Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Overview of Microsoft Excel as a Data Analysis Tool01:13

Overview of Microsoft Excel as a Data Analysis Tool

1.8K
Microsoft Excel is a cornerstone tool for data analysis and statistical operations, offering a wide array of functionalities to manage, analyze, and visualize data efficiently. Recognized for its versatility, Excel facilitates the performance of basic to complex statistical operations, serving as an indispensable asset for analysts, researchers, and students alike. Excel's significance in data analysis emanates from its spreadsheet environment, where data can be organized in rows and...
1.8K
Performing a Simple Data Analysis using MS-Excel Function01:17

Performing a Simple Data Analysis using MS-Excel Function

1.1K
Microsoft Excel offers a suite of functions and tools ideal for statistical analysis, making it accessible to students and researchers. This article outlines fundamental Excel functions pivotal for data analysis.
SUM: This function calculates the total sum of a range of values. It's the foundation for aggregating data, essential for determining overall trends and totals in datasets.
AVERAGE: It computes the mean value of a given set of numbers, providing a quick insight into the central...
1.1K

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Continuous multi-omics pathway enrichment analysis resolves hidden functional heterogeneity.

Briefings in bioinformatics·2026
Same author

Transcriptional repression by TGIF2 coordinates neurogenic priming and neural stem cell maintenance.

Science advances·2026
Same author

UniversalEPI: robust prediction of cell type-specific and differential chromatin interactions from DNA sequence and chromatin accessibility.

Nucleic acids research·2026
Same author

RegVelo: Gene-regulatory-informed dynamics of single cells.

Cell·2026
Same author

Glial multicellular programs reveal distinct patient stratification in Parkinson's disease.

Research square·2026
Same author

TarDis: Achieving robust and structured disentanglement of multiple covariates.

Cell systems·2026

Related Experiment Video

Updated: May 2, 2026

Workflow for High-content, Individual Cell Quantification of Fluorescent Markers from Universal Microscope Data, Supported by Open Source Software
09:57

Workflow for High-content, Individual Cell Quantification of Fluorescent Markers from Universal Microscope Data, Supported by Open Source Software

Published on: December 16, 2014

13.0K

The future of rapid and automated single-cell data analysis using reference mapping.

Mohammad Lotfollahi1, Yuhan Hao2, Fabian J Theis3

  • 1Institute of Computational Biology, Helmholtz Center Munich - German Research Center for Environmental Health, Neuherberg, Germany; Wellcome Sanger Institute, Wellcome Genome Campus, Cambridge, UK.

Cell
|May 10, 2024
PubMed
Summary
This summary is machine-generated.

Single-cell reference mapping algorithms integrate diverse biological datasets, overcoming computational challenges. These advanced workflows promise to replace manual clustering for broader biological insights.

Keywords:
cross-species comparisonsmachine learningmultimodal analysisreference mappingsingle-cell analysis

More Related Videos

Droplet Barcoding-Based Single Cell Transcriptomics of Adult Mammalian Tissues
10:12

Droplet Barcoding-Based Single Cell Transcriptomics of Adult Mammalian Tissues

Published on: January 10, 2019

18.5K
Mapping the Emergent Spatial Organization of Mammalian Cells using Micropatterns and Quantitative Imaging
09:56

Mapping the Emergent Spatial Organization of Mammalian Cells using Micropatterns and Quantitative Imaging

Published on: April 30, 2019

6.6K

Related Experiment Videos

Last Updated: May 2, 2026

Workflow for High-content, Individual Cell Quantification of Fluorescent Markers from Universal Microscope Data, Supported by Open Source Software
09:57

Workflow for High-content, Individual Cell Quantification of Fluorescent Markers from Universal Microscope Data, Supported by Open Source Software

Published on: December 16, 2014

13.0K
Droplet Barcoding-Based Single Cell Transcriptomics of Adult Mammalian Tissues
10:12

Droplet Barcoding-Based Single Cell Transcriptomics of Adult Mammalian Tissues

Published on: January 10, 2019

18.5K
Mapping the Emergent Spatial Organization of Mammalian Cells using Micropatterns and Quantitative Imaging
09:56

Mapping the Emergent Spatial Organization of Mammalian Cells using Micropatterns and Quantitative Imaging

Published on: April 30, 2019

6.6K

Area of Science:

  • Computational biology
  • Genomics
  • Bioinformatics

Background:

  • The rapid expansion of single-cell data necessitates efficient integration methods.
  • Current unsupervised clustering pipelines are often manual and labor-intensive.
  • Reference atlases provide a framework for organizing and interpreting single-cell data.

Purpose of the Study:

  • To discuss computational challenges and opportunities in single-cell reference-mapping algorithms.
  • To highlight the potential of mapping algorithms for integrating diverse single-cell datasets.
  • To explore the future role of these algorithms in biological data analysis.

Main Methods:

  • Perspective-based discussion of computational approaches.
  • Analysis of existing and potential single-cell reference-mapping algorithms.
  • Review of integration strategies across different data types and conditions.

Main Results:

  • Identification of key computational challenges in developing robust mapping algorithms.
  • Elucidation of opportunities for advancing single-cell data integration.
  • Projection of mapping algorithms as a replacement for traditional clustering methods.

Conclusions:

  • Single-cell reference mapping holds significant promise for the biological community.
  • Addressing computational challenges will unlock the full potential of these algorithms.
  • Mapping algorithms are poised to revolutionize single-cell data analysis by enabling seamless integration.