Releases: YeoLab/flotilla
Releases · YeoLab/flotilla
v0.3.2
v0.2.6 (April 10th, 2015)
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 anypandas.DataFrame
on the :py:class:.data_model.Study
object asstudy.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
whenplot_violins=True
- This was changed in :py:meth:
- Add info about data type when reporting that a feature was not found
- Fix lack of tutorial on how to create a datapackage
v0.2.5 (March 3rd, 2015)
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 structureflotilla.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
andflotilla.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 whenTrue
(default) will scale thex
andy
axes by the explained
variance of their individual principal components (PC1 forx
and
PC2 fory
). IfFalse
, then the axes are the same scale, by the
variance in PC1. Often this will "squish" down the samples in they
-axis.
- Keyword argument
API changes
flotilla.data_model.Study.plot_classifier
returns aflotilla.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 replacesNA
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
around0
.
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 themake 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
andmake pep8
, and these
standards will be enforced for all future enhancements. - Add Gitter chat room badge to README
v0.2.4 (November 23rd, 2014)
This is a patch release, with non-breaking changes from v0.2.3.
Plotting functions
- New clustered heatmap and
Study.plot_clustermap
andStudy.plot_correlations
(!!)
API changes
Study.save()
now saves relative instead of paths, which makes for more portabledatapackages
- Underlying code for
DecompositionViz
andClassifierViz
now plots viaplot()
instead of__call__
v0.2.3 (November 17th, 2014)
This is a patch release, with non-breaking changes from v0.2.2.
Compute functions
- Restore
Study.detect_outliers
Study.interactive_choose_outliers
andStudy.interactive_reset_outliers
Plotting functions
- Add
Study
-level NMF space transitions/positions
Bug Fixes
embark
wouldn't work ifmetadata
didn't have apooled
column,
now it doesBaseData.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 singlefig
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"
Docs deployment, fix version info
This is a patch release, with non-breaking changes from v0.2.0.
Documentation updates
- Update documentation (http://yeolab.github.io/docs is release, docs-dev is master)
- Fixed issue with pip install reported by
@roryk
New features and new datapackage spec
0.2.0 changed outlier detection to operate on only 2 PCs