Abstract
This chapter describes a pipeline for basic bioinformatics analysis of single-cell sequencing data (see Chap. 10: Single-Cell Library Preparation). Starting with raw sequencing data, we describe how to quality check samples, to create an index from a reference genome, to align the sequences to an index, and to quantify transcript abundances. The curated data sets will enable differential expression analysis, population analysis, and pathway analysis.
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Acknowledgment
The authors thank the members of the Daadi laboratory for their helpful support and suggestions. This work was supported by the Worth Family Fund, the Perry & Ruby Stevens Charitable Foundation and the Robert J., Jr. and Helen C. Kleberg Foundation, the NIH primate center base grant (Office of Research Infrastructure Programs/OD P51 OD011133), and the National Center for Advancing Translational Sciences, National Institutes of Health, through Grant UL1 TR001120.
Disclosures: Dr. Marcel M. Daadi is founder of the biotech company NeoNeuron.
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Kim, J., Daadi, M.M. (2019). Bioinformatics Analysis of Single-Cell RNA-Seq Raw Data from iPSC-Derived Neural Stem Cells. In: Daadi, M. (eds) Neural Stem Cells. Methods in Molecular Biology, vol 1919. Humana Press, New York, NY. https://doi.org/10.1007/978-1-4939-9007-8_11
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DOI: https://doi.org/10.1007/978-1-4939-9007-8_11
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Publisher Name: Humana Press, New York, NY
Print ISBN: 978-1-4939-9005-4
Online ISBN: 978-1-4939-9007-8
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