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BRIE: Bayesian regression for isoform estimate

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BRIE: Bayesian Regression for Isoform Estimate

About BRIE

Welcome to the new BRIE2 (Bayesian regression for isoform estimate, v2), a scalable Bayesian method to robustly identify splicing phenotypes in single cells RNA-seq designs and accurately estimate isoform proportions and its uncertainty.

BRIE2 supports isoform quantification for different needs:

  1. cell features: informative prior is learned from shared cell processes. It also allows to effectively detect splicing phenotypes by using Evidence Lower Bound gain, an approximate of Bayes factor.
  2. gene features: informative prior is learned from shared gene regulatory features, e.g., sequences and RNA protein binding
  3. no feature: use zero-mean logit-normal as uninformative prior, namely merely data deriven

Note, BRIE1 CLI is still available in this version but changed to brie1 and brie1-diff.


BRIE2 is available through PyPI. To install, type the following command line, and add -U for upgrading:

pip install -U brie

Alternatively, you can install from this GitHub repository for latest (often development) version by following command line

pip install -U git+

In either case, add --user if you don’t have the write permission for your Python environment.

For more instructions, see the installation manual.

Manual and examples

The full manual is at More examples and tutorials are coming soon.

In brief, you need to run brie-count first, which will return a count matrix and hdf5 file for AnnData. Then you can use brie-quant to perform quantification in different settings. Type command line brie-count -h and brie-quant -h to see the full arguments.


Yuanhua Huang and Guido Sanguinetti. BRIE: transcriptome-wide splicing quantification in single cells. Genome Biology, 2017; 18(1):123.

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