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Related Concept Videos

Stratified Sampling Method01:16

Stratified Sampling Method

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Sampling is a technique to select a portion (or subset) of the larger population and study that portion (the sample) to gain information about the population. The sampling method ensures that samples are drawn without bias and accurately represent the population. Because measuring the entire population in a study is not practical, researchers use samples to represent the population of interest.
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Classification of Leukocytes01:30

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Leukocytes are classified into two groups based on the presence or absence of cytoplasmic granules. Granular leukocytes, which contain granules, belong to the myeloid lineage and are divided into three subtypes: neutrophils, eosinophils, and basophils. These cells are roughly spherical and characterized by the granules in their cytoplasm.
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Cluster Sampling Method01:20

Cluster Sampling Method

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Appropriate sampling methods ensure that samples are drawn without bias and accurately represent the population. Because measuring the entire population in a study is not practical, researchers use samples to represent the population of interest.
To choose a cluster sample, divide the population into clusters (groups) and then randomly select some of the clusters. All the members from these clusters are in the cluster sample. For example, if you randomly sample four departments from your...
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Methods of Classification and Identification01:28

Methods of Classification and Identification

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Bacterial identification relies on a diverse array of techniques to classify and understand microorganisms, each tailored to uncover specific characteristics. Traditional morphological approaches, while still valuable, are limited for closely related or structurally simple organisms. Modern methods integrate biochemical, serological, genetic, and advanced molecular tools to achieve greater accuracy.Morphological and Biochemical TechniquesMorphological characteristics, such as cell shape and...
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Overview Of Cell Separation And Isolation01:20

Overview Of Cell Separation And Isolation

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Cell separation was first achieved in 1964 by S. H. Seal, who separated large tumor cells from the smaller blood cells using filtration. Two years later, Pohl and Hawk performed experiments on how cells respond differently to a nonuniform electric field based on the cell type. Such observations were the inception of cell separation methods, which allow isolating a single cell type from a heterogeneous sample.
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Sampling Plans01:23

Sampling Plans

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Sampling is a crucial step in analytical chemistry, allowing researchers to collect representative data from a large population. Common sampling methods include random, judgmental, systematic, stratified, and cluster sampling.
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Updated: Mar 31, 2026

Single-cell RNA-Seq of Defined Subsets of Retinal Ganglion Cells
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scMILD: Single-cell multiple instance learning for sample classification and associated subpopulation discovery.

Kyeonghun Jeong1, Jinwook Choi1,2, Kwangsoo Kim3,4

  • 1Interdisciplinary Program in Bioengineering, Seoul National University, Seoul, Republic of Korea.

Iscience
|March 30, 2026
PubMed
Summary
This summary is machine-generated.

This study introduces single-cell multiple instance learning (scMILD), a new method for identifying disease-associated cells using only sample-level data. scMILD effectively links cellular states to clinical phenotypes across various diseases.

Keywords:
Biocomputational methodClassification of bioinformatical subjectTranscriptomics

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Area of Science:

  • Computational Biology
  • Immunology
  • Genomics

Background:

  • Linking cellular states to clinical phenotypes is crucial but challenging in single-cell analysis.
  • Existing methods often require detailed cell-level labels, limiting their application.

Purpose of the Study:

  • To develop a weakly supervised framework for identifying condition-associated cells using only sample-level labels.
  • To bridge the gap between single-cell observations and high-level clinical phenotypes.

Main Methods:

  • Introduced single-cell multiple instance learning for sample classification and associated subpopulation discovery (scMILD).
  • Validated scMILD's accuracy through controlled simulations and application to diverse disease datasets.
  • Applied scMILD to analyze monocytes in COVID-19 and stratify Lupus patients.

Main Results:

  • scMILD robustly identifies condition-associated cells with sample-level labels.
  • Revealed a temporal transition in COVID-19 monocytes from antiviral to stress-response states.
  • Successfully stratified Lupus patients and distinguished shared vs. disease-specific inflammatory states.

Conclusions:

  • scMILD provides a validated and versatile strategy for dissecting cellular heterogeneity.
  • Enables robust linking of cellular states to clinical phenotypes in various diseases.
  • Facilitates discovery of shared and disease-specific cellular signatures across conditions.