Computational Tools for Quantifying Concordance in Single-Cell Fate.

Clicks: 255
ID: 21352
2019
Article Quality & Performance Metrics
Overall Quality
Not rated
Combines reader engagement with the AI quality analysis. This article has not been analysed, so there is no overall score — reader engagement is measured and shown alongside.
AI Quality Assessment
Not analyzed
Readership in this journal
Steady

Ranked #76 of 139 articles by views in methods in molecular biology (clifton, nj)

Most read Least read

Bar heights use a square-root scale. Only the 120 most-read articles are drawn; the journal has 139 in total.

Mint this article as an NFT
Not yet minted

Create a permanent, verifiable on-chain record of this article on the Scimatic Network. The NFT is held in your Journament account, and you can withdraw it to your own wallet at any time.

5 SUSD one-off · no wallet required
Sign in to mint
Abstract
Cells are dynamic biological systems that interact with each other and their surrounding environment. Understanding how cell extrinsic and intrinsic factors control cell fate is fundamental to many biological experiments. However, due to transcriptional heterogeneity or microenvironmental fluctuations, cell fates appear to be random. Individual cells within well-defined subpopulations vary with respect to their proliferative potential, survival, and lineage potency. Therefore, methods to quantify fate outcomes for heterogeneous populations that consider both the stochastic and deterministic features of single-cell dynamics are required to develop accurate models of cell growth and differentiation. To study random versus deterministic cell behavior, one requires a probabilistic modelling approach to estimate cumulative incidence functions relating the probability of a cell's fate to its lifetime and to model the deterministic effect of cell environment and inheritance, i.e., nature versus nurture. We have applied competing risks statistics, a branch of survival statistics, to quantify cell fate concordance from cell lifetime data. Competing risks modelling of cell fate concordance provides an unbiased, robust statistical modelling approach to model cell growth and differentiation by estimating the effect of cell extrinsic and heritable factors on the cause-specific cumulative incidence function.
Reference Key
cornwell2019computationalmethods Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Cornwell, J A;Nordon, R E;
Journal methods in molecular biology (clifton, nj)
Year 2019
DOI
10.1007/978-1-4939-9224-9_6
URL
Keywords Keywords not found

Citations

No citations found. To add a citation, contact the admin at info@scimatic.org

Comments

No comments yet. Be the first to comment on this article.