Nonlinear methods for dimensionality reduction and clustering of bacterial single-cell sequencing data - intermediate data and figures (MSc thesis)

Data, intermediate results and figures for analyses of my master's thesis in biostatistics at LMU Munich. I took a look on how to use Nonlinear Matrix Decomposition (NMD) (Saul, L., 2022) in the context of bacterial scRNA-seq analysis (Heumos, L., et. al. 2023), replacing Principal Component Analysis in the optimized workflow, as outlined in Ostner, J. (2024).

My thesis was structured along the following objectives:

• implement the algorithms from Seraghiti, G., et. al. (2023) in the Python module nomad in cooperation with Flatiron Institute
• code for the simulation study of the algorithms in Seraghiti, G., et. al. (2023) with varying sparsity can be found in /simulation
• apply NMD in the context of the BacSC workflow (Ostner, J., et. al. (2024)) on raw and normalized counts (found in /application/analysis), also for manually set number of latent dimensions
• explore NMD's potential for imputation of sampling zeros (check /application/NMD_zero_imputation /)
• potential of Poisson-Hurdle model-based clustering (Qiao, Z., et. al. (2023)) for scRNA-seq (/application/poisson_hurdle).