Modisco

Corresponds to PR https://github.com/kundajelab/tfmodisco/pull/77

Corresponds to PR https://github.com/kundajelab/tfmodisco/pull/76

Fix for error when slicing coordinates for revcomp when coordinates go over the edges of the sequence

Also makes a fix for backwards compatibility with numpy version where np.pad requires mode to be provided as an argument

Corresponds to PR https://github.com/kundajelab/tfmodisco/pull/73

Changes:
- Seqlet pruning updated (function `trim_to_positions_with_min_support` in modisco.core). Previously, the limits of `min_support` would be determined by making a histogram of the locations to which seqlet centers align to, and then trimming away positions that didn't have some minimum support. But a location may be supported by the flanks of seqlets, even if is not supported by seqlet centers. Updating this to look at the support from *any* seqlet overlap greatly reduces the amount of seqlets that get unnecessarily trimmed away.
- The previous merging strategy had two components: it looked at both the similarity of motifs as measured by cross-correlation of their contribution score tracks, as well as the density of the clusters (clusters that are less tightly packed should be merged more readily). The density was measured using a t-sne-like strategy, which was a bit ad-hoc and produced values that were hard to interpret intuitively. Now, I still retain the cross-correlation-like similarity, but the 'density' notion is quantified by looking at the distribution of within-cluster and between-cluster pairwise seqlet similarities.

Other small changes:
- Previously, the aforementioned cross-correlation metric in the pattern merging function was implemented by calling scipy.signal.correlate2d, which doesn't do a normalization (thus, correlation values weren't limited to the range -1 to 1). This was ok because I would normalize each track prior to calling scipy.signal.correlate2d - but as a result, the values were scaled according to the number of tracks (e.g. if there were two tasks, each task would generate a contribution score track, and I would have to divide the correlation values to by 2 to put them in the -1 to 1 range). Previously, this scaling was all adjusted for under-the-hood. Now, I just switched to avoid using scipy.signal.correlate2d so that there is no need for all that adjustment.
- `plot_weights_given_ax` now has default values specified for many of the arguments, so it is easier to call

Corresponds to PR https://github.com/kundajelab/tfmodisco/pull/70

Description of changes:
- When I did refactoring to include support for MEME initialization, I had a stray line that effectively caused the "sign consistency check" (which discards motifs for which the signs of the overall contribution scores disagrees with what you expect for the metacluster - such motifs can arise because seqlets get recentered during the various intermediate processing steps) to be bypassed (this effectively means a few extra motifs that seemed to have the wrong sign could have been returned). Related to the error encountered in 66
- Made some minor fixes for tensorflow 2 support
- The final step of tf-modisco is a "reassignment" step where motifs that have a small number of seqlets are disbanded, and an attempt is made to "reassign" their seqlets to the other motifs. If they so desire, users can now access what the tfmodisco motifs are prior to this final reassignment step.

Corresponds to PR https://github.com/kundajelab/tfmodisco/pull/63. Should fix some issues where modisco seems to produce very low-IC motifs; the problem was arising during motif post-processing when the motif was previously recentered around the region of highest average importance; this would sometimes go awry because the high average importance may have been driven by only a few seqlets; now, the motif centering is done based on information content.

There's also support for computing advanced gapped kmer embeddings (which work better than the regular gapped kmer embeddings and also use less memory), but it is still in pure python and I am looking at ways to speed it up.

Corresponds to Pull Request https://github.com/kundajelab/tfmodisco/pull/62. Seqlets comprising a motif are visualized in a tsne plot, and the user can select a subset of the seqlets (by dragging a rectangle around them on the plot) to aggregate and visualize on the fly. Good for dissecting heterogeneity within a motif.

Visualizing a subset of seqlets within the TAL motif from the TAL-GATA toy dataset:
<img width="589" alt="Screenshot 2020-07-09 at 1 47 12 AM" src="https://user-images.githubusercontent.com/2302598/87019085-05300400-c187-11ea-8af8-2ad9d8c836c3.png">