Combining chromosomal arm status and significantly aberrant genomic locations reveals new cancer subtypes

Many types of tumors exhibit chromosomal losses or gains, as well as local amplifications and deletions. Within any given tumor type, sample specific amplifications and deletionsare also observed. Typically, a region that is aberrant in more tumors,or whose copy number change is stronger, would be considered as a more promising candidate to be biologically relevant to cancer. We sought for an intuitive method to define such aberrations and prioritize them. We define V, the volume associated with an aberration, as the product of three factors: a. fraction of patients with the aberration, b. the aberrations length and c. its amplitude. Our algorithm compares the values of V derived from real data to a null distribution obtained by permutations, and yields the statistical significance, p value, of the measured value of V. We detected genetic locations that were significantly aberrant and combined them with chromosomal arm status to create a succint fingerprint of the tumor genome. This genomic fingerprint is used to visualize the tumors, highlighting events that are co ocurring or mutually exclusive. We allpy the method on three different public array CGH datasets of Medulloblastoma and Neuroblastoma, and demonstrate its ability to detect chromosomal regions that were known to be altered in the tested cancer types, as well as to suggest new genomic locations to be tested. We identified a potential new subtype of Medulloblastoma, which is analogous to Neuroblastoma type 1.Comment: 34 pages, 3 figures; to appear in Cancer Informatic

Utilizing microarray spot characteristics to improve cross-species hybridization results

AbstractCross-species hybridization (CSH), i.e., the hybridization of a (target) species RNA to a DNA microarray that represents another (reference) species, is often used to study species diversity. However, filtration of CSH data has to be applied to extract valid information. We present a novel approach to filtering the CSH data, which utilizes spot characteristics (SCs) of image-quantification data from scanned spotted cDNA microarrays. Five SCs that were affected by sequence similarity between probe and target sequences were identified (designated as BS-SCs). Filtration by all five BS-SC thresholds demonstrated improved clustering for two of the three examined experiments, suggesting that BS-SCs may serve for filtration of data obtained by CSH, to improve the validity of the results. This CSH data-filtration approach could become a promising tool for studying a variety of species, especially when no genomic information is available for the target species

Wide-Scale Analysis of Human Functional Transcription Factor Binding Reveals a Strong Bias towards the Transcription Start Site

We introduce a novel method to screen the promoters of a set of genes with shared biological function, against a precompiled library of motifs, and find those motifs which are statistically over-represented in the gene set. The gene sets were obtained from the functional Gene Ontology (GO) classification; for each set and motif we optimized the sequence similarity score threshold, independently for every location window (measured with respect to the TSS), taking into account the location dependent nucleotide heterogeneity along the promoters of the target genes. We performed a high throughput analysis, searching the promoters (from 200bp downstream to 1000bp upstream the TSS), of more than 8000 human and 23,000 mouse genes, for 134 functional Gene Ontology classes and for 412 known DNA motifs. When combined with binding site and location conservation between human and mouse, the method identifies with high probability functional binding sites that regulate groups of biologically related genes. We found many location-sensitive functional binding events and showed that they clustered close to the TSS. Our method and findings were put to several experimental tests. By allowing a "flexible" threshold and combining our functional class and location specific search method with conservation between human and mouse, we are able to identify reliably functional TF binding sites. This is an essential step towards constructing regulatory networks and elucidating the design principles that govern transcriptional regulation of expression. The promoter region proximal to the TSS appears to be of central importance for regulation of transcription in human and mouse, just as it is in bacteria and yeast.Comment: 31 pages, including Supplementary Information and figure

Identifying differentially expressed genes using false discovery rate controlling procedures

Motivation: DNA microarrays have recently been used for the purpose of monitoring expression levels of thousands of genes simultaneously and identifying those genes that are differentially expressed. The probability that a false identification (type I error) is committed can increase sharply when the number of tested genes gets large. Correlation between the test statistics attributed to gene co-regulation and dependency in the measurement errors of the gene expression levels further complicates the problem. In this paper we address this very large multiplicity problem by adopting the false discovery rate (FDR) controlling approach. In order to address the dependency problem, we present three resampling-based FDR controlling procedures, that account for the test statistics distribution, and compare their performance to that of the naïve application of the linear step-up procedure in Benjamini and Hochberg (1995). The procedures are studied using simulated microarray data, and their performance is examined relative to their ease of implementation. Results: Comparative simulation analysis shows that all four FDR controlling procedures control the FDR at the desired level, and retain substantially more power then the family-wise error rate controlling procedures. In terms of power, using resampling of the marginal distribution of each test statistics substantially improves the performance over the naïve one. The highest power is achieved, at the expense of a more sophisticated algorithm, by the resampling-based procedures that resample the joint distribution of the test statistics and estimate the level of FDR control

Trends in decision-making by primary care physicians regarding common infectious complaints

Primary care physicians played an important role in the global response during the COVID-19 pandemic, but with the absence of laboratory and diagnostics services, the move to telehealth and the focus on respiratory assessment, they faced increased uncertainty when making clinical decisions. This paper aims to examine the impact of the pandemic on decisions made by primary care physicians, as measured by referrals to chest X-ray and laboratory tests and by prescriptions of antibiotics. We conducted a retrospective study of all visits recorded with fever or cough, presenting to 209 community clinics in Southern Israel during the years 2018–2022. We describe changes in outcome rates across time and use multivariate generalised linear mixed effects model to compare the odds of referrals and prescriptions between periods, while accounting for gender, age, clinic sector, visit type, diagnosis, and season. In total, 609,823 visits to primary care physicians complied with the cohort definitions. Social restrictions were associated with a decline in all measured outcomes for primary care physician decisions, most prominently among ages 20-59, for throat culture referral during the first lockdown (OR = 0.46) and for cephalosporine prescription during the second lockdown (OR = 0.55). This trend persisted following the cancellation of the restrictions. Despite higher uncertainty during the COVID-19 social restrictions, the overall course of clinical decision-making processes was maintained, and was associated with a reduction in the use of auxiliary resources, which can improve the quality of patient care by lowering costs and supporting prevention of future antibiotics resistance.</p

FDR adjustments of Microarray Experiments (FDR-AME)

Purpose This R package adjusts p-values generated in multiple hypotheses testing of gene expression data obtained by a microarray experiment. The software applies multiple testing procedures that control the False Discovery Rate (FDR) criterion introduced by Benjamini and Hochberg (1995). It applies both theoretical-distribution-based and resampling-based multiple testing procedures, and presents as output adjusted p-values and p-value plots, as described in Reiner et al (2003). It goes beyond Reiner et al in offering adjustments according to the adaptive two stage FDR controlling procedures in Benjamini et al (2001, submitted), and in addressing differences in expression between many classes using one-way ANOVA. The False Discovery Rate (FDR) Criterion The FDR is the expected proportion of erroneously rejected null hypotheses among the rejected ones. Consider a family of m simultaneously tested null hypotheses of which m0 are true. For each hypothesis Hi a test statistic is calculated along with the corresponding p-value Pi. Let R denote the number of hypotheses rejected by a procedure, V the number of true null hypotheses erroneously rejected, and S the number of false hypotheses rejected. Now let Q denote V/R when R&gt;0 and 0 otherwise. Then the FDR is defined as FDR=E(Q). The Linear Step-Up Procedure (BH) This procedure makes use of the ordered p-values P (1)≤... ≤P (m). Denote the corresponding null hypotheses H (1),...,H (m). For a desired FDR level q, the ordered p-value P (i) is compared to the critical value q·i/m. Let k = max { i: P (i) ≤ q·i/m}. Then reject H (1),...,H (k), if such a k exists. Benjamini and Hochberg (1995) show that when the test statistics are independent, this procedure controls the FDR at the level q. Actually, the FDR is controlled at level FDR ≤ q·m0/m ≤ q. 1 Benjamini and Yekutieli (2001) further show that FDR ≤ q·m0/m for positively dependent test statistics as well. The technical condition under which the control holds is that of positive regression dependency on each test statistic corresponding the true null hypotheses. Reiner et al (2003) and Reiner (unpublished thesis) shows FDR ≤ q for two sided tests under positive and negative correlations