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

Empirical Method to Interpret Standard Deviation01:09

Empirical Method to Interpret Standard Deviation

10.2K
The empirical rule, also known as the three-sigma rule, allows a statistician to interpret the standard deviation in a normally distributed dataset. The rule states that 68% of the data lies within one standard deviation from the mean, 95% lies within two standard deviations from the mean, and 99.7% lies within three standard deviations from the mean. Additionally, this rule is also called the 68-95-99.7 rule.
This rule is used widely in statistics to calculate the proportion of data values...
10.2K
Interpreting R Charts01:22

Interpreting R Charts

348
R chart, or range chart, is a fundamental tool in statistical process control used to monitor the variability within a process. It complements the X-bar (x̄) chart by focusing on the range of the data, rather than individual values, providing a clear picture of the process dispersion over time.
An R chart plots the range of subsets of measurements collected from a process. Each point on the chart represents the range—defined as the difference between the maximum and minimum...
348
Interpreting Run Charts01:25

Interpreting Run Charts

3.3K
Run charts, essentially line graphs plotted over time, serve as fundamental yet effective tools for process analysis. They chronicle data sequentially, facilitating the identification of trends, shifts, or cyclical movements. This graphical representation is instrumental in determining whether a process is stable or exhibits signs of potential instability indicative of special cause variation. In the healthcare domain, run charts depict infection rates over time, enabling hospitals to monitor...
3.3K
Interpretation of Confidence Intervals01:19

Interpretation of Confidence Intervals

10.0K
A confidence interval is a better estimate of the population than a point estimate, as it uses a range of values from a sample instead of a single value.
Confidence intervals have confidence coefficients that are crucial for their interpretation. The most common confidence coefficients are 0.90, 0.95, and 0.99, which can be written as percentages–90%, 95%, and 99%, respectively.
Suppose a person calculates a confidence interval with a confidence coefficient of 0.95. In that case, they can...
10.0K
Mass Spectrum: Interpretation01:24

Mass Spectrum: Interpretation

3.3K
An unknown compound can be established by identifying the molecular ion peak in the mass spectrum. The molecular ion peak is often weak or absent due to the predominance of fragmentation in high-energy electron beams. In such cases, a soft-energy electron beam can be used to scan the spectrum to enhance the intensity of the molecular ion peak. Additionally, chemical ionization, field ionization, and desorption ionization spectra are used to obtain a relatively intense molecular ion peak.To...
3.3K
Interpreting X̄ Charts01:13

Interpreting X̄ Charts

308
Interpreting x̄ charts, a type of control chart used in statistical process control helps monitor the variation in processes over time. The x̄ chart is based on the sample mean and allows for monitoring variations in the process mean over time. These charts are pivotal for quality assurance in manufacturing and other sectors.
An x̄ chart plots the values of individual measurements over time against control limits calculated from historical data. The central line...
308

You might also read

Related Articles

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

Sort by
Same author

Neuregulin-1 promotes early regenerative and autophagic responses after ischemic stroke via spatial proteomics.

Frontiers in cellular neuroscience·2026
Same author

Hidden architecture of resistance: The extracellular matrix in melanoma's immune landscape.

Seminars in cancer biology·2026
Same author

Pseudomyxoma peritonei and the microbiome: Emerging observations and unanswered questions.

Trends in cancer·2026
Same author

Interleukin-33: A new frontier in cancer immunotherapy.

International review of cell and molecular biology·2026
Same author

Beyond the bench: teaching grant writing in immunotherapy training.

Trends in immunology·2026
Same author

Dissecting non-small cell lung cancer (NSCLC) with blood proteomics-from surgical to immunotherapeutic responses.

NPJ precision oncology·2026
Same journal

Distinct repeat architecture landscapes in the proteomes of protozoan parasites.

NAR genomics and bioinformatics·2026
Same journal

Long non-coding RNA triplex-dependent regulation of melanoma gene networks.

NAR genomics and bioinformatics·2026
Same journal

Challenges in predicting chromatin accessibility differences between species.

NAR genomics and bioinformatics·2026
Same journal

Power-law penalties correct distance bias in single-cell co-accessibility and deep-learning chromatin interaction predictions.

NAR genomics and bioinformatics·2026
Same journal

LORA: a polymorphic multi-sample long read assembly pipeline.

NAR genomics and bioinformatics·2026
Same journal

Correction to 'Genome sequence assembly and annotation of <i>MATA</i> and <i>MATB</i> strains of <i>Yarrowia lipolytica'</i>.

NAR genomics and bioinformatics·2026
See all related articles

Related Experiment Video

Updated: Jan 30, 2026

Author Spotlight: Exploring Advanced Therapeutic Targets in Osteosarcoma Through Spatial Transcriptomics
07:43

Author Spotlight: Exploring Advanced Therapeutic Targets in Osteosarcoma Through Spatial Transcriptomics

Published on: May 3, 2024

4.4K

SM3DD with segmented PCA: a comprehensive method for interpreting 3D spatial transcriptomics.

Tony Blick1, Aaron Kilgallon1,2, James Monkman1

  • 1Frazer Institute, Faculty of Health, Medicine and Behavioural Sciences, The University of Queensland, Brisbane, QLD 4102, Australia.

NAR Genomics and Bioinformatics
|January 29, 2026
PubMed
Summary
This summary is machine-generated.

We created a new method, Standardised Minimum 3D Distance (SM3DD), to analyze spatial RNA data. This approach revealed significant differences in gene expression patterns between normal lung tissue and that of SARS-CoV-2 patients.

More Related Videos

Mining Spatial Transcriptomics Datasets using DeepSpaceDB
10:16

Mining Spatial Transcriptomics Datasets using DeepSpaceDB

Published on: September 5, 2025

757
The Terroir Concept Interpreted through Grape Berry Metabolomics and Transcriptomics
13:02

The Terroir Concept Interpreted through Grape Berry Metabolomics and Transcriptomics

Published on: October 5, 2016

11.0K

Related Experiment Videos

Last Updated: Jan 30, 2026

Author Spotlight: Exploring Advanced Therapeutic Targets in Osteosarcoma Through Spatial Transcriptomics
07:43

Author Spotlight: Exploring Advanced Therapeutic Targets in Osteosarcoma Through Spatial Transcriptomics

Published on: May 3, 2024

4.4K
Mining Spatial Transcriptomics Datasets using DeepSpaceDB
10:16

Mining Spatial Transcriptomics Datasets using DeepSpaceDB

Published on: September 5, 2025

757
The Terroir Concept Interpreted through Grape Berry Metabolomics and Transcriptomics
13:02

The Terroir Concept Interpreted through Grape Berry Metabolomics and Transcriptomics

Published on: October 5, 2016

11.0K

Area of Science:

  • Spatial transcriptomics
  • Computational biology
  • Pathology

Background:

  • Acute respiratory distress syndrome (ARDS) is a severe complication of SARS-CoV-2 infection.
  • Understanding spatial gene expression in lung tissue is crucial for disease mechanism discovery.

Purpose of the Study:

  • To develop a novel, cell segmentation-free method for analyzing spatial RNA datasets.
  • To compare spatial gene expression patterns between normal lung tissue and SARS-CoV-2 infected lung tissue.

Main Methods:

  • Developed Standardised Minimum 3D Distance (SM3DD) for spatial RNA analysis.
  • Utilized CosMx™ Spatial Molecular Imager for RNA spatial coordinate determination.
  • Applied hierarchical clustering and segmented principal components analysis to SM3DD data.

Main Results:

  • SM3DD successfully identified differences in spatial gene expression between normal and SARS-CoV-2 lung tissue.
  • Hierarchical clustering organized genes by functionality, aiding biological interpretation.
  • Identified significant differences for FKBP11 and MZT2A, suggesting a role in interferon signaling.
  • Detected pathways related to 'SARS-CoV-2 infection' without direct viral transcript detection.

Conclusions:

  • SM3DD is an effective tool for analyzing spatial RNA data without cell segmentation.
  • Spatial gene expression alterations in SARS-CoV-2 infection are identifiable using SM3DD.
  • The method offers insights into disease mechanisms and potential therapeutic targets.