Projektgruppe

Single Cell Data

Tätigkeiten der Projektgruppe

(Dezember 2020) Wir laden ein zur ersten Veranstaltung:

"Power Analyses for single-cell Data" small workshop

Date & Venue: Wednesday Febr 10, 1615-18h, Zoom-meeting

You are cordially invited to a small virtual workshop on
"Power Analyses for single-cell Data". The meeting is intended
to bring together bioinformaticians, statisticians, experimentalists and clinicians
who are wondering how many samples are needed for robust single-cell based insights,
specifically (but not limited to) transcriptomics.

 

For the meeting,  please register here:

https://uni-rostock-de.zoom.us/meeting/register/tZModumhqD0uGNaStJxfcuroE3d5j5F5zlrj

The preliminary program runs as follows:

1615-1630: Introduction

1630-1655: Harald Binder (Freiburg): "Deep generative models for designing single cell sequencing experiments"

1655-1720: Katharina Schmid, Matthias Heinig (Munich): "Design and power analysis for multi-sample single cell genomics experiments"

1720-1745: Kenong Su (Emory University, USA): "Simulation, Power Evaluation, and Sample Size Recommendation for Single Cell RNA-seq" (see abstract below)

 

1745-1800: Discussion

We would be happy about your participation,
and forwarding of this announcement to interested parties.

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(November 2020) Innerhalb des Fachbereich "Medizinische Bioinformatik & Systembiologie" gibt es seit 2020 eine Projektgruppe "Single Cell Data", zunächst geleitet von Georg Fuellen mit Unterstützung durch Harald Binder. Erste Aktivitäten sind ein Workshop zum Thema, mit Unterstützung des German Stem Cell Network, dieser wurde verschoben auf die Tagung in Kiel im September 2021. Bis dahin soll ein zoom-basiertes 1h-Meeting stattfinden in der zweiten Februarwoche 2021, zu "Power Analyses for single-cell Data", mit 2-3 Vorträgen/Softwarevorstellungen, zu scpower vom Helmholtz München und zu „Deep Generative Models“ aus Freiburg.

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ABSTRACTS:

Simulation, Power Evaluation, and Sample Size Recommendation for Single Cell RNA-seq.

Kenong Su (Emory University, USA)

Determining the sample size for adequate power to detect statistical significance is a crucial step at the design stage for high-throughput experiments. This topic in the field of single-cell RNA sequencing becomes more prevalent given the gradually mature single-cell sequencing techniques which allow to sequence thousands of cells at once. However, the unique data characteristics present in scRNA-seq such as sparsity and heterogeneity increase the challenge. We propose POWSC, a simulation-based method, to provide power evaluation and sample size recommendation for single-cell RNA sequencing DE analysis. POWSC consists of a data simulator that creates realistic expression data, and a power assessor that provides a comprehensive evaluation and visualization of the power and sample size relationship. The data simulator in POWSC outperforms two other state-of-art simulators in capturing key characteristics of real datasets. The power assessor in POWSC provides a variety of power evaluations including stratified and marginal power analyses for differential expressions characterized by two forms (phase transition or magnitude tuning), under different comparison scenarios.