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Sampling Plans

Sampling Plans

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.
Random sampling is a method where each member of the population has an equal chance of being selected for the sample. It involves selecting individuals randomly, often using random number generators or lottery-type methods. For example, when analyzing the properties of a...

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A High-throughput Cell Microarray Platform for Correlative Analysis of Cell Differentiation and Traction Forces

A High-throughput Cell Microarray Platform for Correlative Analysis of Cell Differentiation and Traction Forces

Published on: March 1, 2017

Population-level integration of single-cell datasets enables multi-scale analysis across samples.

Carlo De Donno^1,2, Soroor Hediyeh-Zadeh¹, Amir Ali Moinfar^1,3

¹Institute of Computational Biology, Helmholtz Center Munich, Munich, Germany.

|October 9, 2023

Summary

We developed scPoli, a new method for integrating large single-cell datasets. This tool helps analyze complex biological variations and improves the interpretation of population-level atlases.

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Area of Science:

Computational Biology
Genomics
Bioinformatics

Background:

Population-level single-cell atlases are growing, requiring methods to integrate diverse data and metadata.
Integrating heterogeneous cohorts with varying metadata is crucial for constructing comprehensive cellular references.

Purpose of the Study:

To present single-cell population level integration (scPoli), an open-world learner for integrating population-level single-cell data.
To enable data integration, label transfer, and reference mapping using generative models for cell and sample representations.

Main Methods:

scPoli utilizes generative models to learn representations for data integration.
Applied scPoli to large-scale lung and peripheral blood mononuclear cell atlases (7.8 million cells, 2,375 samples).
Demonstrated scPoli's capability in explaining sample-level biological and technical variations through sample embeddings.

Main Results:

scPoli successfully integrated heterogeneous single-cell cohorts.
Sample embeddings revealed genes associated with both batch effects and biological variations.
scPoli demonstrated applicability to chromatin accessibility and cross-species datasets.

Conclusions:

scPoli is a powerful tool for population-level single-cell data integration.
Facilitates the use and interpretation of single-cell atlases through multi-scale analyses.
Enhances understanding of biological and technical variations in large-scale omics data.