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Simple Methods for Peak Detection in Times Series Microarray Data. I. Azzini R. Dell’Anna F. Ciocchetta F. Demichelis A. Sboner Bioinformatics Group, SRA, ITC-Irst Department of Information and C.T. Trento University, Italy E. Blanzieri A. Malossini

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simple methods for peak detection in times series microarray data

Simple Methods for Peak Detection in Times Series Microarray Data

I. Azzini R. Dell’Anna

F. Ciocchetta F. Demichelis

A. Sboner

Bioinformatics Group, SRA, ITC-Irst Department of Information and C.T.Trento University, Italy

E. Blanzieri A. Malossini

Department of Information and Communication Technology

Trento University

preliminary analysis
Preliminary Analysis
  • Visual inspection of images
    • There are blurs on the images
  • We used alternative sw for image analysis
    • TIGR SpotFinder
    • Scananalyse
  • We reapplied the GenePixPro 3.0 quality control algorithm on a sample of images
  • Conclusions
    • From preliminary analysis did not emerged evidence againts reliability of original measures.
    • use QC_Dataset for further analysis
our analysis problem
Our analysis problem
  • To detect and characterize genes that present peaks and singularities over the time series.
  • Motivations:
    • Primary: Peak genes could play an intriguing role
    • Secondary: artifacts detection
our approach
Our approach
  • Detection of spike genes
    • Apply a series of simple methods based on discrete derivative and integral.
  • Characterization of genes
    • Functional Classification using Multiclass SVM
outline of the talk
Outline of the talk
  • Preliminary analysis
  • The analysis problem
  • Methods for peak detection
  • M-SVM for oligo classification
  • Results
  • Discussion
qc dataset
QC_Dataset

Our notation:

X0,t=E(o,t)

missing value managment data imputing
Missing value managment(data imputing)
  • Up to 2 adjacent missing values were replaced by interpolation
  • Oligos with more adjacent missing values were discarded
  • Extrapolation for TP1 and TP48 (For functional classification)
methods for peak detection
Methods for peak detection

None of the methods is

100% precise and

100% accurate

methods for peak detection10
Methods for peak detection

The combination of M1, M2 and M3 are less

prone to detect ramps

Instead of peaks

detection procedure
Detection procedure
  • Each method M1-M6 scores the oligos.
  • We selected the oligos that were ranked among the first ten by at least one method
detection procedure15
Detection procedure
  • We discarded oligos whose signal to noise ratio is less of 2
    • The S/N ratio is higher w.r.t. the one adopted in original work
    • We need such a filter to discard extremely noisy signals
  • We visual inspect all the oligos of the table and discarded the ones that does not present peaks
detection procedure selected genes
opfblob0072

n128_25

f65819_1

m364_2

m12963_1

n159_34

ks244_7

n128_61

opfm60504

l1_28 ET

ks75_15 ET

c154

b593

b597

n176_5

opfh0008

opfblob0105

b541

n132_108

m50253_2

ks1030_4 OM

n128_33

f71224_1

opfh0022

e17542_1

Detection procedure:Selected genes
functional classification m svm
Functional Classification (M-SVM)
  • Multiclass Support Vector Machine
    • Pairwise classification (N-1)*N/2 classifiers for N classes.
    • Majority vote
  • Schema for replacement of missing values
  • Trained on data of Table S2
    • 530 samples and 14 functional classes
    • LOO accuracy is 73%
  • We applied the classifiers to the complete_dataset and scored the results depending on the voting.
significant peaks or artifacts
Significant peaks or artifacts?
  • We tested:
    • Data Quality (from preliminary analysis)
    • We discarded oligos with low signal to noise ratios
    • The peaks have different width and amplitude (not consistent with synchronization induced artifact)
how are the peaks distributed over time
How are the peaksdistributed over time?
  • Plasmodium falciparum has different phases during the 48 hours cycle IDC (Ring, Trophozoide, Schizont)
  • The peaks that we detected seems to concetrate in specific time points.
  • We used Kolgomorov-Smirnov test for ruling out uniform distribution
discussion
Discussion
  • The peaks do not distributed uniformely over time
  • There is a (possibly) interesting high number of peaks near a transition phase.
conclusions
Conclusions
  • We presented
    • Methods for peak detection
    • Functonal classificaton via M-SVM
  • The peaks do not distribute uniformely over time
slide31
Azzini* R. Dell’Anna

F. Ciocchetta F. Demichelis

A. Sboner

Bioinformatics Group, SRA, ITC-Irst Department of Information and C.T.Trento University, Italy

E. Blanzieri A. Malossini

Department of Information and Communication Technology

University of Trento

biological interpretation
Biological Interpretation
  • Critical issue about our analysis
selected genes
opfblob0072

n128_25

f65819_1

m364_2

m12963_1

n159_34

ks244_7

n128_61

opfm60504

l1_28

ks75_15

c154

b593

b597

n176_5

opfh0008

opfblob0105

b541

n132_108

m50253_2

ks1030_4

n128_33

f71224_1

opfh0022

e17542_1

opfblob0072

n128_25

f65819_1

m364_2

m12963_1

n159_34

ks244_7

n128_61

opfm60504

l1_28

ks75_15

c154

b593

b597

n176_5

opfh0008

opfblob0105

b541

n132_108

m50253_2

ks1030_4

n128_33

f71224_1

opfh0022

e17542_1

Selected genes
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