Statistical and Functional Analysis of Genomic and Proteomic Data

Yingchun Liu

Statistical and Functional Analysis of Genomic and Proteomic Data

Yingchun Liu

Research output: Thesis › Doctoral Thesis (compilation)

33 Downloads (Pure)

Abstract

High-throughput technologies have led to an explosion in the availability of data at the genome scale. Such data provide important information about cellular processes and causes of human diseases, as well as for drug discovery. Deciphering the biologically relevant results from these data requires comprehensive analytical methods. In this dissertation, we present methods for gene and protein expression data analysis. Our major contributions include a method for differential in-gelelectrophoresis data analysis capable of removing protein-specific dye bias in the data, a method for finding unknown biological groups using expression data, and a method for identifying active and inactive signaling pathways in a gene expression signature based on the enrichment of downstream target genes of pathways.

Original language	English
Qualification	Doctor
Awarding Institution
Supervisors/Advisors	Ringnér, Markus, Supervisor
Award date	2007 Jan 26
Publisher	Department of Theoretical Physics, Lund University
ISBN (Print)	91-628-6997-3
Publication status	Published - 2007

Bibliographical note

Defence details

Date: 2007-01-26
Time: 10:15
Place: Lecture hall F of the Department of Physics

External reviewer(s)

Name: Mukherjee, Sayan
Title: Assistant Professor
Affiliation: Duke University, USA

---

Subject classification (UKÄ)

Biophysics

Free keywords

Bioinformatik
biomathematics biometrics
unsupervised classification
Bioinformatics
medical informatics
TGF-beta
linear mixed model
expression data
dye bias
2D-gel
signaling pathway
regulatory motif
microarray
medicinsk informatik
biomatematik

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

Access to Document

Yingchun_Liu_kappa

4 Article

Analysis of DIGE data using a linear mixed model allowing for protein-specific dye effects
Krogh, M., Liu, Y., Waldemarson, S., Valestro, B. & James, P., 2007, In: Proteomics. 7, 23, p. 4235-4244
Research output: Contribution to journal › Article › peer-review
Revealing signaling pathway deregulation by using gene expression signatures and regulatory motif analysis
Liu, Y. & Ringnér, M., 2007, In: Genome Biology. 8, 5, R77.
Research output: Contribution to journal › Article › peer-review

Open Access
Gene expression profiling demonstrates that TGF-{beta}1 signals exclusively through receptor complexes involving Alk5 and identifies targets of TGF-{beta} signaling.
Karlsson, G., Liu, Y., Larsson, J., Goumans, M.-J., Lee, J.-S., Thorgeirsson, S. S., Ringnér, M. & Karlsson, S., 2005 May 11, In: Physiological Genomics. 21, 3, p. 396-403
Research output: Contribution to journal › Article › peer-review

Open Access

Cite this

@phdthesis{954eb255c0604873a47dccab2df0de5d,

title = "Statistical and Functional Analysis of Genomic and Proteomic Data",

abstract = "High-throughput technologies have led to an explosion in the availability of data at the genome scale. Such data provide important information about cellular processes and causes of human diseases, as well as for drug discovery. Deciphering the biologically relevant results from these data requires comprehensive analytical methods. In this dissertation, we present methods for gene and protein expression data analysis. Our major contributions include a method for differential in-gelelectrophoresis data analysis capable of removing protein-specific dye bias in the data, a method for finding unknown biological groups using expression data, and a method for identifying active and inactive signaling pathways in a gene expression signature based on the enrichment of downstream target genes of pathways.",

keywords = "Bioinformatik, biomathematics biometrics, unsupervised classification, Bioinformatics, medical informatics, TGF-beta, linear mixed model, expression data, dye bias, 2D-gel, signaling pathway, regulatory motif, microarray, medicinsk informatik, biomatematik",

author = "Yingchun Liu",

note = "Defence details Date: 2007-01-26 Time: 10:15 Place: Lecture hall F of the Department of Physics External reviewer(s) Name: Mukherjee, Sayan Title: Assistant Professor Affiliation: Duke University, USA ---",

year = "2007",

language = "English",

isbn = "91-628-6997-3",

publisher = "Department of Theoretical Physics, Lund University",

type = "Doctoral Thesis (compilation)",

}

TY - THES

T1 - Statistical and Functional Analysis of Genomic and Proteomic Data

AU - Liu, Yingchun

N1 - Defence details Date: 2007-01-26 Time: 10:15 Place: Lecture hall F of the Department of Physics External reviewer(s) Name: Mukherjee, Sayan Title: Assistant Professor Affiliation: Duke University, USA ---

PY - 2007

Y1 - 2007

N2 - High-throughput technologies have led to an explosion in the availability of data at the genome scale. Such data provide important information about cellular processes and causes of human diseases, as well as for drug discovery. Deciphering the biologically relevant results from these data requires comprehensive analytical methods. In this dissertation, we present methods for gene and protein expression data analysis. Our major contributions include a method for differential in-gelelectrophoresis data analysis capable of removing protein-specific dye bias in the data, a method for finding unknown biological groups using expression data, and a method for identifying active and inactive signaling pathways in a gene expression signature based on the enrichment of downstream target genes of pathways.

AB - High-throughput technologies have led to an explosion in the availability of data at the genome scale. Such data provide important information about cellular processes and causes of human diseases, as well as for drug discovery. Deciphering the biologically relevant results from these data requires comprehensive analytical methods. In this dissertation, we present methods for gene and protein expression data analysis. Our major contributions include a method for differential in-gelelectrophoresis data analysis capable of removing protein-specific dye bias in the data, a method for finding unknown biological groups using expression data, and a method for identifying active and inactive signaling pathways in a gene expression signature based on the enrichment of downstream target genes of pathways.

KW - Bioinformatik

KW - biomathematics biometrics

KW - unsupervised classification

KW - Bioinformatics

KW - medical informatics

KW - TGF-beta

KW - linear mixed model

KW - expression data

KW - dye bias

KW - 2D-gel

KW - signaling pathway

KW - regulatory motif

KW - microarray

KW - medicinsk informatik

KW - biomatematik

M3 - Doctoral Thesis (compilation)

SN - 91-628-6997-3

PB - Department of Theoretical Physics, Lund University

ER -

Statistical and Functional Analysis of Genomic and Proteomic Data

Abstract

Bibliographical note

Subject classification (UKÄ)

Free keywords

UN SDGs

Access to Document

Fingerprint

Research output

Analysis of DIGE data using a linear mixed model allowing for protein-specific dye effects

Revealing signaling pathway deregulation by using gene expression signatures and regulatory motif analysis

Gene expression profiling demonstrates that TGF-{beta}1 signals exclusively through receptor complexes involving Alk5 and identifies targets of TGF-{beta} signaling.

Cite this