Rxivist combines preprints from bioRxiv with data from Twitter to help you find the papers being discussed in your field. Currently indexing 70,186 bioRxiv papers from 306,470 authors.
Motivation: Dimension reduction techniques are widely used to interpret high-dimensional biological data. Features learned from these methods are used to discover both technical artifacts and novel biological phenomena. Such feature discovery is critically import to large single-cell datasets, where lack of a ground truth limits validation and interpretation. Transfer learning (TL) can be used to relate the features learned from one source dataset to a new target dataset to perform biologically-driven validation by evaluating their use in or association with additional sample annotations in that independent target dataset. Results: We developed an R/Bioconductor package, projectR, to perform TL for analyses of genomics data via TL of clustering, correlation, and factorization methods. We then demonstrate the utility TL for integrated data analysis with an example for spatial single-cell analysis. Availability: projectR is available on Bioconductor and at https://github.com/genesofeve/projectR.
- Downloaded 380 times
- Download rankings, all-time:
- Site-wide: 30,634 out of 70,177
- In bioinformatics: 3,976 out of 6,878
- Year to date:
- Site-wide: 65,629 out of 70,177
- Since beginning of last month:
- Site-wide: 13,137 out of 70,177
Downloads over time
Distribution of downloads per paper, site-wide
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