classifieR a flexible interactive cloud-application for functional annotation of cancer transcriptomes.
classifieR a flexible interactive cloud-application for functional annotation of cancer transcriptomes.
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DOI：
10.1186/s12859-022-04641-x
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发表时间：
2022-03-31
影响因子：
3
通讯作者：
McDade SS
中科院分区：
文献类型：
--
作者：
Quinn GP;Sessler T;Ahmaderaghi B;Lambe S;VanSteenhouse H;Lawler M;Wappett M;Seligmann B;Longley DB;McDade SS
转录预测对于癌症患者的亚型分型、了解基础生物学和为新型治疗策略提供信息越来越重要。例如，结肠直肠癌（CRC）可以分为四个CRC共有分子亚组（CMS）或五个具有预后和预测价值的内在（CRIS）亚型。乳腺癌（BRCA）有五个具有相似价值的PAM 50分子亚组，OncotypeDX测试提供了基于转录组学的临床可行的治疗风险分层。然而，将样本分配给这些亚型和其他转录推断的预测是耗时的，并且需要大量的生物信息学经验。不存在使用来自不同测定/测序平台的数据来使用已建立的基因分类器组（CMS、CRIS、PAM 50、OncotypeDX）提供亚组分类的“通用”方法，也不存在提供额外有用的功能注释如细胞组成、单样品基因组富集分析或预测转录因子活性的方法。为了解决这个瓶颈，我们开发了classifieR，一个易于使用的基于R-Shiny的Web应用程序，支持来自不同平台的癌症患者样本的转录谱的灵活快速单样本注释。我们证明了实用程序的“classifieR”框架的应用程序集中在从结直肠（classifieRc）和乳腺（classifieRb）的转录谱的分析。用疾病相关转录亚组（classifieRc中的CMS/CRIS亚型和classifieRb中的PAM 50/推断的OncotypeDX）注释样品，使用MCP-计数器和xCell估计细胞组成，单样品基因集富集分析（ssGSEA）和用判别调节子表达分析（DoRothEA）预测转录因子活性。classifieR提供了一个框架，使实验室能够在无法访问专用生物信息的情况下获得有关其样本分子组成的信息，从而深入了解患者预后，可药用性，并作为分析和发现的工具。在https：//generatr.qub.ac.uk上注册一个帐户后，应用程序将在https://generatr.qub.ac.uk/app/classifieRc和https://generatr.qub.ac.uk/app/classifieRb在线版本包含补充材料，可通过10.1186/s12859-022-04641-x获得。
Transcriptionally informed predictions are increasingly important for sub-typing cancer patients, understanding underlying biology and to inform novel treatment strategies. For instance, colorectal cancers (CRCs) can be classified into four CRC consensus molecular subgroups (CMS) or five intrinsic (CRIS) sub-types that have prognostic and predictive value. Breast cancer (BRCA) has five PAM50 molecular subgroups with similar value, and the OncotypeDX test provides transcriptomic based clinically actionable treatment-risk stratification. However, assigning samples to these subtypes and other transcriptionally inferred predictions is time consuming and requires significant bioinformatics experience. There is no "universal" method of using data from diverse assay/sequencing platforms to provide subgroup classification using the established classifier sets of genes (CMS, CRIS, PAM50, OncotypeDX), nor one which in provides additional useful functional annotations such as cellular composition, single-sample Gene Set Enrichment Analysis, or prediction of transcription factor activity. To address this bottleneck, we developed classifieR, an easy-to-use R-Shiny based web application that supports flexible rapid single sample annotation of transcriptional profiles derived from cancer patient samples form diverse platforms. We demonstrate the utility of the " classifieR" framework to applications focused on the analysis of transcriptional profiles from colorectal (classifieRc) and breast (classifieRb). Samples are annotated with disease relevant transcriptional subgroups (CMS/CRIS sub-types in classifieRc and PAM50/inferred OncotypeDX in classifieRb), estimation of cellular composition using MCP-counter and xCell, single-sample Gene Set Enrichment Analysis (ssGSEA) and transcription factor activity predictions with Discriminant Regulon Expression Analysis (DoRothEA). classifieR provides a framework which enables labs without access to a dedicated bioinformation can get information on the molecular makeup of their samples, providing an insight into patient prognosis, druggability and also as a tool for analysis and discovery. Applications are hosted online at https://generatr.qub.ac.uk/app/classifieRc and https://generatr.qub.ac.uk/app/classifieRb after signing up for an account on https://generatr.qub.ac.uk. The online version contains supplementary material available at 10.1186/s12859-022-04641-x.
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影响因子：
4.6
作者：
Allen WL;Dunne PD;McDade S;Scanlon E;Loughrey M;Coleman H;McCann C;McLaughlin K;Nemeth Z;Syed N;Jithesh P;Arthur K;Wilson R;Coyle V;McArt D;Murray GI;Samuel L;Nuciforo P;Jimenez J;Argiles G;Dienstmann R;Tabernero J;Messerini L;Nobili S;Mini E;Sheahan K;Ryan E;Johnston PG;Van Schaeybroeck S;Lawler M;Longley DB
通讯作者：
Longley DB
影响因子：
64.8
作者：
Ghandi, Mahmoud;Huang, Franklin W.;Sellers, William R.
通讯作者：
Sellers, William R.
影响因子：
64.8
作者：
Curtis, Christina;Shah, Sohrab P.;Chin, Suet-Feung;Turashvili, Gulisa;Rueda, Oscar M.;Dunning, Mark J.;Speed, Doug;Lynch, Andy G.;Samarajiwa, Shamith;Yuan, Yinyin;Graef, Stefan;Ha, Gavin;Haffari, Gholamreza;Bashashati, Ali;Russell, Roslin;McKinney, Steven;Langerod, Anita;Green, Andrew;Provenzano, Elena;Wishart, Gordon;Pinder, Sarah;Watson, Peter;Markowetz, Florian;Murphy, Leigh;Ellis, Ian;Purushotham, Arnie;Borresen-Dale, Anne-Lise;Brenton, James D.;Tavare, Simon;Caldas, Carlos;Aparicio, Samuel
通讯作者：
Aparicio, Samuel
影响因子：
16.6
作者：
Isella C;Brundu F;Bellomo SE;Galimi F;Zanella E;Porporato R;Petti C;Fiori A;Orzan F;Senetta R;Boccaccio C;Ficarra E;Marchionni L;Trusolino L;Medico E;Bertotti A
通讯作者：
Bertotti A
影响因子：
5.8
作者：
Gendoo, Deena M. A.;Ratanasirigulchai, Natchar;Haibe-Kains, Benjamin
通讯作者：
Haibe-Kains, Benjamin