A comprehensive evaluation of module detection methods for gene expression data

A critical step in the analysis of large genome-wide gene expression datasets is the use of module detection methods to group genes into co-expression modules. Because of limitations of classical clustering methods, numerous alternative module detection methods have been proposed, which improve upon clustering by handling co-expression in only a subset of samples, modelling the regulatory network, and/or allowing overlap between modules. In this study we use known regulatory networks to do a comprehensive and robust evaluation of these different methods. Overall, decomposition methods outperform all other strategies, while we do not find a clear advantage of biclustering and network inference-based approaches on large gene expression datasets. Using our evaluation workflow, we also investigate several practical aspects of module detection, such as parameter estimation and the use of alternative similarity measures, and conclude with recommendations for the further development of these methods

TinGa : fast and flexible trajectory inference with Growing Neural Gas

Motivation: During the last decade, trajectory inference (TI) methods have emerged as a novel framework to model cell developmental dynamics, most notably in the area of single-cell transcriptomics. At present, more than 70 TI methods have been published, and recent benchmarks showed that even state-of-the-art methods only perform well for certain trajectory types but not others. Results: In this work, we present TinGa, a new TI model that is fast and flexible, and that is based on Growing Neural Graphs. We performed an extensive comparison of TinGa to five state-of-the-art methods for TI on a set of 250 datasets, including both synthetic as well as real datasets. Overall, TinGa improves the state-of-the-art by producing accurate models (comparable to or an improvement on the state-of-the-art) on the whole spectrum of data complexity, from the simplest linear datasets to the most complex disconnected graphs. In addition, TinGa obtained the fastest execution times, showing that our method is thus one of the most versatile methods up to date

Early and late effects of pharmacological ALK inhibition on the neuroblastoma transcriptome

Background: Neuroblastoma is an aggressive childhood malignancy of the sympathetic nervous system. Despite multi-modal therapy, survival of high-risk patients remains disappointingly low, underscoring the need for novel treatment strategies. The discovery of ALK activating mutations opened the way to precision treatment in a subset of these patients. Previously, we investigated the transcriptional effects of pharmacological ALK inhibition on neuroblastoma cell lines, six hours after TAE684 administration, resulting in the 77-gene ALK signature, which was shown to gradually decrease from 120 minutes after TAE684 treatment, to gain deeper insight into the molecular effects of oncogenic ALK signaling. Aim: Here, we further dissected the transcriptional dynamic profiles of neuroblastoma cells upon TAE684 treatment in a detailed timeframe of ten minutes up to six hours after inhibition, in order to identify additional early targets for combination treatment. Results: We observed an unexpected initial upregulation of positively regulated MYCN target genes following subsequent downregulation of overall MYCN activity. In addition, we identified adrenomedullin (ADM), previously shown to be implicated in sunitinib resistance, as the earliest response gene upon ALK inhibition. Conclusions: We describe the early and late effects of ALK inhibitor TAE684 treatment on the neuroblastoma transcriptome. The observed unexpected upregulation of ADM warrants further investigation in relation to putative ALK resistance in neuroblastoma patients currently undergoing ALK inhibitor treatment

Network inference by integrating biclustering and feature selection

In order to develop better therapies to combat specific abnormalities present in the gene regulatory network (GRN) of cancer patients, it is crucial to gain a better understanding of regulatory networks in complex biological systems. An important class of methods in systems biology are network inference (NI) methods, which aim to reconstruct a GRN from high-throughput data (e.g. microarrays or next-generation sequencing). GENIE3 is a state-of-the-art method which employs feature selection to identify the best subset of regulators for each gene. While this method is amongst the best performing NI methods, it fails to take into account expected topological properties of a GRN: a GRN consists of modules, each of which consists of genes coregulated by a common set of regulators. We present BiGENIE, a method which takes the modular topology of a GRN into account. By firstly inferring modules – groups of genes coregulated by a common regulator – using several biclustering methods, the overall topology of the network is reconstructed. Subsequently, the regulator genes for each of the modules is inferred using GENIE3

Trajectory-based differential expression analysis for single-cell sequencing data

Trajectory inference has radically enhanced single-cell RNA-seq research by enabling the study of dynamic changes in gene expression. Downstream of trajectory inference, it is vital to discover genes that are (i) associated with the lineages in the trajectory, or (ii) differentially expressed between lineages, to illuminate the underlying biological processes. Current data analysis procedures, however, either fail to exploit the continuous resolution provided by trajectory inference, or fail to pinpoint the exact types of differential expression. We introduce tradeSeq, a powerful generalized additive model framework based on the negative binomial distribution that allows flexible inference of both within-lineage and between-lineage differential expression. By incorporating observation-level weights, the model additionally allows to account for zero inflation. We evaluate the method on simulated datasets and on real datasets from droplet-based and full-length protocols, and show that it yields biological insights through a clear interpretation of the data. Downstream of trajectory inference for cell lineages based on scRNA-seq data, differential expression analysis yields insight into biological processes. Here, Van den Berge et al. develop tradeSeq, a framework for the inference of within and between-lineage differential expression, based on negative binomial generalized additive models

Essential guidelines for computational method benchmarking

In computational biology and other sciences, researchers are frequently faced with a choice between several computational methods for performing data analyses. Benchmarking studies aim to rigorously compare the performance of different methods using well-characterized benchmark datasets, to determine the strengths of each method or to provide recommendations regarding suitable choices of methods for an analysis. However, benchmarking studies must be carefully designed and implemented to provide accurate, unbiased, and informative results. Here, we summarize key practical guidelines and recommendations for performing high-quality benchmarking analyses, based on our experiences in computational biology.Comment: Minor update

Essential guidelines for computational method benchmarking

In computational biology and other sciences, researchers are frequently faced with a choice between several computational methods for performing data analyses. Benchmarking studies aim to rigorously compare the performance of different methods using well-characterized benchmark datasets, to determine the strengths of each method or to provide recommendations regarding suitable choices of methods for an analysis. However, benchmarking studies must be carefully designed and implemented to provide accurate, unbiased, and informative results. Here, we summarize key practical guidelines and recommendations for performing high-quality benchmarking analyses, based on our experiences in computational biology

arrEYE : a customized platform for high-resolution copy number analysis of coding and noncoding regions of known and candidate retinal dystrophy genes and retinal noncoding RNAs

Purpose: Our goal was to design a customized microarray, arrEYE, for high-resolution copy number variant (CNV) analysis of known and candidate genes for inherited retinal dystrophy (iRD) and retina expressed noncoding RNAs (ncRNAs). Methods: arrEYE contains probes for the full genomic region of 106 known iRD genes, including those implicated in retinitis pigmentosa (RP) (the most frequent iRD), cone rod dystrophies, macular dystrophies, and an additional 60 candidate iRD genes and 196 ncRNAs. Eight CNVs in iRD genes identified by other techniques were used as positive controls. The test cohort consisted of 57 patients with autosomal dominant, X-linked, or simplex RP. Results: In an RP patient, a novel heterozygous deletion of exons 7 and 8 of the HGSNAT gene was identified: c.634-408_820+338delins AGAATATG, p.(G1u2 I 2Glyfs*2). A known variant was found on the second allele: c.1843G>A, p.(A1a615Thr). Furthermore, we expanded the allelic spectrum of USH2A and RCBTB1 with novel CNVs. Conclusion: The arrEYE platform revealed subtle single-exon to larger CNVs in iRD genes that could be characterized at the nucleotide level, facilitated by the high resolution of the platform. We report the first CNV in HGSNAT that, combined with another mutation, leads to RP, further supporting its recently identified role in nonsyndromic iRD

Methyl-CpG-binding domain sequencing reveals a prognostic methylation signature in neuroblastoma

Accurate assessment of neuroblastoma outcome prediction remains challenging. Therefore, this study aims at establishing novel prognostic tumor DNA methylation biomarkers. In total, 396 low- and high-risk primary tumors were analyzed, of which 87 were profiled using methyl-CpG-binding domain (MBD) sequencing for differential methylation analysis between prognostic patient groups. Subsequently, methylation-specific PCR (MSP) assays were developed for 78 top-ranking differentially methylated regions and tested on two independent cohorts of 132 and 177 samples, respectively. Further, a new statistical framework was used to identify a robust set of MSP assays of which the methylation score (i.e. the percentage of methylated assays) allows accurate outcome prediction. Survival analyses were performed on the individual target level, as well as on the combined multimarker signature. As a result of the differential DNA methylation assessment by MBD sequencing, 58 of the 78 MSP assays were designed in regions previously unexplored in neuroblastoma, and 36 are located in non-promoter or non-coding regions. In total, 5 individual MSP assays (located in CCDC177, NXPH1, lnc-MRPL3-2, lnc-TREX1-1 and one on a region from chromosome 8 with no further annotation) predict event-free survival and 4 additional assays (located in SPRED3, TNFAIP2, NPM2 and CYYR1) also predict overall survival. Furthermore, a robust 58-marker methylation signature predicting overall and event-free survival was established. In conclusion, this study encompasses the largest DNA methylation biomarker study in neuroblastoma so far. We identified and independently validated several novel prognostic biomarkers, as well as a prognostic 58-marker methylation signature