November 14-18, 2011
Keywords of the presentation: Allele specific expression, RNA-Seq, Exome-Seq, Genotypes, Susceptibility alleles
In current genetic and clinical research, identification of disease specific variations particularly from non-coding RNA and cis-elements is a major bottleneck. Massively parallel sequencing of exome and transcriptome is widely being used to effectively interrogate the key protein-coding and non-coding RNA regions. In such scenarios, the deep sequencing data of exome and transcriptome could be used for estimating levels of allele-specific expression in diseased vs. control samples (case-control cohorts) and hence the identification of disease specific signatures. This provides a functional basis to identify the differentially expressed alleles, mono-allelic expression, imprinting of alleles and allele regulated alternative splicing. All such data and approaches together make a stronger strategy to predict the disease susceptibility alleles and their functional role in disease mechanism.
Our approaches at BioCOS Life Sciences using the Next Generation Sequencing (NGS) data analysis for the precise detection of allele’s differential expression becomes important in identifying causal/susceptibility genes by mapping their variance in both coding and non-coding DNA/RNA regions.Read More...
I will present our current research work on developing methods and data processing approaches, which can be applied in identification of the susceptibility alleles using the combined approaches from RNA-Seq and Exome-Seq data as well as directly predicting them from RNA-Seq data. The talk will also discuss the existing bottlenecks in the area and approaches to obtain high quality results with a focus on calling genotypes from RNA-Seq data.
Quantification from medical images involves three levels of developments:
- Modeling of the organs
- Extraction of the visual features
- Formulation of the quantification task
Regarding organ modeling, geometric encoding of the shape is designed as a tradeoff between flexibility and robustness. Encoding of the variability within a population is a complex task that can have drawbacks when handling pathological cases. On the other hand, generic anatomical knowledge, especially regarding the context of the organ, can provide rich and more robust information, with spatial relations for example.
Regarding the visual features, images are richer than they appear in terms of tissue signature, embedding multiscale information. The field of image processing has evolved slowly in the design of sophisticated organ-specific visual features, the majority of them remaining very basic. Future challenges remain open regarding the need to correlate multi-modal tissue signatures with physiological characteristics.
Formulation of the quantification task such as segmentation, tracking or detection of longitudinal changes can be formulated either with a deterministic or stochastic formalism. Algorithms remain poorly robust to image quality, lack of image calibration, parameter tuning and presence of pathologies. Finer interactions between algorithmic tuning and image content and better calibration of image content is currently under investigation to address this lack of robustness and reproducibility.
These three components of the pipeline will be discussed, with illustrations on brain, cardiac liver and obstetric data. Emphasis will be paid to the constraints of being fast and robust, in the context of handling large data sets with great variability and pathologies.
Deciphering the structure of gene regulatory networks is crucial to
understanding the functionality of genes as well as the behavior of
cells. To this end, a network topology estimator is developed in this
work based on the structural equation model (SEM) approach, which
capitalizes on naturally occurring genetic variations viewed as
statistical perturbations that enable inference of the causal
relationships between genes. The SEM offers a suitable framework for
the estimation of cyclic directed networks, but typically requires
searching over a huge parameter space, incurring prohibitively high
computational complexity. As gene networks are sparse, meaning that
the number of edges is relatively small when compared to the number of
all possible edges, the present work contributes a SEM-based
sparsity-aware inference methodology. Simulated tests demonstrate that
the novel method can markedly improve inference accuracy.
A joint work with G. B. Giannakis
We formulate the optimal intervention problem in genetic regulatory networks
as a minimal-perturbation of the network in order to force it to converge to
a desired steady-state distribution of gene regulation. We cast optimal
intervention in gene regulation as a convex optimization problem, thus
providing a globally optimal solution which can be efficiently computed
using standard techniques for convex optimization. The criteria adopted for
optimality is chosen to minimize potential adverse effects as a consequence
of the intervention strategy. We consider a perturbation that minimizes (i)
the overall energy of change between the original and controlled networks
and (ii) the time needed to reach the desired steady-state of gene
regulation. Moreover, we show that there is an inherent tradeoff between
minimizing the energy of the perturbation and the convergence rate to the
desired distribution. We further show that the optimal inverse perturbation
control is robust to estimation errors in the original network. The proposed
control is applied to the Human melanoma gene regulatory network.
Keywords of the presentation: genetic regulatory networks, control, perturbation.
We formulate the optimal intervention problem in genetic regulatory networks as a minimal-perturbation of the network in order to force it to converge to a desired steady-state distribution of gene regulation. We cast optimal intervention in gene regulation as a convex optimization problem, thus providing a globally optimal solution which can be efficiently computed using standard techniques for convex optimization. The criteria adopted for optimality is chosen to minimize potential adverse effects as a consequence of the intervention strategy. We consider a perturbation that minimizes (i) the overall energy of change between the original and controlled networks and (ii) the time needed to reach the desired steady-state of gene regulation. Moreover, we show that there is an inherent tradeoff between minimizing the energy of the perturbation and the convergence rate to the desired distribution. We further show that the optimal inverse perturbation control is robust to estimation errors in the original network. The proposed control is applied to the Human melanoma gene regulatory network.
Keywords of the presentation: Bioinformatics, Biomedical Modelling, Genomic Analysis
Solving modern biomedical problems, especially, those involving genome data, requires advanced computational and analytical methods. The huge quantities of data and escalating demands of modern biomedical research increasingly require the sophistication and power of computational techniques for their pattern discovery. Key techniques include relational data management, pattern recognition, data mining, modelling and visualization of biomedical data. In this talk, I will demonstrate recent methodologies and data structures for gathering high-quality approximations and modelling of genomic information, and will use these innovations as the basis for developing methods to cluster and visualize biomedical data in pattern discovery.
In recent years, biomedicine has been faced with difficult high-throughput
small-sample classification problems, which are typically validated with
re-sampling error estimation methods such as cross-validation. While
heuristically designed error estimation techniques may be acceptable in
problems where large amounts of data are available, the small-sample setting
is different because asymptotic results are not meaningful and validation
becomes a critical issue. A recently proposed classifier error estimator
places the problem in a signal estimation framework in the presence of
uncertainty, thereby permitting a rigorous optimal solution in a
minimum-mean-square error (MMSE) sense. The uncertainty in this model is
relative to the parameters of the feature-label distributions, resulting in
a Bayesian approach to error estimation. The same Bayesian framework also
produces the theoretical MSE for both Bayesian error estimators and
arbitrary error estimators, where uncertainty is again relative to the
unknown model parameters and conditioned on the observed sample. Thus, the
Bayesian error estimator has a unique advantage over classical error
estimators in that its mathematical framework naturally gives rise to a
practical expected measure of performance given a fixed sample.
Within only a decade since the first draft of the human genome, we’ve witness astonishing pace of development of technologies for high throughput molecular profiling that probe various aspects of genome biology and its relationship to tumorigenesis and cancer treatment. There have been giant steps towards cataloging massive amounts of data and providing fairly good annotation information. However, computational methods that tried to tease out the relationship between the genotype and its functional readout - in normal and cancer states – revealed a semantic gap that is yet to be bridged. Narrowing this gap is essential in order to develop meaningful clinical decision support technologies.
In addition to imaging modalities which give the gross tissue level properties, crucial decisions in the context of oncology therapy selection require molecular level information that are increasingly captured by the emerging sequencing modalities. We undertook part in multiple studies aiming to understand the tumor heterogeneity and response to chemotherapy. Our efforts span several complementary modalities. In this talk I will provide several examples from our recent high throughput genomic studies:
1. DNA Sequencing: Assembly and downstream analysis of genomic data from normal individuals to understand and establish variation within normal individuals at the single nucleotide and structural level as well as the functional impact of these variations.
2. RNA Sequencing, CNV and DNA methylation: analysis in the context of chemo- and biological therapy response in breast and ovarian cancer.
3. Integration into a computational framework that combines genome-wide DNA methylation, gene expression and copy number variation data in a comprehensive fashion with the aim of finding mechanistic associations as well as signatures indicative of therapy resistance.
Our goal is to include these modalities in a Comprehensive Clinical Decision Support system where we need to integrate sequencing with imaging, pathology and other clinical data.
Keywords of the presentation: tomography, 3-D signal reconstruction, inverse problems, electron microscopy, statistical image processing
Single-particle cryo electron microscopy provides
images of biological macromolecular complexes with
spatial sampling on the order of 1-2 Angstrom.
Combining on the order of 100,000 such images can result
in 3-D reconstructions of the electron scattering
intensity of the complex with a spatial resolution as
fine as 4-5 Angstrom. Due to damage in the imaging
process, each complex is imaged only once and therefore
having a homogeneous ensemble of complexes is
important. Algorithms and results will be presented
for the case where the complexes are not homogeneous
and the reconstruction yields a statistical description
of the electron scattering intensity rather than a
single unique intensity. Related work on computed
electron tomography, where the electron scattering
intensity of individual complexes are determined but at
lower resolution will also be presented.
Keywords of the presentation: biological knowledge, epistemology, high-throughput data, scientific models
A perusal of the contemporary biological literature involving high-throughput data sets reveals the generation of a vast amount of data and an enormous number of models (classifiers, clusters, networks) derived from this data via a plethora of algorithms. There tends to be four interrelated characteristics common to these publications: (1) no experimental deign, (2) data sets where the number of measured variables greatly exceeds the number of replications, (3) algorithms whose performance is unknown for the populations to which they are applied – and often known to work poorly when applied to a small number of replicates, and (4) models that are epistemologically meaningless because they have not been validated. Hence, we find ourselves in a position somewhat akin to that confronted by Immanuel Kant in the Eighteenth Century when he famously asked, “How is metaphysics as a science possible?” Certainly there was a lot of “metaphysical” talk in the air, but to what sureties had it led? To address the problem, Kant had to tackle the meaning of science and then appreciate what constraints had to be placed on metaphysical statements to make them “scientific.” Fortunately for us, we do not have to take on the monumental task of characterizing scientific knowledge, an endeavor that stretched from Galileo to Einstein. But we do have to consider what constraints must be placed on biological statements to make them meaningful, that is, so that they constitute biological scientific knowledge. Moreover, we need to address a critical methodological scientific issue addressed by Kant: What differentiates productive observation of Nature from “groping in the dark,” to use his phrase?
Dynamic processes such as cell motility and deformation are key components of numerous scenarios including cell division & differentiation, morphogenesis, immune response strategies, but also parasite invasion, cancer development & proliferation and host-pathogen interactions. Continuous advances in microscopy imaging techniques have allowed scientists to shed light on many of these processes over extended periods of time, yielding huge amounts of time-lapse imaging data in multiple colors and various experimental conditions. In such context, visual interpretation and manual analysis have proved to be limited by the lack of reproducibility, user bias and fatigue. Scientists thus progressively turn to automatic quantification methods able to process spatiotemporal data in a robust and systematic manner. In this work we present a novel framework for automatic cell segmentation and tracking based on the theory of deformable models, and show how such a versatile mathematical tools can be used to extract various information related to cellular motility, shape analysis and morpho-dynamic studies.
One method used to generate a 3D image in medical imaging with Computerized Tomography (CT) is the helical cone beam scan. This method, obviously, generates a large amount of data. As in all methods of using X-ray scans, one is motivated to reduce the amount of radiation to which the patient is exposed. Combined with the desire to reduce the amounts of data being generated, one is motivated to develop efficient sampling methods. In the work presented here, the essential frequency support of a scanned body is estimated, bounded and then used to develop a sparse sampling method resulting in minimal loss of quality in the reconstructed image. Our initial results show that we can use this sparse sampling to reduce the data by at least factor of two.coauthored by Tamir Ben-Dory.
We present our work toward a statistical model of changes in gene
expression through four stages in the development of cervical cancer.
These stages are characterized in part by changes in the proportion of
cells of particular types. For example, normal tissue is organized in
layers with more well-differentiated cells at the surface and with
less differentiated, but more actively dividing cells further inside
the tissue. Neoplastic lesions shift the balance of types, at least
partly by having relatively more of the less differentiated types and
having fewer of the well-differentiated types. In our model, we make
use of this insight by postulating the existence of several distinct
(and unknown) types of cells which are present in all stages of the
progression, but whose relative proportions (also unknown) change
during the course of the progression. We then study differential
expression across the postulated cell types.
Keywords of the presentation: high dimensional data analysis, screening for high correlations, empirical correlation graphs, differential genomics.
The problem of variable selection is useful for identifying the principal drivers of differential
gene-response under one or more treatments, phenotypes, or conditions. Once identified, such drivers can be targeted as potential
knockouts or enhancers in drug discovery or diagnostic testing. In high throughput data such as gene or
protein expression the large number of variables has made it impractical to implement all but the
simplest univariate methods for variable selection, e.g., detecting significant shifts in t-test or Wilcoxon test statistics.
We propose an alternative approach based on detecting significant shifts in patterns of
connectivity of genes in a correlation graph or concentration graph. Remarkably, it is precisely when the sample size is small
that the approach is scalable, e.g., to whole genome analysis. Furthermore a statistical performance analysis establishes phase transition behaviors and tight approximations to false discovery rate that can be used for error control. We will illustrate the approach on
several gene expression datasets.
Principal component analysis (PCA) is a widely applied method for extracting structure from samples of high dimensional biological data.
Often there exist misalignments between different samples and this can cause severe problems in PCA if not properly taken into account. For example, subject-dependent temporal differences in gene expression response to a treatment will create relative time shifts in the samples that decohere the PCA analysis. The sensitivity of PCA to such misalignments is severe, leading to phase transitions that can be studied using the spectral the theory of high dimensional matrices. With this as motivation, we propose a new method of PCA, called misPCA, that explicitly accounts for the effects of misalignments in the samples. We illustrate misPCA on clustering longitudinal temporal gene expression data.
With Arnau Tibau-Puig, Ami Wiesel, and Raj Rao Nadakuditi
This paper extends the problem of vaccine adverse reaction detection by incorporating historical medical conditions. We propose a novel measure called dual-lift for this task, and formulate this problem in the framework of constraint pattern mining. We present a pattern mining algorithm DLiftMiner which utilizes a novel approach to upper bound the dual-lift measure for reducing the search space. Experimental results on both synthetic and real world datasets show that our method is effective and promising.
In this work, we develop and evaluate a semiautomatic
algorithm for segmentation and morphological assessment of
the dimensions of the ciliary muscle in Visante Anterior
Segment Optical Coherence Tomography images.
Furthermore, we investigate the morphology of the ciliary
muscle during the act of accommodation in a population of
children. Increasing accommodative response was correlated
with increases in the thickness of CMTMAX (p=<0.001) and
CMT1 (p=<0.001), and decreases in the thickness of CMT3