Browse
Submit Data
Databases
API
Help

Dataset Information

0 Views

0 Connections

0 Citations

0 Reanalyses

0 Downloads

Omics score: 0

TooManyCells identifies and visualizes relationships of single-cell clades

ABSTRACT: TooManyCells identifies and visualizes relationships of single-cell clades

PROVIDER: PRJNA577700 | ENA |

REPOSITORIES: ENA

ACCESS DATA

Json Xml

Dataset's files

Source:

			Action	DRS
	SRR10284950.fastq.gz	Fastqsanger.gz
	SRR10284951.fastq.gz	Fastqsanger.gz
	SRR10284952.fastq.gz	Fastqsanger.gz
	SRR10284953.fastq.gz	Fastqsanger.gz
	SRR10284954.fastq.gz	Fastqsanger.gz

Items per page:

1 - 5 of 16

Similar Datasets

TooManyCells identifies and visualizes relationships of single-cell clades.

Project description:Identifying and visualizing transcriptionally similar cells is instrumental for accurate exploration of the cellular diversity revealed by single-cell transcriptomics. However, widely used clustering and visualization algorithms produce a fixed number of cell clusters. A fixed clustering 'resolution' hampers our ability to identify and visualize echelons of cell states. We developed TooManyCells, a suite of graph-based algorithms for efficient and unbiased identification and visualization of cell clades. TooManyCells introduces a visualization model built on a concept intentionally orthogonal to dimensionality-reduction methods. TooManyCells is also equipped with an efficient matrix-free divisive hierarchical spectral clustering different from prevalent single-resolution clustering methods. TooManyCells enables multiresolution and multifaceted exploration of single-cell clades. An advantage of this paradigm is the immediate detection of rare and common populations that outperforms popular clustering and visualization algorithms, as demonstrated using existing single-cell transcriptomic data sets and new data modeling drug-resistance acquisition in leukemic T cells.

| S-EPMC7439807 | biostudies-literature

TooManyCells identifies and visualizes relationships of single-cell clades

Project description:Purpose: To characterize heterogeneity of GSI-responsive and -resistant T-ALL cells Method: We generated GSI-resistant T-ALL DND41 cells and profiled their transcriptome using single-cell RNA sequencing. Result: We report here the presence of resistant-like subpopulation in drug naïve DND41 T-ALL cells. Conclusion: While other popular scRNA-seq clustering algorithms failed, TooManyCells, a suite of clustering and visualization algorithm developed in our lab, could identify rare resistant-like DND41 T-ALL cells.

2020-01-15 | GSE138892 | GEO

High-throughput single cell gene expression analysis in primary blood stem and progenitor cells identifies new links between key transcriptional regulators

Project description:Cellular decision-making is mediated by a complex interplay of external stimuli with the intracellular environment, in particular transcription factor regulatory networks. Here we have determined the expression of a network of 18 key haematopoietic transcription factors (TFs) in 597 single primary blood stem and progenitor cells isolated from mouse bone marrow. We demonstrate that different stem/progenitor populations are characterised by distinctive TF expression states, and through comprehensive bioinformatic analysis reveal positively and negatively correlated TF pairings, including previously unrecognised relationships between Gata2, Gfi1 and Gfi1b. Validation using transcriptional and transgenic assays confirmed direct regulatory interactions consistent with a novel regulatory triad in immature blood stem cells, where Gata2 may function to modulate cross-inhibition between Gfi1 and Gfi1b. Single cell expression profiling therefore identifies network states and allows reconstruction of network hierarchies involved in controlling stem cell fate choices, and provides a blueprint for studying both normal development and human disease. Examination of Gata2 and Gfi1 binding patterns in murine mast cells

2013-04-24 | E-GEOD-42518 | biostudies-arrayexpress

CaSpER identifies and visualizes CNV events by integrative analysis of single-cell or bulk RNA-sequencing data.

Project description:RNA sequencing experiments generate large amounts of information about expression levels of genes. Although they are mainly used for quantifying expression levels, they contain much more biologically important information such as copy number variants (CNVs). Here, we present CaSpER, a signal processing approach for identification, visualization, and integrative analysis of focal and large-scale CNV events in multiscale resolution using either bulk or single-cell RNA sequencing data. CaSpER integrates the multiscale smoothing of expression signal and allelic shift signals for CNV calling. The allelic shift signal measures the loss-of-heterozygosity (LOH) which is valuable for CNV identification. CaSpER employs an efficient methodology for the generation of a genome-wide B-allele frequency (BAF) signal profile from the reads and utilizes it for correction of CNVs calls. CaSpER increases the utility of RNA-sequencing datasets and complements other tools for complete characterization and visualization of the genomic and transcriptomic landscape of single cell and bulk RNA sequencing data.

| S-EPMC6941987 | biostudies-literature

SINGLE CELL RNA SEQUENCING IDENTIFIES IGFBP5 AND QKI

Project description:SINGLE CELL RNA SEQUENCING IDENTIFIES IGFBP5 AND QKI

| PRJNA704098 | ENA

Single-cell RNA sequencing identifies Fgf23-expressing osteocytes

Project description:Single-cell RNA sequencing identifies Fgf23-expressing osteocytes

| PRJNA911730 | ENA

TCM visualizes trajectories and cell populations from single cell data.

Project description:Profiling single cell gene expression data over specified time periods are increasingly applied to the study of complex developmental processes. Here, we describe a novel prototype-based dimension reduction method to visualize high throughput temporal expression data for single cell analyses. Our software preserves the global developmental trajectories over a specified time course, and it also identifies subpopulations of cells within each time point demonstrating superior visualization performance over six commonly used methods.

| S-EPMC6048168 | biostudies-literature

interkingdom relationships

Project description:interkingdom relationships in the soil and rhizosphere

| PRJEB31292 | ENA

Acoel single-cell transcriptomics: cell-type analysis of a deep branching organism

Project description:Bilaterian animals display a wide variety of cell types, organized into defined anatomical structures and organ systems, which are mostly absent in pre-bilaterian animals. Xenacoelomorpha are an early branching bilaterian phylum displaying an apparently relatively simple anatomical organization that have greatly diverged from other Bilaterian clades. In this study, we use whole-body single-cell transcriptomics on the acoel Isodiametra pulchra to identify and characterize different cell types. Our analysis identifies the existence of at least 11 major cell-type categories in acoels all contributing to the main biological functions of the organism: metabolism, locomotion and movements, behavior, defense and development. Interestingly, while most cell clusters express core fate markers shared with other animal clades, we also describe a surprisingly large numbers of clade-specific marker genes, suggesting the emergence of clade-specific common molecular machineries functioning in distinct cell types. Together, these results provide novel insight into the evolution of bilaterian cell-types and open the door to a better understanding of the origins of the bilaterian body plan and their constitutive cell types.

2020-07-09 | GSE154049 | GEO

scChIX-seq infers dynamic relationships between histone modifications in single cells

Project description:scChIX-seq infers dynamic relationships between histone modifications in single cells

| PRJNA649241 | ENA