RNAlysis 1.3.3

RNAlysis provides a modular analysis pipeline for RNA sequencing data. The package includes various methods for filtering, data visualisation, exploratory analyses, enrichment anslyses and clustering.

RNAlysis - RNA sequencing data analysis

What is it?

RNAlysis is a python package providing modular analysis pipeline for RNA sequencing data. The package includes various filtering methods, data visualisation, clustering analyses, enrichment anslyses and exploratory analyses.

RNAlysis allows you to perform filtering and analyses at any order you wish. It has the ability to save or load your progress at any given phase, Wand document the order of operations you performed in the saved file names.

RNAlysis works with sequencing count matrices and differential expression output files in general, and integrates in particular with python’s HTSeq-count and R’s DESeq2. * Documentation: https://guyteichman.github.io/RNAlysis

Main Features

• Filtering of count matrices, fold changes and differential expression tables.

• Normalization of count matrices

• Exploratory data analysis and visualisation

• Enrichment analysis for custom attributes

• Clustering and dimentionality reduction

Dependencies

• numpy

• pandas

• matplotlib

• seaborn

• tissue_enrichment_analysis

• statsmodels

• sklearn

• ipyparallel

• grid_strategy

• Distance

Where to get it

Use the following command in the python prompt:

pip install RNAlysis

Credits

This package was created with Cookiecutter and the audreyr/cookiecutter-pypackage project template.

History

1.3.3 (2020-03-28)

• First stable release on PyPI.

Added

• Added Filter.sort(), and upgraded the functionality of Filter.filter_top_n().

• Added UpSet plots and Venn diagrams to enrichment module.

• User-defined biotype reference tables can now be used.

• Filter operations now print out the result of the operation.

• Enrichment randomization tests now also support non-WBGene indexing.

• Filter.biotypes() and FeatureSet.biotypes() now report genes that don’t appear in the biotype reference table.

• Filter.biotypes() can now give a long-form report with descriptive statistics of all columns, grouped by biotype.

• Added code examples to the user guide and to the docstrings of most functions.

Changed

• Changed argument order and default values in filtering.CountFilter.from_folder().

• Changed default title in scatter_sample_vs_sample().

• Changed default filename in CountFilter.fold_change().

• Settings are now saved in a .yaml format. Reading and writing of settings have been modified.

• Changed argument name ‘deseq_highlight’ to ‘highlight’ in scatter_sample_vs_sample(). It can now accept any Filter object.

• Updated documentation and default ‘mode’ value for FeatureSet.go_enrichment().

• Updated the signature and function of general.load_csv() to be clearer and more predictable.

• Changed argument names in CountFilter.from_folder().

• Modified names and signatures of .csv test files functions to make them more comprehensible.

• Renamed ‘Filter.filter_by_ref_table_attr()’ to ‘Filter.filter_by_attribute()’.

• Renamed ‘Filter.split_by_ref_table_attr()’ to ‘Filter.split_by_attribute()’.

• Renamed ‘Filter.norm_reads_with_size_factor()’ to ‘Filter.normalize_with_scaling_factors()’. It can now use any set of scaling factors to normalize libraries.

• Renamed ‘Filter.norm_reads_to_rpm()’ to ‘Filter.normalize_to_rpm()’.

• Made some functions in the general module hidden.

Fixed

• Various bug fixes

Removed

• Removed the ‘feature_name_to_wbgene’ module from RNAlysis.

1.3.2 (2019-12-11)

• First beta release on PyPI.