What’s new in the package¶

Plotting functions¶

• New clustered heatmap and data_model.Study.plot_clustermap() and data_model.Study.plot_correlations()

API changes¶

• data_model.Study.save() now saves relative instead of absolute paths, which makes for more portable datapackages
• Underlying code for visualize.DecompositionViz and visualize.ClassifierViz now plots via plot()

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 BaseData and Study

Other¶

• Rename modalities that couldn’t be assigned when bootstrapped=True in compute.splicing.Modalities, from “unassigned” to “ambiguous”

Documentation updates¶

• Update documentation
• Fixed issue with pip install reported by @roryk

Documentation updates¶

• Update gallery
• Update tutorial for advanced study making

New features¶

• Plot the expression or splicing of two samples with Study.plot_two_samples()
• Plot the expression or splicing of two features with Study.plot_two_features()
• Detect outliers with Study.interactive_choose_outliers() which performs a OneClassSVM on the PCA-reduced space of data (either expression or splicing), using the first three components
• Study doesn’t filter out the pooled or outlier samples from the data, only technical outliers with fewer reads than specified in the argument mapping_stats_min_reads.
• To filter expression or splicing data on the number of samples that must detect each feature, you can specify expression_thresh, and metadata_min_samples in the Study constructor.
  • For example, if expression_thresh=1 and metadata_min_samples=3, then we will only take genes which have expression values greater than 1 in at least 3 samples. Additionally, we will also take splicing events which were detected in at least three cells, since metadata_min_samples applies to all data types.

API changes¶

• The attribute data in BaseData (i.e. BaseData.data) now contains all the data, including pooled, singles, and outliers
• The attribute data_original in BaseData (i.e. BaseData.data_original) contains the original, unfiltered data. For example, before removing features detected in fewer than 3 samples with expression > 1.
• BaseData now has the attributes BaseData.singles, BaseData.pooled, and BaseData.outliers which are on-the-fly subsets of BaseData.data. This is to maintain data provenance, meaning if “outliers” is changed, this is also changed.
• In Study, you now must specify expression_feature_rename_col, splicing_feature_rename_col, mapping_stats_number_mapped_col explicitly, they are no longer defaulting to, {splicing,expression}_feature_rename_col="gene_name" and mapping_stats_number_mapped_col="Uniquely mapped reads number"

Other Changes¶

• Status messages in embark() have been moved to stdout instead of stderr to avoid confusion that something is going wrong
• In embark(), user gets notified which samples are removed for having too few reads (default minimum number of reads is \(5\times 10^5\), or half a million reads).