A comprehensive re-analysis of the Golden Spike data: Towards a benchmark for differential expression methods

<p>Abstract</p> <p>Background</p> <p>The Golden Spike data set has been used to validate a number of methods for summarizing Affymetrix data sets, sometimes with seemingly contradictory results. Much less use has been made of this data set to evaluate differential expression methods. It has been suggested that this data set should not be used for method comparison due to a number of inherent flaws.</p> <p>Results</p> <p>We have used this data set in a comparison of methods which is far more extensive than any previous study. We outline six stages in the analysis pipeline where decisions need to be made, and show how the results of these decisions can lead to the apparently contradictory results previously found. We also show that, while flawed, this data set is still a useful tool for method comparison, particularly for identifying combinations of summarization and differential expression methods that are unlikely to perform well on real data sets. We describe a new benchmark, AffyDEComp, that can be used for such a comparison.</p> <p>Conclusion</p> <p>We conclude with recommendations for preferred Affymetrix analysis tools, and for the development of future spike-in data sets.</p

A portrait of the Higgs boson by the CMS experiment ten years after the discovery

A Correction to this paper has been published (18 October 2023) : https://doi.org/10.1038/s41586-023-06164-8.Data availability: Tabulated results are provided in the HEPData record for this analysis. Release and preservation of data used by the CMS Collaboration as the basis for publications is guided by the CMS data preservation, re-use and open acess policy.Code availability: The CMS core software is publicly available on GitHub (https://github.com/cms-sw/cmssw).In July 2012, the ATLAS and CMS collaborations at the CERN Large Hadron Collider announced the observation of a Higgs boson at a mass of around 125 gigaelectronvolts. Ten years later, and with the data corresponding to the production of a 30-times larger number of Higgs bosons, we have learnt much more about the properties of the Higgs boson. The CMS experiment has observed the Higgs boson in numerous fermionic and bosonic decay channels, established its spin–parity quantum numbers, determined its mass and measured its production cross-sections in various modes. Here the CMS Collaboration reports the most up-to-date combination of results on the properties of the Higgs boson, including the most stringent limit on the cross-section for the production of a pair of Higgs bosons, on the basis of data from proton–proton collisions at a centre-of-mass energy of 13 teraelectronvolts. Within the uncertainties, all these observations are compatible with the predictions of the standard model of elementary particle physics. Much evidence points to the fact that the standard model is a low-energy approximation of a more comprehensive theory. Several of the standard model issues originate in the sector of Higgs boson physics. An order of magnitude larger number of Higgs bosons, expected to be examined over the next 15 years, will help deepen our understanding of this crucial sector.BMBWF and FWF (Austria); FNRS and FWO (Belgium); CNPq, CAPES, FAPERJ, FAPERGS, and FAPESP (Brazil); MES and BNSF (Bulgaria); CERN; CAS, MoST, and NSFC (China); MINCIENCIAS (Colombia); MSES and CSF (Croatia); RIF (Cyprus); SENESCYT (Ecuador); MoER, ERC PUT and ERDF (Estonia); Academy of Finland, MEC, and HIP (Finland); CEA and CNRS/IN2P3 (France); BMBF, DFG, and HGF (Germany); GSRI (Greece); NKFIH (Hungary); DAE and DST (India); IPM (Iran); SFI (Ireland); INFN (Italy); MSIP and NRF (Republic of Korea); MES (Latvia); LAS (Lithuania); MOE and UM (Malaysia); BUAP, CINVESTAV, CONACYT, LNS, SEP, and UASLP-FAI (Mexico); MOS (Montenegro); MBIE (New Zealand); PAEC (Pakistan); MES and NSC (Poland); FCT (Portugal); MESTD (Serbia); MCIN/AEI and PCTI (Spain); MOSTR (Sri Lanka); Swiss Funding Agencies (Switzerland); MST (Taipei); MHESI and NSTDA (Thailand); TUBITAK and TENMAK (Turkey); NASU (Ukraine); STFC (United Kingdom); DOE and NSF (USA). Individuals have received support from the Marie-Curie programme and the European Research Council and Horizon 2020 Grant, contract Nos. 675440, 724704, 752730, 758316, 765710, 824093, 884104, and COST Action CA16108 (European Union); the Leventis Foundation; the Alfred P. Sloan Foundation; the Alexander von Humboldt Foundation; the Belgian Federal Science Policy Office; the Fonds pour la Formation à la Recherche dans l’Industrie et dans l’Agriculture (FRIA-Belgium); the Agentschap voor Innovatie door Wetenschap en Technologie (IWT-Belgium); the F.R.S.-FNRS and FWO (Belgium) under the “Excellence of Science – EOS” – be.h project n. 30820817; the Beijing Municipal Science & Technology Commission, No. Z191100007219010; the Ministry of Education, Youth and Sports (MEYS) of the Czech Republic; the Stavros Niarchos Foundation (Greece); the Deutsche Forschungsgemeinschaft (DFG), under Germany’s Excellence Strategy – EXC 2121 “Quantum Universe” – 390833306, and under project number 400140256 - GRK2497; the Hungarian Academy of Sciences, the New National Excellence Program - ÚNKP, the NKFIH research grants K 124845, K 124850, K 128713, K 128786, K 129058, K 131991, K 133046, K 138136, K 143460, K 143477, 2020-2.2.1-ED-2021-00181, and TKP2021-NKTA-64 (Hungary); the Council of Science and Industrial Research, India; the Latvian Council of Science; the Ministry of Education and Science, project no. 2022/WK/14, and the National Science Center, contracts Opus 2021/41/B/ST2/01369 and 2021/43/B/ST2/01552 (Poland); the Fundação para a Ciência e a Tecnologia, grant CEECIND/01334/2018 (Portugal); the National Priorities Research Program by Qatar National Research Fund; MCIN/AEI/10.13039/501100011033, ERDF “a way of making Europe”, and the Programa Estatal de Fomento de la Investigación Científica y Técnica de Excelencia María de Maeztu, grant MDM-2017-0765 and Programa Severo Ochoa del Principado de Asturias (Spain); the Chulalongkorn Academic into Its 2nd Century Project Advancement Project, and the National Science, Research and Innovation Fund via the Program Management Unit for Human Resources & Institutional Development, Research and Innovation, grant B05F650021 (Thailand); the Kavli Foundation; the Nvidia Corporation; the SuperMicro Corporation; the Welch Foundation, contract C-1845; and the Weston Havens Foundation (USA)

The CMS Statistical Analysis and Combination Tool: Combine

Metrics: https://link.springer.com/article/10.1007/s41781-024-00121-4/metricsThis paper describes the Combine software package used for statistical analyses by the CMS Collaboration. The package, originally designed to perform searches for a Higgs boson and the combined analysis of those searches, has evolved to become the statistical analysis tool presently used in the majority of measurements and searches performed by the CMS Collaboration. It is not specific to the CMS experiment, and this paper is intended to serve as a reference for users outside of the CMS Collaboration, providing an outline of the most salient features and capabilities. Readers are provided with the possibility to run Combine and reproduce examples provided in this paper using a publicly available container image. Since the package is constantly evolving to meet the demands of ever-increasing data sets and analysis sophistication, this paper cannot cover all details of Combine. However, the online documentation referenced within this paper provides an up-to-date and complete user guide.CERN (European Organization for Nuclear Research)STFC (United Kingdom)Marie-Curie programme and the European Research Council and Horizon 2020 Grant, contract Nos. 675440, 724704, 752730, 758316, 765710, 824093, 101115353, 101002207, and COST Action CA16108 (European Union); the Leventis Foundation; the Alfred P. Sloan Foundatio

Portable Acceleration of CMS Computing Workflows with Coprocessors as a Service

A preprint version of the article is available at: arXiv:2402.15366v2 [physics.ins-det], https://arxiv.org/abs/2402.15366 . Comments: Replaced with the published version. Added the journal reference and the DOI. All the figures and tables can be found at https://cms-results.web.cern.ch/cms-results/public-results/publications/MLG-23-001 (CMS Public Pages). Report numbers: CMS-MLG-23-001, CERN-EP-2023-303.Data Availability: No datasets were generated or analyzed during the current study.Computing demands for large scientific experiments, such as the CMS experiment at the CERN LHC, will increase dramatically in the next decades. To complement the future performance increases of software running on central processing units (CPUs), explorations of coprocessor usage in data processing hold great potential and interest. Coprocessors are a class of computer processors that supplement CPUs, often improving the execution of certain functions due to architectural design choices. We explore the approach of Services for Optimized Network Inference on Coprocessors (SONIC) and study the deployment of this as-a-service approach in large-scale data processing. In the studies, we take a data processing workflow of the CMS experiment and run the main workflow on CPUs, while offloading several machine learning (ML) inference tasks onto either remote or local coprocessors, specifically graphics processing units (GPUs). With experiments performed at Google Cloud, the Purdue Tier-2 computing center, and combinations of the two, we demonstrate the acceleration of these ML algorithms individually on coprocessors and the corresponding throughput improvement for the entire workflow. This approach can be easily generalized to different types of coprocessors and deployed on local CPUs without decreasing the throughput performance. We emphasize that the SONIC approach enables high coprocessor usage and enables the portability to run workflows on different types of coprocessors.SCOAP3. Open access funding provided by CERN (European Organization for Nuclear Research

Identification of new breast cancer candidate genes associated with stromal invasion.

Abstract Abstract #4036 Background: We recently reported our results of a study identifying a number of DNA copy number changes that differentiated low grade ductal carcinoma in situ (DCIS) lesions with and without associated stromal invasion. We now describe a series of validation experiments that support the role of four genes in early breast cancer progression, most of which were not previously implicated in mammary neoplasia.&amp;#x2028; Materials and Methods: DNA was extracted from microdissected paraffin sections of formalin fixed low grade DCIS lesions with an invasive component (n=25), as well as morphologically similar DCIS lesions without associated invasion within at least 5 years (n=20). The DNA was used for comparative PCR, with GAPDH as the reference gene. Specific primers were designed for fourteen candiate genes. Amplicon sizes ranged from 80 to 90 base pairs. Using log2 ratio cutoffs of +1.0 and -0.5, respectively, the frequency of copy number gains and losses was determined for each candidate gene. Additional validation studies employed genomic DNA samples from an independent cohort of 35 microdissected DCIS lesions with (n=17) and without (n=18) an associated invasive component.&amp;#x2028; Results: Our previous whole genome profiling study using high density BAC arrays revealed that DNA copy number changes on 1q, 3p, 5q, 6p, 9p, 10q, 11q, 12q, 16p, 16q, 18q, 20q, and 22q may occur at different rates in low grade DCIS lesions with and without associated stromal invasion. From the differentially amplified or deleted chromosomal sites, thus far we have selected fourteen candidate genes for validation by comparative PCR, utilizing aliquots of the same DNA samples that had previously been used for array CGH analysis. Four genes were confirmed to have differential copy number changes. NCOR2 (on 12q24.31), DYNLRB2 (on 16q23.2), CELSR1 and UPK3A (both on 22q13.31) were more frequently amplified in DCIS lesions that had no invasive component. Moreover, NCOR2 and DYNLRB2 showed more frequent copy number losses in DCIS lesions that had progressed to invasive carcinoma. For NCOR2, these observations held true in an independent cohort of microdissected low grade DCIS cases. The other ten candidate genes revealed significant frequencies of copy number changes that were not differentially distributed, however. TEME9, CALN1, and CENTA1 were amplified in 30-40% of all cases. IRF8, THAP7, and FIGNL1 were deleted in 20-50% of all cases. FBXO31, CDH13, IKZF1, and MAPK9 revealed both gains and losses in &amp;gt;20% of all cases.&amp;#x2028; Discussion: Our validation experiments have provided additional evidence for the role of several novel genes in early breast cancer progression. Four of the candidate genes we assayed so far may function as invasion suppressor genes. Ten additional genes showed significant copy number changes but did not discriminate DCIS lesions with and without the propensity to invade the stroma. Citation Information: Cancer Res 2009;69(2 Suppl):Abstract nr 4036.</jats:p

Search for the lepton-flavor violating decay of the Higgs boson and additional Higgs bosons in the eμ final state in proton-proton collisions at √s = 13 TeV

A search for the lepton-flavor violating decay of the Higgs boson and potential additional Higgs bosons with a mass in the range 110–160 GeV to an ±⁢∓ pair is presented. The search is performed with a proton-proton collision dataset at a center-of-mass energy of 13 TeV collected by the CMS experiment at the LHC, corresponding to an integrated luminosity of 138  fb−1. No excess is observed for the Higgs boson. The observed (expected) upper limit on the ±⁢∓ branching fraction for it is determined to be 4.4⁢(4.7) ×10−5 at 95% confidence level, the most stringent limit set thus far from direct searches. The largest excess of events over the expected background in the full mass range of the search is observed at an ±⁢∓ invariant mass of approximately 146 GeV with a local (global) significance of 3.8 (2.8) standard deviations