Comparison of Methods for Image Analysis on cDNA Microarray Data

By Hwa Yang, Sandrine Dudoit, Teren e P. Speed. 2002

362 citations recorded by the source corpus.

Subfields: Biophysics, Molecular Biology

View Paper: https://www.semanticscholar.org/paper/2f059a371f05ad4a1503885110c5d39206995a49

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Biophysics

1. Gordon K. Smyth
2. J. Shaffer
3. Sandrine Dudoit
4. Hao Wu
5. Alicia Oshlack
6. Jennifer C. Boldrick

In Molecular Biology

1. Gordon K. Smyth
2. Alicia Oshlack
3. Matthew E. Ritchie
4. Bradley Efron
5. Sandrine Dudoit
6. Hao Wu
7. Xiangqin Cui
8. Gary A. Churchill

Citations in the corpus

• 9) TM4 Microarray Software Suite (2006, methodology): “Two examples of this include the spot‐finding procedure based on seeded region growing algorithm (Yang et al., 2002b) or Fourier transform based procedure (Gaidukevitch et al., 2000).”
• MAANOVA: A Software Package for the Analysis of Spotted cDNA Microarray Experiments (2003, methodology): “We assume that the experimenter is satisfied with the quality of the images and that the data have been extracted with a software package such as SPOT (Yang et al., 2002) or GenePix (Axon Instruments, 1999).”
• Multiple Hypothesis Testing in Microarray Experiments (2003, methodology): “The raw data in a microarray experiment consist of image files, and important preprocessing steps include image analysis of these scanned images and normalization (Yang et al., 2001, 2002).”
• Local-pooled-error test for identifying differentially expressed genes with a small number of replicated microarrays (2003, background): “TheM versusA plot provides a very raw look at the data and is useful in detecting outliers and patterns of intensity variation as a function of mean intensity, whereas the log 2 xij1 versus log2 xij2 plot sometimes gives the illusion of better reproducibility than is actually present (Yang et al., 2”
• A comparison of background correction methods for two-colour microarrays (2007, background): “Local background is arguably an unbiased estimate of the local non-specific signal, so subtracting it from the foreground intensity gives in principle an unbiased estimator of the true signal due to hybridization.”