Topologically inferring risk-active pathways toward precise cancer classification by directed random walk Topologically inferring risk-active pathways.

Slides:



Advertisements
Similar presentations
Original Figures for "Molecular Classification of Cancer: Class Discovery and Class Prediction by Gene Expression Monitoring"
Advertisements

Putting genetic interactions in context through a global modular decomposition Jamal.
Genomic analysis of regulatory network dynamics reveals large topological changes Paper Study Speaker: Cai Chunhui Sep 21, 2004.
By Russell Armstrong Supervisor Mrs Wei Ji Diagnosis Analysis of Lung Cancer by Genome Expression Profiles.
Supervised classification performance (prediction) assessment Dr. Huiru Zheng Dr. Franscisco Azuaje School of Computing and Mathematics Faculty of Engineering.
Introduction to molecular networks Sushmita Roy BMI/CS 576 Nov 6 th, 2014.
Protein Classification A comparison of function inference techniques.
Systematic Analysis of Interactome: A New Trend in Bioinformatics KOCSEA Technical Symposium 2010 Young-Rae Cho, Ph.D. Assistant Professor Department of.
Graph-based consensus clustering for class discovery from gene expression data Zhiwen Yum, Hau-San Wong and Hongqiang Wang Bioinformatics, 2007.
A Multivariate Biomarker for Parkinson’s Disease M. Coakley, G. Crocetti, P. Dressner, W. Kellum, T. Lamin The Michael L. Gargano 12 th Annual Research.
Network Analysis and Application Yao Fu
Glasgow 02/02/04 NN k networks for content-based image retrieval Daniel Heesch.
Complex Networks First Lecture TexPoint fonts used in EMF. Read the TexPoint manual before you delete this box.: AA TexPoint fonts used in EMF. Read the.
Intel Confidential – Internal Only Co-clustering of biological networks and gene expression data Hanisch et al. This paper appears in: bioinformatics 2002.
Algorithms for Biological Networks Prof. Tijana Milenković Computer Science and Engineering University of Notre Dame Fall 2010.
Metabolic Network Inference from Multiple Types of Genomic Data Yoshihiro Yamanishi Centre de Bio-informatique, Ecole des Mines de Paris.
Understanding Network Concepts in Modules Dong J, Horvath S (2007) BMC Systems Biology 2007, 1:24.
341- INTRODUCTION TO BIOINFORMATICS Overview of the Course Material 1.
Evaluation of gene-expression clustering via mutual information distance measure Ido Priness, Oded Maimon and Irad Ben-Gal BMC Bioinformatics, 2007.
Gene Set Analysis using R and Bioconductor Daniel Gusenleitner
Response network emerging from simple perturbation Seung-Woo Son Complex System and Statistical Physics Lab., Dept. Physics, KAIST, Daejeon , Korea.
Algorithms and Computational Biology Lab, Department of Computer Science and & Information Engineering, National Taiwan University, Taiwan Network Biology.
From: An open day in the metric space
Graph clustering to detect network modules
David Amar, Tom Hait, and Ron Shamir
Fig. 1. Schematic overview of diffusion maps embedding
From: Global Banking: Recent Developments and Insights from Research*
From: The World Price of Credit Risk
Figure 2. Quality of life (QOL) scores for functional scale items
Fig. 1 Graphical representation
Fig. 2. In adaptive permutation, the pool of candidate SNPs decreases as p-value estimates become more precise. Running time increases with the number.
Figure 2. CONSORT flow diagram.
Fig. 2 Two-dimensional embedding result obtained using nMDS.
From: Growing and Slowing Down Like China
Fig. 3. ACE outperforms plmDCA in recovering the single variable frequencies for models describing (a) ER005, (b) LP SB, (c) PF00014, (d) HIV.
Hyunghoon Cho, Bonnie Berger, Jian Peng  Cell Systems 
Fig. 1. Sample NGS data in FASTQ format (SRA's srr032209), with parts being shortened and numbered: (1) read identifiers; (2) sequence of bases; (3) ‘+’
Figure 1. Annotation and characterization of genomic target of p63 in mouse keratinocytes (MK) based on ChIP-Seq. (A) Scatterplot representing high degree.
Fig. 1. Map of sample collection sites.
Fig. 4 Disease gene prediction based on multiple CSNs
Figure 1. Overview of median-supplement methods
Analysis of bio-molecular networks through RANKS (RAnking of Nodes
Fig. 1. Timeline of the CPAP in Ghana study.
Fig. 1 Selection of patients
Fig. 1 The sucrose breakdown metabolism
Fig. 1. Overview of the entire method for aggregated strings generation. (a) Extraction of 3-tags from the input set of deconvoluted MS/MS spectra. (b)
Figure 7 miRNA and mRNA gene expression changes in the Poor Group
Compact Query Term Selection Using Topically Related Text
Nick Macklon, M.D., Ph.D.  Fertility and Sterility 
Figure 1 Weighted social networks for (a) chase behavior and (b) display behavior. Each node represents an individual, and individuals are in the same.
Volume 5, Issue 1, Pages e6 (July 2017)
Anastasia Baryshnikova  Cell Systems 
Information Networks: State of the Art
Fig. 1. Blood exosomal miRNA changes in patients with SCZ
Figure 4 ROMs have a morph-specific effect relative to active aggression but not display behavior in dominant males. ... Figure 4 ROMs have a morph-specific.
Figure 1 Network topology as a function of dopaminergic state
Single Sample Expression-Anchored Mechanisms Predict Survival in Head and Neck Cancer Yang et al Presented by Yves A. Lussier MD PhD The University.
Label propagation algorithm
We show here the ... We show here the partitioning and analysis strategy for our data set from New York City covering arrests from November 1, 2008, through.
Didi Amar and Tom Hait Group meeting October 2013
Functional classification and visualization of differentially expressed genes. Functional classification and visualization of differentially expressed.
Expression profiles of 87 miRNAs expressed in SC
Hyunghoon Cho, Bonnie Berger, Jian Peng  Cell Systems 
Figure 1. Illustration of aggregation functions on the local network of 1-decene. 1-decene is marked as target. ... Figure 1. Illustration of aggregation.
Inferred promoter–metabolite regulation network (Table EV7)
A, unsupervised hierarchical clustering of the expression of probe sets differentially expressed in the oral mucosa of smokers versus never smokers. A,
Fig. 1. Generic metabolic pathway and its corresponding adjacency matrices. The graph of a static network (A) is ... Fig. 1. Generic metabolic pathway.
Expression of 20 genes significantly associated with reduced survivability in GBM is shown across 33 TCGA diseases. Expression of 20 genes significantly.
Volume 28, Issue 4, Pages e6 (July 2019)
Bioinformatic analyses suggest that PI3K/AKT signaling may be a key downstream pathway of tazarotene signaling. Bioinformatic analyses suggest that PI3K/AKT.
Presentation transcript:

Topologically inferring risk-active pathways toward precise cancer classification by directed random walk Topologically inferring risk-active pathways toward precise cancer classification by directed random walk Wei Liu et al. Bioinformatics Sep 1;29(17): doi: /bioinformatics/btt373. Epub 2013 Jul 10. Bioinformatics. 2013/09/30 Yamada

Target of study With microarray data Assessment of clinical (cancer) conditions Motivation of study Poor/unsatisfied results with methods handling genes individually Usage of gene-pathway information has been promising, but still NOT satisfactory … Particularly POOR REPRODUCABILITY : NOT ROBUST Novelty of study Change in the way to handle gene-pathway: – From sets of genes – To sets of genes with structure of individual sets – Weigh nodes based on network topology using “Directed” random walk instead of “Undirected” random walk

Introduction

Microarray Expression profiles edia.org/wiki/D NA_microarray edia.org/wiki/D NA_microarray Data sets from GEO GEO

Gene pathway n7359/fig_tab/nature10251_F3.html

Pathway information resources a controlled vocabulary of termsa controlled vocabulary of terms for describing gene product characteristics to map elementary datasets (genes, proteins, small molecules, etc.) to network datasets

Network and hubs

Network theory/graph theory and hubs Nodes with many edges ~ high degree nodes Only few hubs Visualized with ba-model in igraphassisted by Cytoscape2.5. Degrees of nodes are indicated by size and color igraphCytoscape2.5 Scale-free graph Random graph Degree Frequency Real-world networks “ “ 複雑系ネット ワーク ” free_network

Topology Network topology Which ones are hubs?

Hubs and Robustness What is Robustness? What is Robustness in expression analysis? What does it mean that hubs are robust in expression networks?

Random walk on Directed and Undirected Graphs

Construction of global-directed pathway graph SubpathwayMiner software (R package NOT MAINTAINED for the latest R version) 300 pathwas from KEGG 1 Global directed graph with 4113 node genes with edges Virtual node

Random Walk on a Graph n nodes are scored with t-test at time 0 (W(0)) W(t): Scores of node 1,2,…,n at time t M: Row-normalized adjacency matrix of graph r: (0,1),Restart probability to respect scores at time t

Directed Random Walk with R

Overview of the DRW-based pathway activity inference method. Liu W et al. Bioinformatics 2013;29: © The Author Published by Oxford University Press. All rights reserved. For Permissions, please Directed Random Walk Genes are RE-scored with DRW

Pathway activity Each gene is weighted based on DRW. Genes in the pathway Pj contributes to the activity. Genes with significant contribution are selected with FDR. Higher activity ~ Higher expression Lower activity ~ Lower expression Over-activity/Under-activity in total matters. Average expression across samples matters. Standardized for number of genes and gene weight to reflect relative weight within the pathway.

Overview of the DRW-based pathway activity inference method. Liu W et al. Bioinformatics 2013;29: © The Author Published by Oxford University Press. All rights reserved. For Permissions, please

Overview of the DRW-based pathway activity inference method. Liu W et al. Bioinformatics 2013;29: © The Author Published by Oxford University Press. All rights reserved. For Permissions, please t-scores for pathways

These t-scores are the “SCORES” of this method for pathways. How robust ~ reproducible are they?

Reproducibility power K-fold cross-validation K-fold cross-validation In k-fold cross-validation, the original sample is randomly partitioned into k equal size subsamples. Of the k subsamples, a single subsample is retained as the validation data for testing the model, and the remaining k − 1 subsamples are used as training data. Validation sets Test sets No. pathways Kind of inner product of two vectors, test set vector and validation set vector.

Classification of samples using highly- scored pathways Using more reproducible ~ reliable scoring method, classification would be more reliable… This part is not today’s target.