New Computational Tool Developed for Large-Scale Analysis of Single Cells

Big Data

DNA strand and cells of a human / Photo by Getty Images


An experimental computational method called BigSCale that can be used in concurrently examining millions of single cells has been created by researchers from the Centro Nacional de Análisis Genómico of the Centre for Genomic Regulation or CNAG-CRG, according to the CRG News.

Holger Heyn, CNAG-CRG team leader, said BigSCale can pinpoint cell type-specific genes and improves the analysis of experiments. Heyn added that BigSCale can contribute a lot to the evaluation of large-scale studies, such as the Human Cell Atlas project. 

BigSCale uses a mathematical model that accurately ascertains the differences among single cells. Once it is determined how individual cells differentiate from one another, they can be classified into various cell populations to describe the cellular intricacy of a given tissue. Since all tissues are made up of various cell types and subtypes, such an analysis can result in an objective and comprehensive characterization sans initial assumptions. There is a section in the BigSCale workflow for the study of millions of cells by a directed convolution technique. By this method, single-cell transcriptomes from the comparable cells are combined into index cells, greatly simplifying data analysis.

The CNAG-CRG researchers used BigSCale in studying one of the largest single-cell gene expression datasets containing 1.3 million cells, available from 10x Genomics. BigSCale enabled them to examine deeply the developmental activities of the mouse brain and to scrutinize rare neuronal cell types. A large number of cells allowed the group to focus into a small transient cell population called Cajal-Retzius cells and to study major substructures related to distinct differentiation stages, spatial organization, and cellular function. 

Results of the research have been published in the Genome Research journal.