computational approaches to cell cycle analysis: cell-cycle microarray analysis
DESCRIPTION
PhD Program in Computational Biology, Instituto Gulbenkian de Ciencia, Oeiras, Portugal, April 29-May 2, 2008TRANSCRIPT
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Cell-cycle microarray analysis
Lars Juhl JensenEMBL Heidelberg
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microarrays 101
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RNA levels
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all genes at once
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two technologies
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mechanical spotting
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cDNAs
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ESTs
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oligomer probes
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photolithography
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Affymetrix
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two types of data
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one-channel
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two-channel
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normalization
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noise vs. bias
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real-world example
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DownloadedSMD data
After intensitynormalization
Spatial biasestimate
After spatialnormalization
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cell-cycle expression
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cell cultures
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synchronization
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isolate mRNA
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microarrays
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time courses
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expression profiles
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Cho et al.
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visual inspection
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Spellman et al.
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Fourier score
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Zhao et al.
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single-pulse model
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Johansson et al.
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partial least squares
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Langmead et al.
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rhythmic analysis
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Wichert et al.
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Fisher’s G-statistic
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Luan et al.
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Luan et al.
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cubic-splines model
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Lu et al.
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periodic-normal mixture
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de Lichtenberg et al.
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dual permutation test
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Willbrand et al.
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up-down patterns
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Ahdesmäki et al.
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Fisher’s G-statistic
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Chen
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Fisher’s G-statistic
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Qiu et al.
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resynchronization
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Glynn et al.
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Lomb-Scargle periodogram
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Andersson et al.
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Bayesian detector
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Ahnert et al.
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low entropy patterns
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Xu et al.
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partial energy ratio
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Lu et al.
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homology transfer
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Liew et al.
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Fisher’s G-statistic
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Morton et al.
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cyclohedron test
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Rowicka et al.
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correlation to known genes
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things to consider
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change
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periodicity
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shape
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loss of synchrony
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stress response
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uneven sampling
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missing data points
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multiple experiments
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one (ranked) list