By Jane Cook
November 29, 2021
What Can scRNA-seq Data Tell Us?
Single-cell sequencing technologies have exploded in popularity for biological research over the last five years. The appeal of scRNA-seq lies in its specificity and scalability compared to older research techniques like Western blotting.
Researchers can use scRNA-seq to examine gene expression across the genome in hundreds of thousands of cells simultaneously while being able to identify the expression patterns unique to individual cells. This allows hundreds of features of gene expression including time-specific, tissue-specific, and individual cell-specific features to be integrated for a better understanding of biological processes.
However, it has become much easier to acquire raw single-cell data, with single-cell assays becoming widely available and less expensive. Extracting meaning from this data is a bioinformatic challenge that depends on the development of high-quality computational tools for scRNA-seq analysis.
The Tools of the Trade
To facilitate progress in scRNA-seq analysis, the Oshlack Lab bioinformatics research group from Melbourne, Australia established the scRNA-tools database, a catalog of tools for scRNA-seq data analysis.
Their bioinformatic database is unique in that it categorizes the type of analysis the tool can perform, its publication status, its coding platform, which repository it’s located in, and any licensing information. The database now lists over 1,000 tools, and due to its size, the researchers analyzed the trends in scRNA-seq analysis tools over the last five years.
Trends in scRNA-seq Analysis
The first trend noted by the research team is the move away from programming these tools in R and towards Python, reflecting the popularity of the language for the more complex computational tasks demanded by the scale of newer scRNA-seq datasets.
Secondly, the types of tasks being tackled by programmers are changing. The biggest increase since 2016 is in tools for integration, or filtering out the noise while preserving biological variation and signals when combining data from multiple samples from different labs. Integration tasks are also critical for combining the data from individual cells to get a readout of a meaningful biological signal.
Integration goes hand in hand with classification, the other type of analysis with the most tools developed in the database. Classification tools use references to label cells with their type or state more efficiently and this is an important step in the analysis process that gets more challenging with scale.
Future Directions
The scRNA-seq analysis landscape is constantly evolving and improving to keep up with the developments in the field. The surface has only been scratched off the insights that can be pulled from scRNA-seq data, and more custom bioinformatic tools and pipelines will be continuously developed for years to come.
Jane Cook, Journalist & Content Writer, Bridge Informatics
Jane is a Content Writer at Bridge Informatics, a professional services firm that helps biotech customers implement advanced techniques in the management and analysis of genomic data. Bridge Informatics focuses on data mining, machine learning, and various bioinformatic techniques to discover biomarkers and companion diagnostics. If you’re interested in reaching out, please email [email protected] or [email protected].
Sources:
https://genomebiology.biomedcentral.com/articles/10.1186/s13059-021-02519-4
