Navigating gene expression using microarrays — a technology review
By A. Schulze, Julian Downward. Nature Cell Biology, 2001
597 citations recorded by the source corpus.
Subfields: Molecular Biology
View Paper: https://www.semanticscholar.org/paper/5bfe9e9bbf29d5dd71dae5759987bfb33c633fed
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In Molecular Biology
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Citations in the corpus
- Estimating the proportion of true null hypotheses, with application to DNA microarray data (2005, background): “Schulze and Downward (2001). It is important to assess the quality of data obtained – both on a global and local scale.”
- Iterative signature algorithm for the analysis of large-scale gene expression data. (2002, background): “DNA microarray experiments monitor the expression levels of thousands of genes simultaneously [1, 2, 3, 4].”
- Fast Batch Alignment of Single Cell Transcriptomes Unifies Multiple Mouse Cell Atlases into an Integrated Landscape (2018, background): “The past few decades have seen a constant increase in the amount of data that can be harvested from single transcriptomics experiments 1,2 , culminating in the development of single cell RNA-Seq 3 .”
- BioArray Software Environment (BASE): a platform for comprehensive management and analysis of microarray data (2002, background): “Microarrays are emerging as one of the most exciting and promising technologies for biological research and clinical practice [1].”
- Revisiting Global Gene Expression Analysis (2012, unknown): “…RNA from two or more cellular sources, introducing similar amounts of RNA from the sources into the experimental platform and analyzing the data by using algorithms that normalize the signal from the samples (Kulkarni, 2011; Mortazavi et al., 2008; Quack-enbush, 2002; Schulze and Downward, 2001).”
- TSCAN: Pseudo-time reconstruction and evaluation in single-cell RNA-seq analysis (2016, methodology): “Unlike single-cell RNA-seq, conventional RNAseq (also referred to as ‘bulk RNA-seq’) (3,4) or microarray (5,6) experiments are used to measure average gene expression of a cell population.”