Data visualization and clustering. Genomics is to no small extend a data science


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1 Data visualization and clustering Genomics is to no small extend a data science [
2 Data visualization and clustering Genomics is to no small extend a data science [Andersson et al., Nature 2015]
3 Data visualization and clustering Data visualization: Look at the data. Why?  Quality control did a experiment work?  Exploratory data analysis what does the data say?  Sanity checks does my code work?  Interpreting data making a point. CAGE signature correlates with other enhancer marks. Fraction enhancers Mean signal [Andersson et al., Nature 2015]
4 Data visualization and clustering 1.Tools for data visualization. How to:  Visualize distributions correlations  Visualize group structure: clustering  Visualize data along genomic coordinates  Visualize dependencies and interactions: Graphs/networks and layouts 2. Examples
5 Visualizing distributions Discrete RV Continuous RV X! {x 1,x 2,...,x n } Random variable Observed realizations (n data points)
6 Visualizing distributions: histogram X! {x 1,x 2,...,x n } {x 1,x 2,...,x n }! Distribution? Number of realizations in bin Bins, e.g. [0.5,0.6)
7 Visualizing distributions: density ESTIMATE of continuous p.d.f The sale of xaxis matters! [Gentleman et al. 2006] 2. mode
8 Visualizing distributions: 2d histograms Binned realizations of RV Y Contour lines Binned realizations of RV X Information about DEPENDENCE between X and Y: P (X, Y )=P (X Y )P (Y ) Joint distribution Conditional distribution Marginal distribution Independence: P (X Y )=P (X) Are the rows in the plot similar?
9 Correlation {(x 1,y 1 ), (x 2,y 2 ),...,(x n,y n )} Joint realizations of X and Y Scatterplot Scatterplot with regression line Linear regression: E[Y X] =f(x) = + x Model the conditional expectation as linear function
10 Correlation Linear regression: E[Y X] =f(x) = + x How well does this work? Coefficient of determination. SS = X i ~Variance of Y Linear relation good: R 2 1 Linear relation bad: R 2 0 Can do the same for nonlinear f (y i ȳ) 2 = X (f i ȳ) 2 i {z } SSreg ~Variance of regression line R 2 =1 SSres SS tot + X (y i f i ) 2 i {z } SSres ~Variance not explained by f
11 Correlation Linear regression: E[Y X] =f(x) = + x How well does this work? Correlation coefficient Pearson s correlation coefficient: ˆ = r = X i r 2 = R 2 = Cov(X, Y ) X Y Cov(X, Y )=E[(X E[X])(Y E[y])] (x i x)(y i ȳ). Xi (x i x) X 2 (y i ȳ) i Coefficient of determination for linear model
12 Correlation coefficient Pearson s correlation coefficient: [1,1] and measures linear dependence
13 Correlation coefficient Pearson s correlation coefficient: [1,1] and measures linear dependence There are measures that capture nonlinear correlations.
14 Bar and Boxplots: comparing distributions [Spitzer et al., Nature Methods 2014]
15 Data visualization and clustering 1.Tools for data visualization. How to:  Visualize distributions correlations Histogram, density estimate, 2d histogram, coefficient of determination, correlation coefficient, scatterplot, boxplot (and variations thereof)  Visualize group structure: clustering  Visualize data along genomic coordinates  Visualize dependencies and interactions: Graphs/networks and layouts 2. Examples
16 Clustering: Grouping data Measurement of Y Measurement of X
17 Clustering: Grouping data 1. Organize data into clusters 2. No prior information (unsupervised) 3. Need some notion of distance/similarity
18 Hierarchical clustering  Euclidean distance  Agglomerative scheme  Average linkage Dendrogram Leafs are data points
19 Hierarchical clustering: distance Euclidean distance  Distance: D(a, b) =D(b, a) D(a, b) =0 iif a = b D(a, b) 0 D(a, b) apple D(a, c)+d(c, b)  Euclidean: D(x, y) = s X (x i y i ) 2 i
20 Hierarchical clustering: distance matrix Euclidean distance D(x, y) = s X (x i y i ) 2 i Heatmap of distances All pairs of data points  Magnitude is color coded  Matrix is symmetric  No apparent order
21 Hierarchical clustering: linkage Agglomerative scheme Start with each data point as its own cluster. Repeat until done: Merge the closest clusters.  Need closeness between points: Euclidean distance.  Need closeness between clusters (sets of points)  Average linkage: Average similarity points.  Single linkage: Take closest pair.  Complete linkage: Take furthest pair.
22 Hierarchical clustering  Euclidean distance  Agglomerative scheme  Average linkage Dendrogram
23 Hierarchical clustering  Euclidean distance  Agglomerative scheme  Linkage method matters Average linkage Single linkage (nearest neighbor)
24 Hierarchical clustering Distance matrix (heatmap) Ordered examples Internal nodes (not all are highlighted) Subtrees can rotate around nodes Ordering of leafs only partially defined
25 Hierarchical clustering Cutting the dendrogram defines clusters Distance matrix (heatmap) Ordered examples Cluster A Cluster B but it is often not clear how many to choose.
26 Prominent example: Clustering gene expression data Samples Genes Group [Gentleman et al. 2006] Group Group Group
27 Clustering: disclaimer There are a lot more clustering methods:  Partition clustering: No hierarchy, just disjoint clusters. Example: kmeans.  Modelbased clustering: Mixture distributions.  Others. P (X, Y )=P (X, Y cluster 1)P (cluster 1) + P (X, Y cluster 2)P (cluster 2) +... P (X, Y )=P (X, Y, Z) Unobserved cluster indicator random variable For each (xi,yi): Find the most likely zi Clustering.
28 Data visualization and clustering 1.Tools for data visualization. How to:  Visualize distributions correlations  Visualize group structure: clustering distance, distance matrix, heatmap, dendrogram, linkage (single, complete, average), partition clustering, modelbased clustering  Visualize data along genomic coordinates  Visualize dependencies and interactions: Graphs/networks and layouts 2. Examples
29 Plotting data along linear genomic coordinates UCSC genome browser
30 Plotting data along linear genomic coordinates UCSC genome browser [Rosenbloom et al., NAR 2015]
31 Circular arrangement Circular visualization: circos Enrichment analysis [Saben et al., Placenta, 2013]
32 Circular arrangement Circular visualization: circos [Zhang et al. 2013]
33 Data visualization and clustering 1.Tools for data visualization. How to:  Visualize distributions correlations  Visualize group structure: clustering  Visualize data along genomic coordinates UCSC browser, circular visualization  Visualize dependencies and interactions: Graphs/networks and layouts 2. Examples
34 Graphs Vertices v 2 V Edges e 2 E Can be directed or undirected (E,V): Graph. Entities and relations between entities [Gentleman et al. 2006] [dzone.com] Tree: acyclic and connected
35 Graphs Tree: acyclic and connected [dzone.com] Directed: edges/arcs have direction
36 Graphs [dzone.com]
37 Rooted trees and DAGs DAG: directed acyclic graph Rooted tree: DAG where each node has one parent. [dzone.com] [cs.cornell.edu]
38 Rooted trees and DAGs DAG: directed acyclic graph Rooted tree: DAG where each node has one parent. [dzone.com] Gene Ontology: heart development Phylogenetic tree
39 Plotting of graphs: layout Same graph, three pictures [Gentleman et al. 2006] dot: hierarchical neato: no edge crossing two: circular structure
40 Plotting of graphs: hairballs Different layout algorithms: an interaction network Gene A Interaction Gene B [ Inferred by:  experimental assay  insilico analyses
41 Data visualization and clustering 1.Tools for data visualization. How to:  Visualize distributions correlations  Visualize group structure: clustering  Visualize data along genomic coordinates  Visualize dependencies and interactions 2. Examples
42 Quality control: Color Number of cells in a well: Handling problem [Gentleman et al. 2006]
43 Example figure Mean signal Fraction enhancers
44 Example figure
45 Example figure
46 Visualizing distributions: microarray probes Intensity stratified by G+C [Gentleman et al. 2006]
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