EXPression ANalyzer and DisplayER
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1 EXPression ANalyzer and DisplayER Tom Hait Aviv Steiner Igor Ulitsky Chaim Linhart Amos Tanay Seagull Shavit Rani Elkon Adi Maron-Katz Dorit Sagir Eyal David Roded Sharan Israel Steinfeld Yossi Shiloh Ron Shamir Ron Shamir s Computational Genomics Group Rani Elkon s Group
2 Schedule Data, preprocessing, grouping (14:15-14:30) Hands-on part I (14:30-14:40) Grouping analysis (14:40-14:55) Hands-on part II (14:55-15:10) Enrichment analysis (15:10-15:20) Hands-on part III (15:20-15:30) ChIP-seq and GSEA (15:30-15:45) Hands-on part IV (15:45-16:00)
3 EXPANDER an integrative package for analysis of gene expression and NGS data Built-in support for 18 organisms: human, mouse, rat, chicken, fly, zebrafish, C.elegans, yeast (s. cereviciae and s. pombe), arabidopsis, tomato, listeria, leishmania, E. coli (two strains), aspargillus, rice. And v.vinifera (grape) Demonstration on human CAL51 cell line experiments*: RNA-Seq data, which contains expression profiles measured in several time points after IR-induction. P53 ChIP-Seq data after 2 hours of IR-induction. *Data from Rashi-Elkeles, Warnatz and Elkon et al, 2014, Science Signaling, DOI: /scisignal
4 EXPANDER status 829 citations since citations since ,062 downloads since since 2017
5 What can it do? Low level analysis Data adjustments (missing values, merging, divide by base, log) Normalization Probes & condition filtering High level analysis Group detection (supervised clustering, differential expression, clustering, bi-clustering, network based grouping). Ascribing biological meaning to patterns via analysis
6 Links to public annotation databases Input data Preprocessing Grouping Gene sets analysis Visualization utilities Functional Location mirna Targets Promoter analyses pathway
7 EXPANDER Data Expression matrix (probe-row; condition-column) One-channel data (e.g., Affymetrix) Dual-channel data, in which data is log R/G (e.g. cdna microarrays).cel files RNA-Seq counts OR absolute/relative intensities data ChIP-Seq data: in BED or GFF3 formats ID conversion file: maps probes to genes Gene groups data: defines gene groups Gene ranks data: defines gene ranking for GSEA Network information (e.g. PPI network) -.sif format
8 First steps with the data load, define, preprocess Load dialog box, Data menu, Preprocessing menu Data definitions Defining condition subsets Data type & scale (log) Define genes of interest Data Adjustments Missing value estimation (KNN or arbitrary) Flooring Condition reordering Merging conditions Merging probes by gene IDs Assigning genes to ChIP-Seq peaks Divide by base Log data (base 2)
9 Data preprocessing Normalization= removal of systematic biases Quantile = equalizes distributions Lowess (locally weighted scatter plot smoothing) = a non linear regression to a base array Visualizations to inspect normalization: box plots Scatter plots (simple and M vs. A) M=log 2 (A1/A2) A = 0.5*log 2 (A1*A2)
10 Data preprocessing Probe filtering Focus downstream analysis on the set of responding genes Fold-Change Variation Statistical tests: T-test, SAM (Significance Analysis of Microarrays), edger and deseq2 (RNAseq count data) It is possible to define VIP genes. Standardization : Mean=0, STD=1 (visualization) Condition filtering Order of operations
11 Hands-on (1-2)
12 Links to public annotation databases Input data Preprocessing Grouping Gene sets analysis Visualization utilities Functional Location mirna Targets Promoter signals pathway
13 Supervised Grouping Differential expression: a) Under normality assumption: t-test, SAM b) No normality assumption (RNA Seq data): Wilcoxon rank sum test, Negative binomial (edger/deseq2) Similarity group (correlation to a selected probe/gene) Rule based grouping (define a pattern)
14 Unsupervised grouping - cluster Analysis Partition into distinct groups, each with a particular expression pattern co-expression co-function co-expression co-regulation Partition the genes attempts to maximize: Homogeneity within clusters Separation between clusters
15 Cluster Analysis within Expander Implemented algorithms: CLICK, K-means, SOM, Hierarchical Visualization: Mean expression patterns Heat-maps Chromosomal positions Network sub-graph (Cytoscape integration) PCA Clustered heat map
16 Biclustering Clustering seeks global partition according to similarity across ALL conditions >> becomes too restrictive on large datasets. Relevant knowledge can be revealed by identifying genes with common pattern across a subset of the conditions Novel algorithmic approach is needed: Biclustering
17 Biclustering II * Bicluster = subset of genes with similar behavior under a subset of conditions Computationally challenging: has to consider many combinations Biclustering methods in EXPANDER: ISA (Iterative Signature Algorithm) - Ihmels et.al Nat Genet 2002 SAMBA = Statistical Algorithmic Method for Bicluster Analysis ( A. Tanay, R. Sharan, R. Shamir RECOMB 02)
18 Drawbacks/ limitations: Useful only for over 20 conditions Parameters How to asses the quality of Bi-clusters
19 Biclustering Visualization
20 Hands-on (3-4)
21 Links to public annotation databases Input data Preprocessing Grouping Gene sets analysis Visualization utilities Functional Location mirna Targets Promoter signals pathway
22 Functional analysis - Ascribing functional meaning to gene groups Gene Ontology (GO) annotations for all supported organisms TANGO: Apply statistical tests that seek overrepresented GO functional categories in the groups
23 Functional Enrichment - Visualization Can be saves as a tabular.txt file
24 Pathway analysis Searches for biological pathways that are overrepresented in gene groups KEGG: Kyoto Encyclopedia of Genes and Genomes (mainly metabolic),all 18 orgs WikiPathways various biological pathways(~20 species, 1765 pathways) open resource Statistical hyper-geometric (HG) cumulative distribution score + multiple testing correction
25 Pathway visualization Same as functional visualization, plus pathway map in browser
26 Links to public annotation databases Input data Preprocessing Grouping Gene sets analysis Visualization utilities Functional Location mirna Targets Promoter Signals (PRIMA/ AMADEUS) pathway
27 Inferring regulatory mechanisms from gene expression data Assumption: co-expression transcriptional co-regulation common cis-regulatory promoter elements Computational identification of cis-regulatory elements over-represention PRIMA - PRomoter Integration in Microarray Analysis (Elkon, et. Al, Genome Research, 2003) AMADEUS novel motif analysis
28 PRIMA general description Input: Target set (e.g. co-expressed genes) Background set (e.g. all genes on the chip) Analysis: Detects TFs with high target set prevalence TF binding site models TRANSFAC DB Default: From bp to 200 bp relative the TSS
29 Promoter Analysis - Visualization Frequency ratio
30 Amadeus A Motif Algorithm for Detecting Enrichment in multiple Species Supports diverse motif discovery tasks: 1. Finding over-represented motifs in one or more given sets of genes. 2. Identifying motifs with global spatial features given only the genomic sequences. Possible Gene-sets: 1. Identified gene sets clusters vs. all genes promoters. 2. ChIP-Seq peaks sequences using Expander built-in FASTA sequences generation.
31 AMADEUS on ChIP-Seq peaks TF binding site map Binding sites distribution
32 Hands-on (4-7)
33 Links to public annotation databases Input data ChIP-Seq Normalization/ Filtering Grouping (Clustering/ Biclustering/ Network based clustering) Gene sets analysis Visualization utilities Functional (TANGO) Location mirna Targets (FAME) Promoter signals (PRIMA/ AMADEUS)
34 Gene Sets Enrichment analysis Goal: Determine whether an a priori defined set of genes shows concordance with a biological pattern (e.g. differences between two phenotypes) Gene set sources: MSigDB (Broad molecular signature database) KEGG Wiki pathways Gene rank sources Phenotype labels Imported Selected condition Significance estimated with permutations FDR correction for multiple comparisons
35 Gene Sets Enrichment visualization
36 ChIP-Seq analysis Searches for over-representation of genes closest to ChIP-Seq data peaks Uses hyper geometric test Multiple testing correction (Bonferroni) Enrichment results visualization (same as other group analysis results)
37 ChIP-Seq visualization Peaks to genomic region distributions Closest gene to peak chromosome visualization Peaks in genomic regions Peaks annotation table including closest gene and genomic region (e.g., 5UTR, Exon etc)
38
39 Integration between different technologies GE data (Microarray/RNA- Seq)? ChIP-Seq peaks data ChIP-Seq vs. GE analysis: GSEA ChIP-Seq target genes as a single set ChIP-Seq of GE s clusters ChIP-Seq target genes distribution in GE Expander tools (e.g., TANGO, PRIMA) select ChIP-Seq target genes as a single cluster
40 GSEA ChIP-Seq vs. GE
41 Rank distribution ChIP-Seq vs. GE
42
43 Hands-on (8-10)
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