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Monod: mechanistic analysis of single-cell RNA sequencing count data

Gorin, Gennady and Pachter, Lior (2022) Monod: mechanistic analysis of single-cell RNA sequencing count data. . (Unpublished)

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We present the Python package Monod for the analysis of single-cell RNA sequencing count data through chemical master equation models. Monod can effectively identify biological and technical components of noise, enabling insights into potential pitfalls of standard normalization techniques. By parameterizing multidimensional distributions with biophysical variables, it provides a route to identifying and studying differential expression patterns that do not cause changes in average gene expression. The Monod framework is open-source and modular, and may be extended to more sophisticated models of variation and further experimental observables.

Item Type:Report or Paper (Discussion Paper)
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URLURL TypeDescription Paper Information ItemMonod Software ItemMonod examples ItemMonod package
Gorin, Gennady0000-0001-6097-2029
Pachter, Lior0000-0002-9164-6231
Additional Information:The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY 4.0 International license. We thank Tara Chari, Meichen Fang, and Sina Booeshaghi for useful discussions in the course of developing Monod. The Monod package uses algorithms implemented in the NumPy [60], SciPy [61], and numdifftools [62] Python packages. G.G. and L.P. were partially funded by NIH U19MH114830. The authors have declared no competing interest.
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Record Number:CaltechAUTHORS:20220614-221628000
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Official Citation:Monod: mechanistic analysis of single-cell RNA sequencing count data Gennady Gorin, Lior Pachter bioRxiv 2022.06.11.495771; doi:
Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:115138
Deposited By: George Porter
Deposited On:15 Jun 2022 16:30
Last Modified:15 Jun 2022 16:30

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