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Trends in Online Biomonitoring Read chapter Read first chapter

2018 | OriginalPaper | Chapter

scFeatureFilter: Correlation-Based Feature Filtering for Single-Cell RNAseq

Authors : Angeles Arzalluz-Luque, Guillaume Devailly, Anagha Joshi

Published in: Bioinformatics and Biomedical Engineering

Publisher: Springer International Publishing

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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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Literature
1.
Tang, F., Barbacioru, C., Bao, S., Lee, C., Nordman, E., Wang, X., Lao, K., Surani, M.A.: Tracing the derivation of embryonic stem cells from the inner cell mass by single-cell RNA-seq analysis. Cell Stem Cell 6(5), 468–478 (2010)CrossRef
2.
Ramskold, D., Luo, S., Wang, Y.C., Li, R., Deng, Q., Faridani, O.R., Daniels, G.A., Khrebtukova, I., Loring, J.F., Laurent, L.C., Schroth, G.P., Sandberg, R.: Full-length mRNA-seq from single-cell levels of RNA and individual circulating tumor cells. Nat. Biotechnol. 30(8), 777–782 (2012)CrossRef
3.
Soneson, C., Robinson, M.D.: Bias, robustness and scalability in differential expression analysis of single-cell RNA-seq data. bioRxiv (2017)
4.
Lun, A., McCarthy, D., Marioni, J.: A step-by-step workflow for low-level analysis of single-cell RNA-seq data with bioconductor. F1000Research 5(2122) (2016). [version 2; referees: 3 approved, 2 approved with reservations]
5.
Stevant, I., Neirijnck, Y., Borel, C., Escoffier, J., Smith, L.B., Antonarakis, S.E., Dermitzakis, E.T., Nef, S.: Deciphering cell lineage specification during male sex determination with single-cell RNA sequencing. bioRxiv (2017)
6.
Petropoulos, S., Edsgärd, D., Reinius, B., Deng, Q., Panula, S.P., Codeluppi, S., Plaza Reyes, A., Linnarsson, S., Sandberg, R., Lanner, F.: Single-cell RNA-seq reveals lineage and X chromosome dynamics in human preimplantation embryos. Cell 165(4), 1012–1026 (2016)CrossRef
7.
Mantsoki, A., Devailly, G., Joshi, A.: Gene expression variability in mammalian embryonic stem cells using single cell RNA-seq data. Computat. Biol. Chem. 63, 52–61 (2016)CrossRef
8.
Yan, L., Yang, M., Guo, H., Yang, L., Wu, J., Li, R., Liu, P., Lian, Y., Zheng, X., Yan, J., Huang, J., Li, M., Wu, X., Wen, L., Lao, K., Li, R., Qiao, J., Tang, F.: Single-cell RNA-seq profiling of human preimplantation embryos and embryonic stem cells. Nat. Struct. Mol. Biol. 20(9), 1131–1139 (2013)CrossRef
9.
Gierlinski, M., Cole, C., Schofield, P., Schurch, N.J., Sherstnev, A., Singh, V., Wrobel, N., Gharbi, K., Simpson, G., Owen-Hughes, T., et al.: Statistical models for RNA-seq data derived from a two-condition 48-replicate experiment. Bioinformatics 31(22), 3625–3630 (2015)CrossRef
10.
Bray, N.L., Pimentel, H., Melsted, P., Pachter, L.: Near-optimal probabilistic RNA-seq quantification. Nat. Biotechnol. 34(5), 525–527 (2016)CrossRef
11.
McCarthy, D.J., Campbell, K.R., Lun, A.T.L., Wills, Q.F.: Scater: pre-processing, quality control, normalization and visualization of single-cell RNA-seq data in R. Bioinformatics 33(8), 1179–1186 (2017)
Metadata
Title
scFeatureFilter: Correlation-Based Feature Filtering for Single-Cell RNAseq
Authors
Angeles Arzalluz-Luque
Guillaume Devailly
Anagha Joshi
Copyright Year
2018
Book
Bioinformatics and Biomedical Engineering

Print ISBN: 978-3-319-78722-0

Electronic ISBN: 978-3-319-78723-7

Copyright Year: 2018

DOI
https://doi.org/10.1007/978-3-319-78723-7_31

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