About: Motivation In recent years, efficient scRNA-seq methods have been developed, enabling the transcriptome profiling of single cells massively in parallel. Meanwhile, its high dimensionality brought challenges in data modeling, analysis, visualization and interpretation. Available analysis tools require extensive knowledge and training of data properties, statistical modeling and computational skills. It is challenging for biologists to efficiently view, browse and interpret the data. Results Here we developed SCANNER, as a public webserver resource to equip the biologists and bioinformatician to share and analyze scRNA-seq data in a comprehensive and collaborative manner. It is effort-less and host-free without requirement on software setup or coding skills, and enables a user-friendly way to compare the activation status of gene sets on single cell basis. Also, it is equipped with multiple data interfaces for easy data sharing and currently provide a database for studying the smoking effect on single cell gene expression in lung. Using SCANNER, we have identified larger proportions of cancer-associated fibroblasts cells and activeness of fibroblast growth related genes in melanoma tissues in females compared to males. Moreover, we found ACE2 is mainly expressed in pneumocytes, secretory cells and ciliated cells with disparity in gene expression by smoking behavior. Availability and implementation SCANNER is available at https://www.thecailab.com/scanner/. Supplementary information Supplementary data are available online. Contact GCAI@mailbox.sc.edu or XIAOF@mailbox.sc.ecu Key Points SCANNER provides a new web server resource for promoting scRNA-seq data analysis SCANNER enables comprehensive and dynamic analysis and visualization, novel functional annotation and activeness inference, online databases and easy data sharing. SCANNER bridges the data analysis and the biological experiment units.   Goto Sponge  NotDistinct  Permalink

An Entity of Type : fabio:Abstract, within Data Space : covidontheweb.inria.fr associated with source document(s)

AttributesValues
type
value
  • Motivation In recent years, efficient scRNA-seq methods have been developed, enabling the transcriptome profiling of single cells massively in parallel. Meanwhile, its high dimensionality brought challenges in data modeling, analysis, visualization and interpretation. Available analysis tools require extensive knowledge and training of data properties, statistical modeling and computational skills. It is challenging for biologists to efficiently view, browse and interpret the data. Results Here we developed SCANNER, as a public webserver resource to equip the biologists and bioinformatician to share and analyze scRNA-seq data in a comprehensive and collaborative manner. It is effort-less and host-free without requirement on software setup or coding skills, and enables a user-friendly way to compare the activation status of gene sets on single cell basis. Also, it is equipped with multiple data interfaces for easy data sharing and currently provide a database for studying the smoking effect on single cell gene expression in lung. Using SCANNER, we have identified larger proportions of cancer-associated fibroblasts cells and activeness of fibroblast growth related genes in melanoma tissues in females compared to males. Moreover, we found ACE2 is mainly expressed in pneumocytes, secretory cells and ciliated cells with disparity in gene expression by smoking behavior. Availability and implementation SCANNER is available at https://www.thecailab.com/scanner/. Supplementary information Supplementary data are available online. Contact GCAI@mailbox.sc.edu or XIAOF@mailbox.sc.ecu Key Points SCANNER provides a new web server resource for promoting scRNA-seq data analysis SCANNER enables comprehensive and dynamic analysis and visualization, novel functional annotation and activeness inference, online databases and easy data sharing. SCANNER bridges the data analysis and the biological experiment units.
Subject
  • RNA
  • Neurons
  • Gene expression
  • Omics
  • Statistical models
  • RNA sequencing
  • Mathematical modeling
  • RNA splicing
  • Data modeling
  • Medical terminology
  • Molecular biology
  • Scientific modeling
  • Statistical theory
part of
is abstract of
is hasSource of
Faceted Search & Find service v1.13.91 as of Mar 24 2020


Alternative Linked Data Documents: Sponger | ODE     Content Formats:       RDF       ODATA       Microdata      About   
This material is Open Knowledge   W3C Semantic Web Technology [RDF Data]
OpenLink Virtuoso version 07.20.3229 as of Jul 10 2020, on Linux (x86_64-pc-linux-gnu), Single-Server Edition (94 GB total memory)
Data on this page belongs to its respective rights holders.
Virtuoso Faceted Browser Copyright © 2009-2024 OpenLink Software