A deep learning model for removing batch effects in microarray data.
- matplotlib == 3.4.2
- numpy == 1.20.3
- pandas == 1.2.4
- torch == 1.8.1
The data is assumed to be in matrix form with columns as samples and features as rows, and \t delimiters.
Expression matrix:
feature Sample_1 Sample_2 ... Sample_n
Feature_1 -0.1661 -1.2160 ... -0.4630
Feature_2 -1.7001 -0.06567 ... 0.8384
...
Feature_m -0.4630 0.8384 ... -0.8774
Sample labels:
sample group batch
Sample_1 1 2
Sample_2 1 1
Sample_3 1 2
...
Sample_n 2 2
- NormAE demo
- Peptide transformer
- Transformer Simple Correction (NEW) - Data for use in the notebook can be found within the shared '\MSSHARE\Camilo_Posso\Batch_Correction' folder.