Flotilla

This is a patch release, with non-breaking changes from v0.3.1.

Plotting
- Removed filling NA of :py:func:`.visualize.gene_ontology.plot_go_enrichment` with arbitrary values

This is a patch release, with non-breaking changes from 0.2.5.

New features
- Add a :py:class:`.data_model.SupplementalData` data type, which allows the
user to store any `pandas.DataFrame` on the :py:class:`.data_model.Study`
object as `study.supplemental`. This is essentially user-driven caching.

Plotting functions
- Changed default loadings plot of PCA to a heatmap of the first 5 PCs

Bug fixes
- Fixed :py:func:`.data_model.Study.save()` to actually save:
- Gene Ontology Data
- Minimum number of mapped reads per sample
- Minimum number of samples to use per feature, at the specified threshold
(e.g. use features with TPM > 1 in at least 20 cells)
- Fixed :py:func:`.data_model.base.subsets_from_metadata` to use boolean
columns properly, which allows for boolean columns in
:py:class:`.data_model.MetaData` and
:py:attr:`.data_model.BaseData.feature_data`

Miscellaneous
- Streamlined test suite to test fewer things at a time, which shortened the
test suite from ~20 minutes to ~3 minutes, about 85% time savings.
- Improved accuracy (fewer false positives) in splicing modality estimation
- Added requirement for new non-plotting features to at least be documented as
IPython notebooks, so the knowledge is shared.
- Changed PCA plot to place legend in "best" place
- Changed default plotting marker from a circle to a randomly chosen symbol
from a list
- Violinplots are now variable width and expand with the number of samples
- This was changed in :py:meth:`.data_model.Study.plot_gene`,
:py:meth:`.data_model.Study.plot_event` and
:py:meth:`.data_model.Study.plot_pca` when `plot_violins=True`
- Add info about data type when reporting that a feature was not found
- Fix lack of tutorial on how to create a datapackage

This is a patch release, with non-breaking changes from v0.2.4. This includes
many changes and bugfixes. Upgrading to this version is highly recommended.

New features
- Added data structure and functions for calculating gene ontology enrichment in `flotilla.data_model.Study.go_enrichment`, using the data structure `flotilla.gene_ontology.GeneOntologyData`

Plotting functions
- New function `flotilla.data_model.Study.plot_expression_vs_inconsistent_splicing()` shows the percent of splicing events in single cells that are inconsistent with the pooled samples. Has the option to choose an expression cutoff.
- Add options to `flotilla.data_model.Study.plot_pca` and `flotilla.data_model.Study.interactive_pca:`
- Keyword argument `color_samples_by` will take a column name from the
`metadata` DataFrame, to color samples by different columns in the
metadata.
- Keyword argument `scale_by_variance` is a boolean which when `True`
(default) will scale the `x` and `y` axes by the explained
variance of their individual principal components (PC1 for `x` and
PC2 for `y`). If `False`, then the axes are the same scale, by the
variance in PC1. Often this will "squish" down the samples in the `y`-axis.

API changes
- `flotilla.data_model.Study.plot_classifier` returns a `flotilla.visualize.predict.ClassifierViz` object
- Multi-index columns for data matrices are no longer supported
- Modalities are now calculated using Bayesian methods
- `flotilla.data_model.Splicing._subset_and_standardize` now doesn't fill
`NA`s with the mean Percent spliced-in/Psi/`\Psi` score for the
event, but rather replaces `NA` with the value 0.5. Then, all values for
that event are transformed with arc cosine
so that all values range from `-\pi` to `+\pi` and are centered
around `0`.

Bug fixes
- Fixed issue with `flotilla.data_model.Study.tidy_splicing_with_expression` and
`flotilla.data_model.Study.filter_splicing_on_expression` which
had an issue with when the index names are not `"miso_id"` or
`"sample_id"`.
- Don't cache `flotilla.data_model.BaseData.feature_renamer_series`, so you
can change the column used to rename features

Miscellaneous
- Add link to PyData NYC talk
- Add scrambled dataset with ~300 samples and both expression and splicing
- Fix build status badge in README
- Removed auto-call to `%matplotlib inline` call within
`flotilla.visualize` because it messes up the `make lint` call
and it's dishonest to the user to be messing with their IPython under the
hood. It's possible they don't want the plotting to be inline, but rather
in a separate X-window as specified by their `$DISPLAY` environment
variable.
- Reformatted all code to pass `make lint` and `make pep8`, and these
standards will be enforced for all future enhancements.
- Add Gitter chat room badge to README

This is a patch release, with non-breaking changes from v0.2.3.

Plotting functions
- New clustered heatmap and `Study.plot_clustermap` and `Study.plot_correlations` (!!)

API changes
- `Study.save()` now saves relative instead of paths, which makes for more portable `datapackages`
- Underlying code for `DecompositionViz` and `ClassifierViz` now plots via `plot()` instead of `__call__`

This is a patch release, with non-breaking changes from v0.2.2.

Compute functions
- Restore `Study.detect_outliers` `Study.interactive_choose_outliers` and `Study.interactive_reset_outliers`

Plotting functions
- Add `Study`-level NMF space transitions/positions

Bug Fixes
- `embark` wouldn't work if `metadata` didn't have a `pooled` column,
now it does
- `BaseData.drop_outliers` would actually drop samples from the data,
but we never want to remove data, only mark it as something to be removed so
all the original data is there
- For all `compute` submodules, add a check to make sure the input
data is truly a probability distribution (non-negative, sums to 1)
- `BaseData.plot_feature` now plots all features with the same name
(e.g. all splicing events within that gene) onto a single `fig` object

Documentation
- Restore some lost documentation on :py:class:`.BaseData` and
:py:class:`.Study`

Other
- Rename modalities that couldn't be assigned when `bootstrapped=True` in
`compute.splicing.Modalities`, from "unassigned" to "ambiguous"