Modisco

Latest version: v0.5.16.4.1

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0.5.5.5

Changes:
- Compatibility with scikit-learn >= 0.22 from https://github.com/kundajelab/tfmodisco/pull/55 (retaining compatibility with versions < 0.22 as well).
- Relaxing of assert statement numerical precision thresholds requested by mmtrebuchet (https://github.com/kundajelab/tfmodisco/pull/54 and https://github.com/kundajelab/tfmodisco/pull/53).

0.5.5.4

The threshold I had to check for symmetry of the coarse-grained affinity matrix within numerical precision was too stringent (presumably because the dot product involves summation so numerical error gets added); relaxed the threshold in commit https://github.com/kundajelab/tfmodisco/commit/adee3119eb1b0c6e83d36692e612dfdd15f56045. atseng95 this should fix the error you messaged Abhi about (I made the numerical threshold much more lax than is probably required - 1e-5 might have been enough - but this is just so that no one gets stuck on that error in the future in some weird edge case).

0.5.5.3

Corresponds to pull request https://github.com/kundajelab/tfmodisco/pull/51 - for situations where the user just wants to extract the seqlets. Demo notebook at https://github.com/kundajelab/tfmodisco/blob/master/examples/H1ESC_Nanog_gkmsvm/JustExtractSeqletsNanog.ipynb

0.5.5.2

The bugfix in https://github.com/kundajelab/tfmodisco/pull/47 broke backward compatibility with some earlier versions of numpy. This tagged release incorporates a fix to restore backward compatibility (commit https://github.com/kundajelab/tfmodisco/commit/6be7ea5084589eec15b79c805317b65bff5573d9) and also makes a minor adjustment to the gapped kmer embedding calculation such that forward and reverse-complement versions of a seqlet now give exactly symmetrical embeddings within numerical precision (commit https://github.com/kundajelab/tfmodisco/commit/19461fab8047617604d97551c3e26d49a49d68cd).

To elaborate on the reason the forward and reverse versions of a seqlet would not give perfectly symmetrical embeddings prior to this fix: consider the case of gapped kmers with a word length of 3 and one gap. Previously, I was treating \*NN and NN\* (e.g. \*AA and AA\*) as though they were redundant with each other, so I only used one of them when computing the embedding. However, \*AA vs. AA\* can produce different results due to the difference in padding; concretely, a seqlet that had a sequence AAGGG contains the AA\* gapped kmer but does NOT contain the \*AA gapped kmer. Thus, when I was only including the AA\* and TT\* gapped kmers in my embedding and was NOT including the \*AA and \*TT gapped kmers, then a seqlet that had the sequence AAGGG would be recorded as containing the AA* gapped kmer but its reverse complement CCCTT would NOT be recorded as having any TT-containing gapped kmer; thus, symmetry was broken. With this fix, I now include BOTH AA\* and \*AA as well as BOTH TT\* and \*TT as features in the gapped kmer embedding; thus, a AAGGG seqlet is recorded as having a match to AA\* while the reverse complement CCCTT is recorded as having a match to \*TT, and symmetry is preserved.

0.5.5.0

Relative to v0.5.4.0, this release incorporates the PRs https://github.com/kundajelab/tfmodisco/pull/47 and https://github.com/kundajelab/tfmodisco/pull/50. The first feature addresses the occurrence of Nan values in modisco.affinitymat.NumpyCosineSimilarity, and the second reduces the memory footprint of graph2binary (thanks hy395!). (Memory usage must be released even further in subsequent releases - see https://github.com/kundajelab/tfmodisco/issues/49 for discussion).

0.5.4.0

Corresponds to pull request https://github.com/kundajelab/tfmodisco/pull/46
* Updated hit scoring strategy in the demo notebook to showcase the combination of the "masked hypothetical CWM cosine similarity" and the "sum of scores" metrics.
* Added associated functions for computing those scores to modisco.util.
* Put in some functionality for trimming motifs (the "AggregatedSeqlet" class in the codebase) according to the information content, or according to the the sum of the absolute value of some score track (e.g. trimming motifs based on the hypothetical contribution scores).
* Did some minor refactoring of the code for computing information-content scaled versions of the position probability matrices.

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