Rooi, J.J. de

Mixture models for baseline estimation (Article)

2011-11-25T00:00:00Z

Various instruments produce data consisting of a series of more or less isolated peaks, superimposed on a drifting baseline. The positions and the heights of the peaks are of interest and the baseline is a nuisance. We model a smooth baseline by weighted regression on P-splines, a combination of B-splines and a discrete penalty to tune smoothness. The weights are computed from a mixture model with two component distributions, relative to the baseline, one for noise, the other for the peaks. The algorithm is fast and it shows excellent performance on simulated and experimental data.

Deconvolution of pulse trains with the L0 penalty (Article)

2011-10-31T00:00:00Z

The output of many instruments can be modeled as a convolution of an impulse response and a series of sharp spikes. Deconvolution considers the inverse problem: estimate the input spike train from an observed (noisy) output signal. We approach this task as a linear inverse problem, solved using penalized regression. We propose the use of an L0penalty and compare it with the more common L2and L1penalties. In all cases a simple and iterative weighted regression procedure can be used. The model is extended with a smooth component to handle drifting baselines. Application to three different data sets shows excellent results.

Integrated Transcript and Genome Analyses Reveal NKX2-1 and MEF2C as Potential Oncogenes in T Cell Acute Lymphoblastic Leukemia (Article)

2011-04-12T00:00:00Z

To identify oncogenic pathways in T cell acute lymphoblastic leukemia (T-ALL), we combined expression profiling of 117 pediatric patient samples and detailed molecular-cytogenetic analyses including the Chromosome Conformation Capture on Chip (4C) method. Two T-ALL subtypes were identified that lacked rearrangements of known oncogenes. One subtype associated with cortical arrest, expression of cell cycle genes, and ectopic NKX2-1 or NKX2-2 expression for which rearrangements were identified. The second subtype associated with immature T cell development and high expression of the MEF2C transcription factor as consequence of rearrangements of MEF2C, transcription factors that target MEF2C, or MEF2C-associated cofactors. We propose NKX2-1, NKX2-2, and MEF2C as T-ALL oncogenes that are activated by various rearrangements.

Intrinsic gene expression profiles of gliomas are a better predictor of survival than histology (Article)

2009-12-01T00:00:00Z

Gliomas are the most common primary brain tumors with heterogeneous morphology and variable prognosis. Treatment decisions in patients rely mainly on histologic classification and clinical parameters. However, differences between histologic subclasses and grades are subtle, and classifying gliomas is subject to a large interobserver variability. To improve current classification standards, we have performed gene expression profiling on a large cohort of glioma samples of all histologic subtypes and grades. We identified seven distinct molecular subgroups that correlate with survival. These include two favorable prognostic subgroups (median survival, >4.7 years), two with intermediate prognosis (median survival, 1-4 years), two with poor prognosis (median survival, <1 year), and one control group. The intrinsic molecular subtypes of glioma are different from histologic subgroups and correlate better to patient survival. The prognostic value of molecular subgroups was validated on five independent sample cohorts (The Cancer Genome Atlas, Repository for Molecular Brain Neoplasia Data, GSE12907, GSE4271, and Li and colleagues). The power of intrinsic subtyping is shown by its ability to identify a subset of prognostically favorable tumors within an external data set that contains only histologically confirmed glioblastomas (GBM). Specific genetic changes (epidermal growth factor receptor amplification, IDH1 mutation, and 1p/19q loss of heterozygosity) segregate in distinct molecular subgroups. We identified a subgroup with molecular features associated with secondary GBM, suggesting that different genetic changes drive gene expression profiles. Finally, we assessed response to treatment in molecular subgroups. Our data provide compelling evidence that expression profiling is a more accurate and objective method to classify gliomas than histologic classification. Molecular classification therefore may aid diagnosis and can guide clinical decision making.