The goal of this project was to create an automated pipeline (nf-core/teamrna) for single-cell RNA sequencing analysis that has UMAP visualization, automatic cell type identification (vi scSorter), differential gene expression analysis, and RNA velocity using the package velocyto. We also aimed to link the databases Metascape and genecards, and integrate the two datasets. We used the nf-core framework for development of this pipeline (more information below).
RNA sequencing (RNA-seq) uses next-generation sequencing to examine the quantity of messenger RNA molecules in a biological sample and uses this transcriptomic information to extrapolate expression levels and changes in expression at the genomic level.[1] The ability to quantify genetic expression in different physiological or pathological states allows researchers to identify potential therapeutic targets and better understand the pathways underlying transitional processes from one state to another. Single-cell RNA sequencing (scRNA-seq), as the name suggests, is an RNA-seq approach that captures the transcriptome of individual cells, allowing researchers to identify cell subtypes and examine differences and similarities among these subtypes at the cellular level.[1] Doing so provides increased resolution into cellular function within different physiological states. Both RNA- and single-cell RNA sequencing are computationally driven processes; as such, robust analysis pipelines with sound mathematical and biological are needed to drive data analysis and research.
With over a decade of use, there are many publicly available resources to help researchers store, analyze, visualize, and share scRNA-seq data, such as Seurat,[2–4] Monocle,[5–9] and Scanpy.[10] Many of these resources require domain specific biological knowledge as well as coding experience. To cater to biologists without previous coding experience, resources like PIVOT[11], CellRanger[12], velocyto,[13] and more have been developed. While the availability of such programs improves scRNA-seq data analysis, researchers often must use a combination of several different programs per dataset to complete analysis. This workflow also leaves room for pre-existing biases to shape analyses at critical steps. For example, cell type identification is usually performed manually, meaning annotation is based on pre-existing knowledge of marker genes. These groups are then used for downstream analyses, so the possibility of manual error can shift the entire analysis and may prevent researchers from identifying cell types that they do not already know.
To address these issues, we have merged Seurat, Monocle, and velocyto into a single automated pipeline that is user-friendly. We are also appending the atumatic cell identification package scSorter to hep eliminate cell-identification bias and compare against a library of marker genes, making cell identification more expansive than manual identification. The overview of our pipeline is detailed below.
Pipeline Workflow. Solid lines represent the steps our pipeline supports, while dashed lines indicate future improvements on this pipeline.
nf-core/teamrna is a bioinformatics best-practice analysis pipeline for Single Cell RNA-seq analysis pipeline.
The pipeline is built using Nextflow, a workflow tool to run tasks across multiple compute infrastructures in a very portable manner. It uses Docker / Singularity containers making installation trivial and results highly reproducible.
On release, automated continuous integration tests run the pipeline on a full-sized dataset on the AWS cloud infrastructure. This ensures that the pipeline runs on AWS, has sensible resource allocation defaults set to run on real-world datasets, and permits the persistent storage of results to benchmark between pipeline releases and other analysis sources. The results obtained from the full-sized test can be viewed on the nf-core website.
Pepline dependency: Seurat, Monocle3, scSORTER, RNA Velocity
- Download data from NCBI GEO dataset
- Read QC (
FastQC) - Present QC for raw reads (
MultiQC) - Trajectory analysis uses monocle 3.
- Reduce dimensionality, clustering and visualize the cells.
- Find marker genes expressed by each cluster.
- Trajectory assignment.
- Pseudotime assign and plot gene along the pseudotime.
-
Install
nextflow -
Install any of
Docker,Singularity,Podman,ShifterorCharliecloudfor full pipeline reproducibility (please only useCondaas a last resort; see docs) -
Download the pipeline and test it on a minimal dataset with a single command:
nextflow run nf-core/teamrna -profile test,<docker/singularity/podman/shifter/charliecloud/conda/institute>
- Please check nf-core/configs to see if a custom config file to run nf-core pipelines already exists for your Institute. If so, you can simply use
-profile <institute>in your command. This will enable eitherdockerorsingularityand set the appropriate execution settings for your local compute environment. - If you are using
singularitythen the pipeline will auto-detect this and attempt to download the Singularity images directly as opposed to performing a conversion from Docker images. If you are persistently observing issues downloading Singularity images directly due to timeout or network issues then please use the--singularity_pull_docker_containerparameter to pull and convert the Docker image instead. It is also highly recommended to use theNXF_SINGULARITY_CACHEDIRorsingularity.cacheDirsettings to store the images in a central location for future pipeline runs. - If you are using
conda, it is highly recommended to use theNXF_CONDA_CACHEDIRorconda.cacheDirsettings to store the environments in a central location for future pipeline runs.
- Please check nf-core/configs to see if a custom config file to run nf-core pipelines already exists for your Institute. If so, you can simply use
-
Start running your own analysis!
nextflow run nf-core/teamrna -profile <docker/singularity/podman/shifter/charliecloud/conda/institute> --input samplesheet.csv --genome GRCh37
See usage docs for all of the available options when running the pipeline.
The nf-core/teamrna pipeline comes with documentation about the pipeline: usage and output.
Demo here- https://asciinema.org/a/WRFzjoXYLOmiGOwJiALt5g0D2
To demonstrate our work's effectiveness, we will analyze the following mouse cancer model dataset: GSE119352 (Reference: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6501221/)
To demonstrate the effectiveness of the RNA velocity pipeline, we used this mouse bone marrow set provided by the velocyto team: http://pklab.med.harvard.edu/velocyto/notebooks/R/SCG71.nb.html (“mouseBM.loom”)
Biological example- GSE119352: compare tumor microenvironment populations between control, anti-PD1 treated, anti-CTLA4, and a combination of the latter two.
mouseBM.loom: observe transcriptional dynamics of neutrophil maturation in mouse bone marrow
Data- GSE119352: four samples of CD45+ cells from syngeneic mice, with one sample being treated with IgG2a isotype control antibodies, another with anti-PD1, with anti-CTLA4, and both anti-PD1 and anti-CTLA4 treatments. mouseBM.loom: Whole bone marrow cells isolated from C57BI/6 mice
Data collection- GSE119352: CD45+ tumor infiltrating cells were sorted into droplets from syngeneic mice using the Chromium Single Cell 3' Reagent Kit v1 from 10X Genomics. The resulting libraries were then sequenced using Illumina HiSeq2500. mouseBM.loom: The bone marrow cells were prepared with inDrop and sequenced using Illumina Next-seq 500.
Data- GSE92332: whole intestines from wild type mice, disaggregated the samples, sorted into single cells and profiled them by single-cell RNA-seq. GSE92332_AtlasFullLength_TPM.txt.gz
nf-core/teamrna was originally written by Edmund Miller. Edmund Miller, Yan Fang, Alexa M. Salsbury, and Kaitlyn Saunders contributed to the development of the pipeline and documentation throughout the NCBI North Texas Codeathon event (2021).
If you find that our pipeline helped you to analyze single cell RNA-seq datasets, please reference the following:
E. Miller, Y. Fang, A. Salsbury, and K. Saunders. All in one scRNA-seq Pipeline: Data Downloading to Analysis. 2021. https://github.com/NCBI-Codeathons/lung-rna-seq.
The nf-core is a framework for community-curated bioinformatics pipelines. To cite nf-core, please reference the following:
Philip Ewels, Alexander Peltzer, Sven Fillinger, Harshil Patel, Johannes Alneberg, Andreas Wilm, Maxime Ulysse Garcia, Paolo Di Tommaso & Sven Nahnsen.
Nat Biotechnol. 2020 Feb 13. doi: 10.1038/s41587-020-0439-x.
[1] Kukurba, K. R.; Montgomery, S. B. RNA Sequencing and Analysis. Cold Spring Harb. Protoc. 2015, 2015 (11), 951–969.
[2] Satija, R.; Farrell, J. A.; Gennert, D.; Schier, A. F.; Regev, A. Spatial Reconstruction of Single-Cell Gene Expression Data. Nat. Biotechnol. 2015, 33 (5), 495–502.
[3] Butler, A.; Hoffman, P.; Smibert, P.; Papalexi, E.; Satija, R. Integrating Single-Cell Transcriptomic Data across Different Conditions, Technologies, and Species. Nat. Biotechnol. 2018, 36 (5), 411–420.
[4] Hao, Y.; Hao, S.; Andersen-Nissen, E.; Mauck, W. M.; Zheng, S.; Butler, A.; Lee, M. J.; Wilk, A. J.; Darby, C.; Zagar, M.; et al. Integrated Analysis of Multimodal Single-Cell Data. bioRxiv. bioRxiv October 12, 2020.
[5] Monocle 3 https://cole-trapnell-lab.github.io/monocle3/docs/citations/ (accessed May 19, 2021).
[6] Trapnell, C.; Cacchiarelli, D.; Grimsby, J.; Pokharel, P.; Li, S.; Morse, M.; Lennon, N. J.; Livak, K. J.; Mikkelsen, T. S.; Rinn, J. L. The Dynamics and Regulators of Cell Fate Decisions Are Revealed by Pseudotemporal Ordering of Single Cells. Nat. Biotechnol. 2014, 32 (4), 381–386.
[7] Qiu, X.; Mao, Q.; Tang, Y.; Wang, L.; Chawla, R.; Pliner, H. A.; Trapnell, C. Reversed Graph Embedding Resolves Complex Single-Cell Trajectories. Nat. Methods 2017, 14 (10), 979–982.
[8] Qiu, X.; Hill, A.; Packer, J.; Lin, D.; Ma, Y. A.; Trapnell, C. Single-Cell MRNA Quantification and Differential Analysis with Census. Nat. Methods 2017, 14 (3), 309–315.
[9] Cao, J.; Spielmann, M.; Qiu, X.; Huang, X.; Ibrahim, D. M.; Hill, A. J.; Zhang, F.; Mundlos, S.; Christiansen, L.; Steemers, F. J.; et al. The Single-Cell Transcriptional Landscape of Mammalian Organogenesis. Nature 2019, 566 (7745), 496–502.
[10] Wolf, F. A.; Angerer, P.; Theis, F. J. SCANPY: Large-Scale Single-Cell Gene Expression Data Analysis. Genome Biol. 2018, 19 (1), 15.
[11] Zhu, Q.; Fisher, S. A.; Dueck, H.; Middleton, S.; Khaladkar, M.; Kim, J. PIVOT: Platform for Interactive Analysis and Visualization of Transcriptomics Data. BMC Bioinformatics 2018, 19 (6), 1–8.
[12] 10x Genomics Cell Ranger 3.0.0. 2021.
[13] La Manno, G.; Soldatov, R.; Zeisel, A.; Braun, E.; Hochgerner, H.; Petukhov, V.; Lidschreiber, K.; Kastriti, M. E.; Lönnerberg, P.; Furlan, A.; et al. RNA Velocity of Single Cells. Nature 2018, 560 (7719), 494–498.