Inferring cells differentiation processes from single-cell Multiome ATACseq+RNAseq data.

我用最近发表在高影响因子期刊上的一种方法证实了这个项目的重要性。我建立了一个计算环境，并致力于估计集群内细胞的基因表达水平。我测试了不同的线性方法，有和没有正则化项。我的结论是，正则化估计，特别是脊回归量，更适合单细胞RNA-seq数据，因为它们减少了数据高稀疏性的影响。我设置了模糊逻辑插值。我对估计的簇特异性基因动力学的插值进行了不同的测试，结果令人满意。通过这些测试，我能够在计算机上模拟细胞从一个簇分化到另一个簇。

I confirmed the significance of this project in terms of a recent method published in a high-impact factor journal.I set up the computational environment and worked on the estimation of gene expression levels of cells within clusters.I tested different linear methods with and without regularization terms. I conclude that estimation with regularization, especially the ridge regressor, was more appropriate for single-cell RNA-seq data as they reduce the impact of the high sparsity of the data.I set up the fuzzy logic interpolation. I performed different tests on the interpolation of the estimated cluster-specific gene dynamics with satisfactory results. With these tests, I was able to perform in-silico simulations of a cell differentiating from one cluster to another.