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Array Platforms. 16K Agilent inkjet printed cDNA arrays The recently developed inkjet printing method (Agilent Technologies) produces more uniform spots than pin spotting techniques Array includes cDNAs selected from the RIKEN FANTOM collection supplemented by cDNAs from AfCS protein list

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Array Platforms

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Array platforms

Array Platforms

  • 16K Agilent inkjet printed cDNA arrays

    • The recently developed inkjet printing method (Agilent Technologies) produces more uniform spots than pin spotting techniques

    • Array includes cDNAs selected from the RIKEN FANTOM collection supplemented by cDNAs from AfCS protein list

  • Affymetrix GeneChip system

    • U74A v.2 chip (represents approx. 13,000 mouse genes)

  • 16k Agilent inkjet printed Oligonucleotide arrays (in preparation)

    • Operon 70mers (13,443) and Compugen 65mers (2,304)


Ligand screen transcript analysis

Ligand Screen Transcript Analysis

  • B cell samples prepared by Cell Lab.

  • Cultured for different time periods (.5, 1, 2, and 4 hr) in the presence or absence of ligands before harvesting for total RNA isolation.

  • Treated and untreated time-course samples hybridized against a spleen reference.

  • After removing the common spleen denominator, comparison to 0 time point data reflects the changes in mRNA levels due to ligand treatment and/or time in culture.

  • All of the experiments were done in triplicate. Including

    in controls >450 arrays


Molecular biology laboratory

Molecular Biology Laboratory

Microarray & Analysis

Sangdun Choi

Xiaocui Zhu

Rebecca Hart

Anna Cao

Mi Sook Chang

Jong Woo Kim

Sun Young Lee


Array platforms

Clustering Analysis of Gene Expression Profile

Using log2Ratio (Treated/0hr)

a. Calculate gene expression value:

Compute log2(Treated/0hr) = log2(Treated/Spleen) – log2(0hr/Spleen) using processedSignalIntensity

b. Hierarchical cluster:

with genes showing >= 2 fold change in at least one condition while keeping ligands in alphabetical/time course order:

Average of 6-23 replicates

Average of triplicates

30min

1hr

2hr

4hr

30min 2MA

1hr 2MA

2hr 2MA

4hr 2MA

30min AIG

1hr AIG

2hr AIG

4hr AIG

….

132 conditions

Gene 1

Gene 2

Gene 3

……..

5281 genes


Array platforms

Ligands, time course ( i.e. medium- 30 min, 1hr, 2hr, 4hr; 2MA- 30 min, 1hr, 2hr, 4hr…)

Genes, clustered


Array platforms

Genes up regulated in AIG, CD40L, IL4, LPS and CpG

CD40L

None

LPS

AIG

CpG

IL4

Hk2

Ak2

Ccnd2

Cdk4

Bax

Ifrd2

cdk6

Atf

Caspase 4

317 features

Image contrast: 1.07


Array platforms

Genes down regulated in AIG, CD40L, IL4, LPS and CpG

CD40L

None

LPS

AIG

CpG

IL4

cAMP-GEFII

Gprk6

Bcap31

Gnai2

id3

Bnip3l

319 features

Image contrast: 1.07


Array platforms

Genes showing AIG & CD40L specific changes

CD40L

None

CpG

LPS

AIG

IL4

Gadd45b

Par-6

Dagk1

IL3ra

IL10ra

Mapk12

235 features

Image contrast: 1.16


Array platforms

Genes up regulated in IL4

CD40L

None

CpG

AIG

LPS

IL4

Socs1

Caspase 6

Xbp1

Dapp1

Rgs14

42 features

Image contrast: 1.14


Array platforms

Genes showing AIG specific changes

None

CD40L

AIG

CpG

LPS

IL4

Stress induced protein

Bak1

Bcl2l11

LTb

apolipoprotein E

65 features

Image contrast: 1.54


Array platforms

Madhusudan Natarajan

Rama Ranganathan


Array platforms

Clustering Analysis of Gene Expression Profile

Using Z Score

Z score: a measurement of the distance between an observed value and the mean of a population

Observed value

basal


Array platforms

Clustering Analysis of Gene Expression Profile

Using Z Score

  • a.Calculate gene expression metric, x:

  • For each gene i on a given chip j: xij ={rMedianIntensity (treated) / gMedianIntensity (spleen) }/ xj , where xj is the mean of intensity ratio of all genes on chip j

  • Calculate the mean and standard deviation of gene expression in 27 sets of 0hr untreated data:

  • For each gene i, calculate the mean(mi) and the standard deviation (i) of expression on

  • 27 0hr chips;

  • Calculate Z score as a measurement of differential expression from 0hr condition

  • For each gene i on a given chip j, Zij = (xij – mi) / i

  • Cluster genes and ligands using Z-score:

  • with genes whose Z > 2 in any of the ligands


Array platforms

Clustering ligand based on Z scores


Afcs data analysis microarray

AfCS Data Analysis- Microarray

Dennis Mock

UC Principal Statistician

University of California, San Diego

Director: Shankar Subramaniam

Acknowledgment: Eugene Ke, Bob Sinkovits, Brian Saunders


Array platforms

Two-way hierarchical clustering –unsupervised- Ligands (n=33)

(0hr, .5h, 1h, 2h, 4h)

Note: the ligand cluster according early –late conditions with 90-100% accuracy

(metrics: sample = Euclidean; gene = Pearson)

mitogenic

Interleukins

early .5-1 hr

(non-mitogenic)

late 2-4 hr

0 hr

late 2-4 hr

early .5-1 hr

.

.

.

.

.

.

.

.

.

Dennis Mock - UCSD


Significance analysis of microarrays sam r tibshirani g chu 2002

Significance analysis of microarrays* (SAM)(R. Tibshirani, G. Chu 2002)

For each gene, define the adjusted “t-statistic” as follows:

Objective: The replicated expression for each gene is taken for the 4hr time condition (untreated vs ligand) to determine whether the gene is statistically differentially up- or down- regulated.

treated - untreated

  •  mean of replicates

      standard deviation for the gene

 + adjustment factor

The t-statistics for all the genes are ordered and noted. The labels are then permutated and the t-statistic is calculated again. After many iterations, the cumulative t-statistics is averaged for each gene. Finally, for a given false positive rate, [called “False Discovery Rate” or FDR], the significant genes are selected.

Dennis Mock - UCSD


Array platforms

Concordance of significantly up (+) or down (-) regulated genes mitogenic ligands(FDR = 1%)

“down-regulated” matches

Mosaic plot

135 (-)

3 (-)

147 (-)

337 (-)

553 (-)

96 (-)

756 (-)

1082 (+)

3 (-)

Example: CD40L had 756 down-regulated and 1082 up-regulated genes.

Those which were similarly regulated in AIG:

337 down

578 up.

119 (-)

341 (-)

2 (-)

72 (-)

446 (-)

887 (+)

143 (-)

151 (-)

3 (-)

152(-)

80(+)

“up-regulated” matches

1 (-)

578 (+)

796 (-)

854 (+)

72 (+)

73 (+)

47 (+)

171 (-)

163 (+)

Discordance matrix

597 (+)

477 (+)

18 (+)

3 (-)

10 (+)

117 (+)

117 (+)

108 (+)

4 (+)

6 (+)

3 (+)

5 (+)

4 (+)


Beyond clustering

Beyond Clustering

  • How can we obtain biological information from array data at the level of individual genes and correlations in expression between genes?

  • Can we use the correlations to build a connection network that reflects correlations in expression? Is there biological significance to this?


Array platforms

Two-way hierarchical cluster:

mean ratio (vs control) of phosphoprotein levels and ligand

Note: the ligands that elicit an ERK response (chemokines + AIG, CD40L) clustered together.


Array platforms

Transcription factor encoded by fos is stabilized by ERK and continues to affect other IE genes such as jun

from Nature Cell Biology august 2002 v 4 issue 8


Array platforms

A clear lesson that we must implement as soon as possible is to decrease the cycle time from experimental design - data collection - data analysis - conclusions, models - to experimental redesign. In the past the rate limiting step has been data analysis


Array platforms

Input Signals

Signal Processing

Translocation

Cytoskeleton

Gene Expression

Transcription

Translation

Transcription

Translation


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