Maximum entropy models for patterns of gene expression
Fuente:
arXiv
Guardado en:
| Autores principales: | , , , , , , |
|---|---|
| Formato: | Preprint |
| Publicado: |
2024
|
| Materias: | |
| Acceso en línea: | |
| Etiquetas: |
Agregar Etiqueta
Sin Etiquetas, Sea el primero en etiquetar este registro!
|
| _version_ | 1866909287930920960 |
|---|---|
| author | Sarra, Camilla Sarra, Leopoldo Di Carlo, Luca GrandPre, Trevor Zhang, Yaojun Callan Jr., Curtis G. Bialek, William |
| author_facet | Sarra, Camilla Sarra, Leopoldo Di Carlo, Luca GrandPre, Trevor Zhang, Yaojun Callan Jr., Curtis G. Bialek, William |
| contents | New experimental methods make it possible to measure the expression levels of many genes, simultaneously, in snapshots from thousands or even millions of individual cells. Current approaches to analyze these experiments involve clustering or low-dimensional projections. Here we use the principle of maximum entropy to obtain a probabilistic description that captures the observed presence or absence of mRNAs from hundreds of genes in cells from the mammalian brain. We construct the Ising model compatible with experimental means and pairwise correlations, and validate it by showing that it gives good predictions for higher-order statistics. We notice that the probability distribution of cell states has many local maxima. By labeling cell states according to the associated maximum, we obtain a cell classification that agrees well with previous results that use traditional clustering techniques. Our results provide quantitative descriptions of gene expression statistics and interpretable criteria for defining cell classes, supporting the hypothesis that cell classes emerge from the collective interaction of gene expression levels. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2408_08037 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Maximum entropy models for patterns of gene expression Sarra, Camilla Sarra, Leopoldo Di Carlo, Luca GrandPre, Trevor Zhang, Yaojun Callan Jr., Curtis G. Bialek, William Biological Physics Statistical Mechanics Molecular Networks New experimental methods make it possible to measure the expression levels of many genes, simultaneously, in snapshots from thousands or even millions of individual cells. Current approaches to analyze these experiments involve clustering or low-dimensional projections. Here we use the principle of maximum entropy to obtain a probabilistic description that captures the observed presence or absence of mRNAs from hundreds of genes in cells from the mammalian brain. We construct the Ising model compatible with experimental means and pairwise correlations, and validate it by showing that it gives good predictions for higher-order statistics. We notice that the probability distribution of cell states has many local maxima. By labeling cell states according to the associated maximum, we obtain a cell classification that agrees well with previous results that use traditional clustering techniques. Our results provide quantitative descriptions of gene expression statistics and interpretable criteria for defining cell classes, supporting the hypothesis that cell classes emerge from the collective interaction of gene expression levels. |
| title | Maximum entropy models for patterns of gene expression |
| topic | Biological Physics Statistical Mechanics Molecular Networks |
| url | https://arxiv.org/abs/2408.08037 |