New Paper: Transformation and Preprocessing of Single-Cell RNA-Seq Data
The paper explores the effect of different variance stabilizing transformations on the downstream analysis of single-cell data. A comprehensive benchmark shows that the simple log-transformation in combination with PCA shows performance on par with more complicated methods, but is computationally much more efficient to calculate.
This study is important for the DECODE project because the choice of preprocessing method influences many of the later analysis steps.
Further information on the publication can be found here.
- Transformation and Preprocessing of Single-Cell RNA-Seq Data
- glmGamPoi: fitting Gamma-Poisson generalized linear models on single cell count data
- Identifying temporal and spatial patterns of variation from multimodal data using MEFISTO
- Computational methods development
- Wolfgang Huber new co-director of the MMPU