- Seurat data visualization. Software tools for the joint analysis of such high dimensional data sets together with clinical data are required. Default is the set of variable genes (VariableFeatures(object = object)) dims. Results We have developed an open source software tool which provides interactive visualization This is done using gene. This vignette introduces the WNN workflow for the analysis of multimodal single-cell datasets. We’ll do this separately for erythroid and lymphoid lineages, but you could explore other strategies building a trajectory for all lineages together. - erilu/single-cell-rnaseq-analysis Oct 31, 2023 · In ( Hao*, Hao* et al, Cell 2021 ), we introduce ‘weighted-nearest neighbor’ (WNN) analysis, an unsupervised framework to learn the relative utility of each data type in each cell, enabling an integrative analysis of multiple modalities. In Seurat v5, we introduce new infrastructure and methods to analyze, interpret, and explore these exciting datasets. Seurat v4 also includes additional functionality for the analysis, visualization, and integration of multimodal datasets. 3 process; 2. pal. rpca) that aims to co-embed shared cell types across batches: We created SeuratData in order to distribute datasets for Seurat vignettes in as painless and reproducible a way as possible. 4 v1. Here we compute a measure of how well mixed a composite dataset is. Analyzing datasets of this size with standard workflows can In Seurat v5, we introduce new infrastructure and methods to analyze, interpret, and explore these exciting datasets. combine. Assay to use for the analysis. v5 <- CreateAssay5Object (data = log1p (pbmc. It also provides plots for the visualization of gene expression at the cell level. While the analytical pipelines are similar to the Seurat workflow for single-cell RNA-seq analysis, we introduce updated interaction and visualization tools, with a particular emphasis on the integration of spatial and molecular information. We also allow users to add the results of a custom dimensional reduction technique (for example, multi-dimensional scaling (MDS), or zero Mar 3, 2024 · 1 Overview. This tutorial demonstrates how to use Seurat (>=3. 3 Analysis, visualization, and integration of spatial datasets with Seurat v4. Oct 31, 2023 · In Seurat v4, we have substantially improved the speed and memory requirements for integrative tasks including reference mapping, and also include new functionality to project query cells onto a previously computed UMAP visualization. A few QC metrics commonly used by the community include. The main modules are described in Fig. integrated. For full details, please read our tutorial. 0 SCTransform v2 v4. "counts" or "data") layer. 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. Conclusions: The SEURAT software meets the growing needs of researchers 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). Now that we have performed our initial Cell level QC, and removed potential outliers, we can go ahead and normalize the data. This should be done if the same normalization approach was applied to all objects. Additional functions allow the user to change the bin-width of Mar 20, 2024 · By default, merge() will combine the Seurat objects based on the raw count matrices, erasing any previously normalized and scaled data matrices. Oct 2, 2020 · This tutorial demonstrates how to use Seurat (>=3. Gene expression data can be analyzed together with associated clinical data, array CGH (comparative genomic hybridization), SNP array (single nucleotide polymorphism) data and available gene Compiled: January 11, 2022. FilterSlideSeq() Filter stray beads from Slide-seq puck. Aug 11, 2022 · Seurat data visualization. Mar 23, 2023 · Overview. stack. 4. We recently released Azimuth ATAC, which uses the bridge integration methodology introduced in Hao, et al 2022. Name of dimension reduction to use. Jun 24, 2021 · (A-E) Benchmarking of Seurat v4 reference-based mapping with scArches. # create an assay using only normalized data assay. 0 - Satija Lab A guide for analyzing single-cell RNA-seq data using the R package Seurat. DietSeurat() Slim down a Seurat object. cells. 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. Option to display pathway enrichments for both negative and positive DE genes. baseplot <- DimPlot (pbmc3k. gene, peak) is frequently affected and unclear, especially when it is overlaid with clustering to annotate cell types. CreateSCTAssayObject() Create a SCT Assay object. Analyzing datasets of this size with standard workflows can The loom format is a file structure imposed on HDF5 files designed by Sten Linnarsson’s group. Mar 20, 2024 · In this vignette, we introduce a Seurat extension to analyze new types of spatially-resolved data. Overview. Cells are label by the annotation that was transferred using each method. 4 Using sctransform in Seurat v4. To use, simply make a ggplot2-based scatter plot (such as DimPlot() or FeaturePlot() ) and pass the resulting plot to HoverLocator() Jun 3, 2010 · All graphics are dynamic and fully linked so that any object selected in a graphic will be highlighted in all other graphics. 0 v2. 1 v3. 0 v4. This interactive plotting feature works with any ggplot2-based scatter plots (requires a geom_point layer). This interactive plotting feature works with any ggplot2-based scatter plots (requires a `geom_point` layer). Both methods utilize reference datasets to assist in the interpretation of query data. Mar 25, 2024 · Existing Seurat workflows for clustering, visualization, and downstream analysis have been updated to support both Visium and Visium HD data. 2 New data visualization methods in v3. For more information, please explore the resources below: Defining cellular identity from multimodal data using WNN analysis in Seurat v4 vignette; Mapping scRNA-seq data onto CITE-seq references vignette Expression visualization¶ Asc-Seurat provides a variety of plots for gene expression visualization of the integrated data. These methods first identify cross-dataset pairs of cells that are in a matched biological state (‘anchors’), can be used both to correct for technical differences between datasets (i. Ask Question Asked 1 year, 6 months ago. Asc-Seurat also implements BioMart for functional annotation and GO term enrichment analysis. Inspired by methods in Goltsev et al, Cell 2018 and He et al, NBT 2022, we consider the ‘local neighborhood’ for each cell Oct 31, 2023 · Seurat allows you to easily explore QC metrics and filter cells based on any user-defined criteria. We also wanted to give users the flexibility to selectively install and load datasets of interest, to minimize disk storage and memory use. In this vignette, we demonstrate how to use a previously established reference to interpret an scRNA-seq query: Seurat object. Identifying cell type-specific peaks. Asc-Seurat is a modular web application implemented using R language and user interface provided by the Shiny framework [] and R []. info The cell meta-data is taken from obj@meta. The method currently supports five integration methods. 4 Violin plots to check; 5 Scrublet Doublet Validation. Mar 20, 2024 · Seurat utilizes R's plotly graphing library to create interactive plots. The annotations are stored in the seurat_annotations field, and are provided as input to the refdata parameter. 0, Asc-Seurat also provides the capacity of generating dot plots and “stacked violin plots” comparing multiple genes. column option; default is ‘2,’ which is gene symbol. To compute, we first examine the local neighborhood for each cell (looking at max. Due to the sparsity observed in single-cell data (e. Jun 3, 2010 · Clinical data and available gene annotations can be selected within the data manager window. Nebulosa is an R package to visualize data from single cells based on kernel density estimation. a gene name - "MS4A1") A column name from meta. If you use Seurat in your research, please considering As high-throughput sequencing applications continue to evolve, the rapid growth in quantity and variety of sequence-based data calls for the development of new software libraries and tools for data analysis and visualization. We note that Visium HD data is generated from spatially patterned olignocleotides labeled in 2um x 2um bins. Features can come from: An Assay feature (e. 2 input data; 2. This tutorial implements the major components of a standard unsupervised clustering workflow including QC and data filtration, calculation of Oct 31, 2023 · Annotate scATAC-seq cells via label transfer. assay. Azimuth ATAC. Let’s first take a look at how many cells and genes passed Quality Control (QC). This is then natural-log transformed using log1p. A multimodal bridge dataset, measuring both scRNA-seq and scATAC-seq data per cell, is used to transfer annotations from our high quality RNA references to an ATAC query. e. '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. Oct 31, 2023 · Seurat v5 enables streamlined integrative analysis using the IntegrateLayers function. Cell class identity 2. Feature counts for each cell are divided by the Jan 17, 2024 · In this vignette, we use sctransform v2 based workflow to perform a comparative analysis of human immune cells (PBMC) in either a resting or interferon-stimulated state. The ability to save Seurat objects as loom files is implemented in SeuratDisk For more details about the loom format, please see the loom file format specification. If you want to merge the normalized data matrices as well as the raw count matrices, simply pass merge. Vector of cells to plot (default is all cells) cols. Vector of features to plot. Visualizing ‘pseudo-bulk’ coverage tracks. (A-B) UMAP visualizations of reference-based mapping of a human PBMC CITE-seq dataset from Kotliarov et al. final, reduction = "umap") # Add custom labels and titles baseplot + labs (title = "Clustering of 2,700 PBMCs") Introductory Vignettes. “ RC ”: Relative counts. Each of these methods performs integration in low-dimensional space, and returns a dimensional reduction (i. To test for DE genes between two specific groups of cells, specify the ident. If set, tree is calculated in dimension reduction space; overrides features. R. SERUAT provides a "Loadings Settings" menu Feb 28, 2024 · Analysis of single-cell RNA-seq data from a single experiment. graph Oct 2, 2023 · Two major visualizations for this data type are tSNE and UMAP. The results data frame has the following columns : avg_log2FC : log fold-change of the average expression between the two groups. Introductory Vignettes. Limit testing to genes which show, on average, at least X-fold difference (log-scale) between the two groups of cells. Most functions now take an assay parameter, but you can set a Default Assay to avoid repetitive statements. idents. Instead of utilizing canonical correlation analysis (‘CCA’) to identify anchors, we instead utilize reciprocal PCA (‘RPCA’). With Seurat, all plotting functions return ggplot2-based plots by default, allowing one to easily capture and manipulate plots just like any other ggplot2-based plot. Source: R/integration. In this vignette, we introduce a sketch-based analysis workflow to analyze a 1. rna) # Add ADT data cbmc[["ADT Seurat object. Slot to pull expression data from (e. Using an rds file containing the clustered data as input, users must provide a csv or tsv file in the same format described in the expression visualization section. 3 Add other meta info; 4. We can convert the Seurat object to a CellDataSet object using the as. 1 Normalize, scale, find variable genes and dimension reduciton; II scRNA-seq Visualization; 4 Seurat QC Cell-level Filtering. DR algorithms are widely used for Method for normalization. data (e. A toolkit for quality control, analysis, and exploration of single cell RNA sequencing data. fill. Jun 14, 2021 · In the central panel, click galaxy-chart-select-data Datatypes tab on the top; In the galaxy-chart-select-data Assign Datatype, select tabular from “New type” dropdown . Viewed 162 times 0 Hi I am using public data pbmc to practice SEURAT is a software tool which provides interactive visualization capability for the integrated analysis of high-dimensional gene expression data. Each of these module network plots are colored based on the color column in the hdWGCNA module assignment table GetModules(seurat_obj). Dynverse allows the evaluation and visualization of developmental trajectories and identifies DEGs on these trajectories. When determining anchors between any two datasets using RPCA, we project each Sep 26, 2019 · The increasing accessibility of single cell RNA sequencing demands tools that enable data visualization and interpretation. 25. factor. It is designed to efficiently hold large single-cell genomics datasets. Seurat wrapper for enhanced processing and visualization of scRNA-seq data. Modified 1 year, 4 months ago. Seurat object summary shows us that 1) number of cells (“samples”) approximately matches the description of each dataset (10194); 2) there are 36601 genes (features) in the reference. 4 Weighted Mar 23, 2022 · We provide here the Seurat data objects containing each of the cluster analyses as R data files Data visualization. Dimensional reduction, visualization, and clustering. To use, simply make a ggplot2-based scatter plot (such as `DimPlot()` or `FeaturePlot()`) and pass the resulting plot to `HoverLocator()` Seurat: Tools for Single Cell Genomics Description. 1038/nbt. 2). “ LogNormalize ”: Feature counts for each cell are divided by the total counts for that cell and multiplied by the scale. After finding anchors, we use the TransferData() function to classify the query cells based on Apr 4, 2024 · Building trajectories with Monocle 3. 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. 2 v3. Using Seurat, users explore scRNA-seq data to identify cell types, markers, and DEGs. Here, we extend this framework to analyze new data types that are captured via highly In data transfer, Seurat has an option (set by default) to project the PCA structure of a reference onto the query, instead of learning a joint structure with CCA. 2 parameters. The output will contain a matrix with predictions and confidence scores for Visualization. 2 Load seurat object; 4. The number of unique genes detected in each cell. All cell groups with less than this expressing the given gene will have no dot drawn. Low-quality cells or empty droplets will often have very few genes. The work highlights the use of UMAP for improved visualization and Slot to pull expression data from (e. e the Seurat object pbmc_10x_v3. mito") A column name from a DimReduc object corresponding to the cell embedding values (e. We can also convert (cast) between Assay and Assay5 objects with as(). Cell class identity 1. Data visualization vignette; SCTransform, v2 regularization; Using Seurat with multi-modal data; Seurat v5 Command Cheat Sheet; Data Integration; Introduction to scRNA-seq integration; Integrative analysis in Seurat v5; Mapping and annotating query datasets; Multi-assay data; Dictionary Learning for cross-modality integration; Weighted Nearest Name of object class Seurat. Scillus can be used in two ways. Starting on v2. Factor to group the cells by. Combine plots into a single patchworked ggplot object. The commonly used resolution ranges between 0. scale. Cells( <SCTModel>) Cells( <SlideSeq>) Cells( <STARmap>) Cells( <VisiumV1>) Get Cell Names. ⓘ Count matrix in Seurat A count matrix from a Seurat object Mar 27, 2023 · The standard Seurat workflow takes raw single-cell expression data and aims to find clusters within the data. plot each group of the split violin plots by multiple or single violin shapes. counts)) # create a Seurat object based on this assay pbmc3k_slim <- CreateSeuratObject (assay. In this vignette we apply sctransform-v2 based normalization to perform the following tasks: Create an ‘integrated’ data assay for downstream analysis. If false, only positive DE gene will be displayed. 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. Scale the size of the points, similar to cex. To visualize the metadata after each processing step inspectdf (ver) [ref] was used. The top 10 hub genes by kME are placed in the center of the plot, while the remaining 15 genes are placed in the outer circle. rna) # Add ADT data cbmc[["ADT Oct 31, 2023 · This can be used to create Seurat objects that require less space. Detailed information about each file and the variables stored can be accessed with a click on the name of the respective dataset. 1 Description; 4. plot. Horizontally stack plots for each feature. cca) which can be used for visualization and unsupervised clustering analysis. Sep 14, 2023 · Seurat provides RunPCA() (pca), and RunTSNE() (tsne), and representing dimensional reduction techniques commonly applied to scRNA-seq data. “ CLR ”: Applies a centered log ratio transformation. 1 and ident. When using these functions, all slots are filled automatically. The BridgeReferenceSet Class The BridgeReferenceSet is an output from PrepareBridgeReference. Genes to use for the analysis. We generally suggest using this option when projecting data between scRNA-seq datasets. Using Seurat with multi-modal data v4. To accomplish this, we opted to distribute datasets through individual R Mar 27, 2023 · Seurat v4 includes a set of methods to match (or ‘align’) shared cell populations across datasets. Seurat v5 is backwards-compatible with previous versions, so that users will continue to be able to re-run Mar 20, 2024 · The fraction of cells at which to draw the smallest dot (default is 0). 3 v3. Seurat - Interaction Tips Seurat - Combining Two 10X Runs Mixscape Vignette Multimodal reference mapping Using Seurat with multimodal data Seurat - Guided Clustering Tutorial Introduction to SCTransform, v2 regularization Using sctransform in Seurat Sketch-based analysis in Seurat v5 Analysis, visualization, and integration of spatial datasets Signac is an R toolkit that extends Seurat for the analysis, interpretation, and exploration of single-cell chromatin datasets. Vector of colors, each color corresponds to an identity class. Inspired by methods in Goltsev et al, Cell 2018 and He et al, NBT 2022, we consider the ‘local neighborhood’ for each cell Oct 31, 2023 · In Seurat v4, we have substantially improved the speed and memory requirements for integrative tasks including reference mapping, and also include new functionality to project query cells onto a previously computed UMAP visualization. If you already have a Seurat object constructed by data integration and would like to try enhanced plotting functions, you can directly go to Plotting after Seurat is an R toolkit for single cell genomics, developed and maintained by the Satija Lab at NYGC. dot. data; Lower-dimensional visualizations are taken each dimensionality reduction in Reductions(obj) These are added using their original names prefixed with “Seurat_” If “pca” has been run, the latentSpace input is taken from its associated cell embeddings By default, Seurat performs differential expression (DE) testing based on the non-parametric Wilcoxon rank sum test. 2) to analyze spatially-resolved RNA-seq data. the PC 1 scores - "PC_1") dims Introductory Vignettes. The dSP pipeline with all its tools is designed to provide a reproducible, almost automatic, workflow that goes from raw reads (FASQ files) to basic data visualization. Jul 19, 2022 · Dimension reduction (DR) algorithms project data from high dimensions to lower dimensions to enable visualization of interesting high-dimensional structure. For tSNE, the parameter perplexity can be changed to best represent the data, while for UMAP the main change would be to change the kNN graph above itself, via the FindNeighbors() function. This tutorial implements the major components of a standard unsupervised clustering workflow including QC and data filtration, calculation of 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. By launching SEURAT the data manager window will appear: The data manager displays the different datasets and the corresponding variables loaded into SEURAT. We can calculate the coordinates for both prior to visualization. However, since the data from this resolution is sparse, adjacent bins are pooled together to May 11, 2021 · Combining dropSeqPipe (dSP) for pre-processing with Seurat for post-processing offers full control over data analysis and visualization. rpca) that aims to co-embed shared cell types across batches: 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. Vision augments the functionality of toolkits like Seurat 45, Scanpy Seurat part 3 – Data normalization and PCA. Seurat is an R package designed for QC, analysis, and exploration of single-cell RNA-seq data. cbmc <- CreateSeuratObject (counts = cbmc. by. g. After identifying anchors, we can transfer annotations from the scRNA-seq dataset onto the scATAC-seq cells. v5) pbmc3k_slim. 4 output; 3 Seurat Pre-process Filtering Confounding Genes. 3192 , Macosko E, Basu A, Satija R, et al (2015) doi:10. Dimensions to plot, must be a two-length numeric vector specifying x- and y-dimensions. 1 and encapsulate several analytical procedures including: (1) the algorithmic capabilities of Seurat for cell clustering, differential expression analysis, and expression visualization; (2) Dynverse functionalities A toolkit for quality control, analysis, and exploration of single cell RNA sequencing data. 5. This may also be a single character or numeric value corresponding to a palette as specified by brewer. major cell types), or the smaller but finer cell groups are returned (e. cell subtypes). See Satija R, Farrell J, Gennert D, et al (2015) doi:10. After this, we will make a Seurat object. Let’s start with a simple case: the data generated using the the 10x Chromium (v3) platform (i. Dec 3, 2018 · Here we apply it to biological data, using three well-characterized mass cytometry and single-cell RNA sequencing datasets. batch effect correction), and to perform comparative Users can individually annotate clusters based on canonical markers. Only used if dims is not NULL. This tutorial implements the major components of a standard unsupervised clustering workflow including QC and data filtration, calculation of Nov 18, 2021 · Overview. reduction. About Seurat. This process consists of data normalization and variable feature selection, data scaling, a PCA on variable features, construction of a shared-nearest-neighbors graph, and clustering using a New data visualization methods in v3. data = TRUE. dims. Nature 2019. SEURAT is a software tool which provides interactive visualization capability for the integrated analysis of high-dimensional gene expression data. 4 Guided tutorial — 2,700 PBMCs v4. Seurat can Nov 18, 2021 · Asc-Seurat is built on three analytical cores. If FALSE, return a list of ggplot. For the visualization of expression, multimodal [X] data and calculated metadata like module scores, the Seurat functions RidgePlot for ridge plots, VlnPlot for violin plots, DotPlot for dot plots and DoHeatmap for heatmaps were used. k neighbors) and determine for each group (could be the dataset after integration) the k nearest neighbor and what rank that neighbor was in Data manager. Default is 0. The software supports the following features: Calculating single-cell QC metrics. The method returns a dimensional reduction (i. By default, Seurat implements a global-scaling normalization method “LogNormalize” that normalizes the gene expression measurements for each cell by the total expression Data visualization vignette; SCTransform, v2 regularization; Using Seurat with multi-modal data; Seurat v5 Command Cheat Sheet; Data Integration; Introduction to scRNA-seq integration; Integrative analysis in Seurat v5; Mapping and annotating query datasets; Multi-assay data; Dictionary Learning for cross-modality integration; Weighted Nearest Jun 3, 2010 · Background In translational cancer research, gene expression data is collected together with clinical data and genomic data arising from other chip based high throughput technologies. Ternary plot visualization was performed as previously described 15. Tip: you can start typing the datatype into the field to filter the dropdown menu; Click the Save button; Click on the galaxy-eye (eye) icon and take a look at the DE Data visualization vignette; SCTransform, v2 regularization; Using Seurat with multi-modal data; Seurat v5 Command Cheat Sheet; Data Integration; Introduction to scRNA-seq integration; Integrative analysis in Seurat v5; Mapping and annotating query datasets; Multi-assay data; Dictionary Learning for cross-modality integration; Weighted Nearest Seurat object. From a list of selected genes, it is possible to visualize the average of each gene expression in each cluster in a heatmap. We are excited to release Seurat v5! This updates introduces new functionality for spatial, multimodal, and scalable single-cell analysis. "counts" or "data") stack. Applying themes to plots. RNA-seq, ATAC-seq), the visualization of cell features (e. 3 million cell dataset of the developing mouse brain, freely available from 10x Genomics. Go from raw data to cell clustering, identifying cell types, custom visualizations, and group-wise analysis of tumor infiltrating immune cells using data from Ishizuka et al. Seurat utilizes R's plotly graphing library to create interactive plots. "counts" or "data") split. (2020). mitochondrial percentage - "percent. and demonstrated in this vignette. 1 and 1, and which is the best option largely depends on the aim of the analysis. Identity classes to include in plot (default is all) group. In this vignette, we present a slightly modified workflow for the integration of scRNA-seq datasets. Optionally, certain visualization parameters can be changed in this plot: Data visualization vignette; SCTransform, v2 regularization; Using Seurat with multi-modal data; Seurat v5 Command Cheat Sheet; Data Integration; Introduction to scRNA-seq integration; Integrative analysis in Seurat v5; Mapping and annotating query datasets; Multi-assay data; Dictionary Learning for cross-modality integration; Weighted Nearest . However, the sctransform normalization reveals sharper biological distinctions compared to the standard Seurat workflow, in a few ways: # These are now standard steps in the Seurat workflow for visualization and clustering # Visualize canonical marker genes as violin plots. Oct 31, 2023 · In ( Hao*, Hao* et al, Cell 2021 ), we introduce ‘weighted-nearest neighbor’ (WNN) analysis, an unsupervised framework to learn the relative utility of each data type in each cell, enabling an integrative analysis of multiple modalities. We have previously introduced a spatial framework which is compatible with sequencing-based technologies, like the 10x Genomics Visium system, or SLIDE-seq. If you want to start from raw data, you should browse all sections below. Layer to pull expression data from (e. Color violins/ridges based on either 'feature' or 'ident' 2. cell_data_set() function from SeuratWrappers and build the trajectories using Monocle 3. 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). 3. Here, the resolution parameter is used to control whether the major and coarsed cell groups (e. Calculates a mixing metric. For exploratory data analysis the software provides unsupervised data analytics like clustering, seriation algorithms and biclustering algorithms. features. SEURAT automatically distinguishes between categorical and continuous data and the associated information is presented in either interactive barcharts or histograms (Figure (Figure2). In this vignette, we demonstrate how to use a previously established reference to interpret an scRNA-seq query: Apr 19, 2024 · 1 Overview. id iz qo rl zz xd yt zl yn yn