# Statistical Methods for the Normalization and Quantification of Single-Cell RNA-Sequencing Data

> **NIH NIH R00** · JOHNS HOPKINS UNIVERSITY · 2020 · $234,822

## Abstract

Project Summary/Abstract
Recent advances in genomic technology have led to quantitatively measuring the transcript abundance in a
single cell, creating an unprecedented opportunity to investigate important biological questions that can only be
answered at the single-cell level such as early cell development, cell identity and changes in cell state. This
technology has already led to new biological discoveries by associating changes in the transcript profiles of
individual cells with phenotypes including immune response, treatment and disease.
 However, this technology also presents new statistical and computational challenges that need to be
addressed to accurately interpret this data. While some methods developed for measuring transcript levels in
bulk populations of cells can be used for single-cell data, such as aligning raw sequencing reads to the
genome, other steps in the processing, such as normalization and quality control, require new methods to
account for the additional sources of variability visible only at the single-cell level. Failure to account for the
additional cell-to-cell variability leads to systematic errors and biased results in downstream analyses including
differential expression detection, classification of cell types and quantification of transcriptional heterogeneity.
The overall goals of the proposed research are to develop novel statistical methods that will 1) remove these
systematic errors induced from this high-resolution technology by accounting for variability visible only at the
single-cell level and 2) quantify biological variability such as transcriptional heterogeneity within and between
populations of cells.
 My long-term goal is to obtain the skills necessary for me to become a highly capable, independent
scientist poised to bring significant statistical and methodological advances to the rapidly evolving field of
genomics and transcriptomics at the single-cell level. Specifically, this award will provide the training,
mentoring and career development to accomplish my research goals and transition to a tenure-track faculty at
a research university with independent funding. At the completion of this award, I will become part of a new
generation of researchers, proficient in statistics, computational biology and functional genomics, enabling me
to work closely with biomedical researchers profiling the transcriptomes of individual cells.

## Key facts

- **NIH application ID:** 9878125
- **Project number:** 5R00HG009007-05
- **Recipient organization:** JOHNS HOPKINS UNIVERSITY
- **Principal Investigator:** Stephanie Carinne Hicks
- **Activity code:** R00 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $234,822
- **Award type:** 5
- **Project period:** 2018-03-14 → 2021-08-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/9878125

## Citation

> US National Institutes of Health, RePORTER application 9878125, Statistical Methods for the Normalization and Quantification of Single-Cell RNA-Sequencing Data (5R00HG009007-05). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/9878125. Licensed CC0.

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