Welcome to the SUMO group! Our collective laboratory works
on problems of computational biology while leveraging
methods from bioinformatics, visualization, and image
analysis. Our group has engineered several workflows for
processing and analyzing images for problems in cancer,
neuro, and wound biology and immunology. We have developed
algorithms that have been adapted for exploration in the
laboratory at the microscopic scale (confocal, brightfield,
histology, etc.) and in radiology clinics (diffusion tensor
MR, fMRI, etc.). Our algorithms are derived from the fields
of computer vision and machine learning. In addition, we are
also developing tools for processing sequence data (micro
array, RNA-seq, CHiP-seq). Kun Huang is also a Co- director
of the Bioinformatics Share Resource facility in the
Comprehensive Cancer Center. This combined focus of image
informatics and bio informatics is serving us well and we
collaborate with several groups on campus and elsewhere.
Projects
SUMO's active projects
iGPSe
A visual analytic system for
integrative genomic based cancer patient
stratification
iGPSe: A Visual Analytic System for Integrative Genomic
Based Cancer Patient Stratification . Ding H, Wang C,
Huang K, Machiraju R. International Symposium on
Biological Data Visualization (BioVis) 2014. [web]
GRAPHIE: Graph based Histology Image Explorer. Ding H,
Wang C, Huang K, Machiraju R. International Symposium
on Biological Data Visualization (BioVis) 2015. [web]
iGPSe
A visual analytic
system for integrative genomic based cancer patient
stratification
We present a visual analytic
system called Interactive
Genomics Patient Stratification explorer
(iGPSe) which significantly reduces the computing
burden for biomedical researchers in the process
of exploring complicated integrative genomics
data. Our system integrates unsupervised
clustering with graph and parallel sets
visualization and allows direct comparison of
clinical outcomes via survival analysis. Using a
breast cancer dataset obtained from the The
Cancer Genome Atlas (TCGA) project, we are able
to quickly explore different combinations of gene
expression (mRNA) and microRNA features and
identify potential combined markers for survival
prediction.
Cancers are highly
heterogeneous with different subtypes. These
subtypes often possess different genetic variants,
present different pathological phenotypes, and most
importantly, show various clinical outcomes such as
varied prognosis and response to treatment and
likelihood for recurrence and metastasis. Recently,
integrative genomics (or panomics) approaches are
often adopted with the goal of combining multiple
types of omics data to identify integrative
biomarkers for stratification of patients into
groups with different clinical outcomes. In this
paper we present a visual analytic system called
Interactive Genomics Patient Stratification explorer
(iGPSe) which significantly reduces the computing
burden for biomedical researchers in the process of
exploring complicated integrative genomics data. Our
system integrates unsupervised clustering with graph
and parallel sets visualization and allows direct
comparison of clinical outcomes via survival
analysis. Using a breast cancer dataset obtained
from the The Cancer Genome Atlas (TCGA) project, we
are able to quickly explore different combinations
of gene expression (mRNA) and microRNA features and
identify potential combined markers for survival
prediction. Visualization plays an important role
in the process of stratifying given population
patients. Visual tools allowed for the selection of
possibly features across various datasets for the
given patient population. We essentially made a case
for visualization for a very important problem in
translational informatics.
GRAPHIE
Graph based
histology image explorer
We introduce Graph
based
Hisportfolio/startup-framework-preview.pngtology
Image Explorer (GRAPHIE)-a visual analytics
tool to explore, annotate and discover potential
relationships in histology image collections within a
biologically relevant context. The design of GRAPHIE
is guided by domain experts' requirements and
well-known InfoVis mantras. By representing each
image with informative features and then subsequently
visualizing the image collection with a graph,
GRAPHIE allows users to effectively explore the image
collection. The features were designed to capture
localized morphological properties in the given
tissue specimen. More importantly, users can perform
feature selection in an interactive way to improve
the visualization of the image collection and the
overall annotation process. Finally, the annotation
allows for a better prospective examination of
datasets as demonstrated in the users study. Thus,
our design of GRAPHIE allows for the users to
navigate and explore large collections of histology
image datasets.
Histology images comprise
one of the important sources of knowledge for
phenotyping studies in systems biology. However, the
annotation and analyses of histological data have
remained a manual, subjective and relatively
low-throughput process. We introduce Graph based
Histology Image Explorer (GRAPHIE)-a visual
analytics tool to explore, annotate and discover
potential relationships in histology image
collections within a biologically relevant context.
The design of GRAPHIE is guided by domain experts'
requirements and well-known InfoVis mantras. By
representing each image with informative features
and then subsequently visualizing the image
collection with a graph, GRAPHIE allows users to
effectively explore the image collection. The
features were designed to capture localized
morphological properties in the given tissue
specimen. More importantly, users can perform
feature selection in an interactive way to improve
the visualization of the image collection and the
overall annotation process. Finally, the annotation
allows for a better prospective examination of
datasets as demonstrated in the users study. Thus,
our design of GRAPHIE allows for the users to
navigate and explore large collections of histology
image datasets. We demonstrated the usefulness of
our visual analytics approach through two case
studies. Both of the cases showed efficient
annotation and analysis of histology image
collection.