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New algorithm uses online learning to accelerate analysis of large cell datasets




The fact that the human body is made up of cells is a basic and well-understood concept. Still surprisingly, scientists are still trying to determine the different types of cells that make up our organs and contribute to our health.

A relatively recent technique called single cell sequencing allows researchers to recognize and classify cell types by characteristics such as the genes they express. However, this type of study produces vast amounts of data, including datasets of hundreds of thousands to millions of cells.

A new algorithm developed by Dr. Joshua Welch of the Faculty of Computational Medicine and Bioinformatics. Candidate Chao Gao and his team will use online learning to significantly speed up this process, allowing researchers around the world to analyze large datasets using the amount of memory found on standard laptop computers. Provides a way.Survey results are published in the journal Nature biotechnology..

“Our technology allows anyone with a computer to perform an analysis on a whole organism scale,” says Welch. “That’s what this field is heading for.”

The team, along with a national research team, including Welch’s lab, uses datasets from the Brain Initiative of the National Institutes of Health, a project aimed at understanding the human brain by mapping all cells. And proved the principle proof.

Welch usually explains. For such projects, each single-cell dataset sent must be reanalyzed with the previous dataset in the order in which it arrived. Their new approach allows new datasets to be added to existing datasets without having to reprocess the old dataset. Researchers can also divide datasets into so-called mini-batch to reduce the amount of memory required to process them.

“This is very important for the increasingly produced set of millions of cells,” says Welch. “This year, we have five to six treatises with more than two million cells, and the amount of memory needed just to store raw data is much higher than everyone has on their computer.”

Welch likens online technology to the ongoing data processing performed by social media platforms such as Facebook and Twitter. These platforms need to process the data that is continually generated by users and provide relevant posts to people’s feeds. “Here, instead of people writing tweets, there are labs that are experimenting and publishing data around the world.”

The findings could significantly improve the efficiency of other ambitious projects such as Human Body Map and Human Cell Atlas. “Understanding the normal compliments of cells in the body is the first step in understanding how they go wrong with the disease,” Welch said.


Journal reference:

Gao, C. , et al. (2021) Iterative single-cell multiomic integration using online learning. Nature biotechnology.


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