The context: Gene-expression levels help in determining cellular function. To understand these roles, scientists have performed thousands of gene-expression studies using microarray assays and next-generation sequencing. Such data are typically stored in tabular files (with rows and columns).

The problem: Different functional-genomics repositories and preprocessing tools store gene-expression data in a cacophony of file formats. Analysis tools (such as Python, R, and Excel) do not support all these formats natively. It is time consuming and labor intensive for bioinformaticians to translate data between file formats and identify samples that match specific criteria.

The solution: We developed the ExpressionAble tool to reduce the time from data acquisition to analysis for researchers studying gene expression. This tool can help researchers more easily work with their own data as well as the immense gene-expression resources in public databases.

This is the official repository for the ExpressionAble Python module, which allows for:

• Transforming tabular data sets from one format to another.
• Querying large data sets to extract useful data.
• Selecting columns/features to include in the resulting data set.
• Merging data sets of various formats into a single file.
• Gzipping resulting data sets, as well as the ability to read gzipped files.

Most people will prefer to use the ExpressionAble command-line tool, which combines the features of ExpressionAble with the ease of the command-line.

Basic use of the Python module is described below. You can see full documentation on the functions in this module here.

pip install expressionable

After installing, import the ExpressionAble class with from expressionable import ExpressionAble. An ExpressionAble object represents the file to be transformed. It is then transformed using the export_filter_results method. Here is a simple example of file called input_file.tsv being transformed into an HDF5 file called output_file.h5, while filtering the data on sex and age:

from expressionable import ExpressionAble

my_expressionable = ExpressionAble("input_file.tsv")
my_expressionable.export_filter_results("output_file.h5", filters="Sex == 'M' and Age > 40")

Note that the type of file being read and exported to were not stated explicitly but inferred by ExpressionAble based on the file extensions provided. If necessary, input_file_type and output_file_type can be named explicitly.

We welcome contributions that help expand ExpressionAble to be compatible with additional file formats. If you are interested in contributing, please follow the instructions here.

• CSV
• TSV (samples as rows, variables as columns)
• Transposed TSV (variables as rows, samples as columns)
• JSON
• Excel
• HDF5
• Parquet
• MsgPack
• Stata
• Pickle
• SQLite
• ARFF
• GCT
• GCTX
• PDF
• Gene Expression Omnibus
• Kallisto (RNA-Sequencing)
• Salmon (RNA-Sequencing)
• STAR (RNA-Sequencing)
• HT-Seq (RNA-Sequencing)
• CBio Portal (RNA expression format)

• CSV
• TSV
• JSON
• Excel
• HDF5
• Parquet
• MsgPack
• Stata
• Pickle
• SQLite
• ARFF
• GCT
• RMarkdown notebook
• Jupyter notebook