Single Cell Power

scPower accelerates and optimizes the design of multi-sample single …

Single cell RNA-seq has revolutionized transcriptomics by providing cell type resolution for differential gene expression and expression quantitative trait loci (eQTL) analyses. However,...

Statistical Power Analysis for Designing Bulk, Single-Cell, and …

In this paper, we review and discuss the power analysis for three types of gene expression profiling technologies from a practical standpoint, including bulk RNA-seq, single-cell RNA-seq, and high-throughput spatial transcriptomics. Specifically, we describe the existing power analysis tools for each research objective for each of ...

scPower accelerates and optimizes the design of multi-sample single …

Here, we present scPower; a statistical framework for the design and power analysis of multi-sample single cell transcriptomic experiments. We modelled the relationship between sample size, the number of cells per individual, sequencing depth, and the power of detecting differentially expressed genes within cell types. We ...

Maximizing statistical power to detect differentially …

Here, we demonstrate the utility of scPOST by simulating thousands of large single-cell datasets to investigate how different study design choices affect power to detect differential abundance between conditions, thus …

Single-cell analysis

In cell biology, single-cell analysis and subcellular analysis [1] refer to the study of genomics, transcriptomics, proteomics, metabolomics, and cell–cell interactions at the level of an individual cell, as opposed to more conventional methods which study bulk populations of many cells.

Statistical Power Analysis for Designing Bulk, Single …

In this paper, we review and discuss the power analysis for three types of gene expression profiling technologies from a practical standpoint, including bulk RNA-seq, single-cell RNA-seq, and high-throughput spatial …

Single-cell analysis

OverviewGenomicsSingle-cell isolationTranscriptomicsProteomicsMetabolomicsReconstructing developmental trajectoriesCell–cell interaction

Single-cell genomics is heavily dependent on increasing the copies of DNA found in the cell so that there is enough statistical power for accurate sequencing. This has led to the development of strategies for whole genome amplification (WGA). Currently, WGA strategies can be grouped into three categories: • Controlled priming and PCR amplification: Adapter-Linker PCR WGA

Power analysis of single-cell RNA-sequencing …

Single-cell RNA sequencing (scRNA-seq) has become an established and powerful method to investigate transcriptomic cell-to-cell variation, thereby revealing new cell types and providing...

The secret power of the single cell

The microscopic world of the single, living cell mirrors our own in so many ways: cells are essentially autonomous, sentient and ingenious. In the lives of single cells we can …

Helmholtz Zentrum München

Detect cell types This section determines the power to detect a sufficient number of cells from a cell type of interest in each individual. This is important as a cell-type specific DE or eQTL analysis is only possible if enough cells of this cell type are detected. The method calculates the minimal number of cells per individual which are ...

The secret power of the single cell

The microscopic world of the single, living cell mirrors our own in so many ways: cells are essentially autonomous, sentient and ingenious. In the lives of single cells we can perceive the roots of our own intelligence.

Power analysis of single-cell RNA-sequencing …

A comparison framework applied to 15 single-cell RNA-seq protocols reveals differences in accuracy and sensitivity and discusses the utility of RNA spike-in standards. Single-cell RNA sequencing ...

Statistical Power Analysis for Designing Bulk, Single-Cell, and

In this paper, we review and discuss the power analysis for three types of gene expression profiling technologies from a practical standpoint, including bulk RNA-seq, single-cell RNA-seq, and high-throughput spatial transcriptomics. Specifically, we describe the existing power analysis tools for each research objective for each of ...

PEM single fuel cell as a dedicated power source for high …

single fuel cell as a dedicated power source for high-inductive superconducting coils. International Journal of Hydrogen Energy, 2018, 43 (11), pp.5913-5921. ￿10.1016/j.ijhydene.2017.09.013￿. ￿hal-01584823￿

Simulation, power evaluation and sample size recommendation for single …

Moreover, cell types with higher proportion are associated with higher power: the power for cell type astr_vs_neur comparison is higher than the other comparisons, since their effective sample sizes are greater due to their higher proportions in the cell population. These results provide detailed information for researchers to choose a proper sample size. For example, if one …

powsimR: power analysis for bulk and single cell RNA-seq ...

Summary: Power analysis is essential to optimize the design of RNA-seq experiments and to assess and compare the power to detect differentially expressed genes in RNA-seq data. PowsimR is a flexible tool to simulate and evaluate differential expression from bulk and especially single-cell RNA-seq data making it suitable for a priori and posterior power …

scPower accelerates and optimizes the design of multi-sample …

Here, we present scPower; a statistical framework for the design and power analysis of multi-sample single cell transcriptomic experiments. We modelled the relationship …

The technological landscape and applications of single-cell multi …

Single-cell multi-omics technologies and methods characterize cell states and activities by simultaneously integrating various single-modality omics methods that profile the...

Method of moments framework for differential expression ...

Single-cell RNA sequencing (scRNA-seq) ... A unique application of Memento on large-scale census data lies in its improved power to compare cell groups, particularly beneficial for those that are rare in individual datasets. To illustrate this, we utilized Memento in its precomputed mode to identify DM genes between conventional (cDC) and plasmacytoid …

Single-cell approaches in human microbiome research

Although such combinatorial barcoding strategies eliminate the need for single-cell isolation, leveraging the power of existing commercial platforms developed for droplet-based isolation and scRNA-seq of eukaryotic cells has also proved possible. 10X Genomics'' Chromium system is a widely used platform comprising commercially available kits ...

Maximizing statistical power to detect differentially abundant cell ...

Here, we demonstrate the utility of scPOST by simulating thousands of large single-cell datasets to investigate how different study design choices affect power to detect differential abundance between conditions, thus informing optimal allocation of limited resources, e.g., cost of patient samples and single-cell assays. scPOST is ...

Harnessing the deep learning power of foundation models in single-cell ...

Foundation models hold great promise for analyzing single-cell omics data, yet various challenges remain that require further advancements. In this Comment, we discuss the progress, limitations ...

The technological landscape and applications of single-cell

Single-cell multi-omics technologies and methods characterize cell states and activities by simultaneously integrating various single-modality omics methods that profile the transcriptome, genome ...

Power analysis of single-cell RNA-sequencing experiments

Single-cell RNA sequencing (scRNA-seq) has become an established and powerful method to investigate transcriptomic cell-to-cell variation, thereby revealing new cell types and providing...

Best practices for single-cell analysis across modalities

Recent advances in single-cell technologies have enabled high-throughput molecular profiling of cells across modalities and locations. Single-cell transcriptomics data can now be complemented by ...

Design and power analysis for multi-sample single cell ...

Single cell RNA-seq is revolutionizing transcriptomics and is enabling interindividual differential gene expression analysis and identification of genetic variants associated with gene expression, so called expression quantitative trait loci at cell-type resolution.