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Microarrays are high-throughput and relatively inexpensive assays that can be automated to analyze large quantities of data at a time. They are used in genome-wide studies to compare gene or protein expression under two varied conditions, such as healthy and diseased states. Microarrays consist of glass or silica slides on which probe molecules are covalently attached through surface functionalization. Most commonly, the slides are prepared through the chemisorption of silanes to silica...
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Combinatorial gene control is the synergistic action of several transcriptional factors to regulate the expression of a single gene. The absence of one or more of these factors may lead to a significant difference in the level of gene expression or repression.
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The gene expression in cells is regulated at different stages: (i) transcription, (ii) RNA processing, (iii) RNA localization, and (iv) translation. Transcriptional regulation is mediated by regulatory proteins such as transcription factors, activators, or repressors—these control gene expression by initiating or inhibiting the transcription of genes. Once a precursor or pre-mRNA is produced, it undergoes post-transcriptional modification, including 5' capping, splicing, and the...
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This lesson introduces two critical methods in pharmacokinetics, the Wagner-Nelson and Loo-Riegelman methods, used for estimating the absorption rate constant (ka) for drugs administered via non-intravenous routes. The Wagner-Nelson method relates ka to the plasma concentration derived from the slope of a semilog percent unabsorbed time plot. However, it is limited to drugs with one-compartment kinetics and can be impacted by factors like gastrointestinal motility or enzymatic degradation.
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Related Experiment Video

Updated: Aug 11, 2025

Single-cell Gene Expression Profiling Using FACS and qPCR with Internal Standards
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Investigating the Complexity of Gene Co-expression Estimation for Single-cell Data.

Jiaqi Zhang1, Ritambhara Singh2

  • 1Department of Computer Science, Brown University.

Biorxiv : the Preprint Server for Biology
|February 7, 2023
PubMed
Summary

Gene co-expression estimation using single-cell RNA sequencing (scRNA-seq) data is challenged by sparsity. Current methods show high false discovery rates, and pre-processing steps like normalization do not improve accuracy.

Keywords:
gene co-expressionscRNA-seq

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Area of Science:

  • Genomics
  • Computational Biology
  • Bioinformatics

Background:

  • Single-cell RNA sequencing (scRNA-seq) enables high-resolution biological process analysis.
  • Gene co-expression estimation is crucial for gene function annotation and gene regulatory network inference.
  • Existing methods require rigorous evaluation on realistic datasets.

Conclusions:

  • The developed benchmark setup aids in the development of better co-expression estimators.
  • High sparsity in scRNA-seq data poses a significant challenge for accurate co-expression estimation.
  • Further research is needed to address sparsity and improve gene co-expression analysis in single-cell studies.