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iCellR: Combined Coverage Correction and Principal Component Alignment for Batch Alignment in Single-Cell Sequencing Analysis

View ORCID ProfileAlireza Khodadadi-Jamayran, View ORCID ProfileJoseph Pucella, Hua Zhou, View ORCID ProfileNicole Doudican, View ORCID ProfileJohn Carucci, Adriana Heguy, View ORCID ProfileBoris Reizis, View ORCID ProfileAristotelis Tsirigos
doi: https://doi.org/10.1101/2020.03.31.019109
Alireza Khodadadi-Jamayran
1Applied Bioinformatics Laboratories, NYU School of Medicine, New York, NY, USA
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  • For correspondence: alireza.Khodadadi-Jamayran@nyulangone.org aristotelis.Tsirigos@nyulangone.org
Joseph Pucella
2Department of Pathology, NYU School of Medicine, New York, NY, USA
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Hua Zhou
1Applied Bioinformatics Laboratories, NYU School of Medicine, New York, NY, USA
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Nicole Doudican
3Ronald O. Perelman Department of Dermatology, New York University Langone Medical Center, New York, NY
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  • ORCID record for Nicole Doudican
John Carucci
3Ronald O. Perelman Department of Dermatology, New York University Langone Medical Center, New York, NY
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Adriana Heguy
4Genome Technology Center (GTC), NYU School of Medicine, New York, NY, USA
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Boris Reizis
2Department of Pathology, NYU School of Medicine, New York, NY, USA
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Aristotelis Tsirigos
1Applied Bioinformatics Laboratories, NYU School of Medicine, New York, NY, USA
2Department of Pathology, NYU School of Medicine, New York, NY, USA
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  • ORCID record for Aristotelis Tsirigos
  • For correspondence: alireza.Khodadadi-Jamayran@nyulangone.org aristotelis.Tsirigos@nyulangone.org
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SUMMARY

Under-sampling RNA molecules and low-coverage sequencing in some single cell sequencing technologies introduce zero counts (also known as drop-outs) into the expression matrices. This issue may complicate the processes of dimensionality reduction and clustering, often forcing distinct cell types to falsely resemble one another, while eliminating subtle, but important differences. Considering the wide range in drop-out rates from different sequencing technologies, it can also affect the analysis at the time of batch/sample alignment and other downstream analyses. Therefore, generating an additional harmonized gene expression matrix is important. To address this, we introduce two separate batch alignment methods: Combined Coverage Correction Alignment (CCCA) and Combined Principal Component Alignment (CPCA). The first method uses a coverage correction approach (analogous to imputation) in a combined or joint fashion between multiple samples for batch alignment, while also correcting for drop-outs in a harmonious way. The second method (CPCA) skips the coverage correction step and uses k nearest neighbors (KNN) for aligning the PCs from the nearest neighboring cells in multiple samples. Our results of nine scRNA-seq PBMC samples from different batches and technologies shows the effectiveness of both these methods. All of our algorithms are implemented in R, deposited into CRAN, and available in the iCellR package.

Footnotes

  • https://github.com/rezakj/iCellR

Copyright 
The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-NC-ND 4.0 International license.
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Posted April 01, 2020.
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iCellR: Combined Coverage Correction and Principal Component Alignment for Batch Alignment in Single-Cell Sequencing Analysis
Alireza Khodadadi-Jamayran, Joseph Pucella, Hua Zhou, Nicole Doudican, John Carucci, Adriana Heguy, Boris Reizis, Aristotelis Tsirigos
bioRxiv 2020.03.31.019109; doi: https://doi.org/10.1101/2020.03.31.019109
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iCellR: Combined Coverage Correction and Principal Component Alignment for Batch Alignment in Single-Cell Sequencing Analysis
Alireza Khodadadi-Jamayran, Joseph Pucella, Hua Zhou, Nicole Doudican, John Carucci, Adriana Heguy, Boris Reizis, Aristotelis Tsirigos
bioRxiv 2020.03.31.019109; doi: https://doi.org/10.1101/2020.03.31.019109

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