Methods for Dissecting High Dimensional Single Cell RNA Sequencing Data
type: CreativeWork
description: Since first being described in 2009 [ 1], single cell RNA sequencing (scRNA-seq) has rapidly advanced into a staple for interrogating cellular identity in heterogeneous populations. Researchers routinely capture transcriptome-wide snapshots of thousands or even millions of individual cells. From these readouts the challenge is to accurately identify distinct patterns of mRNA expression that separate cells with distinct identities. However, several intrinsic properties of scRNA-seq data obfuscate our ability to extract biologically relevant information. Technical error can lead to the loss of resolution in cell similarity or gene co-regulatory relationships, or generate artificial correlations that are related to experimental design rather than biological signals. Often we lack a clear ground truth that would define what cell types should be present in the data set, and which genes are strong discriminators for them. Consequently, it is difficult to know whether any information we have garnered is truly representative of the entire population of cells within the data. The lack of a ground truth is particularly relevant since scRNA-seq data often suffers from the "curse of dimensionality” [ 2– 4], as scRNA-seq data sets typically span tens of thousands of genes. The high number of genes diminishes the validity of common data analysis techniques such as distance and correlation metrics, making it more difficult to identify cell or gene relationships in the data. My aim in this work is to establish a new methodology that can successfully navigate the complexities of scRNA-seq data and maximise our ability to obtain biologically relevant and meaningful insights into a cell population under study. Existing methods designed to interrogate scRNA-seq data struggle to account for two major phenomena: technical drop outs and uninformative genes. Drop outs occur when a gene is measured as having low or zero expression in a cell, but this is a false negative due vi to technical …
abstract: Since first being described in 2009 [ 1], single cell RNA sequencing (scRNA-seq) has rapidly advanced into a staple for interrogating cellular identity in heterogeneous populations. Researchers routinely capture transcriptome-wide snapshots of thousands or even millions of individual cells. From these readouts the challenge is to accurately identify distinct patterns of mRNA expression that separate cells with distinct identities. However, several intrinsic properties of scRNA-seq data obfuscate our ability to extract biologically relevant information. Technical error can lead to the loss of resolution in cell similarity or gene co-regulatory relationships, or generate artificial correlations that are related to experimental design rather than biological signals. Often we lack a clear ground truth that would define what cell types should be present in the data set, and which genes are strong discriminators for them. Consequently, it is difficult to know whether any information we have garnered is truly representative of the entire population of cells within the data. The lack of a ground truth is particularly relevant since scRNA-seq data often suffers from the "curse of dimensionality” [ 2– 4], as scRNA-seq data sets typically span tens of thousands of genes. The high number of genes diminishes the validity of common data analysis techniques such as distance and correlation metrics, making it more difficult to identify cell or gene relationships in the data. My aim in this work is to establish a new methodology that can successfully navigate the complexities of scRNA-seq data and maximise our ability to obtain biologically relevant and meaningful insights into a cell population under study. Existing methods designed to interrogate scRNA-seq data struggle to account for two major phenomena: technical drop outs and uninformative genes. Drop outs occur when a gene is measured as having low or zero expression in a cell, but this is a false negative due vi to technical …
author: Radley, Arthur
language: eng
keywords: Feature selection, Human pre-implantation embryo, Single cell sequencing
license: https://creativecommons.org/licenses/by/4.0/
date published: 2021-10-01
date modified: 2026-07-22T22:24:04Z
provider: Cambridge University
publisher: Networked Digital Library of Theses and Dissertations
type: WebPage
source organization: University of Cambridge

PropertyValue
property ID: DOI
value: 10.17863/CAM.90060