Delineating biological and technical variance in single cell expression data

Arzalluz-Luque, Ángeles; Devailly, Guillaume; Mantsoki, Anna; Joshi, Anagha

doi:10.1016/j.biocel.2017.07.006

Cited by 19 publications

(16 citation statements)

References 38 publications

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“…There is a plethora of data normalization methods that have been, and continue to be, designed to decrease technical noise within and across cells, in order to better perform both gene detection and quantification, and to make these quantities comparable across cells (see [5, 39, 40] for an overview). The challenge we address here is not mitigated by data normalization methods; however, as we argue that when the number of UMIs sequenced per cell decreases drastically, gene quantification information specifically is not present (or useable) in the data, which is a problem that data normalization cannot mitigate.…”

Section: Discussionmentioning

confidence: 99%

scBFA: modeling detection patterns to mitigate technical noise in large-scale single-cell genomics data

Quon

2019

Genome Biol

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Technical variation in feature measurements, such as gene expression and locus accessibility, is a key challenge of large-scale single-cell genomic datasets. We show that this technical variation in both scRNA-seq and scATAC-seq datasets can be mitigated by analyzing feature detection patterns alone and ignoring feature quantification measurements. This result holds when datasets have low detection noise relative to quantification noise. We demonstrate state-of-the-art performance of detection pattern models using our new framework, scBFA, for both cell type identification and trajectory inference. Performance gains can also be realized in one line of R code in existing pipelines.

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Section: Discussionmentioning

confidence: 99%

scBFA: modeling detection patterns to mitigate technical noise in large-scale single-cell genomics data

Quon

2019

Genome Biol

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“…Molecular standards play an important role in deconvolving biological variation from technical variation arising from a measurement tool . In single‐cell sequencing, synthetic spike‐ins and unique molecular identifiers (e.g., short‐random DNA sequences) directly measure error rates, analytical sensitivity, and biases stemming from sample preparation . In flow cytometry, lasers are calibrated before cell sorting using fluorescent microparticles .…”

Section: Introductionmentioning

confidence: 99%

Microparticle Delivery of Protein Markers for Single‐Cell Western Blotting from Microwells

Kim

Chan

Vlassakis

et al. 2018

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Immunoblotting confers protein identification specificity beyond that of immunoassays by prepending protein electrophoresis (sizing) to immunoprobing. To accurately size protein targets, sample analysis includes concurrent analysis of protein markers with known molecular masses. To incorporate protein markers in single-cell western blotting, microwells are used to isolate individual cells and protein marker-coated microparticles. A magnetic field directs protein-coated microparticles to >75% of microwells, so as to 1) deliver a quantum of protein marker to each cell-laden microwell and 2) synchronize protein marker solubilization with cell lysis. Nickel-coated microparticles are designed, fabricated, and characterized, each conjugated with a mixture of histidine-tagged proteins (42.3–100 kDa). Imidazole in the cell lysis buffer solubilizes protein markers during a 30 s cell lysis step, with an observed protein marker release half-life of 4.46 s. Across hundreds of individual microwells and different microdevices, robust log-linear regression fits (R2 > 0.97) of protein molecular mass and electrophoretic mobility are observed. The protein marker and microparticle system is applied to determine the molecular masses of five endogenous proteins in breast cancer cells (GAPDH, β-TUB, CK8, STAT3, ER-α), with <20% mass error. Microparticle-delivered protein standards underpin robust, reproducible electrophoretic cytometry that complements single-cell genomics and transcriptomics.

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“…Our relative importance analysis indicates that high BTM scores relative to reference datasets is associated with certain datasets, suggesting either technical issues or cell misclassifications. Previous studies support the existence of technical variation in different forms [ 8 , 41 ], which can be quantified using synthetic spike-in genes [ 18 ]. Such approaches have estimated that technical factors can explain more of the variation than their biological counterparts [ 42 ].…”

Section: Discussionmentioning

confidence: 95%

DSAVE: Detection of misclassified cells in single-cell RNA-Seq data

Gustafsson¹,

Robinson²,

Inda-Díaz³

et al. 2020

PLoS ONE

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Single-cell RNA sequencing has become a valuable tool for investigating cell types in complex tissues, where clustering of cells enables the identification and comparison of cell populations. Although many studies have sought to develop and compare different clustering approaches, a deeper investigation into the properties of the resulting populations is lacking. Specifically, the presence of misclassified cells can influence downstream analyses, highlighting the need to assess subpopulation purity and to detect such cells. We developed DSAVE (Down-SAmpling based Variation Estimation), a method to evaluate the purity of single-cell transcriptome clusters and to identify misclassified cells. The method utilizes down-sampling to eliminate differences in sampling noise and uses a log-likelihood based metric to help identify misclassified cells. In addition, DSAVE estimates the number of cells needed in a population to achieve a stable average gene expression profile within a certain gene expression range. We show that DSAVE can be used to find potentially misclassified cells that are not detectable by similar tools and reveal the cause of their divergence from the other cells, such as differing cell state or cell type. With the growing use of single-cell RNA-seq, we foresee that DSAVE will be an increasingly useful tool for comparing and purifying subpopulations in single-cell RNA-Seq datasets.

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Delineating biological and technical variance in single cell expression data

Cited by 19 publications

References 38 publications

scBFA: modeling detection patterns to mitigate technical noise in large-scale single-cell genomics data

scBFA: modeling detection patterns to mitigate technical noise in large-scale single-cell genomics data

Microparticle Delivery of Protein Markers for Single‐Cell Western Blotting from Microwells

DSAVE: Detection of misclassified cells in single-cell RNA-Seq data

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