Revisiting Global Gene Expression Analysis

By Jakob Lovén, David A. Orlando, A. Sigova, Charles Y. Lin, P. Rahl, C. Burge, D. Levens, Tong Ihn Lee, Richard A. Young. Cell, 2012

596 citations recorded by the source corpus.

Summary Gene expression analysis is a widely used and powerful method for investigating the transcriptional behavior of biological systems, for classifying cell states in disease and for many other purposes. Recent studies indicate that common assumptions currently embedded in experimental and analytical practices can lead to misinterpretation of global gene expression data. We discuss these assumptions and describe solutions that should minimize erroneous interpretation of gene expression data from multiple analysis platforms.

Subfields: Molecular Biology

View Paper: https://www.semanticscholar.org/paper/64dda1ab3c1adfffbd84a1b0df51c16e44a7a073

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Citations in the corpus

The “Polyploid Hop”: Shifting Challenges and Opportunities Over the Evolutionary Lifespan of Genome Duplications (2018, methodology): “Methods commonly used to evaluate genome-wide expression patterns (microarrays and RNAseq) rely on extensive normalization of the RNA input and therefore are perfectly appropriate to detect relative changes in gene expression but not at all to measure variations in absolute transcriptome size (Lovén”
How many biological replicates are needed in an RNA-seq experiment and which differential expression tool should you use? (2015, background): “…these data (e.g., Anders and Huber 2010; Hardcastle and Kelly 2010; Robinson et al. 2010; Wang et al. 2010; Tarazona et al. 2011; Li et al. 2012; Lund et al. 2012; Trapnell et al. 2012; Leng et al. 2013; Li and Tibshirani 2013; Frazee et al. 2014; Law et al. 2014; Love et al. 2014; Moulos and…”
Normalization of RNA-seq data using factor analysis of control genes or samples (2014, methodology): “Unusual RLE distributions could reveal suspicious samples (e.g., problematic library preparation) or batch effects.”
RNA Sequencing Data: Hitchhiker's Guide to Expression Analysis (2018, methodology): “Moreover, methods based on external spikein features have been introduced to address normalisation for applications where many features are DE or where the basic assumptions of conventional normalization methods are violated [122,123] .”
Selecting between‐sample RNA‐Seq normalization methods from the perspective of their assumptions (2016, methodology): “The method works as follows [23,28], and compares two samples at a time.”
Big Data Analytics in Healthcare (2015, background): “Experiment and analytical practices lead to error as well as batch effects [136, 137].”
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MYC, metabolism, cell growth, and tumorigenesis. (2013, background): “Although ACLY is not highly induced by MYC, ACACA (acetylCoA carboxylase; ACACA converts acetyl-CoA to malonyl-CoA), FASN (fatty acid synthetase; FASN adds two carbons from acetyl-CoA to the growing fatty acid chain to form longer chains), and SCD (stearoyl-CoA desaturase; SCD introduces a double bo”
An atlas of the protein-coding genes in the human, pig, and mouse brain (2020, methodology): “We used normalization strategies to avoid batch effects caused by sampling, technology platforms, and differences in transcriptome size between different types of tissues and also to allow both within-sample and betweensample comparisons (20, 21).”
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Revealing the vectors of cellular identity with single-cell genomics (2016, background): “In bulk RNA-seq it is generally (yet not always 51 ) safe to assume that the starting quantities of RNA are uniform across libraries and to scale read counts accordingly with size factors 52 or relative expression units, such as RPKM 53 (reads per kilobase of exon per million mapped reads), FPKM 54…”
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