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Data-driven detection of age-related arbitrary monotonic changes in single-cell gene expression distributions.
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- Author(s): Jian Hao Cheng; Daigo Okada
- Source:
PeerJ; Feb2024, p1-17, 17p- Subject Terms:
- Source:
- Additional Information
- Abstract: Identification of genes whose expression increases or decreases with age is central to understanding the mechanisms behind aging. Recent scRNA-seq studies have shown that changes in single-cell expression profiles with aging are complex and diverse. In this study, we introduce a novel workflow to detect changes in the distribution of arbitrary monotonic age-related changes in single-cell expression profiles. Since single-cell gene expression profiles can be analyzed as probability distributions, our approach uses information theory to quantify the differences between distributions and employs distance matrices for association analysis. We tested this technique on simulated data and confirmed that potential parameter changes could be detected in a set of probability distributions. Application of the technique to a public scRNA-seq dataset demonstrated its potential utility as a straightforward screening method for identifying aging-related cellular features. [ABSTRACT FROM AUTHOR]
- Abstract: Copyright of PeerJ is the property of PeerJ Inc. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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