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Conquering the Curse of Dimensionality in Gene Expression Cancer Diagnosis: Tough Problem, Simple Models Minca Mramor 1, Gregor Leban 1, Janez Demšar 1.

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Presentation on theme: "Conquering the Curse of Dimensionality in Gene Expression Cancer Diagnosis: Tough Problem, Simple Models Minca Mramor 1, Gregor Leban 1, Janez Demšar 1."— Presentation transcript:

1 Conquering the Curse of Dimensionality in Gene Expression Cancer Diagnosis: Tough Problem, Simple Models Minca Mramor 1, Gregor Leban 1, Janez Demšar 1 and Blaž Zupan 1,2 1 Faculty of Computer and Information Science University of Ljubljana, Slovenia 2 Department of Molecular and Human Genetics Baylor College of Medicine, Houston, USA

2 Cancer epidemiology –2nd cause of death in the developed world –increasing number of patients carcinogenesis –a multi factorial and heterogeneous disease –non-lethal injury to the DNA of one cell –a multi step process

3 Use of gene expression microarrays in cancer research uncovering the genetic mechanisms (loss of cell cycle control) identification of specific genes classification of different tumor types insight into carcinogenesis improvement and individualization of treatment, development of targeted therapeutics identification of biomarkers Final Goals

4 SRBCT Example: 6567 genes, 83 patients, 4 classes 1. Initial cuts (image analysis failed – 2308 genes left) dominant components obtained with PCA feed-forward neural networks 4. Rank genes with the ANN models, select best Clear separation of classes using MDS Khan et al. (Nature Medicine, 2001)

5 Open Questions & Goals Can graphs with clear class separation be found directly from data? Can they include only original attributes? How many of them are needed for good class separation? How are these attributes (genes) related to cancer? How useful are prevailing feature selection methods?

6 Data Sets DatasetSamplesGenesClasses Leukemia Prostate DLBCL MLL SRBCT

7 Methods: VizRank Visualization techniques Visualization scoring and ranking Projection search

8 Methods (VizRank) Visualization techniques Visualization scoring and ranking Projection search score = 0.76 score = 0.98

9 A snapshot of Orange data mining suite, showing VizRank ranking of best visualizations and the corresponding best- ranked scatterplot for the leukemia data set

10 Results LEUKEMIASRBCTDLBCLPROSTATE For all investigated data sets VizRank found visualizations with a small number of genes (2-6) with clear separation of diagnostic classes.

11 Results MIXED LINEAGE LEUKEMIA

12 Results Data setScatterplotRadviz Leukemia98.0%99.6% Prostate91.8%98.3% DLBCL96.8%99.7% MLL94.8%99.8% SRBCT87.7%99.7% Scores for top-ranked visualizations [Probability of correct classification for k-NN classifier in projection plane]

13 Results: biological relevance of the genes in the best visual projections Genes annotated as cancer or cancer related according to the atlas of genetics and cytogenetics in oncology and haematology. The best radviz visualization of the prostate tumor data set: all six genes are cancer related PROSTATE TUMOR

14 Results: biological relevance of the genes in the best visual projections DNTT (terminal deoxynucleotidyl transferase) – a unique DNA polymerase expressed in the lymphoid precursors and their malignant counterparts and an important marker of lymphoblastic leukemias MME (membrane metalloendopeptidase) or common acute lymphocytic leukemia antigen (CALLA) - an important cell surface marker in the diagnosis of human acute lymphocytic leukemia (ALL) MIXED - LINEAGE LEUKEMIA

15 Results: biological relevance of the genes and an explanation of the outliers SMCL and COID class express high levels of neuroendocrine tumors genes (ISL1) For SQ lung carcinomas diagnostic criteria include evidence of squamous differentiation (KRT5) Histological diversity of adenocarcinoma (AD) class in the lung cancer data set: 12 AD were extrapulmonary metastases seven adenocarcinomas display histological evidence of squamous features LUNG CANCER

16 How many “good” projections are there? Only a few [among several millions of possible projections].

17 Gene ranking methods Signal-to-noise (S2N) (Golub et al., Science 1999) – univariate gene scoring statistic derived from the standard parametric t-test S2N = (µ 0 - µ 1 )/(σ 0 + σ 1 ) µ = mean σ = standard deviation ReliefF (Kononenko, 1994) –attribute scoring function sensitive to feature interactions

18 Results: all data sets include a subset of about 100 highly discriminating genes Histogram for actual attribute valuesHistogram for permuted data For all data sets histograms of ReliefF scores are skewed to the right, with a group of 50 – 100 most discriminating genes in the right tail A permutation test to verify if these high discriminatory genes were assigned high scores by chance

19 Results: S2N and ReliefF yield different gene ranking Spearman correlation coefficient (from 0.24 for the DLBCL data set to 0.89 for the MLL data set) 20 best genes from the scatterplot visualizations for the leukemia data set A relatively poor performance of ReliefF, similar to S2N (too large context due to high number of attributes in the data sets?)

20 Conclusion Cancer diagnostic classes can be clearly separated using the expression data of only a few genes Visualizations can –find small sets of most relevant genes –uncover interesting gene interactions –point to outliers Our “visual” models are –simple –understandable and –significantly less sophisticated classification model than prevailing techniques in current cancer gene expression analysis

21 THANK YOU! Small round blue cell tumors, data by Khan et al. (2001) Minca Mramor, Gregor Leban, Janez Demšar and Blaž Zupan


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