Abstract
Single cell RNA sequencing is becoming increasingly popular due to rapidly evolving technology, decreasing costs and its wide applicability. However, the technology suffers from high drop-out rate and high technical noise, mainly due to the low starting material. This hinders the extraction of biological variability, or signal, from the data. One of the first steps in the single cell analysis pipelines is, therefore, to filter the data to keep the most informative features only. This filtering step is often done by arbitrarily selecting a threshold.
In order to establish a data-driven approach for the feature filtering step, we developed an R package, scFeatureFilter, which uses the lack of correlation between features as a proxy for the presence of high technical variability. As a result, the tool filters the input data, selecting for the features where the biological variability is higher than technical noise.
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Acknowledgements
We thank Dr. Anna Mantsoki for her invaluable help and input in the development process.
Funding
AJ is a Chancellor’s fellow and AJ lab is supported by institute strategic funding from Biotechnology and Biological Sciences Research Council (BBSRC, BBSRC-BB/P013732/1-ISPG 2017/22 and BBSRC-BB/P013740/1-ISPG 2017/22). GD is funded by the People Programme (Marie Curie Actions FP7/2007-2013) under REA grant agreement No. PCOFUND-GA-2012-600181.
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Arzalluz-Luque, A., Devailly, G., Joshi, A. (2018). scFeatureFilter: Correlation-Based Feature Filtering for Single-Cell RNAseq. In: Rojas, I., Ortuño, F. (eds) Bioinformatics and Biomedical Engineering. IWBBIO 2018. Lecture Notes in Computer Science(), vol 10813. Springer, Cham. https://doi.org/10.1007/978-3-319-78723-7_31
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DOI: https://doi.org/10.1007/978-3-319-78723-7_31
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