Dual Dean entrainment with volume ratio modulation for efficient co-encapsulation: Extreme single-cell indexing

AbstractThe future of single cell diversity screens involves ever-larger sample sizes, dictating the need for higher throughput methods with low analytical noise to accurately describe the nature of the cellular system. Current approaches are limited by the Poisson statistic, requiring dilute cell suspensions and associated losses in throughput. In this contribution, we apply Dean entrainment to both cell and bead inputs, defining different volume packets to effect efficient co-encapsulation. Volume ratio scaling was explored to identify optimal conditions. This enabled the co-encapsulation of single cells with reporter beads at rates of ~1 million cells/hour, while increasing assay signal-to-noise with cell multiplet rates of ~2.5% and capturing ~70% of cells. The method, called Pirouette-seq, extends our capacity to investigate biological systems.TOC AbstractPirouette-seq involves cell and reporter bead inertial ordering for efficient co-encapsulation, achieving a throughput of 1 million cells/hour, a 2.5% multiplet rate and a 70% cell capture efficiency.

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Faculty Opinions recommendation of Single-cell epigenomics: Recording the past and predicting the future.

Faculty Opinions – Post-Publication Peer Review of the Biomedical Literature ◽

10.3410/f.731793889.793553877 ◽

2018 ◽

Author(s):

Fuchou Tang

Keyword(s):

Single Cell ◽

The Past ◽

The Future

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Disentangling neural cell diversity using single-cell transcriptomics

Nature Neuroscience ◽

10.1038/nn.4366 ◽

2016 ◽

Vol 19 (9) ◽

pp. 1131-1141 ◽

Cited By ~ 189

Author(s):

Jean-Francois Poulin ◽

Bosiljka Tasic ◽

Jens Hjerling-Leffler ◽

Jeffrey M Trimarchi ◽

Rajeshwar Awatramani

Keyword(s):

Single Cell ◽

Neural Cell ◽

Cell Diversity

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Letters to our Future Selves? High-Powered Replication Attempts Question Effects on Future Orientation, Delinquent Decisions, and Risky Investments

Social Cognition ◽

10.1521/soco.2020.38.6.521 ◽

2020 ◽

Vol 38 (6) ◽

pp. 521-554

Author(s):

Laura Quinten ◽

Anja Murmann ◽

Hanna A. Genau ◽

Rafaela Warkentin ◽

Rainer Banse

Keyword(s):

Problem Behavior ◽

How Strong Is the Evidence for the Effectiveness of Working Memory Training?

Cognitive and Working Memory Training ◽

10.1093/oso/9780199974467.003.0004 ◽

2019 ◽

pp. 58-76

Author(s):

Claudia C. von Bastian ◽

Sabrina Guye ◽

Carla De Simoni

Keyword(s):

Working Memory ◽

Cognitive Abilities ◽

Small Sample ◽

Sound Studies ◽

Working Memory Training ◽

Memory Training ◽

Sample Sizes ◽

Small Sample Sizes ◽

Transfer Tasks ◽

Larger Sample

This chapter argues that the question of whether working memory training can induce cognitive plasticity in terms of transfer effects cannot be conclusively answered yet due to persisting methodological issues across the literature. The shortcomings discussed include the lack of theoretically motivated selection of training and transfer tasks, the lack of active control groups, and small sample sizes. These problems call into question the strength of the existing evidence. Indeed, reevaluating published findings with Bayesian inference indicated that only a subset of published studies contributed interpretable evidence. The chapter concludes that the current body of literature cannot conclusively support claims that WM training does or does not improve cognitive abilities and stresses the need for theory-driven, methodologically sound studies with larger sample sizes.

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scHinter: imputing dropout events for single-cell RNA-seq data with limited sample size

Bioinformatics ◽

10.1093/bioinformatics/btz627 ◽

2019 ◽

Author(s):

Pengchao Ye ◽

Wenbin Ye ◽

Congting Ye ◽

Shuchao Li ◽

Lishan Ye ◽

...

Keyword(s):

Gene Expression ◽

Sample Size ◽

Single Cell ◽

Wide Spectrum ◽

Dropout Rate ◽

Supplementary Information ◽

Sample Sizes ◽

Limited Sample ◽

Limited Sample Size ◽

The Impact

Abstract Motivation Single-cell RNA-sequencing (scRNA-seq) is fast and becoming a powerful technique for studying dynamic gene regulation at unprecedented resolution. However, scRNA-seq data suffer from problems of extremely high dropout rate and cell-to-cell variability, demanding new methods to recover gene expression loss. Despite the availability of various dropout imputation approaches for scRNA-seq, most studies focus on data with a medium or large number of cells, while few studies have explicitly investigated the differential performance across different sample sizes or the applicability of the approach on small or imbalanced data. It is imperative to develop new imputation approaches with higher generalizability for data with various sample sizes. Results We proposed a method called scHinter for imputing dropout events for scRNA-seq with special emphasis on data with limited sample size. scHinter incorporates a voting-based ensemble distance and leverages the synthetic minority oversampling technique for random interpolation. A hierarchical framework is also embedded in scHinter to increase the reliability of the imputation for small samples. We demonstrated the ability of scHinter to recover gene expression measurements across a wide spectrum of scRNA-seq datasets with varied sample sizes. We comprehensively examined the impact of sample size and cluster number on imputation. Comprehensive evaluation of scHinter across diverse scRNA-seq datasets with imbalanced or limited sample size showed that scHinter achieved higher and more robust performance than competing approaches, including MAGIC, scImpute, SAVER and netSmooth. Availability and implementation Freely available for download at https://github.com/BMILAB/scHinter. Supplementary information Supplementary data are available at Bioinformatics online.

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Evaluation of tools for highly variable gene discovery from single-cell RNA-seq data

Briefings in Bioinformatics ◽

10.1093/bib/bby011 ◽

2018 ◽

Vol 20 (4) ◽

pp. 1583-1589 ◽

Cited By ~ 32

Author(s):

Shun H Yip ◽

Pak Chung Sham ◽

Junwen Wang

Keyword(s):

Gene Expression ◽

Single Cell ◽

Differentially Expressed Gene ◽

Embryonic Stem ◽

Rna Seq ◽

Software Packages ◽

Homogeneous Cell ◽

Variable Gene ◽

Cell Variation ◽

Larger Sample

Abstract Traditional RNA sequencing (RNA-seq) allows the detection of gene expression variations between two or more cell populations through differentially expressed gene (DEG) analysis. However, genes that contribute to cell-to-cell differences are not discoverable with RNA-seq because RNA-seq samples are obtained from a mixture of cells. Single-cell RNA-seq (scRNA-seq) allows the detection of gene expression in each cell. With scRNA-seq, highly variable gene (HVG) discovery allows the detection of genes that contribute strongly to cell-to-cell variation within a homogeneous cell population, such as a population of embryonic stem cells. This analysis is implemented in many software packages. In this study, we compare seven HVG methods from six software packages, including BASiCS, Brennecke, scLVM, scran, scVEGs and Seurat. Our results demonstrate that reproducibility in HVG analysis requires a larger sample size than DEG analysis. Discrepancies between methods and potential issues in these tools are discussed and recommendations are made.

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A Longitudinal Field Investigation of Auditor Risk Assessments and Sample Size Decisions

The Accounting Review ◽

10.2308/accr.2003.78.4.983 ◽

2003 ◽

Vol 78 (4) ◽

pp. 983-1002 ◽

Cited By ~ 44

Author(s):

Randal J. Elder ◽

Robert D. Allen

Keyword(s):

Sample Size ◽

Field Investigation ◽

Risk Assessments ◽

Sample Sizes ◽

Accounting Firms ◽

Inherent Risk ◽

Changes In Risk ◽

Larger Sample ◽

Average Sample ◽

Do So

This study examines changes in auditor risk assessments and sample size decisions based on information gathered from three large accounting firms for audits during 1994 and 1999. The five-year interval between data collection periods allows us to measure changes in risk assessments and sample sizes between the two periods. Auditors relied on controls and assessed inherent risk below the maximum on most audits, and were more likely to do so in the later period, consistent with a trend of lower risk assessment levels. Average sample sizes declined between 1994 and 1999 for the firms that had larger sample sizes in the earlier period. Overall, we find a significant relationship between inherent risk assessments and sample sizes, but this relationship is stronger in the earlier period and is not significant for all firms, especially in the later period. We find limited evidence of a relationship between control risk and sample sizes.

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