113 research outputs found
RPPanalyzer: Analysis of reverse-phase protein array data
No full textAbstract
Summary: RPPanalyzer is a statistical tool developed to read reverse-phase protein array data, to perform the basic data analysis and to visualize the resulting biological information. The R-package provides different functions to compare protein expression levels of different samples and to normalize the data. Implemented plotting functions permit a quality control by monitoring data distribution and signal validity. Finally, the data can be visualized in heatmaps, boxplots, time course plots and correlation plots. RPPanalyzer is a flexible tool and tolerates a huge variety of different experimental designs.
Availability: The RPPAanalyzer is open source and freely available as an R-Package on the CRAN platform http://cran.r-project.org/
Contact: [email protected]
Supplementary information: Supplementary data are available at Bioinformatics online.</jats:p
Graph based fusion of high-dimensional gene- and microRNA expression data
No full textOne of the main goals in cancer studies including high-throughput microRNA
(miRNA) and mRNA data is to find and assess prognostic signatures capable
of predicting clinical outcome. Both mRNA and miRNA expression changes in
cancer diseases are described to reflect clinical characteristics like staging and
prognosis. Furthermore, miRNA abundance can directly affect target transcripts
and translation in tumor cells. Prediction models are trained to identify either
mRNA or miRNA signatures for patient stratification. With the increasing
number of microarray studies collecting mRNA and miRNA from the same
patient cohort there is a need for statistical methods to integrate or fuse both
kinds of data into one prediction model in order to find a combined signature
that improves the prediction.
Here, we propose a new method to fuse miRNA and mRNA data into one
prediction model. Since miRNAs are known regulators of mRNAs, correlations
between miRNA and mRNA expression data as well as target prediction
information were used to build a bipartite graph representing the relations
between miRNAs and mRNAs.
Feature selection is a critical part when fitting prediction models to high-
dimensional data. Most methods treat features, in this case genes or miRNAs,
as independent, an assumption that does not hold true when dealing with
combined gene and miRNA expression data. To improve prediction accuracy, a
description of the correlation structure in the data is needed. In this work the
bipartite graph was used to guide the feature selection and therewith improve
prediction results and find a stable prognostic signature of miRNAs and genes.
The method is evaluated on a prostate cancer data set comprising 98 patient
samples with miRNA and mRNA expression data. The biochemical relapse, an
important event in prostate cancer treatment, was used as clinical endpoint.
Biochemical relapse coins the renewed rise of the blood level of a prostate
marker (PSA) after surgical removal of the prostate. The relapse is a hint
for metastases and usually the point in clinical practise to decide for further
treatment.
A boosting approach was used to predict the biochemical relapse. It could
be shown that the bipartite graph in combination with miRNA and mRNA
expression data could improve prediction performance. Furthermore the ap-
proach improved the stability of the feature selection and therewith yielded
more consistent marker sets. Of course, the marker sets produced by this new
method contain mRNAs as well as miRNAs.
The new approach was compared to two state-of-the-art methods suited for
high-dimensional data and showed better prediction performance in both cases
Integration of pathway knowledge into a reweighted recursive feature elimination approach for risk stratification of cancer patients
No full textAbstract
Motivation: One of the main goals of high-throughput gene-expression studies in cancer research is to identify prognostic gene signatures, which have the potential to predict the clinical outcome. It is common practice to investigate these questions using classification methods. However, standard methods merely rely on gene-expression data and assume the genes to be independent. Including pathway knowledge a priori into the classification process has recently been indicated as a promising way to increase classification accuracy as well as the interpretability and reproducibility of prognostic gene signatures.
Results: We propose a new method called Reweighted Recursive Feature Elimination. It is based on the hypothesis that a gene with a low fold-change should have an increased influence on the classifier if it is connected to differentially expressed genes. We used a modified version of Google's PageRank algorithm to alter the ranking criterion of the SVM-RFE algorithm. Evaluations of our method on an integrated breast cancer dataset comprising 788 samples showed an improvement of the area under the receiver operator characteristic curve as well as in the reproducibility and interpretability of selected genes.
Availability: The R code of the proposed algorithm is given in Supplementary Material.
Contact: [email protected]; [email protected]
Supplementary information: Supplementary data are available at Bioinformatics online.</jats:p
Graph based fusion of miRNA and mRNA expression data improves clinical outcome prediction in prostate cancer
No full textBackground: One of the main goals in cancer studies including high-throughput microRNA (miRNA) and mRNA data is to find and assess prognostic signatures capable of predicting clinical outcome. Both mRNA and miRNA expression changes in cancer diseases are described to reflect clinical characteristics like staging and prognosis. Furthermore, miRNA abundance can directly affect target transcripts and translation in tumor cells. Prediction models are trained to identify either mRNA or miRNA signatures for patient stratification. With the increasing number of microarray studies collecting mRNA and miRNA from the same patient cohort there is a need for statistical methods to integrate or fuse both kinds of data into one prediction model in order to find a combined signature that improves the prediction. Results: Here, we propose a new method to fuse miRNA and mRNA data into one prediction model. Since miRNAs are known regulators of mRNAs we used the correlations between them as well as the target prediction information to build a bipartite graph representing the relations between miRNAs and mRNAs. This graph was used to guide the feature selection in order to improve the prediction. The method is illustrated on a prostate cancer data set comprising 98 patient samples with miRNA and mRNA expression data. The biochemical relapse was used as clinical endpoint. It could be shown that the bipartite graph in combination with both data sets could improve prediction performance as well as the stability of the feature selection. Conclusions: Fusion of mRNA and miRNA expression data into one prediction model improves clinical outcome prediction in terms of prediction error and stable feature selection. The R source code of the proposed method is available in the supplement
Increasing the sensitivity of reverse phase protein arrays by antibody-mediated signal amplification
No full textAbstract Background Reverse phase protein arrays (RPPA) emerged as a useful experimental platform to analyze biological samples in a high-throughput format. Different signal detection methods have been described to generate a quantitative readout on RPPA including the use of fluorescently labeled antibodies. Increasing the sensitivity of RPPA approaches is important since many signaling proteins or posttranslational modifications are present at a low level. Results A new antibody-mediated signal amplification (AMSA) strategy relying on sequential incubation steps with fluorescently-labeled secondary antibodies reactive against each other is introduced here. The signal quantification is performed in the near-infrared range. The RPPA-based analysis of 14 endogenous proteins in seven different cell lines demonstrated a strong correlation (r = 0.89) between AMSA and standard NIR detection. Probing serial dilutions of human cancer cell lines with different primary antibodies demonstrated that the new amplification approach improved the limit of detection especially for low abundant target proteins. Conclusions Antibody-mediated signal amplification is a convenient and cost-effective approach for the robust and specific quantification of low abundant proteins on RPPAs. Contrasting other amplification approaches it allows target protein detection over a large linear range.</p
First in the Nation in Education : Final Report,1984.
No full textThis report is one step in an ongoing process of change and is a plea for commitment for high standards in education in Iowa. Contains the final reports of the six subcommittees as adopted by the Excellence in Education Task Force, and the five recommendations made by the Task Force
“Kiss me with the hollow of your mouth” – imagining falling in love with Stense Andrea Lind-Valdan
No full textThe essay reflects upon the function of images within the love encounter, drawing on the personal experience of the author and his partner, visual artist Stense Andrea Lind-Valdan. Mixing personal experiences, diaristic notes and academic reflections, the essay moves beyond conventional scholarly style and experiments with more personal and anecdotal modalities, thus creating a text that re-enacts the fascination and imaginary entrapments involved in the love encounter while simultaneously reflecting upon these aspects of love
Ship Surveillance with High Resolution TerraSAR-X Satellite in African Waters
No full textShip detection is an important application of monitoring of environment and security or safety issues in African Waters. In order to overcome the limitations by other monitoring systems, e.g. coastal radar, surveillance with satellite synthetic aperture radar (SAR) is used because of its potential to detect ships at high resolution over wide swaths and in all weather conditions and independent from sun illumination. TerraSAR-X (TS-X) is an X-band polarimetric SAR capable of imaging up to 1m resolution in Spotlight mode. TS-X can be used fo r a wide variety of applications and methods of analysis including visual interpretation, mapping, digital-elevation-model creation, disaster monitoring, and oceanography. Results on the combined use of TS-X ship detection, automatic identification system (AIS), and satellite AIS (Sat AIS) are presented. Using AIS is an effective terrestrial method for tracking vessels in real time typically up to 40 km off the coast. SatAIS is a space-based system with nearly global coverage for monitoring of AIS equipped ships. Since not all ships operate their AIS and smaller ships are not equipped with AIS, space borne SARs provide complimentary means for ship monitoring. As cases , images were acquired over the Somali Coast Area, South African Coast and Gibraltar in Stripmap mode with a resolution of 3m at a coverage of 30km×50km. The rapid tasking performance as well as the short response time of the TS-X data acquisition of the ground segment DLR-BN (Ground Station Neustrelitz, Germany), are very helpful to monitor hotspot areas such as the Gulf of Aden . For ascending orbits the delivery time of ship detection products is less than 20 min. Along with the detected ship positions, estimated wave heights and wind fields derived from large-area TS-X imagery can be used to get a detailed maritime picture of the situation
Narrative as unit of analysis for teaching‐learning praxis and action: tracing the personal growth of a professional voice
No full textNurtured as a teacher and mentored as a mathematics education researcher, the reflective voice of a teacher‐researcher‐educator is portrayed in this paper. While drawing upon experience of creating teaching‐learning environments, the author outlines the growth of praxis within her practice. She then outlines her coming upon narrative as a deployable unit of analysis with which to grasp teaching‐learning practice. With narrative inquiry as strategy, she finally outlines examples of educational action research she is able to conduct. She argues in favour of empowering the praxis of teachers, recognising their language of practice and urges us to listen to their individual voice.</p
NsrR, GadE, and GadX Interplay in Repressing Expression of the Escherichia coli O157: H7 LEE Pathogenicity Island in Response to Nitric Oxide
No full textExpression of genes of the locus of enterocyte effacement (LEE) is essential for adherence of enterohemorrhagic Escherichia coli (EHEC) to intestinal epithelial cells. Gut factors that may modulate LEE gene expression may therefore influence the outcome of the infection. Because nitric oxide (NO) is a critical effector of the intestinal immune response that may induce transcriptional regulation in enterobacteria, we investigated its influence on LEE expression in EHEC O157:H7. We demonstrate that NO inhibits the expression of genes belonging to LEE1, LEE4, and LEE5 operons, and that the NO sensor nitrite-sensitive repressor (NsrR) is a positive regulator of these operons by interacting directly with the RNA polymerase complex. In the presence of NO, NsrR detaches from the LEE1/4/5 promoter regions and does not activate transcription. In parallel, two regulators of the acid resistance pathway, GadE and GadX, are induced by NO through an indirect NsrR-dependent mechanism. In this context, we show that the NO-dependent LEE1 down-regulation is due to absence of NsrR-mediated activation and to the repressor effect of GadX. Moreover, the inhibition of expression of LEE4 and LEE5 by NO is due to loss of NsrR-mediated activation, to LEE1 down-regulation and to GadE up-regulation. Lastly, we establish that chemical or cellular sources of NO inhibit the adherence of EHEC to human intestinal epithelial cells. These results highlight the critical effect of NsrR in the regulation of the LEE pathogenicity island and the potential role of NO in the limitation of colonization by EHEC. Author Summary Enterohemorrhagic Escherichia coli (EHEC) O157:H7 are food-borne pathogens for humans causing bloody diarrhea and, especially in children under five years old, kidney damages leading to death in 5% of cases. Antibiotics are contra-indicated because they are suspected to increase the severity of the disease. Therefore, it is crucial to develop alternative preventive or therapeutic strategies to fight EHEC infection. To reach this goal, a deeper knowledge of host-pathogen interaction is required. A critical step in EHEC infection is the adhesion of bacterial cells to intestinal epithelial cells. In response to the bacterial infection, the host triggers an immune response directed against the pathogen. The current study shows that a main effector of this immune response, nitric oxide (NO), dramatically reduces the capacity of EHEC to adhere to intestinal epithelial cells. We have investigated the molecular mechanisms involved and identified a NO-sensor regulator that controls the expression of the genes required for EHEC adhesion. This finding underlines that NO could be a potential protective factor limiting the development of EHEC-induced diseases and provides a new avenue of investigation for the development of therapeutic strategies against infections with O157:H7 bacteria
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