Functional MRI data analysis : Detection, estimation and modelling

Ph.DDOCTOR OF PHILOSOPH

Genome-wide estimation of firing efficiencies of origins of DNA replication from time-course copy number variation data

<p>Abstract</p> <p>Background</p> <p>DNA replication is a fundamental biological process during S phase of cell division. It is initiated from several hundreds of origins along whole chromosome with different firing efficiencies (or frequency of usage). Direct measurement of origin firing efficiency by techniques such as DNA combing are time-consuming and lack the ability to measure all origins. Recent genome-wide study of DNA replication approximated origin firing efficiency by indirectly measuring other quantities related to replication. However, these approximation methods do not reflect properties of origin firing and may lead to inappropriate estimations.</p> <p>Results</p> <p>In this paper, we develop a probabilistic model - Spanned Firing Time Model (SFTM) to characterize DNA replication process. The proposed model reflects current understandings about DNA replication. Origins in an individual cell may initiate replication randomly within a time window, but the population average exhibits a temporal program with some origins replicated early and the others late. By estimating DNA origin firing time and fork moving velocity from genome-wide time-course S-phase copy number variation data, we could estimate firing efficiency of all origins. The estimated firing efficiency is correlated well with the previous studies in fission and budding yeasts.</p> <p>Conclusions</p> <p>The new probabilistic model enables sensitive identification of origins as well as genome-wide estimation of origin firing efficiency. We have successfully estimated firing efficiencies of all origins in S.cerevisiae, S.pombe and human chromosomes 21 and 22.</p

Biomarker discovery in heterogeneous tissue samples -taking the in-silico deconfounding approach

<p>Abstract</p> <p>Background</p> <p>For heterogeneous tissues, such as blood, measurements of gene expression are confounded by relative proportions of cell types involved. Conclusions have to rely on estimation of gene expression signals for homogeneous cell populations, e.g. by applying micro-dissection, fluorescence activated cell sorting, or <it>in-silico </it>deconfounding. We studied feasibility and validity of a non-negative matrix decomposition algorithm using experimental gene expression data for blood and sorted cells from the same donor samples. Our objective was to optimize the algorithm regarding detection of differentially expressed genes and to enable its use for classification in the difficult scenario of reversely regulated genes. This would be of importance for the identification of candidate biomarkers in heterogeneous tissues.</p> <p>Results</p> <p>Experimental data and simulation studies involving noise parameters estimated from these data revealed that for valid detection of differential gene expression, quantile normalization and use of non-log data are optimal. We demonstrate the feasibility of predicting proportions of constituting cell types from gene expression data of single samples, as a prerequisite for a deconfounding-based classification approach.</p> <p>Classification cross-validation errors with and without using deconfounding results are reported as well as sample-size dependencies. Implementation of the algorithm, simulation and analysis scripts are available.</p> <p>Conclusions</p> <p>The deconfounding algorithm without decorrelation using quantile normalization on non-log data is proposed for biomarkers that are difficult to detect, and for cases where confounding by varying proportions of cell types is the suspected reason. In this case, a deconfounding ranking approach can be used as a powerful alternative to, or complement of, other statistical learning approaches to define candidate biomarkers for molecular diagnosis and prediction in biomedicine, in realistically noisy conditions and with moderate sample sizes.</p

Transcriptomic Analyses of Sexual Dimorphism of the Zebrafish Liver and the Effect of Sex Hormones

10.1371/journal.pone.0053562PLoS ONE81

Hierarchical clustering of the six liver transcriptomes and real-time RT-PCR validation of RNA expression of selected genes.

<p>(a) Hierarchical clustering of the six liver transcriptomes from control males and females, E2-treated males and females, and KT11-treated males and females. The three female samples were closely clustered together, and E2-treated males was clustered with the female samples. Control males and KT11-treated males were clustered together and were distinct from the others. Scale bar represented the pearson correlation score. Heatmap was constructed with transcripts that showed significant differences in at least one comparison. (b–e) Real-time RT-PCR validation of transcripts enriched in the female liver (b), male liver (c), induced by KT11 (d) or E2 (e) in male livers. Fold changes (log2 base) measured by real-time RT-PCR (blue bars) are compared with those measured by RNA-SAGE sequencing (red bars).</p

Top 50 expressed genes in the female and male livers.

<p>Top 50 expressed genes in the female and male livers.</p

Significantly deregulated transcription factor networks.

<p>(a) List of sex-biased transcription factors in the zebrafish liver transcriptome. Regulation z-score indicates the degree of enrichment and p value indicates the level of significance. (b) Transcriptional targets network of <i>ppargc1b</i>. (c) Transcriptional targets network of <i>stat4</i>. Female- and male-biased genes are indicated in red and green, respectively. Non-colored genes are not in the sex-biased gene lists but are associated with the sex-biased genes and are introduced by the software to link up the network.</p

The top three networks as revealed by knowledge-based functional analyses of sex-biased genes.

<p>(a) The top network featured mainly by female-biased transcripts involving in gene expression, protein synthesis, RNA post-transcriptional modification (Score = 46). (b) The second network focuses on hematological system development and function, organismal functions, infectious disease (Score = 44). (c) The third network is associated with lipid metabolism, molecular transport, and small molecule biochemistry (Score = 39). Female-biased transcripts are indicated in red and male-biased transcripts in green.</p

Intersections of differentially expressed genes in the male zebrafish livers after E2 or KT11 treatment with sex-biased genes.

<p>(a) Venn diagram of up-regulated genes in E2-treated males overlapped with sex-biased genes. (b) Venn diagram of down-regulated genes in E2-treated males overlapped with sex-biased genes. (c) Venn diagram of up-regulated genes in KT11-treated males overlapped with sex-biased genes. (d) Venn diagram of down-regulated genes in KT11-treated males overlapped with sex-biased genes . (e) Heat map of expression changes of sex-biased genes in E2- and KT11-treated male livers. For the column corresponding to the sex-biased transcripts, red represents female-biased genes and green represents male-biased genes. For the two columns corresponding to the E2- and KT11-treated male livers, red represents up-regulation and green represents down-regulation. The color intensity is calculated by logarithm-transformed (base 10) p-value. (f) Venn diagram of overlap of up-regulated genes in E2-treated male and down-regulated genes in KT11-treated males. (g) Venn diagram of overlap of down-regulated genes in E2-treated male and up-regulated genes in KT11-treated males. (h) Venn diagram of overlap of up-regulated genes in E2-treated male and up-regulated genes in KT11-treated males. (i) Venn diagram of overlap of down-regulated genes in E2-treated male and down-regulated genes in KT11-treated males.</p