Staff View
Tunable biclustering algorithm for analyzing large gene expression data sets

Descriptive

TitleInfo
Title
Tunable biclustering algorithm for analyzing large gene expression data sets
Name (type = personal)
NamePart (type = family)
Singh
NamePart (type = given)
Amartya
NamePart (type = date)
1990-
DisplayForm
Amartya Singh
Role
RoleTerm (authority = RULIB)
author
Name (type = personal)
NamePart (type = family)
Khiabanian
NamePart (type = given)
Hossein
DisplayForm
Hossein Khiabanian
Affiliation
Advisory Committee
Role
RoleTerm (authority = RULIB)
chair
Name (type = personal)
NamePart (type = family)
Bhanot
NamePart (type = given)
Gyan
DisplayForm
Gyan Bhanot
Affiliation
Advisory Committee
Role
RoleTerm (authority = RULIB)
internal member
Name (type = personal)
NamePart (type = family)
Croft
NamePart (type = given)
Mark
DisplayForm
Mark Croft
Affiliation
Advisory Committee
Role
RoleTerm (authority = RULIB)
internal member
Name (type = personal)
NamePart (type = family)
Morozov
NamePart (type = given)
Alexandre
DisplayForm
Alexandre Morozov
Affiliation
Advisory Committee
Role
RoleTerm (authority = RULIB)
internal member
Name (type = personal)
NamePart (type = family)
De
NamePart (type = given)
Subhajyoti
DisplayForm
Subhajyoti De
Affiliation
Advisory Committee
Role
RoleTerm (authority = RULIB)
outside member
Name (type = corporate)
NamePart
Rutgers University
Role
RoleTerm (authority = RULIB)
degree grantor
Name (type = corporate)
NamePart
School of Graduate Studies
Role
RoleTerm (authority = RULIB)
school
TypeOfResource
Text
Genre (authority = marcgt)
theses
OriginInfo
DateCreated (encoding = w3cdtf); (keyDate = yes); (qualifier = exact)
2019
DateOther (encoding = w3cdtf); (qualifier = exact); (type = degree)
2019-10
Language
LanguageTerm (authority = ISO 639-3:2007); (type = text)
English
Abstract (type = abstract)
Traditional clustering approaches for gene expression data are not well adapted to address the complexity and heterogeneity of tumors, where small sets of genes may be aberrantly co-expressed in specific subsets of tumors. Biclustering algorithms that perform local clustering on subsets of genes and conditions help address this problem. We have proposed a graph-based Tunable Biclustering Algorithm (TuBA) (Chapter 2) based on a novel pairwise proximity measure that leverages the size of the data sets to identify subsets of tumor samples that co-express subsets of genes at their highest or lowest levels relative to other samples.

We applied TuBA to three large gene expression datasets encompassing a total of 3,940 breast invasive carcinoma (BRCA) patients (Chapter 3). We demonstrated that there was significant agreement between the results obtained for each data set, and discovered that about 50% of the altered co-expression signatures were associated with a subtype of the disease that exhibits low levels of expression of the estrogen hormone receptor 1 (ER) and the human epidermal growth factor receptor 2 (HER2) genes. Tumors belonging to this subtype are labelled as ER-/HER2-. Since only 15% of all BRCA patients are estimated to have tumors that belong to this subtype, our algorithm was able to highlight the tremendous heterogeneity in alterations within tumors of this subtype. Quite significantly, more than 50% of these signatures were associated with alterations in the DNA that results in amplification (or deletion) of genes’ copies, which subsequently result in higher (or lower) level of gene expression. Thus, TuBA was especially effective in identifying transcriptionally active copy number variations in tumor samples. Finally, TuBA identified biclusters that were associated with the tumor microenvironment, which included biclusters associated with infiltrating immune and stromal cells. These can improve our understanding about the role played by the microenvironment in modulating tumor progression.

We showed that TuBA outperforms other algorithms in identification of co-expressed genes located in transcriptionally active copy number altered sites (Chapter 4). Moreover, from a differential co-expression perspective, TuBA offers an advantage over other methods since no prior specification of subsets of samples (conditions) is necessary; the nature of our proximity measure ensures that such differential co-expression signatures are preferentially identified.

In summary, our method identified a multitude of altered transcriptional profiles associated with the tremendous heterogeneity of diseased states in breast cancer. Exploring the diversity of these aberrant signatures can help identify potential biomarkers of clinical relevance that can further improve treatment outcomes, especially for ER-/HER2- breast cancers. Although transcriptomic alterations are not the ultimate determinants of progression of disease, our algorithm holds the promise to improve therapeutic selection and design by identifying significantly altered transcriptional patterns associated with tumors.
Subject (authority = RUETD)
Topic
Physics and Astronomy
Subject (authority = local)
Topic
Biclustering
Subject (authority = LCSH)
Topic
Gene expression -- Computer programs
RelatedItem (type = host)
TitleInfo
Title
Rutgers University Electronic Theses and Dissertations
Identifier (type = RULIB)
ETD
Identifier
ETD_10119
PhysicalDescription
Form (authority = gmd)
InternetMediaType
application/pdf
InternetMediaType
text/xml
Extent
1 online resource (xiii, 103 pages) : illustrations
Note (type = degree)
Ph.D.
Note (type = bibliography)
Includes bibliographical references
RelatedItem (type = host)
TitleInfo
Title
School of Graduate Studies Electronic Theses and Dissertations
Identifier (type = local)
rucore10001600001
Location
PhysicalLocation (authority = marcorg); (displayLabel = Rutgers, The State University of New Jersey)
NjNbRU
Identifier (type = doi)
doi:10.7282/t3-ys8f-ct92
Genre (authority = ExL-Esploro)
ETD doctoral
Back to the top

Rights

RightsDeclaration (ID = rulibRdec0006)
The author owns the copyright to this work.
RightsHolder (type = personal)
Name
FamilyName
Singh
GivenName
Amartya
Role
Copyright Holder
RightsEvent
Type
Permission or license
DateTime (encoding = w3cdtf); (qualifier = exact); (point = start)
2019-07-12 10:07:42
AssociatedEntity
Name
Amartya Singh
Role
Copyright holder
Affiliation
Rutgers University. School of Graduate Studies
AssociatedObject
Type
License
Name
Author Agreement License
Detail
I hereby grant to the Rutgers University Libraries and to my school the non-exclusive right to archive, reproduce and distribute my thesis or dissertation, in whole or in part, and/or my abstract, in whole or in part, in and from an electronic format, subject to the release date subsequently stipulated in this submittal form and approved by my school. I represent and stipulate that the thesis or dissertation and its abstract are my original work, that they do not infringe or violate any rights of others, and that I make these grants as the sole owner of the rights to my thesis or dissertation and its abstract. I represent that I have obtained written permissions, when necessary, from the owner(s) of each third party copyrighted matter to be included in my thesis or dissertation and will supply copies of such upon request by my school. I acknowledge that RU ETD and my school will not distribute my thesis or dissertation or its abstract if, in their reasonable judgment, they believe all such rights have not been secured. I acknowledge that I retain ownership rights to the copyright of my work. I also retain the right to use all or part of this thesis or dissertation in future works, such as articles or books.
Copyright
Status
Copyright protected
Availability
Status
Open
Reason
Permission or license
Back to the top

Technical

RULTechMD (ID = TECHNICAL1)
ContentModel
ETD
OperatingSystem (VERSION = 5.1)
windows xp
CreatingApplication
Version
1.5
DateCreated (point = end); (encoding = w3cdtf); (qualifier = exact)
2019-07-15T12:02:46
DateCreated (point = end); (encoding = w3cdtf); (qualifier = exact)
2019-07-15T12:02:46
ApplicationName
pdfTeX-1.40.18
Back to the top
Version 8.3.13
Rutgers University Libraries - Copyright ©2021