Peax is a tool for interactive visual pattern search and exploration in epigenomic data based on unsupervised representation learning with autoencoders
Currently, the search is based on a sliding-window approach over the input data. Each window is taken as is and encoded using the associated autoencoder. This approach is geared towards search in a global context, i.e., find two peaks that appear as peaks in the global context. This requires the underlying distribution is roughly the same across the entire datasets.
In cases where this is not the case, it can be useful to allow instance-based normalization. I.e., prior to encoding a window, the window could be normalized based on its local neighborhood. This could theoretically be done prior to the search but it would be convenient if Peax supports this out of the box at runtime.
Currently, the search is based on a sliding-window approach over the input data. Each window is taken as is and encoded using the associated autoencoder. This approach is geared towards search in a global context, i.e., find two peaks that appear as peaks in the global context. This requires the underlying distribution is roughly the same across the entire datasets.
In cases where this is not the case, it can be useful to allow instance-based normalization. I.e., prior to encoding a window, the window could be normalized based on its local neighborhood. This could theoretically be done prior to the search but it would be convenient if Peax supports this out of the box at runtime.