RankAggreg, an R package for weighted rank aggregation

<p>Abstract</p> <p>Background</p> <p>Researchers in the field of bioinformatics often face a challenge of combining several ordered lists in a proper and efficient manner. Rank aggregation techniques offer a general and flexible framework that allows one to objectively perform the necessary aggregation. With the rapid growth of high-throughput genomic and proteomic studies, the potential utility of rank aggregation in the context of meta-analysis becomes even more apparent. One of the major strengths of rank-based aggregation is the ability to combine lists coming from different sources and platforms, for example different microarray chips, which may or may not be directly comparable otherwise.</p> <p>Results</p> <p>The <it>RankAggreg </it>package provides two methods for combining the ordered lists: the Cross-Entropy method and the Genetic Algorithm. Two examples of rank aggregation using the package are given in the manuscript: one in the context of clustering based on gene expression, and the other one in the context of meta-analysis of prostate cancer microarray experiments.</p> <p>Conclusion</p> <p>The two examples described in the manuscript clearly show the utility of the <it>RankAggreg </it>package in the current bioinformatics context where ordered lists are routinely produced as a result of modern high-throughput technologies.</p

OptCluster : an R package for determining the optimal clustering algorithm and optimal number of clusters.

Determining the best clustering algorithm and ideal number of clusters for a particular dataset is a fundamental difficulty in unsupervised clustering analysis. In biological research, data generated from Next Generation Sequencing technology and microarray gene expression data are becoming more and more common, so new tools and resources are needed to group such high dimensional data using clustering analysis. Different clustering algorithms can group data very differently. Therefore, there is a need to determine the best groupings in a given dataset using the most suitable clustering algorithm for that data. This paper presents the R package optCluster as an efficient way for users to evaluate up to ten clustering algorithms, ultimately determining the optimal algorithm and optimal number of clusters for a given set of data. The selected clustering algorithms are evaluated by as many as nine validation measures classified as “biological”, “internal”, or “stability”, and the final result is obtained through a weighted rank aggregation algorithm based on the calculated validation scores. Two examples using this package are presented, one with a microarray dataset and the other with an RNA-Seq dataset. These two examples highlight the capabilities the optCluster package and demonstrate its usefulness as a tool in cluster analysis

Effective Alu repeat based RT-qPCR normalization in cancer cell perturbation experiments

Background: Measuring messenger RNA (mRNA) levels using the reverse transcription quantitative polymerase chain reaction (RT-qPCR) is common practice in many laboratories. A specific set of mRNAs as internal control reference genes is considered as the preferred strategy to normalize RT-qPCR data. Proper selection of reference genes is a critical issue, especially in cancer cells that are subjected to different in vitro manipulations. These manipulations may result in dramatic alterations in gene expression levels, even of assumed reference genes. In this study, we evaluated the expression levels of 11 commonly used reference genes as internal controls for normalization of 19 experiments that include neuroblastoma, T-ALL, melanoma, breast cancer, non small cell lung cancer (NSCL), acute myeloid leukemia (AML), prostate cancer, colorectal cancer, and cervical cancer cell lines subjected to various perturbations. Results: The geNorm algorithm in the software package qbase+ was used to rank the candidate reference genes according to their expression stability. We observed that the stability of most of the candidate reference genes varies greatly in perturbation experiments. Expressed Alu repeats show relatively stable expression regardless of experimental condition. These Alu repeats are ranked among the best reference assays in all perturbation experiments and display acceptable average expression stability values (M<0.5). Conclusions: We propose the use of Alu repeats as a reference assay when performing cancer cell perturbation experiments

Detection of KRAS Synthetic Lethal Partners through Integration of Existing RNAi Screens

KRAS is a gene that plays a very important role in the initiation and development of several types of cancer. In particular, 90% of human pancreatic cancers are due to KRAS mutations. KRAS is difficult to target directly and a promising therapeutic path is its indirect inactivation by targeting one of its Synthetic Lethal Partners (SLPs). A gene G is a Synthetic Lethal Partner of KRAS if the simultaneous perturbation of KRAS and G leads to cell death. In the past, efforts to identify KRAS SLPs with high-throughput RNAi screens have been performed. These studies have reported only few top-ranked SLPs. To our knowledge, these screens have never been considered in combination for further examination. This thesis employs integrative analysis of the published screens, utilizing additional, independent data aiming at the detection of more robust therapeutic targets. To this aim, RankSLP, a novel statistical analysis approach was implemented, which for the first time i) consistently integrates existing KRAS-specific RNAi screens, ii) consistently integrates and normalizes the results of various ranking methods, iii) evaluates its findings with the use of external data and iv) explores the effects of random data inclusion. This analysis was able to predict novel SLPs of KRAS and confirm some of the existing ones

Reference Genes for Real-Time PCR Quantification of MicroRNAs and Messenger RNAs in Rat Models of Hepatotoxicity

Hepatotoxicity is associated with major changes in liver gene expression induced by xenobiotic exposure. Understanding the underlying mechanisms is critical for its clinical diagnosis and treatment. MicroRNAs are key regulators of gene expression that control mRNA stability and translation, during normal development and pathology. The canonical technique to measure gene transcript levels is Real-Time qPCR, which has been successfully modified to determine the levels of microRNAs as well. However, in order to obtain accurate data in a multi-step method like RT-qPCR, the normalization with endogenous, stably expressed reference genes is mandatory. Since the expression stability of candidate reference genes varies greatly depending on experimental factors, the aim of our study was to identify a combination of genes for optimal normalization of microRNA and mRNA qPCR expression data in experimental models of acute hepatotoxicity. Rats were treated with four traditional hepatotoxins: acetaminophen, carbon tetrachloride, D-galactosamine and thioacetamide, and the liver expression levels of two groups of candidate reference genes, one for microRNA and the other for mRNA normalization, were determined by RT-qPCR in compliance with the MIQE guidelines. In the present study, we report that traditional reference genes such as U6 spliceosomal RNA, Beta Actin and Glyceraldehyde-3P-dehydrogenase altered their expression in response to classic hepatotoxins and therefore cannot be used as reference genes in hepatotoxicity studies. Stability rankings of candidate reference genes, considering only those that did not alter their expression, were determined using geNorm, NormFinder and BestKeeper software packages. The potential candidates whose measurements were stable were further tested in different combinations to find the optimal set of reference genes that accurately determine mRNA and miRNA levels. Finally, the combination of MicroRNA-16/5S Ribosomal RNA and Beta 2 Microglobulin/18S Ribosomal RNA were validated as optimal reference genes for microRNA and mRNA quantification, respectively, in rat models of acute hepatotoxicity

Typical Phone Use Habits: Intense Use Does Not Predict Negative Well-Being

Not all smartphone owners use their device in the same way. In this work, we uncover broad, latent patterns of mobile phone use behavior. We conducted a study where, via a dedicated logging app, we collected daily mobile phone activity data from a sample of 340 participants for a period of four weeks. Through an unsupervised learning approach and a methodologically rigorous analysis, we reveal five generic phone use profiles which describe at least 10% of the participants each: limited use, business use, power use, and personality- & externally induced problematic use. We provide evidence that intense mobile phone use alone does not predict negative well-being. Instead, our approach automatically revealed two groups with tendencies for lower well-being, which are characterized by nightly phone use sessions.Comment: 10 pages, 6 figures, conference pape

Reference gene validation for quantitative RT-PCR during biotic and abiotic stresses in Vitis vinifera

Grapevine is one of the most cultivated fruit crop worldwide with Vitis vinifera being the species with the highest economical importance. Being highly susceptible to fungal pathogens and increasingly affected by environmental factors, it has become an important agricultural research area, where gene expression analysis plays a fundamental role. Quantitative reverse transcription polymerase chain reaction (qRT-PCR) is currently amongst the most powerful techniques to perform gene expression studies. Nevertheless, accurate gene expression quantification strongly relies on appropriate reference gene selection for sample normalization. Concerning V. vinifera, limited information still exists as for which genes are the most suitable to be used as reference under particular experimental conditions. In this work, seven candidate genes were investigated for their stability in grapevine samples referring to four distinct stresses (Erysiphe necator, wounding and UV-C irradiation in leaves and Phaeomoniella chlamydospora colonization in wood). The expression stability was evaluated using geNorm, NormFinder and BestKeeper. In all cases, full agreement was not observed for the three methods. To provide comprehensive rankings integrating the three different programs, for each treatment, a consensus ranking was created using a non-weighted unsupervised rank aggregation method. According to the last, the three most suitable reference genes to be used in grapevine leaves, regardless of the stress, are UBC, VAG and PEP. For the P. chlamydospora treatment, EF1, CYP and UBC were the best scoring genes. Acquaintance of the most suitable reference genes to be used in grapevine samples can contribute for accurate gene expression quantification in forthcoming studiesinfo:eu-repo/semantics/publishedVersio

DISTANCE MEASURES IN AGGREGATING PREFERENCE DATA

The aim of this paper is to present aggregation methods of individual preferences scores by means of distance measures. Three groups of distance measures are discussed: measures  which use preference distributions for all pairs of objects (e.g. Kemeny’s measure, Bogart’s measure), distance measures based on ranking data (e.g. Spearman distance, Podani distance) and distance measures using permissible transformations to ordinal scale (GDM2 distance). Adequate distance formulas are presented and the aggregation of individual preference by using separate distance measures was carried out with the use of the R program

Identification of suitable reference genes for miRNA quantitation in bumblebee (Hymenoptera: Apidae) response to reproduction

International audienceAbstractThe precise quantification of microRNAs (miRNAs) expression level is a critical factor in mastering its functions. We evaluate the suitability of two common genes and ten miRNAs as normalizers for miRNA quantification in the head and ovary at different reproductive status of bumblebees, Bombus lantschouensis by using four different algorithms and one consensus rank approach. For the head and ovary combination, miR-275 was the best candidate. For different tissues, miR-275 was the most stable candidate in the head, while the candidate for the ovary was miR-277. To test the best candidate accuracy, miR-315 was demonstrated to be downregulated based on miR-275 normalization in ovipositor bumblebees. The miR-275 and miR-277 combination is identified to be the most reliable and suitable reference genes for the head and ovary of bumblebees