updated docs to match website versions

MustafaElshani · Nov 14, 2023 · c68a678 · c68a678
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diff --git a/_pkgdown.yaml b/_pkgdown.yaml
@@ -14,70 +14,72 @@ navbar:
   title: "Seurat"
   left:
     - text: "Install"
-      href: articles/install.html
-    - text: "Seurat v5"
-      href: articles/get_started_v5.html
+      href: articles/install_v5.html
     - text: "Get started"
-      href: articles/get_started.html
+      href: articles/get_started_v5_new.html
     - text: "Vignettes"
       menu:
         - text: Introductory Vignettes
         - text: "PBMC 3K guided tutorial"
           href: articles/pbmc3k_tutorial.html
+        - text: "Data visualization vignette"
+          href: articles/visualization_vignette.html
+        - text: "SCTransform, v2 regularization"
+          href: articles/sctransform_vignette.html
         - text: "Using Seurat with multi-modal data"
           href: articles/multimodal_vignette.html
+        - text: "Seurat v5 Command Cheat Sheet"
+          href: articles/essential_commands.html
         - text: -------
         - text: Data Integration
         - text: "Introduction to scRNA-seq integration"
           href: articles/integration_introduction.html
+        - text: "Integrative analysis in Seurat v5"
+          href: articles/seurat5_integration.html
         - text: "Mapping and annotating query datasets"
           href: articles/integration_mapping.html
-        - text: "Fast integration using reciprocal PCA (RPCA)"
-          href: articles/integration_rpca.html
-        - text: "Tips for integrating large datasets"
-          href: articles/integration_large_datasets.html
-        - text: "Integrating scRNA-seq and scATAC-seq data"
-          href: articles/atacseq_integration_vignette.html
-        - text: "Multimodal reference mapping"
-          href: articles/multimodal_reference_mapping.html
         - text: -------
-        - text: New Statistical Methods
+        - text: Multi-assay data
+        - text: "Dictionary Learning for cross-modality integration"
+          href: articles/seurat5_integration_bridge.html
         - text: "Weighted Nearest Neighbor Analysis"
           href: articles/weighted_nearest_neighbor_analysis.html
+        - text: "Integrating scRNA-seq and scATAC-seq data"
+          href: articles/seurat5_atacseq_integration_vignette.html
+        - text: "Multimodal reference mapping"
+          href: articles/multimodal_reference_mapping.html
         - text: "Mixscape Vignette"
           href: articles/mixscape_vignette.html
-        - text: "Using sctransform in Seurat"
-          href: articles/sctransform_vignette.html
-        - text: "SCTransform, v2 regularization"
-          href: articles/sctransform_v2_vignette.html
+        - text: -------
+        - text: Massively scalable analysis
+        - text: "Sketch-based analysis in Seurat v5"
+          href: articles/seurat5_sketch_analysis.html
+        - text: "Sketch integration using a 1 million cell dataset from Parse Biosciences"
+          href: articles/ParseBio_sketch_integration.html
+        - text: "Map COVID PBMC datasets to a healthy reference"
+          href: articles/COVID_SCTMapping.html
+        - text: "BPCells Interaction"
+          href: articles/seurat5_bpcells_interaction_vignette.html
+        - text: -------
+        - text: Spatial analysis
+        - text: "Analysis of spatial datasets (Imaging-based)"
+          href: articles/seurat5_spatial_vignette_2.html
+        - text: "Analysis of spatial datasets (Sequencing-based)"
+          href: articles/spatial_vignette.html
         - text: -------
         - text: Other
-        - text: "Data visualization vignette"
-          href: articles/visualization_vignette.html
         - text: "Cell-cycle scoring and regression"
           href: articles/cell_cycle_vignette.html
         - text: "Differential expression testing"
           href: articles/de_vignette.html
         - text: "Demultiplexing with hashtag oligos (HTOs)"
           href: articles/hashing_vignette.html
-        - text: "Interoperability between single-cell object formats"
-          href: articles/conversion_vignette.html
-        - text: "Parallelization in Seurat with future"
-          href: articles/future_vignette.html
-        - text: "Dimensional reduction vignette"
-          href: articles/dim_reduction_vignette.html
-        - text: "Seurat essential commands list"
-          href: articles/essential_commands.html
-        - text: "Seurat interaction tips"
-          href: articles/interaction_vignette.html
-        - text: "Merging Seurat objects"
-          href: articles/merge_vignette.html
     - text: Extensions
       href: articles/extensions.html
     - text: FAQ
       href: "https://github.com/satijalab/seurat/discussions"
     - text: "News"
-      href: news/index.html
+      href: articles/announcements.html
     - text: "Reference"
       href: reference/index.html
     - text: "Archive"
@@ -157,6 +159,16 @@ reference:
 - contents:
   - has_concept("convenience")
 
+- title: "Multimodal"
+  desc: "Functions for multimodal analysis"
+- contents:
+  - has_concept("multimodal")
+
+- title: "Re-exports"
+  desc: "Functions for flexible analysis of massively scalable datasets"
+- contents:
+  - has_concept("sketching")
+
 - title: "Re-exports"
   desc: "Functions re-exported from other packages"
 - contents:

diff --git a/index.md b/index.md
@@ -1,14 +1,9 @@
 ![](articles/assets/seurat_banner.jpg)
 
-## **Beta release of Seurat v5**
+## **Seurat v5**
 
-We are excited to release an initial beta version of Seurat v5! This update brings the following new features and functionality:
+We are excited to release Seurat v5! To install, please follow the instructions in our [install page](install.html). This update brings the following new features and functionality:
 
-* **Analysis of sequencing and imaging-based spatial datasets:** Spatially resolved datasets are redefining our understanding of cellular interactions and the organization of human tissues. Both sequencing-based(i.e. Visium, SLIDE-seq, etc.), and imaging-based (MERFISH/Vizgen, Xenium, CosMX, etc.) technologies have unique advantages, and require tailored analytical methods and software infrastructure. In Seurat v5, we introduce flexible and diverse support for a wide variety of spatially resolved data types, and support for analytical techniqiues for scRNA-seq integration, deconvolution, and niche identification.
-
-    - Vignette: [Analysis of spatial datasets (Sequencing-based)](articles/seurat5_spatial_vignette.html)
-    - Vignette: [Analysis of spatial datasets (Imaging-based)](articles/seurat5_spatial_vignette_2.html)\
-\
 * **Integrative multimodal analysis:** The cellular transcriptome is just one aspect of cellular identity, and recent technologies enable routine profiling of chromatin accessibility, histone modifications, and protein levels from single cells. In Seurat v5, we introduce 'bridge integration', a statistical method to integrate experiments measuring different modalities (i.e. separate scRNA-seq and scATAC-seq datasets), using a separate multiomic dataset as a molecular 'bridge'. For example, we demonstrate how to map scATAC-seq datasets onto scRNA-seq datasets, to assist users in interpreting and annotating data from new modalities.\
 \
 We recognize that while the goal of matching shared cell types across datasets may be important for many problems, users may also be concerned about which method to use, or that integration could result in a loss of biological resolution. In Seurat v5, we also introduce flexible and streamlined workflows for the integration of multiple scRNA-seq datasets. This makes it easier to explore the results of different integration methods, and to compare these results to a workflow that excludes integration steps.
@@ -28,15 +23,24 @@ We enable high-performance via the BPCells package, developed by Ben Parks in th
     - Vignette: [Interacting with BPCell matrices in Seurat v5](articles/seurat5_bpcells_interaction_vignette.html)
     - BPCells R Package: [Scaling Single Cell Analysis to Millions of Cells](https://bnprks.github.io/BPCells/)\
 \
-* **Backwards compatibility:** While Seurat v5 introduces new functionality, we have ensured that the software is backwards-compatible with previous versions, so that users will continue to be able to re-run existing workflows. As v5 is still in beta, the CRAN installation (`install.packages("Seurat")`) will continue to install Seurat v4, but users can opt-in to test Seurat v5 by following the instructions in our [install page](install.html).\
+* **Analysis of sequencing and imaging-based spatial datasets:** Spatially resolved datasets are redefining our understanding of cellular interactions and the organization of human tissues. Both sequencing-based(i.e. Visium, SLIDE-seq, etc.), and imaging-based (MERFISH/Vizgen, Xenium, CosMX, etc.) technologies have unique advantages, and require tailored analytical methods and software infrastructure. In Seurat v5, we introduce flexible and diverse support for a wide variety of spatially resolved data types, and support for analytical techniqiues for scRNA-seq integration, deconvolution, and niche identification.
+
+    - Vignette: [Analysis of spatial datasets (Sequencing-based)](articles/seurat5_spatial_vignette.html)
+    - Vignette: [Analysis of spatial datasets (Imaging-based)](articles/seurat5_spatial_vignette_2.html)\
+\
+* **Backwards compatibility:** While Seurat v5 introduces new functionality, we have ensured that the software is backwards-compatible with previous versions, so that users will continue to be able to re-run existing workflows. Previous versions of Seurat, such as Seurat v4, can also be installed following the instructions in our [install page](install.html).\
+
+## **Changes between v4 and v5**
+
+We have documented major changes between Seurat v4 and v5 in our [News page](announcements.html) for reference. 
 
 ## **About Seurat**
 
 Seurat is an R package designed for QC, analysis, and exploration of single-cell RNA-seq data. 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.
 
 If you use Seurat in your research, please considering citing:
 
-* [Hao, et al., bioRxiv 2022](https://doi.org/10.1101/2022.02.24.481684) [Seurat v5]
+* [Hao, et al., Nature Biotechnology 2023](https://www.nature.com/articles/s41587-023-01767-y) [Seurat v5]
 * [Hao\*, Hao\*, et al., Cell 2021](https://doi.org/10.1016/j.cell.2021.04.048) [Seurat v4]
 * [Stuart\*, Butler\*, et al., Cell 2019](https://www.cell.com/cell/fulltext/S0092-8674(19)30559-8) [Seurat v3]
 * [Butler, et al., Nat Biotechnol 2018](https://doi.org/10.1038/nbt.4096) [Seurat v2]

diff --git a/vignettes/COVID_SCTMapping.Rmd b/vignettes/COVID_SCTMapping.Rmd
@@ -56,7 +56,7 @@ We first load the reference (available [here](https://zenodo.org/record/7779017#
 
 ```{r load.data}
 reference <- readRDS("/brahms/hartmana/vignette_data/pbmc_multimodal_2023.rds")
-object <- readRDS("/brahms/hartmana/vignette_data/merged_covid_object.rds")
+object <- readRDS("/brahms/mollag/seurat_v5/vignette_data/merged_covid_object.rds")
 object <- NormalizeData(object, verbose = FALSE)
 ```
 

diff --git a/vignettes/ParseBio_sketch_integration.Rmd b/vignettes/ParseBio_sketch_integration.Rmd
@@ -52,7 +52,6 @@ library(ggrepel)
 library(patchwork)
 # set this option when analyzing large datasets
 options(future.globals.maxSize = 3e9)
-options(Seurat.object.assay.version = "v5")
 ```
 ## Create a Seurat object containing data from 24 patients
 We downloaded the original dataset and donor metadata from [Parse Biosciences](https://cdn.parsebiosciences.com/1M_PBMC_T1D_Parse.zip). While the BPCells package can work directly with h5ad files, for optimal performance, we converted the dataset to the compressed sparse format used by BPCells, as described [here](seurat5_bpcells_interaction_vignette.html).
@@ -117,7 +116,6 @@ plot.s1 + plot.s2 + plot_layout(ncol = 1)
 Now that we have integrated the subset of atoms of each dataset, placing them each in an integrated low-dimensional space, we can now place each cell from each dataset in this space as well. We load the full datasets back in individually, and use the `ProjectIntegration` function to integrate all cells. After this function is run, the `integrated.rpca.full` space now embeds all cells in the dataset.Even though all cells in the dataset have been integrated together, the non-sketched cells are not loaded into memory. Users can still switch between the `sketch` (sketched cells, in-memory) and `RNA` (full dataset, on disk) for analysis. After integration, we can also project cell type labels from the sketched cells onto the full dataset using `ProjectData`.
 
 ```{r}
-
 # resplit the sketched cell assay into layers
 # this is required to project the integration onto all cells
 object[['sketch']] <- split(object[['sketch']], f = object$sample)
@@ -159,7 +157,8 @@ After we aggregate cells, we can perform celltype-specific differential expressi
 ```{r}
 bulk <- AggregateExpression(object, return.seurat = T, slot = 'counts',
             assays  = 'RNA', group.by = c("celltype.full","sample", 'disease'))
-
+```
+```{r}
 # each sample is an individual-specific celltype-specific pseudobulk profile
 tail(Cells(bulk))
 
@@ -182,8 +181,6 @@ We do not necessarily expect to see a strong transcriptomic signature of diabete
 ```{r,height = 12, width=6}
 # each dot represents a pseudobulk average from an individual
 VlnPlot(bulk, features = c("C1R"),group.by = 'celltype', split.by = 'disease', cols = c('#377eb8','#e41a1c'))
-
-
 ```
 
 

diff --git a/vignettes/announcements.Rmd b/vignettes/announcements.Rmd
@@ -0,0 +1,50 @@
+---
+title: "News"
+output:
+  html_document:
+    theme: united
+    df_print: kable
+---
+
+## **Changes in Seurat v5**
+
+We are excited to release Seurat v5 on CRAN, where it is now the default version for new installs. Seurat v5 is designed to be backwards compatible with Seurat v4 so existing code will continue to run, but we have made some changes to the software that will affect user results. We note that users who aim to reproduce their previous workflows in Seurat v4 can still install this version using the instructions on our [install page](link).
+
+In particular, we have made changes to:
+
+* **Seurat Object and Assay class:** 
+\
+Seurat v5 now includes support for additional assay and data types, including on-disk matrices. To facilitate this, we have introduced an updated Seurat v5 assay. Users can check out this [vignette for more information]. Briefly, Seurat v5 assays store data in layers (previously referred to as 'slots'). 
+
+    For example, these layers can store: raw counts `(layer='counts')`, normalized data `(layer='data')`, or z-scored/variance-stabilized data `(layer='scale.data')`. 
+
+    Data can be accessed using the `$` accessor (i.e. `obj[["RNA"]]$counts`), or the ``LayerData` function (i.e. `LayerData(obj, assay="RNA", layer='counts')`.
+\
+
+    We've designed these updates to minimize changes for users. Existing Seurat functions and workflows from v4 continue to work in v5. For example, the command `GetAssayData(obj, assay="RNA", slot='counts')`, will run successfully in both Seurat v4 and Seurat v5.
+
+
+* **Integration workflow:** 
+\
+Seurat v5 introduces a [streamlined integration](integration_introduction.html) and [data transfer](intregration_mapping.html) workflows that performs integration in low-dimensional space, and improves speed and memory efficiency.  The results of integration are not identical between the two workflows, but users can still run the [v4 integration workflow](integration_introduction.html) in Seurat v5 if they wish.
+\
+
+    In previous versions of Seurat, the integration workflow required a list of multiple Seurat objects as input. In Seurat v5, all the data can be kept as a single object, but prior to integration, users can simply split the layers. See our [introduction to integration](integration_introduction.html) vignette for more information.
+
+
+* **Differential expression:** 
+\
+Seurat v5 now uses the [presto package](https://github.com/immunogenomics/presto) (from the Korunsky and Raychaudhari labs), when available, to perform differential expression analysis. Using presto can dramatically speed up DE testing, and we encourage users to install it.
+\
+In addition, in Seurat v5 we implement a pseudocount (when calculating log-FC) at the group level instead of the cell level. As a result, users will observe higher logFC estimates in v5 - but should note that these estimates may be more unstable - particularly for genes that are very lowly expressed in one of the two groups. We gratefully acknowledge feedback from the McCarthy and Pachter labs on this topic.
+
+* **SCTransform v2:** 
+\
+In [Choudhary and Satija, Genome Biology, 2022](https://genomebiology.biomedcentral.com/articles/10.1186/s13059-021-02584-9), we implement an updated version 2 of sctransform. This is now the default version when running `SCTransform` in Seurat v5. Users who wish to run the previous workflow can set the `vst.flavor = "v1"` argument in the `SCTransform` function.
+\
+\
+* **Pseudobulk analysis:** 
+\
+Once a single-cell dataset has been analyzed to annotate cell subpopulations, pseudobulk analyses (i.e. aggregating together cells within a given subpopulation and sample) can reduce noise, improve quantification of lowly expressed genes, and reduce the size of the data matrix. In Seurat v5, we encourage the use of the `AggregateExpression` function to perform pseudobulk analysis.
+\
+Check out our [differential expression vignette](de_vignette.html) as well as our [pancreatic/healthy PBMC comparison](ParseBio_sketch_integration.html), for examples of how to use `AggregateExpression` to perform robust differential expression of scRNA-seq data from multiple different conditions.