Cell Ranger6.0, printed on 12/26/2024
In this tutorial, you will learn how to:
The cellranger aggr pipeline is optional. It is used to aggregate, or combine two cellranger count runs together. With experiments involving multiple samples, and multiple 10X Chromium GEM wells, libraries must each be processed in separate runs of cellranger count.
To compare samples to each other for differential expression analysis, cellranger aggr is used to combine output files from each run of cellranger count to produce one single feature-barcode matrix and a .cloupe file for visualizing with Loupe Browser.
First run the cellranger aggr pipeline with the ----help command to print the usage statement and view the input requirements.
cellranger aggr --help
The output is similar to the following:
/mnt/home/user.name/yard/apps/cellranger-3.1.0/cellranger-cs/3.1.0/bin
cellranger aggr (3.1.0)
Copyright (c) 2019 10x Genomics, Inc. All rights reserved.
-------------------------------------------------------------------------------
...
The commands below should be preceded by 'cellranger':
Usage:
aggr
--id=ID
--csv=CSV
[options]
aggr [options]
aggr -h | --help | --version
...
This pipeline has two inputs:
CSV stands for comma separated value. For specific instructions for creating this CSV, see the cellranger aggr page.
The CSV file is a two column file. The first column is for the library id. This id can be anything you want. Choose descriptive ids since they are used later in analysis. The second column contains the paths to the molecule_info.h5 output files from the cellranger count pipelines.
Use the following publicly available molecule_info.h5 files:
Start by making a directory to run the aggr pipeline in.
mkdir ~/yard/run_cellranger_aggr cd ~/yard/run_cellranger_aggr
Next, download the data files.
wget https://cf.10xgenomics.com/samples/cell-exp/3.0.0/pbmc_1k_v3/pbmc_1k_v3_molecule_info.h5 wget https://cf.10xgenomics.com/samples/cell-exp/3.0.0/pbmc_10k_v3/pbmc_10k_v3_molecule_info.h5
These are small files, less than1GB each and usually take less than one minute to download.
The next step is to build the CSV file. The path to the hdf5 f just downloaded is needed. From the same directory where the files were downloaded , use the pwd command to find the path.
pwd
The output is similar to the following:
/mnt/home/user.name/yard/run_cellranger_aggr
Copy the path to make the CSV file. Use the text editor of your choice to make this file. This example uses nano.
nano pbmc_aggr.csv
This opens the nano text editor. This is a Linux program. To access the command prompt again, exit from this program.
library_id,molecule_h5 1k_pbmcs,/mnt/home/user.name/yard/run_cellranger_aggr/pbmc_1k_v3_molecule_info.h5 10k_pbmcs,/mnt/home/user.name/yard/run_cellranger_aggr/pbmc_10k_v3_molecule_info.h5
Paste the text above into the editor. Edit the path to each molecule_info.h5file so it matches the path copied above, pointing to the path of the file on your system.
Exit out of the nano text edtior by pressing Ctrl-x and answering Y to save the file.
Save modified buffer (ANSWERING "No" WILL DESTROY CHANGES) ? Y Yes N No ^C Cancel
It asks you:
File Name to Write: pbmc_aggr.csv
Press Enter to confirm keeping this filename and saving the file. Now you are back to the command prompt.
Save the file and out of the nano text editor, which is a Linux-formatted CSV file.
Next, build the command line and run it.
cellranger aggr --id=1k_10K_pbmc_aggr --csv=pbmc_aggr.csv
The output is similar to the following:
/mnt/home/user.name/yard/apps/cellranger-3.1.0/cellranger-cs/3.1.0/bin
cellranger aggr (3.1.0)
Copyright (c) 2019 10x Genomics, Inc. All rights reserved.
-------------------------------------------------------------------------------
...
Waiting 6 seconds for UI to do final refresh.
Pipestance completed successfully!
2019-09-13 18:22:07 Shutting down.
Saving pipestance info to 1k_10K_pbmc_aggr/1k_10K_pbmc_aggr.mri.tgz
Just like the other pipelines, when you see “Pipestance completed successfully!” the job is done, and the pipeline outputs are in the pipestance directory in the outs folder. List the contents of this directory:
ls -1 1k_10K_pbmc_aggr/outs/
The output is similar to the following:
aggregation.csv analysis cloupe.cloupe filtered_feature_bc_matrix filtered_feature_bc_matrix.h5 raw_feature_bc_matrix raw_feature_bc_matrix.h5 summary.json web_summary.html
The outputs are similar to those from the cellranger count pipeline, with the exception of the BAM files and molecule_info.h5 files.