ArticleeLife2021
Distinguishing different modes of growth using single-cell data.
Article in eLife, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
16 citing papers in PubMed, 35 citations in OpenAlex.
- Stress-dependent growth in breast cancer arises from a mechano-osmotic coupling and cell-sizing checkpoint.Proceedings of the National Academy of Sciences of the United States of America · 2026Article
- Nonlinear memory in cell-division dynamics across species.Proceedings of the National Academy of Sciences of the United States of America · 2025Article
- Gene expression cycles drive non-exponential bacterial growth.Physical review research · 2025Article
- Linking Cell Size, VEnvironmental microbiology reports · 2025Article
- Are cell length and volume interchangeable in cell cycle analysis?Biophysical journal · 2025Article
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- Determining the rate-limiting processes for cell division in Escherichia coli.Nature communications · 2024Article
- Differential growth regulates asymmetric size partitioning inLife science alliance · 2024Article
- Cell size homeostasis is tightly controlled throughout the cell cycle.PLoS biology · 2024Article
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- Super-exponential growth and stochastic size dynamics in rod-like bacteria.Biophysical journal · 2023Article
- Using conditional independence tests to elucidate causal links in cell cycle regulation inProceedings of the National Academy of Sciences of the United States of America · 2023Article
- Correlation between speed and turning naturally arises for sparsely sampled cell movements.Physical biology · 2023Article
- Analytical cell size distribution: lineage-population bias and parameter inference.Journal of the Royal Society, Interface · 2022Article
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Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors at 2 institutions in 1 country.
Funding
Abstract
Collection of high-throughput data has become prevalent in biology. Large datasets allow the use of statistical constructs such as binning and linear regression to quantify relationships between variables and hypothesize underlying biological mechanisms based on it. We discuss several such examples in relation to single-cell data and cellular growth. In particular, we show instances where what appears to be ordinary use of these statistical methods leads to incorrect conclusions such as growth being non-exponential as opposed to exponential and vice versa. We propose that the data analysis and its interpretation should be done in the context of a generative model, if possible. In this way, the statistical methods can be validated either analytically or against synthetic data generated via the use of the model, leading to a consistent method for inferring biological mechanisms from data. On applying the validated methods of data analysis to infer cellular growth on our experimental data, we find the growth of length in
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What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.