C ovariate- a djusted M atrix V isualization via C orrelation D ecomposition 吳漢銘 淡江大學...
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Transcript of C ovariate- a djusted M atrix V isualization via C orrelation D ecomposition 吳漢銘 淡江大學...
Covariate-adjusted Matrix
Visualization via
Correlation Decomposition吳漢銘
淡江大學 數學系 資料科學與數理統計組
http://www.hmwu.idv.tw
Outlines
Data/Information Visualization Two Demo Data Sets Generalized Association Plots (GAP) Related Works with Matrix Visualization Covariate-adjusted Matrix Visualization
For a discrete covariate: Within And Between Analysis (WABA) For a continuous covariate: Partial Correlations
Examples GAP Software Concluding Remarks
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Data/Information Visualization
Exploiting the human visual system to extract information from data.
Provides an overview of complex data sets. Identifies structure, patterns, trends,
anomalies, and relationships in data. Assists in identifying the areas of interest.
Matrix Visualization: reorderable matrix, the heatmap, color histogram, data image.
Visualization =
Data
information
Graphing for Data
+ Fitting
+ Graphing for Model
Raw Data Matrix Raw Data Map
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The Iris Data (Anderson 1935; Fisher 1936)
The iris data published by Fisher (1936) have been widely used for examples in discriminant analysis and cluster analysis.
Images source: http://www.stat.auckland.ac.nz/~ihaka/120/Lectures/lecture27.pdf
4 variables
50x3=150 subjects
Raw Data Matrix
1 covariate
setosa
versicolor
virginica
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Psychosis Disorder Data (Chen 2002)
All the symptoms are recorded on a six point scale
(0-5).
Scale for Assessment of Negative Symptoms
(SANS): 20 items, 5 subgroups.
Expression (NA1-7)Scale for Assessment of Positive Symptoms
(SAPS): 30 items, 4 subgroups.
Hallucinations (AH1-6)
69 schizophrenic
26 bipolar disorders
Speech (NB1-4)
Hygiene (NC1-3)
Activity (ND1-4)
Inattentiveness (NE1-2)
Behavior (BE1-4)
Delusions (DL1-12)
Thought disorder (TH1-8)
50 Variables
95 Subjects
正性症狀 : 行為的過量
負性症狀 : 行為的不足
精神分裂症
躁鬱症
精神疾病
幻覺
妄想行為
思考失序
表達語言
社交
做事的意志
衛生
Raw Data Matrix胡海國 國立臺灣大學 精神科教授
國立臺灣大學醫學院附設醫院 精神部主任
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(4) Sufficient
充分
Generalized Association Plots (GAP) (Chen, 2002)
Four Steps of Generalized Association Plots (GAP)
(1)Presentation
呈現
(2) Seriation
排序
(3) Partition
分割
Raw Data Matrix
Proximity Matrices for Rows and Columns
Clustering Summarization
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Presentation of Raw Data Matrix
0. Data Transformation
1. Selection of Proximity Measures
2. Color Spectrum
3. Display Conditions
The 1st Step of GAP
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Presentation of Raw Data Matrix: iris data
(2) Color Spectrum
(1) Selection of Proximity Measures
(3) Range Matrix Condition
Pearson Correlation Matrix for Variables
Eculidean Distance Matrix for Subjects
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Presentation of Raw Data Matrix: Psychosis Disorder Data
Pearson Correlation CoefficientCorrelation Matrix for Variables
Correlation Matrix for Subjects
Raw Data Matrix
(2) Color Spectrum
(1) Selection of Proximity Measures
(3) Range Matrix Condition
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Seriation of Proximity Matrices and Raw Data Matrix
Relativity of a Statistical Graph Global Criterion
GAP Rank-Two Elliptical Seriation
Local Criterion Tree Seriation Flipping of Tree Intermediate Nodes
The 2nd Step of GAP
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Relativity of a Statistical Graph
Placing similar objects at closer positions.Placing different objects at distant positions.
Seriation Methods
(1) Rank Two Ellipse Ordering (Chen, 2002)
(2) Hierarchical Clustering Tree (Average-Linkage)
Seriation Methods
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GAP Rank-Two Elliptical Seriation
The p objects fall on an ellipse and have unique relative position on the ellipse (Chen 2002).
Seriation Algorithms with Converging Correlation Matrices
First two Eigenvectors
Correlation Matrix (without ordering)
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Hierarchical Clustering Tree with a Dendrogram
Different Seriations Generated from Identical
Tree Structure
Tree seriation for proximity matrices
Tree seriation for raw data matrices
3 flips1 flipmany flips5 flips
ideal model
Tree seriation
Internal Tree Flips External Tree Flips Ziv Bar-Joseph, David K. Gifford, and Tommi S. Jaakkola, (2001), Fast Optimal Leaf Ordering for Hierarchical Clustering. Bioinformatics 17(Suppl. 1):S22–S29.
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GAP Rank-two elliptical seriation Michael Eisen (1998) tree seriation
Global vs. Local Seriation
Data: 517 genes by 13 arrays
Tien, Y. J., Lee, Y. S, Wu, H. M. and Chen, C. H.* (2008), Methods for Simultaneously Identifying Coherent Local Clusters with Smooth Global Patterns in Gene Expression Profiles. BMC Bioinformatics 9:155, 1-16.
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Related Works of Matrix Visualization
Concept:1. Bertin (1967): reorderable matrix.2. Carmichael and Sneath (1969): taxometric maps.
Clustering of data arrays:1. Hartigan (1972): direct clustering of a data matrix. 2. Tibshirani (1999): block clustering. 3. Lenstra (1974): traveling-salesman problem.4. Slagle et al. (1975): shortest spanning path.
Colour Representation:1. Wegman (1990): colour histogram.2. Minnotte and West (1998): data image.3. Marchette and Solka (2003): outlier detection.
1
12
1 2 3
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Exploring proximity matrices only:1. Ling (1973): shaded correlation matrix.2. Murdoch and Chow (1996): elliptical glyphs.3. Friendly (2002): corrgrams.
Integration of raw data matrix with two proximity matrices1. Chen (1996, 1999, and 2002): generalized association plots (GAP).
Reordering of variables and samples1. Chen (2002): concept of relativity of a statistical graph.2. Friendly and Kwan (2003): effect ordering of data displays.3. Hurley (2004): placing interesting displays in prominent positions.
Matrix Visualization (MV): reorderable matrix, the heatmap, color histogram, data image.
Related Works of MV (conti.)
1
2
3
1
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Covariate-adjusted
First two PCAs for Iris Data Psychosis Disorder Data
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A Model18/42
Correlation Decomposition19/42
Covariate-adjusted MV for Discrete Case
Correlation (Distance) for rows
based on(1) raw data matrix(2) fitted data matrix(3) residual data matrix
Correlations for
columns
Discrete Covariate
Y
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Within And Between AnalysisDansereau, F., Alutto, J. A., & Yammarino, F. J. (1984).
Total correlation
Between-group correlation Within-group correlation
Between-eta correlation Within-eta correlation
Between component Within component
WABA equation
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Three Steps to WABA
WABA I: Assessment of Variation: eta Each variable is assessed to determine whether the variable varies
between group (suggesting within-group homogeneity). within groups (suggesting within-group heterogeneity). both between and within groups (suggesting individual differences
rather than within-group homogeneity or heterogeneity).
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Three Steps to WABA (conti.)
WABA II: Assessment of Covariation: RB, RW Relationship among variables are assessed to determine whether the
correlation between variables is primarily a function of between-group covariance within-group covariance within- and between-group covariance (suggesting individual differences).
Drawing Inferences: Combination of WABA I and WABA II: R, B, W
The results of the first two steps are assessed for consistency and combined to draw the best overall conclusion from the data.
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Covariate-adjusted MV for Continuous Case
Correlation (Distance) for rows
based on(1) raw data matrix(2) fitted data matrix(3) residual data matrix
Correlations for
columns
Continuous Covariate
Y
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Partial Correlations
Conditional correlation is equivalent to partial correlation under some assumptions (Kurowicka and Cooke, 2000).
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Assessing the Goodness of Fit of the Model Component
+=
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Significance Analysis of the Residual Component Dunn and Clark’s z test for the equality of two dependent
correlations in the case of N exceeds 20 (Steiger, 1980). Test whether the correlations between variables Xj and Xk are
different significantly before and after a covariate adjustment.
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z-score Significant Map
This z-score significant map is helpful identifying variable pairs with the most significant differences in correlation before and after a covariate adjustment.
R Radj
zDunn and
Clark’s z test
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Simulation Study29/42
Psychosis Disorder Data: R
Rank-two ellipse orderingFive symptom groups
identified by Chen (2002).
thought disorder ( 思考失序 )
Negative ( 負性症狀 )
auditory hallucination ( 聽幻覺 )
loss of ego boundary ( 分際喪失 )
Mania ( 狂躁 )
NOTE: the mania symptoms are negatively related to the negative symptoms and the auditory hallucination symptoms.
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Psychosis Disorder Data: R=B+W
By comparing B and R, the negative correlations between the mania symptoms
V5 (DL4, TH6-8) with the negative symptoms V2 (NC1-ND4) and the auditory
hallucination symptoms V3 are mostly due to the patients‘ subtypes.
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Psychosis Disorder Data: B
Average-linkage + GrandPa Flip
mania symptoms (DL4, TH6-8)
negative symptoms (NC1-ND4)
auditory hallucination symptoms
Delusions ( 妄想 )
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Psychosis Disorder Data: RB
Average-linkage + GrandPa Flip
All correlations are either positive one or negative one since there are only two subtypes for patients.
Two clusters (DL2-TH6) and (NA7-NA6) are formed and are negatively correlated.
For 50 between-eta correlations, symptom TH6 with the darkest between-eta has the most significant difference between schizophrenic and bipolar disorders.
話停不下來的 (Pressure of Speech)
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Psychosis Disorder Data: W
Rank-two ellipse ordering
Residual Patterns
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Psychosis Disorder Data: RW
Rank-two ellipse ordering
Four new symptom groups: (ND2-NE1), (TH5-TH7), (Th3-Th4) and (DL4-DL6).
Four symptoms NE1, DL2, BE1, and BE2 were grouped into the original negative symptoms group.
The symptoms in the TH (thought disorder) were grouped into two highly correlated subgroups (TH3-TH4, Th5-TH7).
All hallucination symptoms (AH1-6) and most of the delusion symptoms (except DL2, DL3) were clustered together.
negative symptoms
thought disorder
hallucination
delusion
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Psychosis Disorder Data: Z
Positive z scores: between and within the group of
the negative symptoms (V2, except NA6, DL3, and BE4) and the group of the auditory hallucination symptoms (V3, except DL6)
within the group of the mania symptoms (V5, except DL5 and BE3)
Negative z scores: between the group of mania
symptoms V5 and the group of the negative symptoms (V2, except DL3, and BE4) and the group of the auditory hallucination symptoms (V3, except DL6).
negative
auditory hallucination
mania
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Psychosis Disorder Data: Z
Changed significantly: the symptom TH4 ( 不合邏
輯 ) of V1 to the group of the negative symptoms (V2, except NA6, DL3, and BE4), the group of the auditory hallucination symptoms (V3, except DL6).
Without significant relationship with any other symptom for different patients' subtypes:
Eleven symptoms (TH5, NE2, DL2, BE1, BE2, DL3, BE4, AH6, AH5, DL5,and BE3)
Note: positive symptoms of behavior (BE1-BE4) are all included.
thought disorder
loss of ego boundary
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Psychosis Disorder Data: Z
Single-linkage+GrandPa Flip
Most significant difference A right slash: A reversed slash:
(AH1, DL4), (AH1, TH7), (DL1, DL4), (TH6, NC2), (TH7, NA1), (TH7, NA2), (TH7, NA3) (TH7, NA4), (TH7, NA5), (TH7, NB1), and (TH7, ND1).
Bipolar disorders patients tend to have higher distractible speech score (TH7).
Schizophrenic patients are more likely having higher negative symptoms scores.
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Psychosis Disorder Data: Z
Single-linkage+GrandPa Flip
Most significant difference A right slash: A reversed slash:
(AH1, NA5), (DL1, NA4), (DL7, NA1) and (DL7, NA5).
Bipolar disorders patients have lower scores on these symptoms than schizophrenic patients.
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GAP Software verison 0.2.7
Generalized Association Plots Input Data Type: continuous or binary. Various seriation algorithms and clustering analysis. Various display conditions.
Modules: Covaraite Adjusted. Proximity Modelling. Nonlinear Association Analysis. Missing Value Imputation.
http://gap.stat.sinica.edu.tw/Software/GAP
Statistical Plots2D Scatterplot, 3D Scatterplot (Rotatable)
Download
Wu, H. M., Tien, Y. J. and Chen, C. H.* (2010). GAP: A Graphical Environment for Matrix Visualization and Cluster Analysis, Computational Statistics and Data Analysis, 54, 767-778.
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Concluding Remarks
Suggestions A preliminary step in modern exploratory data analysis. A continuing and active topic of research and application. New generation of exploratory data analysis (EDA) tool.
Matrix Visualization Color order-based representation of data
matrices. Provide several levels of information.
Covariate-adjusted Matrix Visualization Decomposition of correlations. Working on fitted and residual data matrix. Interactive Software: GAP. Extension to multi-level data.
GAP
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Acknowledgment
[email protected] http://gap.stat.sinica.edu.tw
[email protected] http://www.hmwu.idv.tw
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