Biology subjects

Buffa, F. M.

Publications and source records attributed to Buffa, F. M..

18 recordsLinked to original sources

bioRxiv2026

PHAROS: turning single-cell perturbation models into target-directed drug-combination screens

Combination therapies are central to cancer treatment, but exhaustive screening is impractical. We introduce PHAROS, a framework that turns a pretrained single-cell perturbation model into a target-directed search engine for drug combinations. PHAROS predicts how a cell population changes under a drug, one drug at a time, then chains these predictions together to simulate drug combinations. It scores each simulated outcome against the desired target state and uses a search algorithm to find the most promising combinations, all without retraining the underlying model. Across two independent combinatorial perturbation datasets, PHAROS recovered exact or mechanism-matched two-drug responses in cell lines, both seen and unseen during model training. Its rankings were specific to the requested conversion and were not explained by single-drug effects, additive effects, or shared mechanism of action. In exploratory analyses of patient-derived metastatic HR+/HER2$-$ breast tumors and basal cell carcinoma (BCC), PHAROS prioritized FDA-approved regimens, distinguished combinations by their predicted tumor-versus-immune objective profiles, and nominated pathway-level hypotheses, while explicitly identifying both tumor cohorts as outside the model's supported distribution. PHAROS provides a modular route from pretrained virtual-cell models to inverse, single-cell combination-screening platforms.

bioinformatics↗

bioRxiv2026

Scaling recipes for single-cell RNA sequencing foundation models: when do scaling laws hold?

Deep learning models exhibit empirical scaling laws whereby performance changes predictably with model size, dataset size, and training compute. Although these relationships are well established in domains such as language and image modelling, their applicability to biological data remains unclear. Here, we investigate scaling behaviour in foundation models trained on large collec tions of single-cell transcriptomes. We show that pre-training loss decreases systematically with model capacity and training compute, exhibiting a power law dependence on model size. The strength and regularity of these trends differ between model formulations. We identify and quantify empirical relationships linking the optimal learning rate and depth-to-width ratio to model size and depth or compute. These results demonstrate that scaling principles extend to transcriptomic modelling. More broadly, they provide a quantitative framework for estimating the expected returns from additional resources and selecting suit able hyperparameters and architectures, thereby supporting the development of increasingly capable foundation models for omics data.

bioinformatics↗

bioRxiv2026

pysigscore: gene signatures scoring across bulk and single-cell transcriptomics

SummaryHigh-throughput transcriptomics has made gene signatures central to interpreting gene expression data, with applications in diagnosis, prognosis, and prediction. Quantifying signature activity and assessing its robustness remain challenging because scoring methods primarily rely on various assumptions, and no single approach is universally optimal. Here, we present pysigscore, a Python framework for gene set scoring in bulk and single-cell RNA-seq data. pysigscore integrates 18 built-in scoring methods with a fully customisable scorer, allowing users to define and benchmark new scoring functions. It also provides reliability analyses, including p-value estimation and leave-one-out experiments, to assess the significance of scores and gene-level contributions. We validated pysigscore on the CCLE, TCGA, and PBMC datasets, recovering the expected enrichment in liver, hypoxia, inflammatory, and cell-cycle signatures. Availability and ImplementationSource code is available at https://github.com/bioinformatics-hub/pysigscore. Contact: tommaso.giacomello@phd.unibocconi.it, francesca.buffa@unibocconi.it Supplementary informationSupplementary data are available at Bioinformatics online.

bioinformatics↗

bioRxiv2026

DNA2Graph enables automated identification of non-linear DNA molecules in electron microscopy

Electron microscopy of spread and rotary-shadowed DNA molecules provides a direct readout of DNA structure and remains uniquely informative for studying replication, recombination and repair intermediates. However, quantitative EM analysis is limited by the rarity of biologically informative structures and by the time required for expert operators to inspect very large numbers of molecules. Automated image acquisition and stitching have increased the scale of EM datasets but have shifted the main bottleneck from image collection to image analysis. Standard image segmentation tools and machine-learning approaches fail to faithfully preserve molecular continuity in EM images of DNA molecules contrasted by rotary shadowing. Here we present DNA2Graph, an open-source software for segmentation of DNA molecules from EM images. Rather than treating segmentation as a purely pixel-level task, DNA2Graph represents each molecule as a spatial graph of nodes and edges. It applies dedicated graph-based error-correction algorithms that repair signal interruptions and spurious connections introduced during segmentation, while enforcing the biological priors of continuity and thinness. DNA2Graph classifies molecules as linear or non-linear, thereby converting large imaging datasets into focused lists of candidate structures for operator review. We validated DNA2Graph on two genomic DNA datasets: a structure-poor, non-enriched human sample and a structure-rich yeast sample. DNA2Graph reduced the number of molecules requiring operator review by 45-fold and 7-fold, respectively. In addition, DNA2Graph-assisted review slightly improved the operators structure-detection sensitivity compared to unaided image review. The software also enables automated length measurement of individual DNA molecules and generates machine-readable outputs for downstream quantitative and computational analyses. DNA2Graph does not require training on manually annotated data, can run on a personal computer and can be adapted across experimental preparations through interpretable parameters.

molecular biology↗

bioRxiv2024

Multiomics-based assessment of 2D and 3D human iPSC-cardiomyocyte models of insulin resistance demonstrate metabolic and contractile dysfunction that recapitulates diabetic cardiomyopathy.

In type II diabetes (T2DM), the heart is exposed to hyperglycaemia, hyperlipidaemia, and hyperinsulinaemia, leading to insulin resistance and metabolic dysfunction, culminating in diabetic cardiomyopathy (DbCM). Human-centric models of DbCM are needed to provide mechanistic insights and therapeutic targets in a translationally relevant setting. We hypothesised that culturing human-induced pluripotent stem cell-derived cardiomyocytes (hiPSC-CMs) in an "insulin resistance" (IR) media, and assessing this using a systems biology approach, would offer a comprehensive evaluation of dysregulated pathways, establishing their suitability as a model of DbCM. Culturing hiPSC-CMs in 2D or 3D as engineered heart tissue (EHT) in IR media induced insulin resistance and activated numerous pathways implicated in DbCM, including metabolic remodelling, mitochondrial dysfunction, extracellular matrix remodelling, and endoplasmic reticulum stress. Pathways involved in fatty acid oxidation were upregulated, while those involved in glucose metabolism were downregulated, which was validated using radioisotope flux measurements. Adaptation to hypoxia, a key component of post-ischaemic remodelling, was blunted in the 2D IR hiPSC-CMs. Combining proteomic and transcriptomic analyses in the IR 3D EHT revealed significant enrichment of DbCM pathways, with subnetworks enriched for several metabolic and diabetes-related pathways. Additionally, IR 3D EHT displayed impaired relaxation, mimicking the diastolic dysfunction observed in T2DM patients. In conclusion, culturing hiPSC-CM in 2D or 3D in an IR media activates multiple mechanisms implicated in the development of DbCM, with IR 3D EHT also recapitulating the diastolic dysfunction present in patients with T2DM.

molecular biology↗

bioRxiv2023

SCF cyclin F - EXO1 axis controls cell cycle dependent execution of double strand break repair

Ubiquitination is a crucial post-translational modification required for the proper repair of DNA double-strand breaks (DSBs) induced by ionising radiation (IR). DSBs are mainly repaired through homologous recombination (HR) when template DNA is present and non-homologous end joining (NHEJ) in its absence. Additionally, microhomology-mediated end joining (MMEJ) and single strand annealing (SSA) provide back-up DSBs repair pathways. However, the mechanisms controlling their use remain poorly understood. By employing a high-resolution CRISPR screen of the ubiquitin system after IR, we systematically uncover genes required for cell survival and elucidate a critical role of the E3 ubiquitin ligase SCFcyclin F in cell cycle-dependent DSB repair. We show that SCFcyclin F -mediated EXO1 degradation prevents DNA end resection in mitosis, allowing MMEJ to take place. Moreover, we identify a conserved cyclin F recognition motif, distinct from the one used by other cyclins, with broad implications in cyclin specificity for cell cycle control.

cell biology↗

bioRxiv2023

Molecular analysis of archival diagnostic prostate cancer biopsies identifies genomic similarities in cases with progression post-radiotherapy, and those with de novo metastatic disease.

PurposeIt is important to identify molecular features that improve prostate cancer (PCa) risk stratification before radical treatment with curative intent. Molecular analysis of historical diagnostic formalin-fixed paraffin-embedded (FFPE) prostate biopsies from cohorts with post-radiotherapy (RT) long-term clinical follow-up has been limited. Utilizing parallel sequencing modalities, we performed a proof-of-principle sequencing analysis of historical diagnostic FFPE prostate biopsies. We compared patients with i) stable PCa post-primary or salvage RT (sPCa), ii) progressing PCa post-RT (pPCa), and iii) de novo metastatic PCa (mPCa). Experimental DesignA cohort of 19 patients with diagnostic prostate biopsies (n=6 sPCa, n=5 pPCa, n=8 mPCa) and mean 4 years 10 months follow-up (diagnosed 2009-2016) underwent nucleic acid extraction from demarcated malignancy. Samples underwent 3RNA sequencing (3RNAseq) (n=19), nanoString analysis (n=12) and Illumina 850k methylation (n=8) sequencing. Bioinformatic analysis was performed to coherently identify differentially expressed genes (DEGs) and methylated genomic regions (MGRs). Results18 of 19 samples provided useable 3RNAseq data. Principal Component Analysis (PCA) demonstrated similar expression profiles between pPCa and mPCa cases, versus sPCa. Coherently differentially methylated probes between these groups identified [~]600 differentially MGRs. The top 50 genes with increased expression in pPCa patients were associated with reduced progression-free survival post-RT (p<0.0001) in an external cohort. Conclusions3RNAseq, nanoString and 850K-methylation analyses are each achievable from historical FFPE diagnostic pre-treatment prostate biopsies, unlocking the potential to utilize large cohorts of historic clinical samples. Profiling similarities between individuals with pPCa and mPCa suggests biological similarities and historical radiological staging limitations, which warrant further investigation.

genomics↗

bioRxiv2023

Metabolic Symbiosis between Oxygenated and Hypoxic Tumour Cells: An Agent-based Modelling Study

Deregulated metabolism is one of the hallmarks of cancer. It is well-known that tumour cells tend to metabolize glucose via glycolysis even when oxygen is available and mitochondrial respiration is functional. However, the lower energy efficiency of aerobic glycolysis with respect to mitochondrial respiration makes this behaviour, namely the Warburg effect, counter-intuitive, although it has now been recognized as a source of anabolic precursors. On the other hand, there is evidence that oxygenated tumour cells could be fuelled by exogenous lactate produced from glycolysis, and that glycolytic tumour cells export lactate to the environment. Using a multi-scale approach combining multi-agent modelling, diffusion-reaction and stoichiometric equations, we simulated these cell populations and their environment, and studied the metabolic co-operation between cells exposed to different oxygen concentration and nutrient abundance. The results show that the metabolic cooperation between cells reduces the depletion of environmental glucose, resulting in an overall advantage of using aerobic glycolysis. In addition, the environmental oxygen level was found to be decreased by the symbiosis, promoting a further shift towards anaerobic glycolysis. However, the oxygenated and hypoxic cell populations may gradually reach quasi-equilibrium. A sensitivity analysis based on Latin hypercube sampling and partial rank correlation shows that the symbiotic dynamics depends on properties of the specific cell such as the minimum glucose level needed for glycolysis. Our results suggest that strategies that block glucose transporters may be more effective than those blocking lactate intake transporters to reduce tumour growth by blocking lactate production. AUTHOR SUMMARYMetabolic alteration is one of the hallmarks of cancer and the well-known metabolic alteration of tumour cells is that cells prefer to do glycolysis over mitochondrial respiration even under well-oxygenated and functional mitochondrial conditions. On the other hand, there is evidence that oxygenated tumour cells could be fuelled by exogenous lactate produced from hypoxic glycolytic cells in which it can create a metabolic co-operation between oxygenated and hypoxic cell populations. This metabolic co-operation could allow tumour cells to economically share oxygen and glucose and promote tumour survival. Using a multi-scale approach combining multi-agent modelling, diffusion-reaction and stoichiometric equations, we studied this metabolic co-operation between different populations of cells, exposed to a changing microenvironment. We predict that the tumour environmental glucose depletion is decreased while the oxygen depletion is increased by this metabolic symbiosis, promoting a further shift towards glycolysis. Our results also show that blocking glucose transporters could be more effective than blocking lactate intake transporters, because the former would disrupt both glycolysis and lactate production, drastically reducing tumour growth.

systems biology↗

bioRxiv2023

Intra-prostatic tumour evolution, steps in metastatic spread and histogenomic associations revealed by integration of multi-region whole genome sequencing with histopathological features

Extension of prostate cancer beyond the primary site into the surrounding organs by local invasion or nodal metastasis is associated with poor prognosis. The emergence and evolution of cancer clones at this early stage of expansion and spread has not been studied in detail. We performed whole genome sequencing on 42 prostate cancer samples from the prostate, seminal vesicles and regional lymph nodes of five treatment-naive patients with locally advanced disease who underwent radical prostatectomy. Using cancer cell fractions computed from single nucleotide variants and copy number alterations, we reconstructed the tumour phylogenies, which in turn allowed us to infer key molecular steps in the progression of prostate cancer in these individuals. We mapped the clonal composition of cancer sampled across the prostate in each individual and inferred the routes of spread of cancer cells within the prostate and to seminal vesicles and lymph nodes. Based on these data, we delineated the route of tumour progression and metastasis following the transformation of adenocarcinoma to amphicrine morphology, the molecular events leading to whole genome duplication associated with a single clonal expansion and identified putative driver events associated with local invasion and lymph node metastasis. We also correlated genomic changes associated with differences in morphology and identified putative driver events associated with spread to seminal vesicle invasion and lymph node metastasis. Taken together, these findings have implications for diagnosis and risk stratification, in addition to providing a rationale for further studies to characterise the genetic changes associated with morphological transformation. Our results demonstrate the value of integrating multi-region sequencing with histopathological data to study tumour evolution and identify mechanisms of prostate cancer spread.

cancer biology↗

bioRxiv2022

Ingres: from single-cell RNA-seq data to single-cell probabilistic Boolean networks

MotivationThe current explosion of omics data has provided scientists with an unique opportunity to elucidate the inner workings of biological processes that remained opaque. For this, computational models are essential. Gene regulatory networks (GRN) have long been used as a way to integrate heterogeneous data into a discrete model, and are very useful to generate actionable hypotheses on the mechanisms governing these biological processes. Boolean networks are particularly popular for this kind of discrete models. When working with single-cell RNA-seq datasets a main focus of analysis is the differential expression between subpopulations of cells. Boolean networks are limited in this task, since they cannot easily represent different levels of expression, confined as they are to binary states. We set out to develop an algorithm that can fit Boolean networks with this kind of data, maintaining both the heterogeneity of the data and the simplicity and computational efficiency of these type of networks. ResultsHere we present Ingres (Inferring Probabilistic Boolean Networks of Gene Regulation Using Protein Activity Enrichment Scores) an open-source tool that uses single-cell sequencing data and prior knowledge GRNs to produce a probabilistic Boolean network (PBN) per each cell and/or cluster of cells in the dataset. Ingres allows to better capture the differences between cell phenotypes, using a continuous measure of protein activity while still confined to the simplicity of a GRN. We believe Ingres will be useful to better understand the heterogeneous makeup of cell populations, to gain insight into the specific circuits that drive certain phenotypes, and to use expression and other omics to infer computational cellular models in bulk or single-cell data. Availability and implementationIngres has been implemented as an R package, and it is publicly available at https://github.com/CBigOxf/ingres. It is currently being submitted to the public repository CRAN too. works seamlessly with existing software for single-cell RNA-seq analysis, and for network analysis, modelling and visualization. Contactpedro.victori@oncology.ox.ac.uk; francesca.buffa@unibocconi.it; francesca.buffa@imm.ox.ac.uk Supplementary informationSupplementary data are available online. Software documentation and an explanatory vignette are available on GitHub and as part of the R package.

bioinformatics↗

bioRxiv2022

Reconstructing the functional effect of TP53 somatic mutations on its regulon using causal signalling network modelling

AO_SCPLOWBSTRACTC_SCPLOWThe gene encoding tumor protein p53 (TP53) is the most frequently mutated gene in human cancer. Mutations in both coding and non-coding regions of TP53 can disrupt the regulatory function of the transcription factor, but the functional impact of different somatic mutations on the global TP53 regulon is complex and poorly understood. To address this, we first proceed with a machine learning (ML) approach, and then propose an integrated computational network modelling approach that reconstructs signalling networks using a comprehensive collection of experimental and predicted regulons, and compares their topology. We evaluate both these approaches in a scrutinized pan-cancer analysis of matched genomics and transcriptomics data from 1,457 cell lines (22 cancer types) and 12,531 clinical samples (54 cancer sub-types). Using a ML approach based on penalized generalized linear regression we were able to predict TP53 mutation, but failed to resolve different mutation types. Thus, to infer the impact of different TP53 mutations we compared the topological characteristics of the optimized and reconstructed (upwards of twenty thousand) gene networks and extracted gene signatures for each mutation type using network analysis. We demonstrate that by accounting for TP53 mutation characteristics such as i) mutation type (e.g. missense, nonsense), ii) deleterious consequences of the mutation, or iii) mapping to previously identified hotspots, we can infer a much richer understanding of gene expression regulation, than when simply grouping samples based on their mutation/wild type or gene expression status. Our study highlights a powerful strategy exploiting signalling networks to systematically characterize the functional impact of the full spectrum of somatic mutations. This approach can be applied in general to genetic variation, with clear implications for, but not limited to, the biomedical domain and precision medicine.

systems biology↗

bioRxiv2021

Dicer-to-Argonaute switch controls biogenesis of oncogenic miRNA

miRNAs are post-transcriptional regulators of gene expression, controlling biological processes from development to pathogenesis. We asked whether the reshaped functional miRNA landscape in cancers is driven by altered transcription of its precursors, or altered biogenesis and maturation of miRNAs. Integrated analysis of genomic and transcriptomic data in 9,111 samples across 10 cancer types and healthy tissues revealed a recurrent genomic switch from DICER-dependent to non-canonical Argonaute-mediated, DICER-independent, miRNA biogenesis. Experimental validation in AGO2-amplified clinical samples and cancer cell lines confirmed that canonical miRNAs can undergo maturation in a DICER-independent manner, and that elevated Argonaute levels promote selective maturation of the oncogenic miR-106b/25 cluster as shown by the altered ratio of mature miRNA to immature pri-miRNA levels. The preferential maturation of these oncogenic miRNAs, whose processing bypasses DICER1, promotes cancer progression and predicts poor prognosis. This highlights the evolution of non-canonical AGO2-dependent oncomiR processing as a novel driver pathway in cancer.

genomics↗

bioRxiv2020

Switching On Static Gene Regulatory Networks to Compute Cellular Decisions

MotivationGene networks are complex sets of regulators and interactions that govern cellular processes. Their perturbations can disrupt regular biological functions, translating into a change in cell behaviour and ability to respond to internal and external cues. Computational models of these networks can boost translation of our scientific knowledge into medical applications by predicting how cells will behave in health and disease, or respond to stimuli such as a drug treatment. The development of such models requires effective ways to read, manipulate and analyse the increasing amount of existing, and newly deposited gene network data. ResultsWe developed BioSWITCH, a command-line program using the BioPAX standardised language to "switch on" static regulatory networks so that they can be executed in GINML to predict cellular behaviour. Using a previously published haematopoiesis gene network, we show that BioSWITCH successfully and faithfully automates the network de-coding and re-coding into an executable logical network. BioSWITCH also supports the integration of a BioPAX model into an existing GINML graph. AvailabilitySource code available at https://github.com/CBigOxf/BioSWITCH. Contactclara.pavillet@msdtc.ox.ac.uk; francesca.buffa@oncology.ox.ac.uk

bioinformatics↗

bioRxiv2020

Hypoxia-induced SETX links replication stress with the unfolded protein response

The levels of hypoxia associated with resistance to radiotherapy significantly impact cancer patient prognosis. These levels of hypoxia initiate a unique transcriptional response with the rapid activation of numerous transcription factors in a background of global repression of transcription. Here, we show that the biological response to radiobiological hypoxia includes the induction of the DNA/RNA helicase SETX. In the absence of hypoxia-induced SETX, R-loop levels increase, DNA damage accumulates, and DNA replication rates decrease. SETX plays a key role in protecting cells from DNA damage induced during transcription in hypoxia. Importantly, we show that the mechanism of SETX induction is reliant on the PERK/ATF4 arm of the unfolded protein response. These data not only highlight the unique cellular response to radiobiological hypoxia, which includes both a replication stress dependent DNA damage response and an unfolded protein response but uncover a novel link between these two distinct pathways.

cancer biology↗

bioRxiv2019

Heritable genetic variants in key cancer genes link cancer risk with anthropometric traits

Inherited genetic variants in tumour suppressors and oncogenes can increase the cancer risk, but little is known about their influence on anthropometric traits. Through the integration of inherited and somatic cancer genetic data, we define functional single nucleotide polymorphisms (SNPs) associated with cancer risk and explore potential pleiotropic associations with anthropometic traits in a cohort of 500,000 individuals. We identify three regulatory SNPs for three important cancer genes that associate with both anthropometric traits and cancer risk. We describe a novel association of a SNP in TP53 (rs78378222) with height, lean body mass measures and basal metabolic rate, as well as validating its known associations with brain and non-melanomatous skin cancer susceptibility. Our results clearly demonstrate that heritable variants in key cancer genes can associate with both differential cancer risk and anthropometric traits in the general population, thereby lending support for a role of genetics in linking these human phenotypes.

genetics↗

bioRxiv2018

Endogenous miRNA sponges mediate the generation of oscillatory dynamics for a non-coding RNA network

Oscillations are crucial to the sustenance of living organisms, across a wide variety of biological processes. In eukaryotes, oscillatory dynamics are thought to arise from interactions at the protein and RNA levels; however, the role of non-coding RNA in regulating these dynamics remains understudied. In this work, using a mathematical model, we show how non-coding RNA acting as microRNA (miRNA) sponges in a conserved miRNA - transcription factor feedback motif, can give rise to oscillatory behaviour. Control of these non-coding RNA can dynamically create oscillations or stability, and we show how this behaviour predisposes to oscillations in the stochastic limit. These results, supported by emerging evidence for the role of miRNA sponges in development, point towards key roles of different species of miRNA sponges, such as circular RNA, potentially in the maintenance of yet unexplained oscillatory behaviour. These results help to provide a paradigm for understanding functional differences between the many redundant, but distinct RNA species thought to act as miRNA sponges in nature, such as long non-coding RNA, pseudogenes, competing mRNA, circular RNA, and 3 UTRs.\n\nAuthor summaryWe analyze the effects of a newly discovered species of non-coding RNA, acting as microRNA (miRNA) sponges, on intracellular signalling dynamics. We show that oscillatory behaviour can arise in a time-varying manner in an over-represented transcriptional feedback network. These results point towards novel hypotheses for the roles of different species of miRNA sponges, such as their increasingly understood role in neural development.

systems biology↗

bioRxiv2017

Pan-cancer characterisation of microRNA with hallmarks of cancer reveals role of microRNA-mediated downregulation of tumour suppressor genes

microRNA are key regulators of the human transcriptome across a number of diverse biological processes, such as development, aging, and cancer, where particular miRNA have been identified as tumour suppressive and oncogenic. In this work, we sought to elucidate, in a comprehensive manner, across 15 epithelial cancer types comprising 7,316 clinical samples from the Cancer Genome Atlas, the association of miRNA expression and target regulation with the pheno-typic hallmarks of cancer. Utilising penalized regression techniques to integrate transcriptomic, methylation and mutation data, we find evidence for a complex map of interactions underlying the relationship of miRNA regulation and the hallmarks of cancer. This highlighted high redundancy for the oncomiR-1 cluster of oncogenic miRNAs, in particular hsa-miR-17-5p. In addition, we reveal extensive miRNA regulation of tumour suppressor genes such as PTEN, FAT4, and CDK12, uncovering an alternative mechanism of repression in the absence of mutation, methylation or copy number changes.

genomics↗

bioRxiv2017

sigQC: A procedural approach for standardising the evaluation of gene signatures

With the increase in next generation sequencing generating large amounts of genomic data, gene expression signatures are becoming critically important tools, poised to make a large impact on the diagnosis, management and prognosis for a number of diseases. Increasingly, it is becoming necessary to determine whether a gene expression signature may apply to a dataset, but no standard quality control methodology exists. In this work, we introduce the first protocol, implemented in an R package sigQC, enabling a streamlined methodological and standardised approach for the quality control validation of gene signatures on independent data sets. The emphasis in this work is in showing the critical quality control steps involved in the generation of a clinically and biologically useful, transportable gene signature, including ensuring sufficient expression, variability, and autocorrelation of a signature. We demonstrate the application of the protocol in this work, showing how the outputs created from sigQC may be used for the evaluation of gene signatures on large-scale gene expression data in cancer.

bioinformatics↗