-
Notifications
You must be signed in to change notification settings - Fork 33
Commit
This commit does not belong to any branch on this repository, and may belong to a fork outside of the repository.
- Loading branch information
1 parent
0127a35
commit 3798470
Showing
6 changed files
with
175 additions
and
38 deletions.
There are no files selected for viewing
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,44 @@ | ||
| ## ----setup, include = FALSE---------------------------------------------- | ||
| library('Matrix') | ||
| library('ggplot2') | ||
| library('reshape2') | ||
| library('sctransform') | ||
| library('knitr') | ||
| knitr::opts_chunk$set( | ||
| collapse = TRUE, | ||
| comment = "#>", | ||
| digits = 2, | ||
| fig.width=8, fig.height=5, dpi=100, out.width = '70%' | ||
| ) | ||
| library('Seurat') | ||
| #old_theme <- theme_set(theme_classic(base_size=8)) | ||
|
|
||
| ## ----eval=FALSE---------------------------------------------------------- | ||
| # devtools::install_github(repo = 'ChristophH/sctransform', ref = 'develop') | ||
| # devtools::install_github(repo = 'satijalab/seurat', ref = 'release/3.0') | ||
| # library(Seurat) | ||
| # library(sctransform) | ||
|
|
||
| ## ----load_data, warning=FALSE, message=FALSE, cache = T------------------ | ||
| pbmc_data <- Read10X(data.dir = "~/Downloads/pbmc3k_filtered_gene_bc_matrices/hg19/") | ||
| pbmc <- CreateSeuratObject(counts = pbmc_data) | ||
|
|
||
| ## ----apply_sct, warning=FALSE, message=FALSE, cache = T------------------ | ||
| # Note that this single command replaces NormalizeData, ScaleData, and FindVariableFeatures. | ||
| # Transformed data will be available in the SCT assay, which is set as the default after running sctransform | ||
| pbmc <- SCTransform(object = pbmc, verbose = FALSE) | ||
|
|
||
| ## ----pca, fig.width=5, fig.height=5, cache = T--------------------------- | ||
| # These are now standard steps in the Seurat workflow for visualization and clustering | ||
| pbmc <- RunPCA(object = pbmc, verbose = FALSE) | ||
| pbmc <- RunUMAP(object = pbmc, dims = 1:20, verbose = FALSE) | ||
| pbmc <- FindNeighbors(object = pbmc, dims = 1:20, verbose = FALSE) | ||
| pbmc <- FindClusters(object = pbmc, verbose = FALSE) | ||
| DimPlot(object = pbmc, label = TRUE) + NoLegend() | ||
|
|
||
| ## ----fplot, fig.width = 10, fig.height=10, cache = F--------------------- | ||
| # These are now standard steps in the Seurat workflow for visualization and clustering | ||
| FeaturePlot(object = pbmc, features = c("CD8A","GZMK","CCL5","S100A4"), pt.size = 0.3) | ||
| FeaturePlot(object = pbmc, features = c("S100A4","CCR7","CD4","ISG15"), pt.size = 0.3) | ||
| FeaturePlot(object = pbmc, features = c("TCL1A","FCER2","XCL1","FCGR3A"), pt.size = 0.3) | ||
|
|
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,68 @@ | ||
| --- | ||
| title: "Using sctransform in Seurat" | ||
| author: "Christoph Hafemeister & Rahul Satija" | ||
| date: '`r Sys.Date()`' | ||
| output: | ||
| html_document: default | ||
| pdf_document: default | ||
| vignette: > | ||
| %\VignetteIndexEntry{Using sctransform in Seurat} | ||
| %\VignetteEngine{knitr::rmarkdown} | ||
| %\VignetteEncoding{UTF-8} | ||
| --- | ||
|
|
||
| ```{r setup, include = FALSE} | ||
| library('Matrix') | ||
| library('ggplot2') | ||
| library('reshape2') | ||
| library('sctransform') | ||
| library('knitr') | ||
| knitr::opts_chunk$set( | ||
| collapse = TRUE, | ||
| comment = "#>", | ||
| digits = 2, | ||
| fig.width=8, fig.height=5, dpi=100, out.width = '70%' | ||
| ) | ||
| library('Seurat') | ||
| #old_theme <- theme_set(theme_classic(base_size=8)) | ||
| ``` | ||
|
|
||
| This vignette shows how to use the sctransform wrapper in Seurat. | ||
| Install sctransform and Seurat v3 | ||
| ```{r eval=FALSE} | ||
| devtools::install_github(repo = 'ChristophH/sctransform', ref = 'develop') | ||
| devtools::install_github(repo = 'satijalab/seurat', ref = 'release/3.0') | ||
| library(Seurat) | ||
| library(sctransform) | ||
| ``` | ||
| Load data and create Seurat object | ||
| ```{r load_data, warning=FALSE, message=FALSE, cache = T} | ||
| pbmc_data <- Read10X(data.dir = "~/Downloads/pbmc3k_filtered_gene_bc_matrices/hg19/") | ||
| pbmc <- CreateSeuratObject(counts = pbmc_data) | ||
| ``` | ||
| Apply sctransform normalization | ||
| ```{r apply_sct, warning=FALSE, message=FALSE, cache = T} | ||
| # Note that this single command replaces NormalizeData, ScaleData, and FindVariableFeatures. | ||
| # Transformed data will be available in the SCT assay, which is set as the default after running sctransform | ||
| pbmc <- SCTransform(object = pbmc, verbose = FALSE) | ||
| ``` | ||
| Perform dimensionality reduction by PCA and UMAP embedding | ||
| ```{r pca, fig.width=5, fig.height=5, cache = T} | ||
| # These are now standard steps in the Seurat workflow for visualization and clustering | ||
| pbmc <- RunPCA(object = pbmc, verbose = FALSE) | ||
| pbmc <- RunUMAP(object = pbmc, dims = 1:20, verbose = FALSE) | ||
| pbmc <- FindNeighbors(object = pbmc, dims = 1:20, verbose = FALSE) | ||
| pbmc <- FindClusters(object = pbmc, verbose = FALSE) | ||
| DimPlot(object = pbmc, label = TRUE) + NoLegend() | ||
| ``` | ||
| Users can individually annotate clusters based on canonical markers. However, the sctransform normalization reveals sharper biological distinctions compared to the [standard Seurat workflow](https://satijalab.org/seurat/pbmc3k_tutorial.html), in a few ways: | ||
| * Clear separation of three CD8 T cell populations (naive, memory, effector), based on CD8A, GZMK, CCL5, GZMK expression | ||
| * Clear separation of three CD4 T cell populations (naive, memory, IFN-activated) based on S100A4, CCR7, IL32, and ISG15 | ||
| * Additional developmental sub-structure in B cell cluster, based on TCL1A, FCER2 | ||
| * Additional separation of NK cells into CD56dim vs. bright clusters, based on XCL1 and FCGR3A | ||
| ```{r fplot, fig.width = 10, fig.height=10, cache = F} | ||
| # These are now standard steps in the Seurat workflow for visualization and clustering | ||
| FeaturePlot(object = pbmc, features = c("CD8A","GZMK","CCL5","S100A4"), pt.size = 0.3) | ||
| FeaturePlot(object = pbmc, features = c("S100A4","CCR7","CD4","ISG15"), pt.size = 0.3) | ||
| FeaturePlot(object = pbmc, features = c("TCL1A","FCER2","XCL1","FCGR3A"), pt.size = 0.3) | ||
| ``` |
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters