Batch Effect Correction Tools

Variability in datasets not only results from biological processes, but also from technical bias [Lander1999]. InMoose offers a collection of tools for the correction of such technical bias, also called batch effects.

Please refer to [Behdenna2023] for a detailed comparison of InMoose implementation with the original R implementations.

Batch effect correction per type of data:

• for microarray data
• for RNASeq data

References

[Behdenna2023]

A. Behdenna, M. Colange, J. Haziza, A. Gema, G. Appé, C.-A. Azencott and A. Nordor. 2023. pyComBat, a Python tool for batch effects correction in high-throughput molecular data using empirical Bayes methods. BMC Bioinformatics 7;24(1):459. doi:10.1186/s12859-023-05578-5

[Johnson2007]

W. E. Johnson, C. Li, A. Rabinovic. 2007. Adjusting batch effects in microarray expression data using empirical Bayes methods. Biostatistics, 8, 118–12. doi:10.1093/biostatistics/kxj037

[Lander1999]

E. S. Lander. 1999. Array of hope. Nature Genetics, 21(1 Suppl), 3-4. doi:10.1038/4427

[Zhang2020]

Y. Zhang, G. Parmigiani, W. E. Johnson. 2020. ComBat-Seq: batch effect adjustment for RNASeq count data. NAR Genomics and Bioinformatics, 2(3). doi:10.1093/nargab/lqaa078