Recognizing the role of scRNA-seq, this list aims to help inform newcomers to omics. Whether you are looking to begin your journey into single-cell analysis, deepen your understanding of scRNA-seq, or stay abreast of the latest tools and techniques, the information should be helpful.
Foundational Talks and Articles
The foundation of single-cell RNA sequencing is built upon a deep understanding of its principles and applications. This section introduces vital resources that provide comprehensive insights into this field.
1. Single-cell Sequencing Made Simple – Article on Nature.com
This Nature article simplifies the concept of single-cell RNA sequencing, making it accessible to a wide audience. It emphasizes the effectiveness of the technique in uncovering unique cell types and subtle genetic variations.
The article delves into the challenges of handling large datasets generated by single-cell sequencing and the evolving tools that facilitate this analysis. It is particularly insightful for understanding the methodological advancements and analytical perspectives in single-cell sequencing.
2. Dr. Eric Chow - Talk on Single Cell Sequencing
Dr. Eric Chow, from the University of California, San Francisco, delivers a detailed presentation on single-cell sequencing, emphasizing RNA sequencing. Chow highlights how single-cell sequencing can differentiate various cell types by analyzing their gene expression. He describes the process of clustering and re-clustering cell data for more detailed analysis, showcasing the high-dimensional data readout that single-cell sequencing offers. He explains various methodologies, including plate-based, microfluidic, combinatorial indexing, and others extending beyond RNA sequencing.
Databases to Use for Data Comparison
Two examples of critical databases in the field of single-cell RNA sequencing (scRNA-seq) offer invaluable resources for researchers and scientists. These databases are helpful for comparing data, understanding gene expression at the single-cell level, and advancing our comprehension of human health and disease.
1. Single Cell Expression Atlas (EMBL-EBI)
The Single Cell Expression Atlas, hosted by EMBL-EBI, is a treasure trove for scRNA-seq experiment analyses. As of today, this platform extends across 21 species, encompassing 355 studies and over 10.5 million cells. It provides single-cell gene expression data across various species, making it an extensive resource for researchers interested in comparative omics.
The Expression Atlas offers a flexible pipeline for scRNA-seq analysis, integrating numerous tools for filtering, mapping, quantifying expression, clustering, and identifying marker and variable genes. The workflows, designed for reproducibility, can be run on the cloud, local machines, or local premises, facilitating versatile use for diverse research needs.
The Human Cell Atlas aims to create comprehensive reference maps of all human cells, a monumental task critical for understanding human health and disease. It seeks to map every cell type in the human body, identifying their molecular characteristics and locations.
This international collaboration charts cell types from development to old age and is considered larger in scale than the Human Genome Project. By discovering new cell types and their functions, the Human Cell Atlas provides a critical foundation for studying health and disease. The Human Cell Atlas has already provided insights into COVID-19, cancer, cystic fibrosis, heart disease, and more.
These databases represent invaluable tools for scientists and researchers, offering deep insights and data crucial for advancing our understanding of omics and single-cell research.
RNA Seq Data Analysis Software
In single-cell RNA sequencing (scRNA-seq), data analysis software is crucial in processing, visualizing, and interpreting vast and complex datasets. This section introduces some of the key software tools that have become indispensable in the field.
1. Seurat
Seurat is an R-based toolkit for quality control, analysis, and exploration of single-cell RNA sequencing data. Seurat filters data, maps, and quantifies expression in single-cell transcriptomic measurements, enabling users to identify and interpret variability. Its capabilities extend to clustering and integrating diverse types of single-cell databases. Seurat is recognized for its contributions to understanding single-cell omics and is widely referenced in scientific literature.
Scanpy is a scalable toolkit designed for analyzing single-cell gene expression data using Python. Developed with anndata, another Python package, Scanpy offers preprocessing, visualization, clustering, trajectory inference, and differential expression testing. Scanpy efficiently handles more than one million cell datasets, making it a go-to tool for large-scale single-cell gene expression studies.
3. g.nome® by Almaden Genomics
g.nome® is a bioinformatics platform designed for scientists of all skill levels. It features a user-friendly drag-and-drop interface, enabling the construction of powerful bioinformatic workflows. The cloud-native platform offers scalable and interoperable workflows for next-generation sequencing analysis.
g.nome provides a low-code/no-code pipeline build, integrating pre-built workflows and toolkits from a curated library, and supports custom code importation. It significantly streamlines the process of genomic workflow development and facilitates effective team collaboration.
4. Cell Ranger
Developed by 10x Genomics, Cell Ranger is a set of analysis pipelines for processing Chromium single-cell data. It aligns reads, generates feature-barcode matrices, performs clustering, and conducts other secondary analyses. Cell Ranger is a complementary tool for 10x Genomics consumers as it offers pipelines for 3' Single Cell Gene Expression Solutions and associated products.
5. UCSC Cell Browser
The UCSC Cell Browser is a lightweight, fast viewer for single-cell data. Users can view 2D plots of cells arranged by algorithms like t-SNE or UMAP, color cells by metadata and gene expression, and view cluster marker genes. It also enables renaming clusters, adding custom annotations to selected cells, and the visualization of a heatmap of selected gene expression across clusters.
Each software tool provides unique capabilities essential in the scRNA-seq field, catering to different aspects of data analysis, from basic visualization to complex bioinformatic workflows.
Online Learning Materials
Several resources provide excellent educational content RNA seq data analysis course for those new to single-cell RNA sequencing or seeking to deepen their understanding. Here, we highlight some key learning materials in the field.
1. Single Cell RNA-Seq for Dummies - Lior Pachter
Lior Pachter's blog, "Single Cell RNA-Seq for Dummies," presents a simplified introduction to single-cell RNA sequencing. The blog is designed to make the complex subject matter accessible to a broader audience, including those with minimal prior knowledge.
2. Seurat Tutorials
The Seurat package, widely used for single-cell omics analysis, offers a range of tutorials and examples. These resources are collected in vignettes that cover various aspects of Seurat's capabilities, including data preprocessing, visualization, clustering, and differential expression testing. These tutorials are an excellent resource for beginners and advanced users wishing to leverage Seurat for single-cell RNA-seq data analysis.
These resources provide a comprehensive overview of single-cell RNA sequencing, offering valuable insights for both novices and seasoned researchers in the field.
Advanced Tutorials and Courses
For those looking to delve deeper into the complexities of single-cell RNA sequencing, advanced tutorials and courses provide in-depth knowledge and hands-on experience.
1. Advanced Analysis of Single Cell RNA-Seq Data
This course taught through the University of Cambridge Bioinformatics training unit, offers comprehensive training on genome-wide transcriptome data from single cells using high-throughput sequencing (scRNA-seq). The course addresses the need for novel methods in analyzing scRNA-seq data, which differ from those used in bulk RNA-seq. It focuses on computational and statistical techniques implemented in R and is updated twice a year. University of Cambridge-registered students can access RNA seq analysis course materials via the course's GitHub repository, including a Docker image with all essential software.
2. Single-Cell Tutorial
The site offers a course on analysing single-cell RNA-seq database and highlights how scRNA-seq obtains genome-wide transcriptome data from single cells using high-throughput sequencing. The RNA seq analysis course emphasizes unique methodologies for scRNA-seq data analysis as traditional approaches for bulk RNA-seq methods are not applicable. This biannually updated course from the University of Cambridge's Bioinformatics training section is for those interested in computational analysis of scRNA-seq data.
These advanced courses are tailored for individuals with a basic understanding of single-cell RNA sequencing. The aim is to expand their knowledge further, especially in computational analysis and using specific tools like R for in-depth data analysis.
These advanced courses are tailored for individuals with a basic understanding of single-cell RNA sequencing who want to expand their knowledge further, especially in computational analysis and using specific tools like R for in-depth data analysis.
RNA Seq Analysis Workshop for Hands-on Learning
For those seeking practical, hands-on experience in single-cell RNA sequencing, specialized workshops provide intensive training and deep dives into the subject matter.
1. Single Cell RNA-Seq Analysis Bootcamp - Columbia University Mailman School of Public Health
The Single Cell Analysis Bootcamp is a two-day intensive training consisting of seminars and hands-on analytical sessions. The online boot camp scheduled for December 14-15, 2023, and May 20-21, 2024, will guide participants toward scRNASeq data analysis methods used in health studies.
The curriculum includes gene expression analysis, cluster analysis, cell type identification, regulatory network analysis, and master regulator analysis. Participants will learn to compare whole transcriptional profiles to categorize single cells into distinct populations. Additionally, they will translate gene expression patterns into protein activity profiles and phenotype single-cell populations using systems-biology techniques.
The RNA seq analysis workshop is open to investigators from all institutions and career stages. A basic understanding of statistics, familiarity with R, and a basic RStudio Cloud account are required.
2. Introduction to Single Cell RNA-Seq - Harvard Informatics Group
Harvard's Informatics Group offers a 2-day, hands-on workshop titled "Introduction to single-cell RNA-seq analysis." This workshop instructs participants on designing single-cell RNA-seq experiments and efficiently managing and analyzing data starting from count matrices. It primarily focuses on using the Seurat package in R/RStudio and requires a working knowledge of R.
The workshop covers various aspects, such as quality control metrics, cell clustering based on expression data, and integrating different sample conditions. It is suitable for both trainer-led workshops and self-guided learning.
3. Hemberg Lab scRNA-seq Course - University of Cambridge's Bioinformatics
The Hemberg Lab's scRNA-seq course, as part of the University of Cambridge's Bioinformatics training unit, provides in-depth computational analysis of scRNA-seq data. It covers various topics related to scRNA-seq, such as addressing intractable issues using other methods like bulk RNA-seq or single-cell RT-qPCR. It focuses on computational and statistical methods needed for scRNA-seq analysis.
The course material is updated twice a year and is available for anyone interested in learning about computational analysis of scRNA-seq data. Participants are expected to be familiar with tools implemented in R and run reasonably fast. The course includes updated repositories and can be reproduced using a Docker image containing all required packages.
These workshops provide an ideal platform for researchers, scientists, and students to get hands-on experience and an in-depth understanding of scRNA-seq, from basic analysis to advanced computational methods, under the guidance of experts in the field.
Current Literature and Discussions
Staying updated with the latest developments in single-cell RNA sequencing is crucial for researchers and enthusiasts alike. Two platforms stand out for sharing cutting-edge developments and fostering discussions in the field.
1. Nature Methods Community
The Nature Methods Community is a platform where articles on the latest methods development in various scientific fields, including single-cell RNA sequencing, are shared and discussed. It is a hub for researchers to stay informed about new techniques, breakthroughs, and collaborative opportunities in scientific methodologies. The platform encourages active participation and discussion among scientists and researchers worldwide.
2. The Single Cell Blog - Single Cell Discoveries
The Single Cell Blog by Single Cell Discoveries is a valuable resource that offers updates on tools and datasets in single-cell research. The blog covers a range of topics relevant to single-cell RNA sequencing. These include advances in single-cell technologies and understanding specific techniques like scATAC-seq. It also addresses considerations around transcriptome sequencing, basics of single-cell ATAC-seq, and discussions on tools like 10x Chromium and CellRanger. It is an informative platform for beginners and experts interested in the nuances of single-cell omics.
These platforms provide a comprehensive overview of the latest trends, research, and discussions in the rapidly evolving field of single-cell RNA sequencing. As a result, researchers and enthusiasts stay at the forefront of scientific advancements.
Opening New Doors
The resources and tools outlined in this guide are more than just informational assets. Staying current with developments in scRNA-seq is essential for any researcher or enthusiast who wishes to contribute meaningfully to the scientific community. I strongly encourage you to use these resources with a spirit of inquiry and a drive for discovery. The journey of learning in scRNA-seq is ongoing, and each resource listed here can open new doors of understanding and innovation for you.
Mark Kunitomi
Mark Kunitomi is the Chief Scientific Officer at Almaden Genomics. He was a post-doctoral fellow at UC San Francisco with a background in genomics, bioinformatics and microbiology, and he has a Ph.D. in Biochemistry & Molecular Biology from the University of California, San Francisco.