On the importance of data transformation for data integration in single-cell RNA sequencing analysis

This repository is for the investigation of the impact of data normalization in integrative analysis of the single-cell RNA-seq data.

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More information

• bioRxiv
• Poster

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Reference

• Luecken, M.D., Büttner, M., Chaichoompu, K. et al. Benchmarking atlas-level data integration in single-cell genomics. Nat Methods 19, 41–50 (2022).
• Constantin Ahlmann-Eltze, Wolfgang Huber. Comparison of Transformations for Single-Cell RNA-Seq Data bioRxiv (2021).
• Tran, H.T.N., Ang, K.S., Chevrier, M. et al. A benchmark of batch-effect correction methods for single-cell RNA sequencing data. Genome Biol 21, 12 (2020).
• Wang, C., Gao, X. & Liu, J. Impact of data preprocessing on cell-type clustering based on single-cell RNA-seq data. BMC Bioinformatics 21, 440 (2020).
• Tian, L., Dong, X., Freytag, S. et al. Benchmarking single cell RNA-sequencing analysis pipelines using mixture control experiments. Nat Methods 16, 479–487 (2019).