Browse
Submit Data
Databases
API
Help

Dataset Information

0 Views

0 Connections

0 Citations

0 Reanalyses

0 Downloads

Omics score: 0

MultiK: An Automated Tool to Infer Optimal Cluster Numbers in Single-Cell RNA Sequencing Data

ABSTRACT: This paper proposes MultiK, a data-driven tool for objective selection of multiple insightful numbers of clusters (K) from single-cell RNA-seq data.

ORGANISM(S): Mus musculus

PROVIDER: GSE165336 | GEO | 2021/07/19

REPOSITORIES: GEO

ACCESS DATA

Json Xml

Dataset's files

Source:

			Action	DRS
		Other

Items per page:

1 - 1 of 1

Similar Datasets

MultiK: An Automated Tool to Infer Optimal Cluster Numbers in Single-Cell RNA Sequencing Data

Project description:MultiK: An Automated Tool to Infer Optimal Cluster Numbers in Single-Cell RNA Sequencing Data

| PRJNA694120 | ENA

MultiK: an automated tool to determine optimal cluster numbers in single-cell RNA sequencing data.

Project description:Single-cell RNA sequencing (scRNA-seq) provides new opportunities to characterize cell populations, typically accomplished through some type of clustering analysis. Estimation of the optimal cluster number (K) is a crucial step but often ignored. Our approach improves most current scRNA-seq cluster methods by providing an objective estimation of the number of groups using a multi-resolution perspective. MultiK is a tool for objective selection of insightful Ks and achieves high robustness through a consensus clustering approach. We demonstrate that MultiK identifies reproducible groups in scRNA-seq data, thus providing an objective means to estimating the number of possible groups or cell-type populations present.

| S-EPMC8375188 | biostudies-literature

Cluster randomised trials with different numbers of measurements at baseline and endline: Sample size and optimal allocation.

Project description:Background/aimsPublished methods for sample size calculation for cluster randomised trials with baseline data are inflexible and primarily assume an equal amount of data collected at baseline and endline, that is, before and after the intervention has been implemented in some clusters. We extend these methods to any amount of baseline and endline data. We explain how to explore sample size for a trial if some baseline data from the trial clusters have already been collected as part of a separate study. Where such data aren't available, we show how to choose the proportion of data collection devoted to the baseline within the trial, when a particular cluster size or range of cluster sizes is proposed.MethodsWe provide a design effect given the cluster size and correlation parameters, assuming different participants are assessed at baseline and endline in the same clusters. We show how to produce plots to identify the impact of varying the amount of baseline data accounting for the inevitable uncertainty in the cluster autocorrelation. We illustrate the methodology using an example trial.ResultsBaseline data provide more power, or allow a greater reduction in trial size, with greater values of the cluster size, intracluster correlation and cluster autocorrelation.ConclusionInvestigators should think carefully before collecting baseline data in a cluster randomised trial if this is at the expense of endline data. In some scenarios, this will increase the sample size required to achieve given power and precision.

| S-EPMC7334046 | biostudies-literature

FastMEDUSA: a parallelized tool to infer gene regulatory networks.

Project description:In order to construct gene regulatory networks of higher organisms from gene expression and promoter sequence data efficiently, we developed FastMEDUSA. In this parallelized version of the regulatory network-modeling tool MEDUSA, expression and sequence data are shared among a user-defined number of processors on a single multi-core machine or cluster. Our results show that FastMEDUSA allows a more efficient utilization of computational resources. While the determination of a regulatory network of brain tumor in Homo sapiens takes 12 days with MEDUSA, FastMEDUSA obtained the same results in 6 h by utilizing 100 processors.Source code and documentation of FastMEDUSA are available at https://wiki.nci.nih.gov/display/NOBbioinf/FastMEDUSA

| S-EPMC2894517 | biostudies-other

MetaCNV - a consensus approach to infer accurate copy numbers from low coverage data.

Project description:BackgroundThe majority of copy number callers requires high read coverage data that is often achieved with elevated material input, which increases the heterogeneity of tissue samples. However, to gain insights into smaller areas within a tissue sample, e.g. a cancerous area in a heterogeneous tissue sample, less material is used for sequencing, which results in lower read coverage. Therefore, more focus needs to be put on copy number calling that is sensitive enough for low coverage data.ResultsWe present MetaCNV, a copy number caller that infers reliable copy numbers for human genomes with a consensus approach. MetaCNV specializes in low coverage data, but also performs well on normal and high coverage data. MetaCNV integrates the results of multiple copy number callers and infers absolute and unbiased copy numbers for the entire genome. MetaCNV is based on a meta-model that bypasses the weaknesses of current calling models while combining the strengths of existing approaches. Here we apply MetaCNV based on ReadDepth, SVDetect, and CNVnator to real and simulated datasets in order to demonstrate how the approach improves copy number calling.ConclusionsMetaCNV, available at https://bitbucket.org/sonnhammergroup/metacnv, provides accurate copy number prediction on low coverage data and performs well on high coverage data.

| S-EPMC7268502 | biostudies-literature

Individuals with excess numbers of germline de novo CNVs

Project description:Individuals with excess numbers of germline de novo CNVs

| PRJNA348186 | ENA

Multiplexed phosphoproteomics of low cell numbers using SPARCE

Project description:Understanding cellular diversity and disease mechanisms requires global analysis of proteins and their modifications. Ultrasensitive mass spectrometry-based proteomics enables unbiased protein-level analysis of low cell numbers, down to single cells. However, phosphoproteomics remains limited to high-input samples due to sample losses and poor reaction efficiencies associated with processing low cell numbers. In this study, we have used isobaric stable isotope labelling as a promising approach for reproducible and accurate quantification of low abundant phosphopeptides. Here, we introduce SPARCE (Streamlined Phosphoproteomic Analysis of Rare CElls) for multiplexed phosphoproteomic analysis of low cell numbers. SPARCE outperforms traditional methods by enhancing labelling efficiency and phosphoproteome coverage. To demonstrate the utility of SPARCE, we analysed four patient-derived glioblastoma stem cell lines, reliably quantifying phosphosite changes from 1,000 FACS-sorted cells. This workflow expands the possibilities for signalling analysis of rare cell populations.

2025-04-01 | PXD056902 | Pride

Global Gene Expression of H9 hESC with different passage numbers

Project description:Global Gene Expression of H9 hESC with different passage numbers

| PRJNA489086 | ENA

Nucleoporin downregulation modulates keratinocyte differentiation independent of nuclear pore numbers

Project description:Nucleoporin downregulation modulates keratinocyte differentiation independent of nuclear pore numbers

| PRJNA862059 | ENA

An optimized FAIRE procedure for low cell numbers in yeast.

Project description:An optimized FAIRE procedure for low cell numbers in yeast.

| PRJNA411771 | ENA