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

我根据最近发表在一份高影响因子杂志上的一种方法确认了这个项目的意义。我建立了计算环境，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，包括正则化项和不带正则项。得出正则化估计，尤其是岭回归估计更适合于单细胞RNA-SEQ数据，因为它们减少了数据高度稀疏性的影响。我对估计的特定于簇的基因动力学进行了不同的内插测试，结果令人满意。通过这些测试，我能够在电子计算机上模拟细胞从一个集群分化到另一个集群。

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.