven analysis Here I present two script for sending Single cell and more precisely Spatial Transciptomics data from R (Seurat) to Python (Scanpy) without losing the Spatial information. data (e. packages ('Seurat') library ( Seurat) If you see the warning message below, enter y: package which is only available in source form, and may need compilation of C / C ++/ Fortran: 'Seurat' Do you want to attempt to install Seurat label transfer: Mapping approach that can be used to “anchor” diverse datasets together, including different types of single cell data (transcriptomic, epigenomic, and proteomic) and single cell and spatial data. Mar 8, 2021 · Spatial transcriptomic and proteomic technologies have provided new opportunities to investigate cells in their native microenvironment. In this vignette, we introduce a sketch-based analysis workflow to analyze a 1. by. reference. png , scalefactors_json. Overlay boundaries from a single image to create a single plot; if TRUE, then boundaries are stacked in the order they're given (first is lowest) axes. ids parameter with an c(x, y) vector, which will prepend the given identifier to the beginning of each cell name. data column to group the data by. Seurat object. Could you briefly explain what type of data you have, and how the transcriptional measurements relate to regions in the image? Jan 27, 2022 · 4 Analysis and Visualization in the Expression Domain. For new users of Seurat, we suggest starting with a guided walk through of a dataset of 2,700 Peripheral Blood Mononuclear Cells (PBMCs) made publicly available by 10X Genomics. Examples Mar 31, 2020 · satijalab / seurat Public. Seurat has a vast, ggplot2-based plotting library. Name for the image, used to populate the instance's key. Some of these methods must be overridden in order to ensure proper functionality of the derived classes (see Required methods below). A vector of variables to group cells by; pass 'ident' to group by cell identity classes. Cell type identification usually starts with the dimensionality reduction Signac is an R toolkit that extends Seurat for the analysis, interpretation, and exploration of single-cell chromatin datasets. We now release an updated version (‘v2’), based on our broad analysis of 59 scRNA-seq datasets spanning a range of technologies, systems, and sequencing depths. Combine plots into a single patchwork ggplot object. 1. page/newsletter. We also provide SpatialFeaturePlot and SpatialDimPlot as wrapper functions around SpatialPlot for a consistent naming framework. pool. Low-quality cells or empty droplets will often have very few genes. 0系列教程22：空间转录组的分析、可视化与整合; Seurat(v4. SingleCellExperiment() Support for Visium probe information introduced in Spaceranger 2. For more information, please explore the resources below: Defining cellular identity from multimodal data using WNN analysis in Seurat v4 vignette. About Seurat. R, . visualization, clustering, etc. Specifies the bin sizes to read in - defaults to c (16, 8) filter. This tutorial implements the major components of a standard unsupervised clustering workflow including QC and data filtration, calculation of Jan 12, 2024 · However, established workflows such as Seurat still employ pipelines designed for single-cell RNA-seq (scRNA-seq) analysis, which primarily focuses on the gene expression data and ignores the spatial arrangement of cells. 4):空间转录组基本流程; 重温seurat官方教程四：空间转录组分析; 不可错过的单细胞转录组研究新维度：空间转录组 Sketch-based analysis in Seurat v5; Sketch integration using a 1 million cell dataset from Parse Biosciences; Map COVID PBMC datasets to a healthy reference; BPCells Interaction; Spatial analysis; Analysis of spatial datasets (Imaging-based) Analysis of spatial datasets (Sequencing-based) Other; Cell-cycle scoring and regression; Differential Nov 10, 2023 · Merging Two Seurat Objects. Comprehensive Integration of Single-Cell Data. The number of unique genes detected in each cell. The results of integration are not identical between the two workflows, but users can still run the v4 integration workflow in Seurat v5 if they wish. Add a color bar showing group status for cells. group. Analyzing datasets of this size with standard workflows can Seurat object. features. STUtility can be used for normalization, identification of spatial expression patterns alignment of consecutive stacked tissue images and visualizations. Seurat. crop. csv. g. Now it’s time to fully process our data using Seurat. Azimuth: local annotation of scRNA-seq and scATAC-seq queries across multiple organs and tissues. Identifying cell type-specific peaks. 1038/nbt. new. k. combine. A first step in the spatial transcriptomic analysis is to identify the cell type (for datasets of single-cell resolution) or cell mixture (for datasets of multicellular resolution) of each spatial unit or spot. Returns a matrix with genes as rows, identity classes as columns. A vector of features to use for integration. In this example, we map one of the first scRNA-seq datasets released by 10X Genomics of 2,700 PBMC to our recently described CITE-seq reference of 162,000 PBMC measured with 228 antibodies. radius. In re-designing our spatial data support, it would be useful to know some common data types that users have. Older tutorials #. Sign up for my newsletter to not miss a post like this. type Thank you so much for building up SpatialExperiment! As I'm transition from Seurat to SpatialExperiment I wondered f there was a way to convert Seurat objecs to SpatialExperiments. Name of the image to use in the plot. min Fix bug in as. Each of these methods performs integration in low-dimensional space, and returns a dimensional reduction (i. integrated. Returns a Seurat object with a new integrated Assay. 9 min read. data' is set to the aggregated values. Ok, the spatial branch has now been updated with a fix that should restore the proper behavior of cells. A reference Seurat object. bar. If FALSE , return a list of ggplot Seurat utilizes R’s plotly graphing library to create interactive plots. image. A few QC metrics commonly used by the community include. spatial, . Here we present Seurat, a computational strategy to infer cellular localization by integrating single-cell RNA-seq data with in situ RNA patterns. upper. neighbors. json and tissue_positions_list. Vector of features to plot. Seurat, a well-known method for integrating single-cell expression datasets that works by identifying ‘anchors’ between datasets, can be used with spatial data as well 14. cluster assignments) as spots over the image that was collected. 3M neurons), Unsupervised integration and comparison of 1M PBMC from healthy and diabetic patients, and Supervised mapping of 1. 3 million cell dataset of the developing mouse brain, freely available from 10x Genomics. cca) which can be used for visualization and unsupervised clustering analysis Nov 10, 2021 · 2 Seurat object. However, since the data from this resolution is sparse, adjacent bins are pooled together to Sep 12, 2023 · a Spatial regions of ground truth and those detected by different methods, including SiGra, BayesSpace, and Seurat. 3 % (473 of 1798) of A Seurat object. To test for DE genes between two specific groups of cells, specify the ident. First, create the directories and folder-sample names where you want to allocate the data and write the correct path in both of the scripts where it is stated. Name of meta. Feature counts for each cell are divided by the Sketch-based analysis in Seurat v5; Sketch integration using a 1 million cell dataset from Parse Biosciences; Map COVID PBMC datasets to a healthy reference; BPCells Interaction; Spatial analysis; Analysis of spatial datasets (Imaging-based) Analysis of spatial datasets (Sequencing-based) Other; Cell-cycle scoring and regression; Differential Seurat "objects" are a type of data that contain your UMI counts, barcodes, and gene features all in one variable. alpha. single-cell. Provide as a vector specifying the min and max for Mar 29, 2023 · Discover how you can take advantage of cutting-edge single-cell and spatial approaches with Seurat’s developer Dr. seurat = TRUE and slot is 'scale. To install, run: # Enter commands in R (or R studio, if installed) install. Value. This interactive plotting feature works with any ggplot2-based scatter plots (requires a geom_point layer). Nov 16, 2023 · The Seurat v5 integration procedure aims to return a single dimensional reduction that captures the shared sources of variance across multiple layers, so that cells in a similar biological state will cluster. reduction. factors. b Boxplot of the adjusted Rand index (ARI) scores of six methods in all 20 FOVs Seurat v4 also includes additional functionality for the analysis, visualization, and integration of multimodal datasets. https://divingintogeneticsandgenomics. Visualization: Plotting- Core plotting func Reading the data#. If a named vector is given, the cell barcode names will be It holds all molecular information and associated metadata, including (for example) nearest-neighbor graphs, dimensional reduction information, spatial coordinates and image data, and cluster labels. Higher-scoring pairs have more shared nearest neighbours in low Sketch-based analysis in Seurat v5; Sketch integration using a 1 million cell dataset from Parse Biosciences; Map COVID PBMC datasets to a healthy reference; BPCells Interaction; Spatial analysis; Analysis of spatial datasets (Imaging-based) Analysis of spatial datasets (Sequencing-based) Other; Cell-cycle scoring and regression; Differential Apr 14, 2015 · Seurat – Spatial reconstruction of single-cell gene expression data. Basic workflows: Basics- Preprocessing and clustering, Preprocessing and clustering 3k PBMCs (legacy workflow), Integrating data using ingest and BBKNN. The method returns a dimensional reduction (i. Oct 31, 2023 · Seurat v5 enables streamlined integrative analysis using the IntegrateLayers function. And it cannot be Sketch-based analysis in Seurat v5; Sketch integration using a 1 million cell dataset from Parse Biosciences; Map COVID PBMC datasets to a healthy reference; BPCells Interaction; Spatial analysis; Analysis of spatial datasets (Imaging-based) Analysis of spatial datasets (Sequencing-based) Other; Cell-cycle scoring and regression; Differential Mar 15, 2021 · Hi @derek-atlas, we're currently thinking about how best to support custom spatial assays in Seurat. merge() merges the raw count matrices of two Seurat objects and creates a new Seurat object with the resulting combined raw count matrix. Crop the plots to area with cells only. cells. The function datasets. 0% (377 of 2894) of highly variable genes (HVGs) identified by Seurat while ignoring spatial context, or less than 26. A vector of features to plot, defaults to VariableFeatures(object = object) cells. 1 and ident. R. Seurat allows you to easily explore QC metrics and filter cells based on any user-defined criteria. to. Spatial localization is a key determinant of cellular fate and behavior, but methods for spatially resolved, transcriptome-wide gene expression profiling across complex tissues are lacking. This software includes the option to select multiple clustering methods that only utilize gene expression information. Feb 4, 2020 · I was confused because the argument explanation for data. Converts all feature names to upper case. Name of FOV to plot. Name for spatial neighborhoods assay. dir in the Load10X_Spatial function reads "Directory containing the matrix. in Workflows April 14, 2015 10,376 Views. Dec 15, 2023 · Thanks for the update of Seurat to process the spatial transcriptome data. the PC 1 scores - "PC_1") dims Sketch-based analysis in Seurat v5; Sketch integration using a 1 million cell dataset from Parse Biosciences; Map COVID PBMC datasets to a healthy reference; BPCells Interaction; Spatial analysis; Analysis of spatial datasets (Imaging-based) Analysis of spatial datasets (Sequencing-based) Other; Cell-cycle scoring and regression; Differential May 24, 2021 · MNN approaches, such as mnnCorrect/FastMNN 107 or Seurat v3 21, identify the most similar cells (MNNs), called ‘anchors’, across data sets that are used to estimate and correct the cell type Feb 5, 2024 · This tutorial is adapted from the Seurat vignette. This speeds up plotting, especially when looking at large areas, where cell boundaries are too small to visualize. In this dataset, scRNA-seq and scATAC-seq profiles were simultaneously collected in the same cells. rpca) that aims to co-embed shared cell types across batches: We would like to show you a description here but the site won’t allow us. R, R/convenience. We applied Seurat to spatially map 851 single cells from dissociated zebrafish (Danio rerio) embryos and generated a transcriptome-wide map of spatial patterning. Path to directory with 10X Genomics visium image data; should include files tissue_lowres_image. e. Other methods are designed to work across all SpatialImage-derived subclasses, and should only be overridden if necessary A Seurat object. After performing integration, you can rejoin the layers. mitochondrial percentage - "percent. filter. Yutong Wang (4th PhD candidate in the Biostatistics group at UC Berkeley) lead Introductory Vignettes. seurat is TRUE, returns an object of class Seurat. This vignette introduces the process of mapping query datasets to annotated references in Seurat. Sep 19, 2022 · Genes identified by scGCO accounted for less than 13. In Seurat v5, we introduce new infrastructure and methods to analyze, interpret, and explore these exciting datasets. highlight. Here we present Giotto, a comprehensive and open-source toolbox for spatial data analysis and visualization. You’ve previously done all the work to make a single cell matrix. Key for these spatial coordinates. Mar 31, 2020 · (@ Seurat maintainers, I think it could be helpful to add a section in the spatial vignette pointing towards the canonical way of constructing it. fov. matrix. Jan 17, 2024 · We recently introduced sctransform to perform normalization and variance stabilization of scRNA-seq datasets. A data frame with tissue coordinate information. layers. Seurat is an R package designed for QC, analysis, and exploration of single-cell RNA-seq data. SpatialPlot plots a feature or discrete grouping (e. 6b, c Jun 18, 2021 · Seurat Integration readily imputes by using the scRNA-seq gene expression profile of the spatial cell’s highest scoring anchor. Cell (2019) Tool: Seurat Oct 31, 2023 · Prior to performing integration analysis in Seurat v5, we can split the layers into groups. '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. data slot and can be treated as centered, corrected Pearson residuals. The SpatialFeaturePlot() function in Seurat extends FeaturePlot(), and can overlay molecular data on top of tissue histology. Visualize spatial clustering and expression data. We also support rapid and on-disk conversion between h5Seurat and AnnData objects, with the goal of enhancing interoperability between Seurat and Mar 23, 2023 · In Seurat, we have functionality to explore and interact with the inherently visual nature of spatial data. Oct 31, 2023 · In Seurat v5, we introduce support for ‘niche’ analysis of spatial data, which demarcates regions of tissue (‘niches’), each of which is defined by a different composition of spatially adjacent cell types. Controls opacity of spots. Number of bins of aggregate expression levels for all analyzed features. “ CLR ”: Applies a centered log ratio transformation. Nov 29, 2023 · SpatialScope performs comparably to state-of-the-art methods SpaGE, stPlus, and Seurat in terms of predicting spatial gene expression of the seven cortical layer-specific markers (Fig. If normalization. method. Single numeric value giving the radius of the spots. 1 ; Add LoadCurioSeeker to load sequencing-based spatial datasets generated using the Curio Seeker; Fix fold change calculation for assays ; Fix pt. A vector of cells to plot. A list of vectors of features for expression programs; each entry should be a vector of feature names. “ RC ”: Relative counts. method = "SCT", the integrated data is returned to the scale. Publication: Stuart, Tim, et al. name. A dimensional reduction to correct. tsv), and barcodes. Rahul Satija of the New York Genome Center as he introduces Seurat v5. Could you please help me with converting the patial data from Scanpy (python) to Seurat (R) ? I got the h5ad file (spatial transcriptome data. In data transfer, Seurat has an option (set by default) to project the PCA structure of a reference Seurat utilizes R’s plotly graphing library to create interactive plots. May 16, 2022 · Clinical Cancer Bulletin (2024) Spatial transcriptomics (ST) measures mRNA expression across thousands of spots from a tissue slice while recording the two-dimensional (2D) coordinates of each May 29, 2024 · Set plot background to black. ai wu rx xw nt xl ug lb cs vq