Equivariant layers for RC-complement symmetry in DNA sequence data
This is a repository that implements the layers as described in "Reverse-Complement Equivariant Networks for DNA Sequences" in Keras and Pytorch. The simplest way to use it is to include the appropriate standalone python script in your code.
Just install Keras or Pytorch and you can start importing the layers.
The reg_in, reg_out arguments should be understood as the number of cycles. This correspond to half the input dimension (so for the input, we have 4 nucleotides, so reg=2)
The a_n are of the dimensions of type +1, the b_n of type -1
This class used for the Binary Prediction task is implemented as an example. One can refer to this implementation and for testing, simply run :
python keras_example.py
The equivalent class is also written in Pytorch, and can be ran with :
python pytorch_example.py