Modisco

The main reason for creating this version tag is that I'm about the change the hit scoring strategy in the demo notebook so I can send the newer version of the hit scoring to David & Han. The change between version 0.5.3.0 and 0.5.3.1 is that I added an option to skip the fine-grained clustering step (I don't recommend people actually use this option; I had just added in to see how things behaved without the fine-grained step). I had also added in a version of the demo notebook that ran on Google colab, which I will also update when I put in the newer hit scoring.

Corresponds to PR https://github.com/kundajelab/tfmodisco/pull/45; some modifications for cutting down on the memory footprint.

The auxiliary tracks are not used during the clustering but can be useful for visualization purposes (e.g. if you want to visualize the value of methylation/conservation/dnase footprints at a modisco motif). In the demo notebook at https://github.com/kundajelab/tfmodisco/blob/886f4815c89756a5d010a191c944061d8760c564/test/nb_test/talgata/TF%20MoDISco%20TAL%20GATA%20with%20Activations.ipynb, I use it to visualize the activations of the conv layer for each motif. The extra data tracks are supplied in the call to TfModiscoWorkflow via the `other_tracks` argument. `other_tracks` accepts a list of instances of `modisco.core.DataTrack`.

If the data are such that there is no concept of reverse complements (e.g. RNA-based data), then when instantiating the DataTrack objects, leave the value of `rev_tracks` to None (and also make sure `revcomp=False` when calling TfModiscoWorkflow). Otherwise, rev_tracks should be the value that fwd_tracks would have if the reverse-complement of the input sequence was provided (e.g. for conv layer activations, you can reverse-complement the original input sequence and recompute the conv layer activations). (At the time of writing, I have not personally tested out how TFMoDISco behaves for RNA-type data extensively, though others have)

If the data is such that there is no positional axis (e.g. if you want to visualize the activations of the fully-connected layer for each motif), set `has_pos_axis` to False when instantiating the DataTrack object. Note that I have not tested the functionality with `has_pos_axis=False` at all.

Minor bugfix release corresponding to pull request https://github.com/kundajelab/tfmodisco/pull/40

rosaxma received the error `ValueError: Negative values in data passed to 'pairwise_distances'. Precomputed distance need to have non-negative values` when scikit's NearestNeighbors functions were called. This fix shifts all the distances upwards so that they are all nonnegative, which appears to eliminate the error without affecting the results. I am not sure why this error wasn't encountered before - it may have to do with the particular version of scikit.

Release corresponds to pull request https://github.com/kundajelab/tfmodisco/pull/39

Percentile-based thresholding is triggered if the number of passing windows produced through null-distribution-based thresholding does not fall within min_passing_windows_frac and max_passing_windows_frac. By default, the percentiles are taken w.r.t. the absolute values. This feature adds an argument separate_pos_neg_thresholds which can be set to True when instantiating a TfModiscoWorkflow object to take the percentiles for positive values and negative values separately, as opposed to taking the percentiles w.r.t. the absolute values. The default value of the argument is False, for backward compatibility. A notebook testing out the feature is at https://github.com/kundajelab/tfmodisco/blob/68bef1575ddec5f55e7605f64fd3753d43d2ca5c/test/nb_test/NoRevcompAndSepPosNegThresh.ipynb

There were a couple of other very minor changes that can cause differences within numerical precision. The first was that in `window_sum_function` in line 103 of coordproducers.py, the running window sums are now computed using `np.cumsum`, rather than with a python loop. The second was that in lines 548 and 549 of coordproducers.py, the criterion for meeting the threshold has been changed to `y > pos_threshold` and `y < neg_threshold`, whereas previously it was `y >= pos_threshold` and `y <= neg_threshold`.

Pull request here: https://github.com/kundajelab/tfmodisco/pull/38

To avoid using reverse complements (e.g. if working with splicing motifs), set the argument revcomp=False when calling a TfModiscoWorkflow instance on your data. If reloading a saved TfModisco results object, then you also have to set revcomp=False when calling `prep_track_set`. Otherwise, the revcomp argument is by default True (for backwards compatibility). Permalink to a notebook demonstrating the functionality is here: https://github.com/kundajelab/tfmodisco/blob/d88a1dba7f59f6dc8f62aa267ac42eb5e53037d4/test/nb_test/NoRevcomp.ipynb