The challenge with transcript expression estimation is the limited number of reads per gene, which makes the assignment of (non unique) reads to transcripts difficult. To address this, Bambu performs a clustered EM, in which ....
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...reads are only assigned to transcripts after similar cells are clustered into groups, reducing the noise in transcript expression estimates due to missing data. We also provide unique counts for single cells (and EM counts for genes/experiments with high sequencing depth)
The recommended way to run bambu-clump is through the nextflow pipeline (bambu-pipe), which optimises barcode detection, alignment, transcript discocery and quantification, and it also includes an option to identify fusion transcripts in single cells https://github.com/GoekeLab/bambu-singlecell-spatial
This work was down by Andre Sim, Min Hao Ling, Chen Ying, Sui Yue, together with Jay Shin's team at GIS. Let us know your comments and feedback, we aim to improve Bambu with every release! https://github.com/GoekeLab/bambu
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