Quick start

BRIE estimates the isoform proportion for two-isoform events across many single cells. For getting started quickly, there are two main steps to go. A demo file is available at brie2_demo.sh. Sepcifically, you can follow the steps below.

Step1: read counts

First, you need to count the isoform specific reads in each splicing events in each cell. For alternative splicing, e.g., exon-skipping event, you can download the splicing annotations generated by us or make your own, e.g., with briekit.

Then you can use the brie-count fetch the read count tensor, which will be stored in hdf5 format as AnnData. See more details on brie-count CLI, and you can use this example command line.

brie-count -a AS_events/SE.gold.gtf -S sam_and_cellID.tsv -o out_dir -p 15

Besides the SE event, other types of alternative splicing, e.g., intron retaining is also applicable with BRIE. Some pre-processing utilities will be available soon.

Step2: quantification

Step2.1: isoform quantification

Second, you can quantify the isoform with cell / gene or none features. Usually, we recommend to use aggregated imputation even if you don’t have any feature, namely mode 2 in brie-quant CLI as follows (please add --interceptMode gene),

brie-quant -i out_dir/brie_count.h5ad -o out_dir/brie_quant_aggr.h5ad --interceptMode gene

Step2.2 phenotype detection

If you have cell level features, e.g., disease condition or cell type or continuous variable, you can use it in cell features to detect variable splicing events as phenotypes for further analysis. This is mode 3 in brie-quant, so requires -c and --LRTindex

brie-quant -i out_dir/brie_count.h5ad -o out_dir/brie_quant_cell.h5ad \
    -c $DATA_DIR/cell_info.tsv --interceptMode gene --LRTindex=All

Step3: downstream analysis

The BRIE output AnnData files are compatible with Scanpy, hence you can easily use it for dimension reduction, clustering, and other visualization. Example analyses are coming soon.