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With Seurat

There are a number of ways to create a cell browser using Seurat:

  • Import a Seurat rds file - create a cell browser with the Unix command line tool cbImportSeurat.
  • Using RStudio and a Seurat object - create a cell browser directly using the ExportToCellbrowser() R function.
  • Run our basic Seurat pipeline - with just an expression matrix, you can run our cbSeurat pipeline to create a cell browser.

Each of these methods are described in more detail below.

Convert a Seurat rds or ``.rdata` file

First, create an .rds file in R as described in the Seurat tutorial:

saveRDS(pbmc, "pbmc3k_small.rds")

Next, on the Unix command line, use the cbImportSeurat script to convert this rds file into a cell browser:

cbImportSeurat -i pbmc3k_small.rds -o pbmc3kImport

This works with objects created by versions 2 and 3 of Seurat. cbImportSeurat can read both .rds and .rdata files, for .rdata it assume the first object is the Seurat object. Make sure that you have the same major version of Seurat installed that was used to create the object. You cannot open Seurat2 objects with Seurat3 or vice versa. (We often need to switch between Seurat versions and found conda environments very helpful for this.)

The -i option specifies the input rds file and the -o option specifies a name for the output directory. You can use the -n option to change the dataset name in the cell browser; if it is not specified, it will default to the output directory name.

A Seurat object does not contain the marker genes by default, as FindAllMarkers() does not save its output. You can add it to the object when you save the .rds file with a command like this:

object@misc$markers <- FindAllMarkers(object)

cbImportSeurat will then use these markers. Otherwise, if misc$markers is not present in the object, it will run FindAllMarkers with the default values (Wilcoxon and 0.25 as the cutoff). Alternatively, you can also save the markers to a tab-separated file yourself and provide this file with the --markerFile option.

Lastly, go into the pbmc3kImport directory and run cbBuild to create the cell browser output files:

cd pbmc3kImport
cbBuild -o ~/public_html/cb

Alternatively, you can use the --htmlDir option for cbImportSeurat to automatically run cbBuild for you:

cbImportSeurat -i pbmc3k_small.rds -o pbmc3kImport --htmlDir=~/public_html/cb

Convert a Seurat object from R

The function ExportToCellbrowser() is already part of Seurat 3. You can install pre-release Seurat3 like this:

install.packages("devtools")
devtools::install_github("satijalab/seurat", ref = "release/3.0")

For Seurat 2, you have to load the function with this command:

source("https://raw.githubusercontent.com/maximilianh/cellBrowser/master/src/cbPyLib/cellbrowser/R/ExportToCellbrowser-seurat2.R")

You can then write a Seurat object to a directory from which you can run cbBuild:

ExportToCellbrowser(pbmc_small, dir="pbmcSmall", dataset.name="pbmcSmall")

Or, you can build a cell browser from this dataset into the htdocs directory, serve the result on port 8080 via http, and open a web browser from within R:

ExportToCellbrowser(pbmc_small, dir="pbmcSmall", cb.dir="htdocs", dataset.name="pbmcSmall", port=8080)

Writing the expression matrix is somewhat slow. If you have already exported into the same output directory before and just updated a part of the cell annotation data (e.g. clustering), you can use the argument skip.matrix=TRUE to save some time:

ExportToCellbrowser(pbmc_small, dir="pbmcSmall", dataset.name="pbmcSmall", skip-matrix=TRUE)

Run a basic Seurat pipeline

If you have never used Seurat before and just want to process an expression matrix as quickly as possible, this section is for you.

If you do not have R installed yet, we recommend that you install it via conda. To install miniconda, follow their installation instructions.

After setting up conda, install R:

conda install r

Then, install Seurat:

conda install -c bioconda r-seurat

To process an example dataset now, download the 10X pbmc3k expression matrix:

rsync -Lavzp genome-test.gi.ucsc.edu::cells/datasets/pbmc3k/ ./pbmc3k/ --progress

Now run the expression matrix filtered_gene_bc_matrices/hg19/matrix.mtx through Seurat:

cbSeurat -e filtered_gene_bc_matrices/hg19 --name pbmc3kSeurat -o seuratOut

This will create a script (seuratOut/runSeurat.R), run it through Rscript, and will fill the directory seuratOut/ with everything needed to create a cell browser. After the cbSeurat script completes, you can build your cell browser from the output:

cd seuratOut
cbBuild -o ~/public_html/cells

Changing the defaults using seurat.conf

This set of steps will run a basic Seurat pipeline with the default settings. You can modify the settings for Seurat by creating a seurat.conf file:

cbSeurat --init

You can edit the settings in seurat.conf and re-run the cbSeurat command to generate a new set of Seurat output using these new settings.