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In silico deconvolution of small cancer cell ratios using transcriptomics gene signatures
KTH, School of Engineering Sciences (SCI), Mathematics (Dept.), Numerical Analysis, NA.
2014 (English)Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE creditsStudent thesisAlternative title
n silico dekonvolutionsmetod för bestämning av haltencancerceller genom transkriptomik (Swedish)
Abstract [en]

In this work we suggest a deconvolution method, based on a convex optimization problem, to calculate the cancer amount from heterogeneous cell type gene expression profiles generated in silico.

Expression profiling is a technique for identifying global expression patterns within cellular groups, its multiple purposes may include the identification of disease biomarkers and the basic understanding of cellular processes. Given the necessity for understanding complex biological processes such as development and carcinogenesis, it is of main importance to distinguish between contributions to gene expression profiles from either regulation processes or abundance of cellular groups. Unfortunately, many biological samples contain mixtures of cell-types. This severely limits the conclusions that can be made about the specificity of gene expression in the cell-type of interest.

We describe a model to estimate the proportions of cell types in a given test data set based on a gene expression profile derived from transcriptomics. Our model is based on least squares estimation and the solution of a convex optimization problem. The technical aim is to solve an undetermined system of linear equations, which must satisfy several constraints and under a particular sparsity assumption.

Cell type mixtures were simulated in silico using a special procedure based on mean and standard deviations. Variable selection was performed by Analysis of Variance (ANOVA) using “cell type” as main factor and genes were ranked by F-statistics. We tested our model in breast and liver tissues, employing four cell types (three normal and one cancerous). We also performed a bootstrap procedure to test the robustness of our method concluding that our method is stable and accurate enough to estimate cancer portions of at least 10%.

Abstract [sv]

I denna studie föreslås en in silico dekonvolutionsmetod baserad på ett konvext optimeringsproblem för att bestämma mängden cancerceller genom genuttrycksprofilering av en heterogen blandning av celltyper. Profilering av genuttryck är en metod som används for identifieringen av genuttrycksmönster inom olika cellgrupper. Metoden kan till exempel användas för att identifiera biomarkörer för sjukdomar och för att studera cellprocesser. En svårighet är att biologiska prover innehåller många olika celltyper, vilket har hittills begränsat metodens användbarhet för att studera genuttryck i en specifik celltyp.  

För att uppskatta mängden av olika celltyper från test data föreslås en modell baserat på transkriptomik. I modellen används minsta kvadratmetoden och lösningen av ett konvext optimeringsproblem för att lösa ett underbestämt system av linjära ekvationer. Test data med blandade celltyper simulerats in silico, baserat på medelvärden och standardavvikelser. Selektion av variabler gjordes med hjälp av variansanalys (ANOVA), och F-statistik användes för att ordna av gener. Modellen tillämpades på simulerade vävnadsprover från bröst och lever, innehållande tre normala celltyper och en typ av cancercell. Modellens tillförlitlighet testades med hjälp av en bootstrapping metod. Vi fann att den föreslagna modellen är stabil och tillräckligt noggrann för att bestämma cancerhalt av minst 10%.

Place, publisher, year, edition, pages
2014.
Series
TRITA-MAT-E, 2014:61
National Category
Computational Mathematics
Identifiers
URN: urn:nbn:se:kth:diva-154267OAI: oai:DiVA.org:kth-154267DiVA: diva2:756318
External cooperation
Science for Life Laboratory (SciLifeLab), Stockholm
Subject / course
Scientific Computing
Educational program
Master of Science - Computer Simulation for Science and Engineering
Supervisors
Examiners
Available from: 2014-10-16 Created: 2014-10-16 Last updated: 2014-10-16Bibliographically approved

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