Browse
Submit Data
Databases
API
Help

Dataset Information

0 Views

0 Connections

0 Citations

0 Reanalyses

0 Downloads

Omics score: 0

Unsupervised analysis of flow cytometry data in a clinical setting captures cell diversity and allows population discovery

ABSTRACT: Unsupervised analysis of flow cytometry data in a clinical setting captures cell diversity and allows population discovery

PROVIDER: PRJNA680799 | ENA |

REPOSITORIES: ENA

ACCESS DATA

Json Xml

Dataset's files

Source:

			Action	DRS
	SRR13144535.fastq.gz	Fastqsanger.gz
	SRR13144536.fastq.gz	Fastqsanger.gz
	SRR13144537.fastq.gz	Fastqsanger.gz
	SRR13144538.fastq.gz	Fastqsanger.gz
	SRR13144539.fastq.gz	Fastqsanger.gz

Items per page:

1 - 5 of 9

Similar Datasets

Unsupervised analysis of flow cytometry data in a clinical setting captures cell diversity and allows population discovery

Project description:In our study, MegaClust - an unsupervised, data-driven algorithm - barely described mDCs and pDC subsets were identified. To confirm these findings we performed RNA sequencing Facs sorted of mDCs (CD123-CD11c+CD4+HLA-DR+), pDCs (CD123+CD11c-CD4+HLA-DR+) and monocytes (CD14+) from healthy donors and compared these with publicly available data.

2021-04-27 | GSE162177 | GEO

Unsupervised Analysis of Flow Cytometry Data in a Clinical Setting Captures Cell Diversity and Allows Population Discovery.

Project description:Data obtained with cytometry are increasingly complex and their interrogation impacts the type and quality of knowledge gained. Conventional supervised analyses are limited to pre-defined cell populations and do not exploit the full potential of data. Here, in the context of a clinical trial of cancer patients treated with radiotherapy, we performed longitudinal flow cytometry analyses to identify multiple distinct cell populations in circulating whole blood. We cross-compared the results from state-of-the-art recommended supervised analyses with results from MegaClust, a high-performance data-driven clustering algorithm allowing fast and robust identification of cell-type populations. Ten distinct cell populations were accurately identified by supervised analyses, including main T, B, dendritic cell (DC), natural killer (NK) and monocytes subsets. While all ten subsets were also identified with MegaClust, additional cell populations were revealed (e.g. CD4+HLA-DR+ and NKT-like subsets), and DC profiling was enriched by the assignment of additional subset-specific markers. Comparison between transcriptomic profiles of purified DC populations and publicly available datasets confirmed the accuracy of the unsupervised clustering algorithm and demonstrated its potential to identify rare and scarcely described cell subsets. Our observations show that data-driven analyses of cytometry data significantly enrich the amount and quality of knowledge gained, representing an important step in refining the characterization of immune responses.

| S-EPMC8119773 | biostudies-literature

Unsupervised clustering and epigenetic classification of single cells

Project description:Unsupervised clustering and epigenetic classification of single cells

| PRJNA420974 | ENA

Human Bone Marrow Assessment by Single Cell RNA Sequencing, Mass Cytometry and Flow Cytometry

Project description:Human Bone Marrow Assessment by Single Cell RNA Sequencing, Mass Cytometry and Flow Cytometry

| PRJNA493078 | ENA

Human Bone Marrow Assessment by Single Cell RNA Sequencing, Mass Cytometry and Flow Cytometry [bulk]

Project description:Human Bone Marrow Assessment by Single Cell RNA Sequencing, Mass Cytometry and Flow Cytometry [bulk]

| PRJNA493081 | ENA

Human Bone Marrow Assessment by Single Cell RNA Sequencing, Mass Cytometry and Flow Cytometry [scRNA]

Project description:Human Bone Marrow Assessment by Single Cell RNA Sequencing, Mass Cytometry and Flow Cytometry [scRNA]

| PRJNA492227 | ENA

Intraoperative Flow Cytometry In Colorectal Cancer

Project description:This study was designed to investigate the efficacy of flow cytometry to accurately identify between normal and cancer cells in colon epithelium in humans diagnosed with colorectal cancer.

| 2406350 | ecrin-mdr-crc

Unsupervised embedding of trajectories captures the latent structure of scientific migration.

Project description:Human migration and mobility drives major societal phenomena including epidemics, economies, innovation, and the diffusion of ideas. Although human mobility and migration have been heavily constrained by geographic distance throughout the history, advances, and globalization are making other factors such as language and culture increasingly more important. Advances in neural embedding models, originally designed for natural language, provide an opportunity to tame this complexity and open new avenues for the study of migration. Here, we demonstrate the ability of the model word2vec to encode nuanced relationships between discrete locations from migration trajectories, producing an accurate, dense, continuous, and meaningful vector-space representation. The resulting representation provides a functional distance between locations, as well as a "digital double" that can be distributed, re-used, and itself interrogated to understand the many dimensions of migration. We show that the unique power of word2vec to encode migration patterns stems from its mathematical equivalence with the gravity model of mobility. Focusing on the case of scientific migration, we apply word2vec to a database of three million migration trajectories of scientists derived from the affiliations listed on their publication records. Using techniques that leverage its semantic structure, we demonstrate that embeddings can learn the rich structure that underpins scientific migration, such as cultural, linguistic, and prestige relationships at multiple levels of granularity. Our results provide a theoretical foundation and methodological framework for using neural embeddings to represent and understand migration both within and beyond science.

| S-EPMC10756268 | biostudies-literature

Transposable element activity captures human pluripotent cell states.

Project description:Transposable element activity captures human pluripotent cell states.

| PRJNA1135496 | ENA

Temporal iCLIP captures co-transcriptional RNA-protein interactions

Project description:Temporal iCLIP captures co-transcriptional RNA-protein interactions

| PRJNA837938 | ENA