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

Data: Types and Distribution01:19

Data: Types and Distribution

1.6K
In biostatistics, data are the observations collected for analysis. There are two main types: parametric and non-parametric. Parametric data, which include continuous (e.g., weight) and discrete numerical data (e.g., number of tablets), assume a particular distribution pattern, often the normal distribution. Non-parametric data do not adhere to a specific distribution and typically comprise nominal (e.g., gender) and ordinal categorical data (e.g., pain scale ratings).
Distributions in...
1.6K
Deconvolution01:20

Deconvolution

560
Deconvolution, also known as inverse filtering, is the process of extracting the impulse response from known input and output signals. This technique is vital in scenarios where the system's characteristics are unknown, and they must be inferred from the observable signals.
Deconvolution involves several mathematical techniques to derive the impulse response. One common approach is polynomial division. In this method, the input and output sequences are treated as coefficients of...
560
Mixtures of Acids03:27

Mixtures of Acids

21.6K
The pH of a solution containing an acid can be determined using its acid dissociation constant and its initial concentration. If a solution contains two different acids, then its pH can be determined using one of several methods depending upon the relative strength of the acids and their dissociation constants.
A Mixture of a Strong Acid and a Weak Acid
In a mixture of a strong acid and a weak acid, the strong acid dissociates completely and becomes a source of almost all the hydronium ions...
21.6K
Mixtures of Acids01:19

Mixtures of Acids

1.1K
The pH of a solution containing an acid can be determined using its acid dissociation constant and initial concentration. If a solution contains two different acids, then its pH can be determined using one of several methods depending on the relative strength of the acids and their dissociation constants.
In a strong and weak acid mixture, the strong acid dissociates completely and becomes a source of almost all the hydronium ions present in the solution. In contrast, the weak acid shows...
1.1K
Model Approaches for Pharmacokinetic Data: Distributed Parameter Models01:06

Model Approaches for Pharmacokinetic Data: Distributed Parameter Models

247
Pharmacokinetic models are mathematical constructs that represent and predict the time course of drug concentrations in the body, providing meaningful pharmacokinetic parameters. These models are categorized into compartment, physiological, and distributed parameter models.
The distributed parameter models are specifically designed to account for variations and differences in some drug classes. This model is particularly useful for assessing regional concentrations of anticancer or...
247
Racemic Mixtures and the Resolution of Enantiomers02:30

Racemic Mixtures and the Resolution of Enantiomers

21.6K
A racemic mixture, or racemate, is an equimolar mixture of enantiomers of a molecule that can be separated using their unique interaction with chiral molecules or media. Racemic mixtures are denoted by the (±)- prefix. This ‘optical rotation descriptor’ applies to the whole solution of a racemic mixture rather than a specific stereoisomer. Enantiomers typically have the same physical and chemical properties. Hence, they are not easily separable. However, enantiomers can exhibit...
21.6K

You might also read

Related Articles

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

Sort by
Same author

Distinct Clinical Phenotypes in Moyamoya Disease: A Multicenter Comparison of Ischemic and Hemorrhagic Presentations.

Neurosurgery·2026
Same author

Direct Versus Indirect Bypass in Early-Stage Moyamoya (Suzuki I-III): A Propensity Score-Weighted Study.

Translational stroke research·2026
Same author

Systemic Cardiovascular Factors and Outcomes in Dural Arteriovenous Fistulas: Insights From the CONDOR Registry.

Stroke·2026
Same author

Prehospital Detection of Large Vessel Occlusion and Intracerebral Hemorrhage Using a Dual-Biomarker Point-of-Care Test.

Stroke (Hoboken, N.J.)·2026
Same author

Safety and long-term outcomes following bypass surgery in pediatric versus adult patients with Moyamoya disease: a multicenter cohort study.

Child's nervous system : ChNS : official journal of the International Society for Pediatric Neurosurgery·2026
Same author

Microsurgical management of tentorial dural arteriovenous fistula: an analysis from the Consortium for Dural Arteriovenous Fistula Outcomes Research (CONDOR).

Journal of neurosurgery·2026
Same journal

Cross-Domain Transfer Learning from Peptides to Metabolites Using a Multi-Property Fine-Tuned LLM.

Bioinformatics (Oxford, England)·2026
Same journal

Biomedical Concept Recognition with Error-aware Negative-enhanced Ranking Framework.

Bioinformatics (Oxford, England)·2026
Same journal

TEDLH: Domain HMMs for sensitive detection of remote homologues.

Bioinformatics (Oxford, England)·2026
Same journal

PLNFGL: Joint Estimation of Multi-Condition Gene Networks from Single-cell RNA-seq Data.

Bioinformatics (Oxford, England)·2026
Same journal

MCFST: Spatial domain identification method based on multi-view graph convolutional network and graph fusion network.

Bioinformatics (Oxford, England)·2026
Same journal

SpaBiT: Enhancing Spatial Transcriptomics Resolution via Bidirectional Attention Transformers.

Bioinformatics (Oxford, England)·2026
See all related articles

Related Experiment Video

Updated: Jan 24, 2026

Analysis of SEC-SAXS data via EFA deconvolution and Scatter
10:59

Analysis of SEC-SAXS data via EFA deconvolution and Scatter

Published on: January 28, 2021

9.8K

deconvSeq: deconvolution of cell mixture distribution in sequencing data.

Rose Du1,2, Vince Carey2, Scott T Weiss2

  • 1Department of Neurosurgery, Boston, MA, USA.

Bioinformatics (Oxford, England)
|June 1, 2019
PubMed
Summary
This summary is machine-generated.

A new computational deconvolution method, deconvSeq, accurately predicts cell type mixtures from bulk tissue sequencing data, including RNA and bisulfite sequencing. This method is effective for challenging samples like intracranial aneurysms and outperforms existing approaches.

More Related Videos

Author Spotlight: Investigating the Role of Repetitive DNA Misregulation in Cancer Initiation and Immunotherapy Resistance
04:58

Author Spotlight: Investigating the Role of Repetitive DNA Misregulation in Cancer Initiation and Immunotherapy Resistance

Published on: December 13, 2024

4.0K
2D-HELS MS Seq: A General LC-MS-Based Method for Direct and de novo Sequencing of RNA Mixtures with Different Nucleotide Modifications
05:41

2D-HELS MS Seq: A General LC-MS-Based Method for Direct and de novo Sequencing of RNA Mixtures with Different Nucleotide Modifications

Published on: July 10, 2020

2.3K

Related Experiment Videos

Last Updated: Jan 24, 2026

Analysis of SEC-SAXS data via EFA deconvolution and Scatter
10:59

Analysis of SEC-SAXS data via EFA deconvolution and Scatter

Published on: January 28, 2021

9.8K
Author Spotlight: Investigating the Role of Repetitive DNA Misregulation in Cancer Initiation and Immunotherapy Resistance
04:58

Author Spotlight: Investigating the Role of Repetitive DNA Misregulation in Cancer Initiation and Immunotherapy Resistance

Published on: December 13, 2024

4.0K
2D-HELS MS Seq: A General LC-MS-Based Method for Direct and de novo Sequencing of RNA Mixtures with Different Nucleotide Modifications
05:41

2D-HELS MS Seq: A General LC-MS-Based Method for Direct and de novo Sequencing of RNA Mixtures with Different Nucleotide Modifications

Published on: July 10, 2020

2.3K

Area of Science:

  • Genomics
  • Computational Biology
  • Bioinformatics

Background:

  • Single-cell sequencing is limited for fibrous, minute tissues like intracranial aneurysms.
  • Analyzing cell type heterogeneity in such tissues requires advanced computational methods.

Purpose of the Study:

  • To develop a computational deconvolution method, deconvSeq, for analyzing cell type heterogeneity in bulk tissue sequencing data.
  • To enable accurate cell type mixture prediction from RNA and bisulfite sequencing data.

Main Methods:

  • DeconvSeq employs a generalized linear model tailored to sequencing data structures.
  • The method estimates model coefficients to predict cell type composition.
  • It can be applied to both bulk and single-cell RNA sequencing data.

Main Results:

  • DeconvSeq achieved high prediction accuracy in whole blood samples, with mean correlations of 0.998 for RNA-seq and 0.95 for RRBS-seq.
  • The method demonstrated robust performance across different sequencing types and sample compositions.
  • Compared to other deconvolution tools, deconvSeq requires fewer genes or CpG sites for accurate predictions.

Conclusions:

  • DeconvSeq offers a powerful computational solution for cell type deconvolution in bulk tissue samples.
  • The method is particularly valuable for tissues difficult to dissociate into single cells.
  • Its efficiency and accuracy make it a significant advancement in genomic data analysis.