Integrative Multi-Scale Analyses

Integrative Multi-Scale Analyses

Joel Saltz MD, PhD
Emory University
February 2013

a.k.a “Big Data”
Center for Comprehensive Informatics

• Integrative Spatio-Temporal Analytics
• Deep Integrative Biomedical Research
• High End Computing/”Big Data” Computers,
Systems Software
• Analysis of Patient Populations

Application Targets

• Multi-dimensional spatial-temporal datasets
– Radiology and Microscopy Image Analyses
– Oil Reservoir Simulation/Carbon
Sequestration/Groundwater Pollution Remediation
– Biomass monitoring and disaster surveillance using
multiple types of satellite imagery
– Weather prediction using satellite and ground sensor
data
– Analysis of Results from Large Scale Simulations
• Correlative and cooperative analysis of data from
multiple sensor modalities and sources
• What-if scenarios and multiple design choices or
initial conditions

Core Transformations

• Data Cleaning and Low Level Transformations
• Data Subsetting, Filtering, Subsampling
• Spatio-temporal Mapping and Registration
• Object Segmentation
• Feature Extraction, Object Classification
• Spatio-temporal Aggregation
• Change Detection, Comparison, and Quantification

Emory In Silico Center for Brain Tumor
Research (PI = Dan Brat, PD= Joel Saltz)

National Science Foundation Grand Challenge
in Land Cover Dynamics
• Remote sensing analysis of
high resolution satellite
images.
• Databases of land cover
dynamics are essential for
global carbon models,
biogeochemical cycling,
hydrological modeling and
ecosystem response
modeling
• Maps of the world's tropical
rain forest during the past
three decades.
Larry Davis , Rama Chellappa , Joel Saltz , Alan Sussman , John
Townshend

Analysis of Computational Data; Uncertainty
Quantification, Comparisons with Experimental Results

Dimitri Mavriplis, Raja Das, Joel Saltz


Whole Slide Imaging: Scale

Pathology Computer Assisted Diagnosis

Shimada, Gurcan, Kong, Saltz

Computerized Classification System
for Grading Neuroblastoma
Initialization Yes
Image Tile Background? Label
I=L
• Background Identification
No

Create Image I(L)
• Image Decomposition (Multi-
Training Tiles resolution levels)
Segmentation I = I -1 • Image Segmentation
Down-sampling
(EMLDA)
Segmentation
Feature Construction
• Feature Construction (2nd
Yes

No order statistics, Tonal
Feature Extraction I > 1?
Feature Construction
Features)
Feature Extraction
Classification
• Feature Extraction (LDA) +
Classification (Bayesian)
Classifier Training
No
• Multi-resolution Layer
Within Confidence
Region ? Controller (Confidence
Yes
TRAINING Region)
TESTING

Using TCGA Data to Study
Glioblastoma

Diagnostic Improvement

Molecular Classification

Predictors of Progression

TCGA Network

Digital Pathology

Neuroimaging

Morphological Tissue Classification

Whole Slide Imaging Cellular Features

Nuclei Segmentation

Lee Cooper,
Jun Kong

Can we use image analysis of TCGA GBMs TO INFORM
diagnostic criteria based on molecular or clinical
endpoints?

Nuclear Qualities

Oligodendroglioma Astrocytoma

Application: Oligodendroglioma Component in GBM

Millions of Nuclei Defined by n Features

• Bottom-up analysis: let features define
and drive the analysis

• Top-down analysis: use the features
with existing diagnostic constructs

TCGA Whole Slide Images
Step 1:
Nuclei
• Identify individual nuclei
Segmentation
and their boundaries

Jun Kong

Nuclear Analysis Workflow
Step 1: Step 2:
Nuclei Feature
Segmentation Extraction

• Describe individual nuclei in terms of size,
shape, and texture

Step 3:
Nuclei
Nuclear Qualities
Classification

1 10

Oligodendroglioma Astrocytoma

Representative Nuclei

Oligo Astro

Comparison of Machine-based Classification
to Human Based Classification

Separation of GBM, Oligo1, Oligo2 Separation of GBM, Oligo1 and
as Designated by Oligo2 as Designated by Machine
Neuropathologists

Survival Analysis

Human Machine

Gene Expression Correlates of High Oligo-Astro
Ratio on Machine-based Classification

Oligo Related Genes

Myelin Basic Protein
Proteolipoprotein
HoxD1

Nuclear features most
Associated with Oligo
Signature Genes:

Circularity (high)
Eccentricity (low)

Millions of Nuclei Defined by n Features

• Bottom-up analysis: let nuclear features
define and drive the analysis

• Top-down analysis: analyze features in
context of existing diagnostic constructs

Direct Study of Relationship Between
vs

Lee Cooper,
Carlos Moreno

Nuclear Features Used to Classify GBMs

50
3 2 1
20
1
45
40
Silhouette Area

40 60

Cluster
80
2
35
100

120
30
140
3

25 160
2 3 4 5 6 7 20 40 60 80 100 120 140 160
# Clusters 0 0.5 1
Silhouette Value

Consensus clustering of morphological
signatures

Study includes 200 million nuclei taken from 480
slides corresponding to 167 distinct patients

Each possibility evaluated using 2000 iterations of K-
means to quantify co-clustering

Clustering identifies three morphological groups

• Analyzed 200 million nuclei from 162 TCGA GBMs (462 slides)
• Named for functions of associated genes:
Cell Cycle (CC), Chromatin Modification (CM),
Protein Biosynthesis (PB)
• Prognostically-significant (logrank p=4.5e-4)

CC CM PB
1
CC
10 0.8 CM
PB
20
Feature Indices

0.6

Survival
30 0.4

40 0.2

50
0
0 500 1000 1500 2000 2500 3000
Days


Associations

Molecular Correlates of MR Features Using TCGA Data

MRIs of TCGA GBMs reviewed by 3-6 neuroradiologists using VASARI feature set and In
Vivo Imaging tools

MR Features compared to TCGA Transcriptional Classes and Genetic Alterations

David Gutman

Capturing structured annotations
and markups/ AIM Data Service

VASARI
Feature Set

Scott Hwang
Chad Holder
Adam Flanders

Prognostic Significance of Vasari Features
Tests Between Groups: 0-33% vs. 34-95% Proportion enhancing

Test ChiSquare DF P-Value
Log-Rank 12.4775 3 0.0059*
Wilcoxon 10.0802 3 0.0179*

Titan – Peak Speed 30,000,000,000,000,000
floating point operations per second!

Extreme DataCutter Prototype

DataCutter
Pipeline of filters connected though logical streams
In transit processing
Flow control between filters and streams
Developed 1990s-2000s; led to IBM System S
Extreme DataCutter
Two level hierarchical pipeline framework
In transit processing
Coarse grained components coordinated by Manager that
coordinates work on pipeline stages between nodes
Fine grained pipeline operations managed at the node level
Both levels employ filter/stream paradigm
Bottom line – everything ends up as DAGS

Extreme DataCutter – Two Level Model


Node Level Work Scheduling

Brain Tumor Pipeline Scaling on Keeneland
(100 Nodes)

Challenge: Structured/Unstructured Grid
Calculations with Unpredictable Runtime

Dependencies

Key Kernel in Distance Transform,
Morphological Reconstruction, Delaney
Triagulation

“Speedup” relative to single CPU core

Large Scale Data Management

 Represented by a complex data model capturing
multi-faceted information including markups,
annotations, algorithm provenance, specimen, etc.
 Support for complex relationships and spatial
query: multi-level granularities, relationships
between markups and annotations, spatial and
nested relationships
 Highly optimized spatial query and analyses
 Implemented in a variety of ways including
optimized CPU/GPU, Hadoop/HDFS and IBM DB2

Spatial Centric – Pathology Imaging “GIS”
Point query: human marked point Window query: return markups
inside a nucleus contained in a rectangle

.

Containment query: nuclear feature Spatial join query: algorithm
aggregation in tumor regions validation/comparison

Algorithm Validation: Intersection
between Two Result Sets (Spatial Join)
PAIS: Example Queries

. .

VLDB 2012

Change Detection, Comparison, and Quantification

Approach to Integrated Sensor Data Analysis
Framework

• Abstract templates specify
dataset geometry
• Templates describe
collections of space-time
regions
• Mapping to memory
hierarchies provided by user
defined mapping functions
• Leverages Parashar’s
DataSpaces

Clinical Phenotype Characterization and the Emory
Analytic Information Warehouse

• Example Project: Find hot spots in readmissions within 30 days
– What fraction of patients with a given principal diagnosis will be
readmitted within 30 days?
– What fraction of patients with a given set of diseases will be readmitted
within 30 days?
– How does severity and time course of co-morbidities affect
readmissions?
– Geographic analyses

• Compare and contrast with UHC Clinical Data Base
– Repeat analyses across all UHC hospitals
– Are we performing the same?
– How are UHC-curated groupings of patients (e.g., product lines) useful?

• Need a repeatable process that we can apply identically to both
local and UHC data

Andrew Post, Sharath Cholleti, Doris Gao, Michel Monsour, Himanshu Rathod

Overall System

Metadata
Repository
I2b2 Web I2b2
Server
Database

Investigator Metadata
Manager

Data Modeler

Data Query
Processing Specification

Data Analyst
Investigator

Database
Mapper

Data Analyst
Study-
Query tools specific
Database Source Source Source
Investigator
data data data

5-year Datasets from Emory and
University Healthcare Consortium

• EUH, EUHM and WW (inpatient encounters)
• Removed encounter pairs with chemotherapy and radiation
therapy readmit encounters (CDW data)

• Encounter location (down to unit for Emory)
• Providers (Emory only)
• Discharge disposition
• Primary and secondary ICD9 codes
• Procedure codes
• DRGs
• Medication orders (Emory only)
• Labs (Emory only)
• Vitals (Emory only)
• Geographic information (CDW only + US Census and American
Community Survey)
Analytic Information

Using Emory & UHC Data to Find
Associations With 30-day Readmits

• Problem: “Raw” clinical and administrative variables
are difficult to use for associative data mining
– Too many diagnosis codes, procedure codes
– Continuous variables (e.g., labs) require interpretation
– Temporal relationships between variables are implicit
• Solution: Transform the data into a much smaller set
of variables using heuristic knowledge
– Categorize diagnosis and procedure codes using code
hierarchies
– Classify continuous variables using standard
interpretations (e.g., high, normal, low)
– Identify temporal patterns (e.g., frequency, duration,
sequence)
– Apply standard data mining techniques

Analytic Information

Derived Variables

• 30-day readmit
• The 9 Emory Enhanced Risk Assessment Tool diagnosis categories
• UHC product lines
• Variables derived from a combination of codes and/or laboratory test results
– Obesity
– Diabetes/uncontrolled diabetes
– End-stage renal disease (ESRD)
– Pressure ulcer
– Sickle cell disease/sickle cell crisis
• Temporal variables derived over multiple encounters
– Multiple MI
– Multiple 30-day readmissions
– Chemotherapy within 180 (or 365) days before surgery
– Previous encounter within the last 90 (or 180) days

30-Day Readmission Rates for Derived
Variables

Emory Health Care

Geographic Analyses
UHC Medicine General Product Line (#15)

Analytic Information Warehouse

Predictive Modeling for Readmission

• Random forests (ensemble of decision trees)
– Create a decision tree using a random subset of the
variables in the dataset
– Generate a large number of such trees
– All trees vote to classify each test example in a
training dataset
– Generate a patient-specific readmission risk for each
encounter
• Rank the encounters by risk for a subsequent 30-
day readmission

Sharath Cholleti

Emory Readmission Rates for High and
Low Risk Groups Generated with

Random Forest

Status of Clinical Phenotype
Characterization

• Integrative dataset analysis can leverage patient
information gathered over many encounters
• Temporal analyses can generate derived variables that
appear to correlate with readmissions
• Predictive modeling has promise of providing decision
support
• Data Analytics arm of the Emory New Care Model
Initiative led by Greg Esper
• Ongoing analyses involve characterization of clinical
phenotype in GWAS, biomarker and quality
improvement efforts
• Co-lead (with Bill Hersh) of CTSA CER Informatics
taskforce dedicated to this issue

Thanks to:
• In silico center team: Dan Brat (Science PI), Tahsin Kurc, Ashish

Sharma, Tony Pan, David Gutman, Jun Kong, Sharath Cholleti,
Carlos Moreno, Chad Holder, Erwin Van Meir, Daniel Rubin, Tom
Mikkelsen, Adam Flanders, Joel Saltz (Director)
• Digital Pathology R01 (s): Foran and Saltz; Jun Kong, Sharath
Cholleti, Fusheng Wang, Tony Pan, Tahsin Kurc, Ashish Sharma,
David Gutman (Emory), Wenjin Chen, Vicky Chu, Jun Hu, Lin Yang,
David J. Foran (Rutgers)
• Analytic Warehouse team: Andrew Post, Sharath Cholleti, Doris
Gao, Michel Monsour, Himanshu Rathod
• In vivo imaging Emory team: Tony Pan, Ashish Sharma, Joel Saltz
• NIH/in silico TCGA Imaging Group: Scott Hwang, Bob Clifford, Erich
Huang, Dima Hammoud, Manal Jilwan, Prashant Raghavan, Max
Wintermark, David Gutman, Carlos Moreno, Lee Cooper, John
Freymann, Justin Kirby, Arun Krishnan, Seena Dehkharghani, Carl
Jaffe
• ACTSI Biomedical Informatics Program: Marc Overcash, Tim
Morris, Tahsin Kurc, Alexander Quarshie, Circe Tsui, Adam Davis,
Sharon Mason, Andrew Post, Alfredo Tirado-Ramos
• ORNL HPC collaboration: Scott Klasky, David Pugmire ORNL

Thanks to

• National Cancer Institute
• National Library of Medicine
• National Science Foundation
• Cardiovascular Research Grid (NHLBI)
• Minority Health Grid (ARRA)
• Emory Health Care
• Kaiser Health Care
• Winship Cancer Institute
• Oak Ridge National Laboratory
• Woodruff Health Sciences

Integrative Multi-Scale Analyses

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