Computational approaches to cell cycle analysis: Cell-cycle microarray analysis

Cell-cycle microarray analysis

Lars Juhl JensenEMBL Heidelberg

microarrays 101

RNA levels

all genes at once

two technologies

mechanical spotting

cDNAs

ESTs

oligomer probes

photolithography

Affymetrix

two types of data

one-channel

two-channel

normalization

noise vs. bias

real-world example

DownloadedSMD data

After intensitynormalization

Spatial biasestimate

After spatialnormalization

cell-cycle expression

cell cultures

synchronization

isolate mRNA

microarrays

time courses

expression profiles

Cho et al.

visual inspection

Spellman et al.

Fourier score

Zhao et al.

single-pulse model

Johansson et al.

partial least squares

Langmead et al.

rhythmic analysis

Wichert et al.

Fisher’s G-statistic

Luan et al.

Luan et al.

cubic-splines model

Lu et al.

periodic-normal mixture

de Lichtenberg et al.

dual permutation test

Willbrand et al.

up-down patterns

Ahdesmäki et al.

Fisher’s G-statistic

Chen

Fisher’s G-statistic

Qiu et al.

resynchronization

Glynn et al.

Lomb-Scargle periodogram

Andersson et al.

Bayesian detector

Ahnert et al.

low entropy patterns

Xu et al.

partial energy ratio

Lu et al.

homology transfer

Liew et al.

Fisher’s G-statistic

Morton et al.

cyclohedron test

Rowicka et al.

correlation to known genes

things to consider

change

periodicity

shape

loss of synchrony

stress response

uneven sampling

missing data points

multiple experiments

one (ranked) list