Split Conformal Prediction (SCP) provides a computationally efficient way to construct confidence intervals in prediction problems. Notably, most of the theory built around SCP is focused on the single test point setting. In real-life, inference sets consist of multiple points, which raises the question of coverage guarantees for many points simultaneously. While on average, the False Coverage Proportion (FCP) remains controlled, it can fluctuate strongly around its mean, the False Coverage Rate (FCR). We observe that when a dataset is split multiple times, classical SCP may not control the FCP in a majority of the splits. We propose CoJER, a novel method that achieves sharp FCP control in probability for conformal prediction, based on a recent characterization of the distribution of conformal $p$-values in a transductive setting. This procedure incorporates an aggregation scheme which provides robustness with respect to modeling choices. We show through extensive real data experiments that CoJER provides FCP control while standard SCP does not. Furthermore, CoJER yields shorter intervals than the state-of-the-art method for FCP control and only slightly larger intervals than standard SCP.

MOTIVATION: The 3D organization of the genome plays a crucial role in various biological processes. Hi-C technology is widely used to investigate chromosome structures by quantifying 3D proximity between genomic regions. While numerous computational tools exist for detecting differences in Hi-C data between conditions, a comprehensive review and benchmark comparing their effectiveness is lacking. RESULTS: This study offers a comprehensive review and benchmark of 10 generic tools for differential analysis of Hi-C matrices at the interaction count level. The benchmark assesses the statistical methods, usability, and performance (in terms of precision and power) of these tools, using both real and simulated Hi-C data. Results reveal a striking variability in performance among the tools, highlighting the substantial impact of preprocessing filters and the difficulty all tools encounter in effectively controlling the false discovery rate across varying resolutions and chromosome sizes. AVAILABILITY: The complete benchmark is available at https://forgemia.inra.fr/scales/replication-chrocodiff using processed data deposited at https://doi.org/10.57745/LR0W9R. CONTACT: [email protected].

MOTIVATION: Characterizing the structure of flexible proteins, particularly within the realm of intrinsic disorder, presents a formidable challenge due to their high conformational variability. Currently, their structural representation relies on (possibly large) conformational ensembles derived from a combination of experimental and computational methods. The detailed structural analysis of these ensembles is a difficult task, for which existing tools have limited effectiveness. RESULTS: This study proposes an innovative extension of the concept of contact maps to the ensemble framework, incorporating the intrinsic probabilistic nature of disordered proteins. Within this framework, a conformational ensemble is characterized through a weighted family of contact maps. To achieve this, conformations are first described using a refined definition of contact that appropriately accounts for the geometry of the inter-residue interactions and the sequence context. Representative structural features of the ensemble naturally emerge from the subsequent clustering of the resulting contact-based descriptors. Importantly, transiently populated structural features are readily identified within large ensembles. The performance of the method is illustrated by several use cases and compared with other existing approaches, highlighting its superiority in capturing relevant structural features of highly flexible proteins. AVAILABILITY AND IMPLEMENTATION: An open-source implementation of the method is provided together with an easy-to-use Jupyter notebook, available at https://gitlab.laas.fr/moma/WARIO.

Controlled variable selection is an important analytical step in various scientific fields, such as brain imaging or genomics. In these high-dimensional data settings, considering too many variables leads to poor models and high costs, hence the need for statistical guarantees on false positives. Knockoffs are a popular statistical tool for conditional variable selection in high dimension. However, they control for the expected proportion of false discoveries (FDR) and not the actual proportion of false discoveries (FDP). We present a new method, KOPI, that controls the proportion of false discoveries for Knockoff-based inference. The proposed method also relies on a new type of aggregation to address the undesirable randomness associated with classical Knockoff inference. We demonstrate FDP control and substantial power gains over existing Knockoff-based methods in various simulation settings and achieve good sensitivity/specificity tradeoffs on brain imaging data.

MOTIVATION: The standard approach for statistical inference in differential expression (DE) analyses is to control the false discovery rate (FDR). However, controlling the FDR does not in fact imply that the proportion of false discoveries is upper bounded. Moreover, no statistical guarantee can be given on subsets of genes selected by FDR thresholding. These known limitations are overcome by post hoc inference, which provides guarantees of the number of proportion of false discoveries among arbitrary gene selections. However, post hoc inference methods are not yet widely used for DE studies. RESULTS: In this article, we demonstrate the relevance and illustrate the performance of adaptive interpolation-based post hoc methods for two-group DE studies. First, we formalize the use of permutation-based methods to obtain sharp confidence bounds that are adaptive to the dependence between genes. Then, we introduce a generic linear time algorithm for computing post hoc bounds, making these bounds applicable to large-scale two-group DE studies. The use of the resulting Adaptive Simes bound is illustrated on a RNA sequencing study. Comprehensive numerical experiments based on real microarray and RNA sequencing data demonstrate the statistical performance of the method. AVAILABILITY AND IMPLEMENTATION: A cross-platform open source implementation within the R package sanssouci is available at https://sanssouci-org.github.io/sanssouci/. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

A number of bioinformatic or biostatistical methods are available for analyzing DNA copy number profiles measured from microarray or sequencing technologies. In the absence of rich enough gold standard data sets, the performance of these methods is generally assessed using unrealistic simulation studies, or based on small real data analyses. To make an objective and reproducible performance assessment, we have designed and implemented a framework to generate realistic DNA copy number profiles of cancer samples with known truth. These profiles are generated by resampling publicly available SNP microarray data from genomic regions with known copy-number state. The original data have been extracted from dilutions series of tumor cell lines with matched blood samples at several concentrations. Therefore, the signal-to-noise ratio of the generated profiles can be controlled through the (known) percentage of tumor cells in the sample. This article describes this framework and its application to a comparison study between methods for segmenting DNA copy number profiles from SNP microarrays. This study indicates that no single method is uniformly better than all others. It also helps identifying pros and cons of the compared methods as a function of biologically informative parameters, such as the fraction of tumor cells in the sample and the proportion of heterozygous markers. This comparison study may be reproduced using the open source and cross-platform R package jointseg, which implements the proposed data generation and evaluation framework: http://r-forge.r-project.org/R/?group_id=1562.

UNLABELLED: We describe the implementation of the method introduced by Chambaz et al. in 2012. We also demonstrate its genome-wide application to the integrative search of new regions with strong association between DNA copy number and gene expression accounting for DNA methylation in breast cancers. AVAILABILITY AND IMPLEMENTATION: An open-source R package tmle.npvi is available from CRAN (http://cran.r-project.org/). CONTACT: [email protected].

BACKGROUND: Genome-wide association studies (GWAS) aim at finding genetic markers that are significantly associated with a phenotype of interest. Single nucleotide polymorphism (SNP) data from the entire genome are collected for many thousands of SNP markers, leading to high-dimensional regression problems where the number of predictors greatly exceeds the number of observations. Moreover, these predictors are statistically dependent, in particular due to linkage disequilibrium (LD). We propose a three-step approach that explicitly takes advantage of the grouping structure induced by LD in order to identify common variants which may have been missed by single marker analyses (SMA). In the first step, we perform a hierarchical clustering of SNPs with an adjacency constraint using LD as a similarity measure. In the second step, we apply a model selection approach to the obtained hierarchy in order to define LD blocks. Finally, we perform Group Lasso regression on the inferred LD blocks. We investigate the efficiency of this approach compared to state-of-the art regression methods: haplotype association tests, SMA, and Lasso and Elastic-Net regressions. RESULTS: Our results on simulated data show that the proposed method performs better than state-of-the-art approaches as soon as the number of causal SNPs within an LD block exceeds 2. Our results on semi-simulated data and a previously published HIV data set illustrate the relevance of the proposed method and its robustness to a real LD structure. The method is implemented in the R package BALD (Blockwise Approach using Linkage Disequilibrium), available from http://www.math-evry.cnrs.fr/publications/logiciels . CONCLUSIONS: Our results show that the proposed method is efficient not only at the level of LD blocks by inferring well the underlying block structure but also at the level of individual SNPs. Thus, this study demonstrates the importance of tailored integration of biological knowledge in high-dimensional genomic studies such as GWAS.

SUMMARY: CalMaTe calibrates preprocessed allele-specific copy number estimates (ASCNs) from DNA microarrays by controlling for single-nucleotide polymorphism-specific allelic crosstalk. The resulting ASCNs are on average more accurate, which increases the power of segmentation methods for detecting changes between copy number states in tumor studies including copy neutral loss of heterozygosity. CalMaTe applies to any ASCNs regardless of preprocessing method and microarray technology, e.g. Affymetrix and Illumina. AVAILABILITY: The method is available on CRAN (http://cran.r-project.org/) in the open-source R package calmate, which also includes an add-on to the Aroma Project framework (http://www.aroma-project.org/).

MOTIVATION: High-throughput techniques facilitate the simultaneous measurement of DNA copy number at hundreds of thousands of sites on a genome. Older techniques allow measurement only of total copy number, the sum of the copy number contributions from the two parental chromosomes. Newer single nucleotide polymorphism (SNP) techniques can in addition enable quantifying parent-specific copy number (PSCN). The raw data from such experiments are two-dimensional, but are unphased. Consequently, inference based on them necessitates development of new analytic methods. METHODS: We have adapted and enhanced the circular binary segmentation (CBS) algorithm for this purpose with focus on paired test and reference samples. The essence of paired parent-specific CBS (Paired PSCBS) is to utilize the original CBS algorithm to identify regions of equal total copy number and then to further segment these regions where there have been changes in PSCN. For the final set of regions, calls are made of equal parental copy number and loss of heterozygosity (LOH). PSCN estimates are computed both before and after calling. RESULTS: The methodology is evaluated by simulation and on glioblastoma data. In the simulation, PSCBS compares favorably to established methods. On the glioblastoma data, PSCBS identifies interesting genomic regions, such as copy-neutral LOH. AVAILABILITY: The Paired PSCBS method is implemented in an open-source R package named PSCBS, available on CRAN (http://cran.r-project.org/).

BACKGROUND: High-throughput genotyping microarrays assess both total DNA copy number and allelic composition, which makes them a tool of choice for copy number studies in cancer, including total copy number and loss of heterozygosity (LOH) analyses. Even after state of the art preprocessing methods, allelic signal estimates from genotyping arrays still suffer from systematic effects that make them difficult to use effectively for such downstream analyses. RESULTS: We propose a method, TumorBoost, for normalizing allelic estimates of one tumor sample based on estimates from a single matched normal. The method applies to any paired tumor-normal estimates from any microarray-based technology, combined with any preprocessing method. We demonstrate that it increases the signal-to-noise ratio of allelic signals, making it significantly easier to detect allelic imbalances. CONCLUSIONS: TumorBoost increases the power to detect somatic copy-number events (including copy-neutral LOH) in the tumor from allelic signals of Affymetrix or Illumina origin. We also conclude that high-precision allelic estimates can be obtained from a single pair of tumor-normal hybridizations, if TumorBoost is combined with single-array preprocessing methods such as (allele-specific) CRMA v2 for Affymetrix or BeadStudio's (proprietary) XY-normalization method for Illumina. A bounded-memory implementation is available in the open-source and cross-platform R package aroma.cn, which is part of the Aroma Project (http://www.aroma-project.org/).

MOTIVATION: One of the most challenging tasks in the post-genomic era is the reconstruction of transcriptional regulation networks. The goal is to identify, for each gene expressed in a particular cellular context, the regulators affecting its transcription, and the co-ordination of several regulators in specific types of regulation. DNA microarrays can be used to investigate relationships between regulators and their target genes, through simultaneous observations of their RNA levels. RESULTS: We propose a data mining system for inferring transcriptional regulation relationships from RNA expression values. This system is particularly suitable for the detection of cooperative transcriptional regulation. We model regulatory relationships as labelled two-layer gene regulatory networks, and describe a method for the efficient learning of these bipartite networks from discretized expression data sets. We also evaluate the statistical significance of such inferred networks and validate our methods on two public yeast expression data sets. AVAILABILITY: http://www.lri.fr/~elati/licorn.html. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

MOTIVATION: Microarray-based CGH (Comparative Genomic Hybridization), transcriptome arrays and other large-scale genomic technologies are now routinely used to generate a vast amount of genomic profiles. Exploratory analysis of this data is crucial in helping to understand the data and to help form biological hypotheses. This step requires visualization of the data in a meaningful way to visualize the results and to perform first level analyses. RESULTS: We have developed a graphical user interface for visualization and first level analysis of molecular profiles. It is currently in use at the Institut Curie for cancer research projects involving CGH arrays, transcriptome arrays, SNP (single nucleotide polymorphism) arrays, loss of heterozygosity results (LOH), and Chromatin ImmunoPrecipitation arrays (ChIP chips). The interface offers the possibility of studying these different types of information in a consistent way. Several views are proposed, such as the classical CGH karyotype view or genome-wide multi-tumor comparison. Many functionalities for analyzing CGH data are provided by the interface, including looking for recurrent regions of alterations, confrontation to transcriptome data or clinical information, and clustering. Our tool consists of PHP scripts and of an applet written in Java. It can be run on public datasets at http://bioinfo.curie.fr/vamp AVAILABILITY: The VAMP software (Visualization and Analysis of array-CGH,transcriptome and other Molecular Profiles) is available upon request. It can be tested on public datasets at http://bioinfo.curie.fr/vamp. The documentation is available at http://bioinfo.curie.fr/vamp/doc.

BACKGROUND: Array-based comparative genomic hybridization (array-CGH) is a recently developed technique for analyzing changes in DNA copy number. As in all microarray analyses, normalization is required to correct for experimental artifacts while preserving the true biological signal. We investigated various sources of systematic variation in array-CGH data and identified two distinct types of spatial effect of no biological relevance as the predominant experimental artifacts: continuous spatial gradients and local spatial bias. Local spatial bias affects a large proportion of arrays, and has not previously been considered in array-CGH experiments. RESULTS: We show that existing normalization techniques do not correct these spatial effects properly. We therefore developed an automatic method for the spatial normalization of array-CGH data. This method makes it possible to delineate and to eliminate and/or correct areas affected by spatial bias. It is based on the combination of a spatial segmentation algorithm called NEM (Neighborhood Expectation Maximization) and spatial trend estimation. We defined quality criteria for array-CGH data, demonstrating significant improvements in data quality with our method for three data sets coming from two different platforms (198, 175 and 26 BAC-arrays). CONCLUSION: We have designed an automatic algorithm for the spatial normalization of BAC CGH-array data, preventing the misinterpretation of experimental artifacts as biologically relevant outliers in the genomic profile. This algorithm is implemented in the R package MANOR (Micro-Array NORmalization), which is described at http://bioinfo.curie.fr/projects/manor and available from the Bioconductor site http://www.bioconductor.org. It can also be tested on the CAPweb bioinformatics platform at http://bioinfo.curie.fr/CAPweb.