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1. Mixture of Gaussian-distributed Prototypes with Generative Modelling for Interpretable and Trustworthy Image Recognition

Author

Wang, Chong, Chen, Yuanhong, Liu, Fengbei, Liu, Yuyuan, McCarthy, Davis James, Frazer, Helen, and Carneiro, Gustavo

Subjects
Computer Science - Computer Vision and Pattern Recognition
Abstract

Prototypical-part methods, e.g., ProtoPNet, enhance interpretability in image recognition by linking predictions to training prototypes, thereby offering intuitive insights into their decision-making. Existing methods, which rely on a point-based learning of prototypes, typically face two critical issues: 1) the learned prototypes have limited representation power and are not suitable to detect Out-of-Distribution (OoD) inputs, reducing their decision trustworthiness; and 2) the necessary projection of the learned prototypes back into the space of training images causes a drastic degradation in the predictive performance. Furthermore, current prototype learning adopts an aggressive approach that considers only the most active object parts during training, while overlooking sub-salient object regions which still hold crucial classification information. In this paper, we present a new generative paradigm to learn prototype distributions, termed as Mixture of Gaussian-distributed Prototypes (MGProto). The distribution of prototypes from MGProto enables both interpretable image classification and trustworthy recognition of OoD inputs. The optimisation of MGProto naturally projects the learned prototype distributions back into the training image space, thereby addressing the performance degradation caused by prototype projection. Additionally, we develop a novel and effective prototype mining strategy that considers not only the most active but also sub-salient object parts. To promote model compactness, we further propose to prune MGProto by removing prototypes with low importance priors. Experiments on CUB-200-2011, Stanford Cars, Stanford Dogs, and Oxford-IIIT Pets datasets show that MGProto achieves state-of-the-art image recognition and OoD detection performances, while providing encouraging interpretability results.

Published
2023

2. Technical outlier detection via convolutional variational autoencoder for the ADMANI breast mammogram dataset

Author

Li, Hui, Solorzano, Carlos A. Pena, Wei, Susan, and McCarthy, Davis J.

Subjects
Electrical Engineering and Systems Science - Image and Video Processing, Computer Science - Artificial Intelligence, Computer Science - Computer Vision and Pattern Recognition
Abstract

The ADMANI datasets (annotated digital mammograms and associated non-image datasets) from the Transforming Breast Cancer Screening with AI programme (BRAIx) run by BreastScreen Victoria in Australia are multi-centre, large scale, clinically curated, real-world databases. The datasets are expected to aid in the development of clinically relevant Artificial Intelligence (AI) algorithms for breast cancer detection, early diagnosis, and other applications. To ensure high data quality, technical outliers must be removed before any downstream algorithm development. As a first step, we randomly select 30,000 individual mammograms and use Convolutional Variational Autoencoder (CVAE), a deep generative neural network, to detect outliers. CVAE is expected to detect all sorts of outliers, although its detection performance differs among different types of outliers. Traditional image processing techniques such as erosion and pectoral muscle analysis can compensate for the poor performance of CVAE in certain outlier types. We identify seven types of technical outliers: implant, pacemaker, cardiac loop recorder, improper radiography, atypical lesion/calcification, incorrect exposure parameter and improper placement. The outlier recall rate for the test set is 61% if CVAE, erosion and pectoral muscle analysis each select the top 1% images ranked in ascending or descending order according to image outlier score under each detection method, and 83% if each selects the top 5% images. This study offers an overview of technical outliers in the ADMANI dataset and suggests future directions to improve outlier detection effectiveness.

Published
2023

6. BRAIxDet: Learning to Detect Malignant Breast Lesion with Incomplete Annotations

Author

Chen, Yuanhong, Liu, Yuyuan, Wang, Chong, Elliott, Michael, Kwok, Chun Fung, Pena-Solorzano, Carlos, Tian, Yu, Liu, Fengbei, Frazer, Helen, McCarthy, Davis J., and Carneiro, Gustavo

Subjects
Computer Science - Computer Vision and Pattern Recognition, Computer Science - Artificial Intelligence, Computer Science - Machine Learning
Abstract

Methods to detect malignant lesions from screening mammograms are usually trained with fully annotated datasets, where images are labelled with the localisation and classification of cancerous lesions. However, real-world screening mammogram datasets commonly have a subset that is fully annotated and another subset that is weakly annotated with just the global classification (i.e., without lesion localisation). Given the large size of such datasets, researchers usually face a dilemma with the weakly annotated subset: to not use it or to fully annotate it. The first option will reduce detection accuracy because it does not use the whole dataset, and the second option is too expensive given that the annotation needs to be done by expert radiologists. In this paper, we propose a middle-ground solution for the dilemma, which is to formulate the training as a weakly- and semi-supervised learning problem that we refer to as malignant breast lesion detection with incomplete annotations. To address this problem, our new method comprises two stages, namely: 1) pre-training a multi-view mammogram classifier with weak supervision from the whole dataset, and 2) extending the trained classifier to become a multi-view detector that is trained with semi-supervised student-teacher learning, where the training set contains fully and weakly-annotated mammograms. We provide extensive detection results on two real-world screening mammogram datasets containing incomplete annotations, and show that our proposed approach achieves state-of-the-art results in the detection of malignant breast lesions with incomplete annotations., Comment: Under Review

Published
2023

7. Learning Support and Trivial Prototypes for Interpretable Image Classification

Author

Wang, Chong, Liu, Yuyuan, Chen, Yuanhong, Liu, Fengbei, Tian, Yu, McCarthy, Davis J., Frazer, Helen, and Carneiro, Gustavo

Subjects
Computer Science - Computer Vision and Pattern Recognition
Abstract

Prototypical part network (ProtoPNet) methods have been designed to achieve interpretable classification by associating predictions with a set of training prototypes, which we refer to as trivial prototypes because they are trained to lie far from the classification boundary in the feature space. Note that it is possible to make an analogy between ProtoPNet and support vector machine (SVM) given that the classification from both methods relies on computing similarity with a set of training points (i.e., trivial prototypes in ProtoPNet, and support vectors in SVM). However, while trivial prototypes are located far from the classification boundary, support vectors are located close to this boundary, and we argue that this discrepancy with the well-established SVM theory can result in ProtoPNet models with inferior classification accuracy. In this paper, we aim to improve the classification of ProtoPNet with a new method to learn support prototypes that lie near the classification boundary in the feature space, as suggested by the SVM theory. In addition, we target the improvement of classification results with a new model, named ST-ProtoPNet, which exploits our support prototypes and the trivial prototypes to provide more effective classification. Experimental results on CUB-200-2011, Stanford Cars, and Stanford Dogs datasets demonstrate that ST-ProtoPNet achieves state-of-the-art classification accuracy and interpretability results. We also show that the proposed support prototypes tend to be better localised in the object of interest rather than in the background region., Comment: ICCV 2023, Code: https://github.com/cwangrun/ST-ProtoPNet

Published
2023

8. Knowledge Distillation to Ensemble Global and Interpretable Prototype-Based Mammogram Classification Models

Author

Wang, Chong, Chen, Yuanhong, Liu, Yuyuan, Tian, Yu, Liu, Fengbei, McCarthy, Davis J., Elliott, Michael, Frazer, Helen, and Carneiro, Gustavo

Subjects
Computer Science - Computer Vision and Pattern Recognition
Abstract

State-of-the-art (SOTA) deep learning mammogram classifiers, trained with weakly-labelled images, often rely on global models that produce predictions with limited interpretability, which is a key barrier to their successful translation into clinical practice. On the other hand, prototype-based models improve interpretability by associating predictions with training image prototypes, but they are less accurate than global models and their prototypes tend to have poor diversity. We address these two issues with the proposal of BRAIxProtoPNet++, which adds interpretability to a global model by ensembling it with a prototype-based model. BRAIxProtoPNet++ distills the knowledge of the global model when training the prototype-based model with the goal of increasing the classification accuracy of the ensemble. Moreover, we propose an approach to increase prototype diversity by guaranteeing that all prototypes are associated with different training images. Experiments on weakly-labelled private and public datasets show that BRAIxProtoPNet++ has higher classification accuracy than SOTA global and prototype-based models. Using lesion localisation to assess model interpretability, we show BRAIxProtoPNet++ is more effective than other prototype-based models and post-hoc explanation of global models. Finally, we show that the diversity of the prototypes learned by BRAIxProtoPNet++ is superior to SOTA prototype-based approaches., Comment: MICCAI 2022

Published
2022
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9. Multi-view Local Co-occurrence and Global Consistency Learning Improve Mammogram Classification Generalisation

Author

Chen, Yuanhong, Wang, Hu, Wang, Chong, Tian, Yu, Liu, Fengbei, Elliott, Michael, McCarthy, Davis J., Frazer, Helen, and Carneiro, Gustavo

Subjects
Computer Science - Computer Vision and Pattern Recognition
Abstract

When analysing screening mammograms, radiologists can naturally process information across two ipsilateral views of each breast, namely the cranio-caudal (CC) and mediolateral-oblique (MLO) views. These multiple related images provide complementary diagnostic information and can improve the radiologist's classification accuracy. Unfortunately, most existing deep learning systems, trained with globally-labelled images, lack the ability to jointly analyse and integrate global and local information from these multiple views. By ignoring the potentially valuable information present in multiple images of a screening episode, one limits the potential accuracy of these systems. Here, we propose a new multi-view global-local analysis method that mimics the radiologist's reading procedure, based on a global consistency learning and local co-occurrence learning of ipsilateral views in mammograms. Extensive experiments show that our model outperforms competing methods, in terms of classification accuracy and generalisation, on a large-scale private dataset and two publicly available datasets, where models are exclusively trained and tested with global labels., Comment: MICCAI 2022

Published
2022

10. Fancm has dual roles in the limiting of meiotic crossovers and germ cell maintenance in mammals

Author

Tsui, Vanessa, Lyu, Ruqian, Novakovic, Stevan, Stringer, Jessica M., Dunleavy, Jessica E.M., Granger, Elissah, Semple, Tim, Leichter, Anna, Martelotto, Luciano G., Merriner, D. Jo, Liu, Ruijie, McNeill, Lucy, Zerafa, Nadeen, Hoffmann, Eva R., O’Bryan, Moira K., Hutt, Karla, Deans, Andrew J., Heierhorst, Jörg, McCarthy, Davis J., and Crismani, Wayne

Published
2023
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12. Erratum: Sequence data and association statistics from 12,940 type 2 diabetes cases and controls.

Author

Flannick, Jason, Fuchsberger, Christian, Mahajan, Anubha, Teslovich, Tanya M, Agarwala, Vineeta, Gaulton, Kyle J, Caulkins, Lizz, Koesterer, Ryan, Ma, Clement, Moutsianas, Loukas, McCarthy, Davis J, Rivas, Manuel A, Perry, John RB, Sim, Xueling, Blackwell, Thomas W, Robertson, Neil R, Rayner, N William, Cingolani, Pablo, Locke, Adam E, Tajes, Juan Fernandez, Highland, Heather M, Dupuis, Josee, Chines, Peter S, Lindgren, Cecilia M, Hartl, Christopher, Jackson, Anne U, Chen, Han, Huyghe, Jeroen R, van de Bunt, Martijn, Pearson, Richard D, Kumar, Ashish, Müller-Nurasyid, Martina, Grarup, Niels, Stringham, Heather M, Gamazon, Eric R, Lee, Jaehoon, Chen, Yuhui, Scott, Robert A, Below, Jennifer E, Chen, Peng, Huang, Jinyan, Go, Min Jin, Stitzel, Michael L, Pasko, Dorota, Parker, Stephen CJ, Varga, Tibor V, Green, Todd, Beer, Nicola L, Day-Williams, Aaron G, Ferreira, Teresa, Fingerlin, Tasha, Horikoshi, Momoko, Hu, Cheng, Huh, Iksoo, Ikram, Mohammad Kamran, Kim, Bong-Jo, Kim, Yongkang, Kim, Young Jin, Kwon, Min-Seok, Lee, Juyoung, Lee, Selyeong, Lin, Keng-Han, Maxwell, Taylor J, Nagai, Yoshihiko, Wang, Xu, Welch, Ryan P, Yoon, Joon, Zhang, Weihua, Barzilai, Nir, Voight, Benjamin F, Han, Bok-Ghee, Jenkinson, Christopher P, Kuulasmaa, Teemu, Kuusisto, Johanna, Manning, Alisa, Ng, Maggie CY, Palmer, Nicholette D, Balkau, Beverley, Stančáková, Alena, Abboud, Hanna E, Boeing, Heiner, Giedraitis, Vilmantas, Prabhakaran, Dorairaj, Gottesman, Omri, Scott, James, Carey, Jason, Kwan, Phoenix, Grant, George, Smith, Joshua D, Neale, Benjamin M, Purcell, Shaun, Butterworth, Adam S, Howson, Joanna MM, Lee, Heung Man, Lu, Yingchang, Kwak, Soo-Heon, Zhao, Wei, Danesh, John, and Lam, Vincent KL

Abstract

This corrects the article DOI: 10.1038/sdata.2017.179.

Published
2018

13. Sequence data and association statistics from 12,940 type 2 diabetes cases and controls.

Author

Flannick, Jason, Fuchsberger, Christian, Mahajan, Anubha, Teslovich, Tanya M, Agarwala, Vineeta, Gaulton, Kyle J, Caulkins, Lizz, Koesterer, Ryan, Ma, Clement, Moutsianas, Loukas, McCarthy, Davis J, Rivas, Manuel A, Perry, John RB, Sim, Xueling, Blackwell, Thomas W, Robertson, Neil R, Rayner, N William, Cingolani, Pablo, Locke, Adam E, Tajes, Juan Fernandez, Highland, Heather M, Dupuis, Josee, Chines, Peter S, Lindgren, Cecilia M, Hartl, Christopher, Jackson, Anne U, Chen, Han, Huyghe, Jeroen R, van de Bunt, Martijn, Pearson, Richard D, Kumar, Ashish, Müller-Nurasyid, Martina, Grarup, Niels, Stringham, Heather M, Gamazon, Eric R, Lee, Jaehoon, Chen, Yuhui, Scott, Robert A, Below, Jennifer E, Chen, Peng, Huang, Jinyan, Go, Min Jin, Stitzel, Michael L, Pasko, Dorota, Parker, Stephen CJ, Varga, Tibor V, Green, Todd, Beer, Nicola L, Day-Williams, Aaron G, Ferreira, Teresa, Fingerlin, Tasha, Horikoshi, Momoko, Hu, Cheng, Huh, Iksoo, Ikram, Mohammad Kamran, Kim, Bong-Jo, Kim, Yongkang, Kim, Young Jin, Kwon, Min-Seok, Lee, Juyoung, Lee, Selyeong, Lin, Keng-Han, Maxwell, Taylor J, Nagai, Yoshihiko, Wang, Xu, Welch, Ryan P, Yoon, Joon, Zhang, Weihua, Barzilai, Nir, Voight, Benjamin F, Han, Bok-Ghee, Jenkinson, Christopher P, Kuulasmaa, Teemu, Kuusisto, Johanna, Manning, Alisa, Ng, Maggie CY, Palmer, Nicholette D, Balkau, Beverley, Stančáková, Alena, Abboud, Hanna E, Boeing, Heiner, Giedraitis, Vilmantas, Prabhakaran, Dorairaj, Gottesman, Omri, Scott, James, Carey, Jason, Kwan, Phoenix, Grant, George, Smith, Joshua D, Neale, Benjamin M, Purcell, Shaun, Butterworth, Adam S, Howson, Joanna MM, Lee, Heung Man, Lu, Yingchang, Kwak, Soo-Heon, Zhao, Wei, Danesh, John, and Lam, Vincent KL

Subjects
Humans, Diabetes Mellitus, Type 2, European Continental Ancestry Group, Genetic Variation, Diabetes, Human Genome, Genetics, 2.1 Biological and endogenous factors, Metabolic and endocrine
Abstract

To investigate the genetic basis of type 2 diabetes (T2D) to high resolution, the GoT2D and T2D-GENES consortia catalogued variation from whole-genome sequencing of 2,657 European individuals and exome sequencing of 12,940 individuals of multiple ancestries. Over 27M SNPs, indels, and structural variants were identified, including 99% of low-frequency (minor allele frequency [MAF] 0.1-5%) non-coding variants in the whole-genome sequenced individuals and 99.7% of low-frequency coding variants in the whole-exome sequenced individuals. Each variant was tested for association with T2D in the sequenced individuals, and, to increase power, most were tested in larger numbers of individuals (>80% of low-frequency coding variants in ~82 K Europeans via the exome chip, and ~90% of low-frequency non-coding variants in ~44 K Europeans via genotype imputation). The variants, genotypes, and association statistics from these analyses provide the largest reference to date of human genetic information relevant to T2D, for use in activities such as T2D-focused genotype imputation, functional characterization of variants or genes, and other novel analyses to detect associations between sequence variation and T2D.

Published
2017

15. Genomic variation in human health and disease

Author

McCarthy, Davis James and Donnelly, Peter

Subjects
576.5
Abstract

Understanding the structure and function of genomic variation within and be- tween individuals will be crucial for the translation of genomics into improved health and clinical outcomes. This thesis addresses current issues around the study of genomic variation in that context. Variant annotation is a vital step in the analysis of whole-genome and whole- exome sequence data. I compared variant annotations for 80 million variants from a clinically-focused whole-genome sequencing study, obtaining annotations with two different sets of transcripts and two different software tools. I found that choice of transcripts and choice of software both have a large effect on variant annotation. The extent of discrepancy in annotations has implications for all research that relies on variant annotation, especially as we try to use whole-genome sequencing in the clinic. Type 2 diabetes (T2D) is a common, complex genetic disease imposing a large global health burden. Although over 80 genomic loci have been associated with increased risk for T2D, many questions remain about the genomic architecture of the disease. I used 11 million rare, low-frequency and common single-nucleotide variants obtained from whole-genome sequence data from 2,657 individuals with and without T2D to assess the contributions of different classes of genomic variation to T2D susceptibility. Using linear mixed model methods and variance partitioning approaches I characterised contributions from variants in different allele frequency classes. Partitioning variance into different functional classes revealed significant four-fold enrichment (P < 0.01) for variants in enhancer regions identified in pancreatic islet cells and significant depletion for variants without any functional annotation (P < 0.01). Single-cell RNA-sequencing (scRNA-seq) technologies are rapidly gaining traction to interrogate transcriptomic heterogeneity across individual cells. How- ever, raw scRNA-seq data require a large amount of processing to obtain a clean, tidy dataset ready for statistical modeling. I have developed a new, self- contained R software package, SCATER, to fill the niche between raw scRNA-seq data and downstream analysis. The package streamlines the pre-processing, quality control and data normalisation procedures while enabling flexible ways to visualise data and integration with other scRNA-seq analysis tools.

Published
2015

18. Benchmarking single-cell RNA-sequencing protocols for cell atlas projects

Author

Mereu, Elisabetta, Lafzi, Atefeh, Moutinho, Catia, Ziegenhain, Christoph, McCarthy, Davis J., Álvarez-Varela, Adrián, Batlle, Eduard, Sagar, Grün, Dominic, Lau, Julia K., Boutet, Stéphane C., Sanada, Chad, Ooi, Aik, Jones, Robert C., Kaihara, Kelly, Brampton, Chris, Talaga, Yasha, Sasagawa, Yohei, Tanaka, Kaori, Hayashi, Tetsutaro, Braeuning, Caroline, Fischer, Cornelius, Sauer, Sascha, Trefzer, Timo, Conrad, Christian, Adiconis, Xian, Nguyen, Lan T., Regev, Aviv, Levin, Joshua Z., Parekh, Swati, Janjic, Aleksandar, Wange, Lucas E., Bagnoli, Johannes W., Enard, Wolfgang, Gut, Marta, Sandberg, Rickard, Nikaido, Itoshi, Gut, Ivo, Stegle, Oliver, and Heyn, Holger

Published
2020
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19. Count-based differential expression analysis of RNA sequencing data using R and Bioconductor

Author

Anders, Simon, McCarthy, Davis J., Chen, Yunshen, Okoniewski, Michal, Smyth, Gordon K., Huber, Wolfgang, and Robinson, Mark D.

Subjects
Quantitative Biology - Genomics
Abstract

RNA sequencing (RNA-seq) has been rapidly adopted for the profiling of transcriptomes in many areas of biology, including studies into gene regulation, development and disease. Of particular interest is the discovery of differentially expressed genes across different conditions (e.g., tissues, perturbations), while optionally adjusting for other systematic factors that affect the data collection process. There are a number of subtle yet critical aspects of these analyses, such as read counting, appropriate treatment of biological variability, quality control checks and appropriate setup of statistical modeling. Several variations have been presented in the literature, and there is a need for guidance on current best practices. This protocol presents a "state-of-the-art" computational and statistical RNA-seq differential expression analysis workflow largely based on the free open-source R language and Bioconductor software and in particular, two widely-used tools DESeq and edgeR. Hands-on time for typical small experiments (e.g., 4-10 samples) can be <1 hour, with computation time <1 day using a standard desktop PC.

Published
2013
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20. Choice of transcripts and software has a large effect on variant annotation

Author

McCarthy, Davis J, Humburg, Peter, Kanapin, Alexander, Rivas, Manuel A, Gaulton, Kyle, The WGS500 Consortium, Cazier, Jean-Baptiste, and Donnelly, Peter

Subjects
Biological Sciences, Bioinformatics and Computational Biology, Genetics, Human Genome, Clinical Sciences
Abstract

BackgroundVariant annotation is a crucial step in the analysis of genome sequencing data. Functional annotation results can have a strong influence on the ultimate conclusions of disease studies. Incorrect or incomplete annotations can cause researchers both to overlook potentially disease-relevant DNA variants and to dilute interesting variants in a pool of false positives. Researchers are aware of these issues in general, but the extent of the dependency of final results on the choice of transcripts and software used for annotation has not been quantified in detail.MethodsThis paper quantifies the extent of differences in annotation of 80 million variants from a whole-genome sequencing study. We compare results using the RefSeq and Ensembl transcript sets as the basis for variant annotation with the software Annovar, and also compare the results from two annotation software packages, Annovar and VEP (Ensembl's Variant Effect Predictor), when using Ensembl transcripts.ResultsWe found only 44% agreement in annotations for putative loss-of-function variants when using the RefSeq and Ensembl transcript sets as the basis for annotation with Annovar. The rate of matching annotations for loss-of-function and nonsynonymous variants combined was 79% and for all exonic variants it was 83%. When comparing results from Annovar and VEP using Ensembl transcripts, matching annotations were seen for only 65% of loss-of-function variants and 87% of all exonic variants, with splicing variants revealed as the category with the greatest discrepancy. Using these comparisons, we characterised the types of apparent errors made by Annovar and VEP and discuss their impact on the analysis of DNA variants in genome sequencing studies.ConclusionsVariant annotation is not yet a solved problem. Choice of transcript set can have a large effect on the ultimate variant annotations obtained in a whole-genome sequencing study. Choice of annotation software can also have a substantial effect. The annotation step in the analysis of a genome sequencing study must therefore be considered carefully, and a conscious choice made as to which transcript set and software are used for annotation.

Published
2014

22. Eleven grand challenges in single-cell data science

Author

Lähnemann, David, Köster, Johannes, Szczurek, Ewa, McCarthy, Davis J., Hicks, Stephanie C., Robinson, Mark D., Vallejos, Catalina A., Campbell, Kieran R., Beerenwinkel, Niko, Mahfouz, Ahmed, Pinello, Luca, Skums, Pavel, Stamatakis, Alexandros, Attolini, Camille Stephan-Otto, Aparicio, Samuel, Baaijens, Jasmijn, Balvert, Marleen, Barbanson, Buys de, Cappuccio, Antonio, Corleone, Giacomo, Dutilh, Bas E., Florescu, Maria, Guryev, Victor, Holmer, Rens, Jahn, Katharina, Lobo, Thamar Jessurun, Keizer, Emma M., Khatri, Indu, Kielbasa, Szymon M., Korbel, Jan O., Kozlov, Alexey M., Kuo, Tzu-Hao, Lelieveldt, Boudewijn P.F., Mandoiu, Ion I., Marioni, John C., Marschall, Tobias, Mölder, Felix, Niknejad, Amir, Rączkowska, Alicja, Reinders, Marcel, Ridder, Jeroen de, Saliba, Antoine-Emmanuel, Somarakis, Antonios, Stegle, Oliver, Theis, Fabian J., Yang, Huan, Zelikovsky, Alex, McHardy, Alice C., Raphael, Benjamin J., Shah, Sohrab P., and Schönhuth, Alexander

Published
2020
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24. ADMANI: Annotated Digital Mammograms and Associated Non-Image Datasets

Author

Frazer, Helen M. L., primary, Tang, Jennifer S. N., additional, Elliott, Michael S., additional, Kunicki, Katrina M., additional, Hill, Brendan, additional, Karthik, Ravishankar, additional, Kwok, Chun Fung, additional, Peña-Solorzano, Carlos A., additional, Chen, Yuanhong, additional, Wang, Chong, additional, Al-Qershi, Osamah, additional, Fox, Samantha K., additional, Li, Shuai, additional, Makalic, Enes, additional, Nguyen, Tuong L., additional, Schmidt, Daniel F., additional, Basnayake Ralalage, Prabhathi, additional, Lippey, Jocelyn F., additional, Brotchie, Peter, additional, Hopper, John L., additional, Carneiro, Gustavo, additional, and McCarthy, Davis J., additional

Published
2023
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25. Common genetic variation drives molecular heterogeneity in human iPSCs

Author

Kilpinen, Helena, Goncalves, Angela, Leha, Andreas, Afzal, Vackar, Alasoo, Kaur, Ashford, Sofie, Bala, Sendu, Bensaddek, Dalila, Casale, Francesco Paolo, Culley, Oliver J., Danecek, Petr, Faulconbridge, Adam, Harrison, Peter W., Kathuria, Annie, McCarthy, Davis, McCarthy, Shane A., Meleckyte, Ruta, Memari, Yasin, Moens, Nathalie, Soares, Filipa, Mann, Alice, Streeter, Ian, Agu, Chukwuma A., Alderton, Alex, Nelson, Rachel, Harper, Sarah, Patel, Minal, White, Alistair, Patel, Sharad R., Clarke, Laura, Halai, Reena, Kirton, Christopher M., Kolb-Kokocinski, Anja, Beales, Philip, Birney, Ewan, Danovi, Davide, Lamond, Angus I., Ouwehand, Willem H., Vallier, Ludovic, Watt, Fiona M., Durbin, Richard, Stegle, Oliver, and Gaffney, Daniel J.

Subjects
Genetic variation -- Health aspects, Stem cells -- Genetic aspects -- Health aspects, Environmental issues, Science and technology, Zoology and wildlife conservation
Abstract

Technology utilizing human induced pluripotent stem cells (iPS cells) has enormous potential to provide improved cellular models of human disease. However, variable genetic and phenotypic characterization of many existing iPS cell lines limits their potential use for research and therapy. Here we describe the systematic generation, genotyping and phenotyping of 711 iPS cell lines derived from 301 healthy individuals by the Human Induced Pluripotent Stem Cells Initiative. Our study outlines the major sources of genetic and phenotypic variation in iPS cells and establishes their suitability as models of complex human traits and cancer. Through genome-wide profiling we find that 546% of the variation in different iPS cell phenotypes, including differentiation capacity and cellular morphology, arises from differences between individuals. Additionally, we assess the phenotypic consequences of genomic copy-number alterations that are repeatedly observed in iPS cells. In addition, we present a comprehensive map of common regulatory variants affecting the transcriptome of human pluripotent cells., Author(s): Helena Kilpinen [1]; Angela Goncalves [2]; Andreas Leha [2]; Vackar Afzal [3]; Kaur Alasoo [2]; Sofie Ashford [4]; Sendu Bala [2]; Dalila Bensaddek [3]; Francesco Paolo Casale [1]; Oliver [...]

Published
2017
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29. A comparison of marker gene selection methods for single-cell RNA sequencing data

Author

Pullin, Jeffrey and McCarthy, Davis

Subjects
bioinformatics, benchmarking, single cell, humanities
Abstract

Processed datasets used in the manuscript "A comparison of computational methods for selecting marker genes in single-cell RNA-sequencing data". The Endothelial and Mesenchymal datasets are excluded pending their formal publication. See the manuscript and associated code for details of the data processing.

Published
2022
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30. The genetic architecture of type 2 diabetes

Author

Fuchsberger, Christian, Flannick, Jason, Teslovich, Tanya M., Mahajan, Anubha, Agarwala, Vineeta, Gaulton, Kyle J., Ma, Clement, Fontanillas, Pierre, Moutsianas, Loukas, McCarthy, Davis J., Rivas, Manuel A., Perry, John R. B., Sim, Xueling, Blackwell, Thomas W., Robertson, Neil R., Rayner, N. William, Cingolani, Pablo, Locke, Adam E., Tajes, Juan Fernandez, Highland, Heather M., Dupuis, Josee, Chines, Peter S., Lindgren, Cecilia M., Hartl, Christopher, Jackson, Anne U., Chen, Han, Huyghe, Jeroen R., van de Bunt, Martijn, Pearson, Richard D., Kumar, Ashish, Müller-Nurasyid, Martina, Grarup, Niels, Stringham, Heather M., Gamazon, Eric R., Lee, Jaehoon, Chen, Yuhui, Scott, Robert A., Below, Jennifer E., Chen, Peng, Huang, Jinyan, Go, Min Jin, Stitzel, Michael L., Pasko, Dorota, Parker, Stephen C. J., Varga, Tibor V., Green, Todd, Beer, Nicola L., Day-Williams, Aaron G., Ferreira, Teresa, Fingerlin, Tasha, Horikoshi, Momoko, Hu, Cheng, Huh, Iksoo, Ikram, Mohammad Kamran, Kim, Bong-Jo, Kim, Yongkang, Kim, Young Jin, Kwon, Min-Seok, Lee, Juyoung, Lee, Selyeong, Lin, Keng-Han, Maxwell, Taylor J., Nagai, Yoshihiko, Wang, Xu, Welch, Ryan P., Yoon, Joon, Zhang, Weihua, Barzilai, Nir, Voight, Benjamin F., Han, Bok-Ghee, Jenkinson, Christopher P., Kuulasmaa, Teemu, Kuusisto, Johanna, Manning, Alisa, Ng, Maggie C. Y., Palmer, Nicholette D., Balkau, Beverley, Stančáková, Alena, Abboud, Hanna E., Boeing, Heiner, Giedraitis, Vilmantas, Prabhakaran, Dorairaj, Gottesman, Omri, Scott, James, Carey, Jason, Kwan, Phoenix, Grant, George, Smith, Joshua D., Neale, Benjamin M., Purcell, Shaun, Butterworth, Adam S., Howson, Joanna M. M., Lee, Heung Man, Lu, Yingchang, Kwak, Soo-Heon, Zhao, Wei, Danesh, John, Lam, Vincent K. L., Park, Kyong Soo, Saleheen, Danish, So, Wing Yee, Tam, Claudia H. T., Afzal, Uzma, Aguilar, David, Arya, Rector, Aung, Tin, Chan, Edmund, Navarro, Carmen, Cheng, Ching-Yu, Palli, Domenico, Correa, Adolfo, Curran, Joanne E., Rybin, Denis, Farook, Vidya S., Fowler, Sharon P., Freedman, Barry I., Griswold, Michael, Hale, Daniel Esten, Hicks, Pamela J., Khor, Chiea-Chuen, Kumar, Satish, Lehne, Benjamin, Thuillier, Dorothée, Lim, Wei Yen, Liu, Jianjun, van der Schouw, Yvonne T., Loh, Marie, Musani, Solomon K., Puppala, Sobha, Scott, William R., Yengo, Loïc, Tan, Sian-Tsung, Taylor Jr., Herman A., Thameem, Farook, Wilson, Gregory, Wong, Tien Yin, Njølstad, Pål Rasmus, Levy, Jonathan C., Mangino, Massimo, Bonnycastle, Lori L., Schwarzmayr, Thomas, Fadista, João, Surdulescu, Gabriela L., Herder, Christian, Groves, Christopher J., Wieland, Thomas, Bork-Jensen, Jette, Brandslund, Ivan, Christensen, Cramer, Koistinen, Heikki A., Doney, Alex S. F., Kinnunen, Leena, Esko, Tõnu, Farmer, Andrew J., Hakaste, Liisa, Hodgkiss, Dylan, Kravic, Jasmina, Lyssenko, Valeriya, Hollensted, Mette, Jørgensen, Marit E., Jørgensen, Torben, Ladenvall, Claes, Justesen, Johanne Marie, Käräjämäki, Annemari, Kriebel, Jennifer, Rathmann, Wolfgang, Lannfelt, Lars, Lauritzen, Torsten, Narisu, Narisu, Linneberg, Allan, Melander, Olle, Milani, Lili, Neville, Matt, Orho-Melander, Marju, Qi, Lu, Qi, Qibin, Roden, Michael, Rolandsson, Olov, Swift, Amy, Rosengren, Anders H., Stirrups, Kathleen, Wood, Andrew R., Mihailov, Evelin, Blancher, Christine, Carneiro, Mauricio O., Maguire, Jared, Poplin, Ryan, Shakir, Khalid, Fennell, Timothy, DePristo, Mark, Hrabé de Angelis, Martin, Deloukas, Panos, Gjesing, Anette P., Jun, Goo, Nilsson, Peter, Murphy, Jacquelyn, Onofrio, Robert, Thorand, Barbara, Hansen, Torben, Meisinger, Christa, Hu, Frank B., Isomaa, Bo, Karpe, Fredrik, Liang, Liming, Peters, Annette, Huth, Cornelia, O’Rahilly, Stephen P., Palmer, Colin N. A., Pedersen, Oluf, Rauramaa, Rainer, Tuomilehto, Jaakko, Salomaa, Veikko, Watanabe, Richard M., Syvänen, Ann-Christine, Bergman, Richard N., Bharadwaj, Dwaipayan, Bottinger, Erwin P., Cho, Yoon Shin, Chandak, Giriraj R., Chan, Juliana C. N., Chia, Kee Seng, Daly, Mark J., Ebrahim, Shah B., Langenberg, Claudia, Elliott, Paul, Jablonski, Kathleen A., Lehman, Donna M., Jia, Weiping, Ma, Ronald C. W., Pollin, Toni I., Sandhu, Manjinder, Tandon, Nikhil, Froguel, Philippe, Barroso, Inês, Teo, Yik Ying, Zeggini, Eleftheria, Loos, Ruth J. F., Small, Kerrin S., Ried, Janina S., DeFronzo, Ralph A., Grallert, Harald, Glaser, Benjamin, Metspalu, Andres, Wareham, Nicholas J., Walker, Mark, Banks, Eric, Gieger, Christian, Ingelsson, Erik, Im, Hae Kyung, Illig, Thomas, Franks, Paul W., Buck, Gemma, Trakalo, Joseph, Buck, David, Prokopenko, Inga, Mägi, Reedik, Lind, Lars, Farjoun, Yossi, Owen, Katharine R., Gloyn, Anna L., Strauch, Konstantin, Tuomi, Tiinamaija, Kooner, Jaspal Singh, Lee, Jong-Young, Park, Taesung, Donnelly, Peter, Morris, Andrew D., Hattersley, Andrew T., Bowden, Donald W., Collins, Francis S., Atzmon, Gil, Chambers, John C., Spector, Timothy D., Laakso, Markku, Strom, Tim M., Bell, Graeme I., Blangero, John, Duggirala, Ravindranath, Tai, E. Shyong, McVean, Gilean, Hanis, Craig L., Wilson, James G., Seielstad, Mark, Frayling, Timothy M., Meigs, James B., Cox, Nancy J., Sladek, Rob, Lander, Eric S., Gabriel, Stacey, Burtt, Noël P., Mohlke, Karen L., Meitinger, Thomas, Groop, Leif, Abecasis, Goncalo, Florez, Jose C., Scott, Laura J., Morris, Andrew P., Kang, Hyun Min, Boehnke, Michael, Altshuler, David, and McCarthy, Mark I.

Published
2016
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31. Case Report: Hypoglycemia Due to a Novel Activating Glucokinase Variant in an Adult – a Molecular Approach

Author

Koneshamoorthy, Anojian, primary, Seniveratne-Epa, Dilan, additional, Calder, Genevieve, additional, Sawyer, Matthew, additional, Kay, Thomas W. H., additional, Farrell, Stephen, additional, Loudovaris, Thomas, additional, Mariana, Lina, additional, McCarthy, Davis, additional, Lyu, Ruqian, additional, Liu, Xin, additional, Thorn, Peter, additional, Tong, Jason, additional, Chin, Lit Kim, additional, Zacharin, Margaret, additional, Trainer, Alison, additional, Taylor, Shelby, additional, MacIsaac, Richard J., additional, Sachithanandan, Nirupa, additional, Thomas, Helen E., additional, and Krishnamurthy, Balasubramanian, additional

Published
2022
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39. Additional file 3 of Combined single-cell profiling of expression and DNA methylation reveals splicing regulation and heterogeneity

Author

Linker, Stephanie M., Urban, Lara, Clark, Stephen J., Chhatriwala, Mariya, Shradha Amatya, McCarthy, Davis J., Ebersberger, Ingo, Vallier, Ludovic, Reik, Wolf, Stegle, Oliver, and Bonder, Marc Jan

Abstract

Supplementary Results & Figure S1-S11. Supplementary results on the deep modeling of the splicing states in single cells and the Figures S1 to S11. (PDF 2190 kb)

Published
2020
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40. Additional file 1 of Eleven grand challenges in single-cell data science

Author

Lähnemann, David, Köster, Johannes, Szczurek, Ewa, McCarthy, Davis, Hicks, Stephanie, Robinson, Mark, Vallejos, Catalina, Campbell, Kieran, Beerenwinkel, Niko, Mahfouz, Ahmed, Pinello, Luca, Skums, Pavel, Stamatakis, Alexandros, Attolini, Camille, Aparicio, Samuel, Baaijens, Jasmijn, Balvert, Marleen, Buys Barbanson, Cappuccio, Antonio, Corleone, Giacomo, Dutilh, Bas, Florescu, Maria, Guryev, Victor, Holmer, Rens, Jahn, Katharina, Lobo, Thamar, Keizer, Emma, Khatri, Indu, Kielbasa, Szymon, Korbel, Jan, Kozlov, Alexey, Tzu-Hao Kuo, Lelieveldt, Boudewijn, Mandoiu, Ion, Marioni, John, Marschall, Tobias, Mölder, Felix, Niknejad, Amir, Lukasz Raczkowski, Reinders, Marcel, Ridder, Jeroen, Antoine-Emmanuel Saliba, Somarakis, Antonios, Stegle, Oliver, Theis, Fabian, Yang, Huan, Zelikovsky, Alex, McHardy, Alice, Raphael, Benjamin, Shah, Sohrab, and Schönhuth, Alexander

Abstract

Additional file 1 Review history.

Published
2020
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41. Corrigendum: Common genetic variation drives molecular heterogeneity in human iPSCs

Author

Kilpinen, Helena, Goncalves, Angela, Leha, Andreas, Afzal, Vackar, Alasoo, Kaur, Ashford, Sofie, Bala, Sendu, Bensaddek, Dalila, Casale, Francesco Paolo, Culley, Oliver J., Danecek, Petr, Faulconbridge, Adam, Harrison, Peter W., Kathuria, Annie, McCarthy, Davis, McCarthy, Shane A., Meleckyte, Ruta, Memari, Yasin, Moens, Nathalie, Soares, Filipa, Mann, Alice, Streeter, Ian, Agu, Chukwuma A., Alderton, Alex, Nelson, Rachel, Harper, Sarah, Patel, Minal, White, Alistair, Patel, Sharad R., Clarke, Laura, Halai, Reena, Kirton, Christopher M., Kolb-Kokocinski, Anja, Beales, Philip, Birney, Ewan, Danovi, Davide, Lamond, Angus I., Ouwehand, Willem H., Vallier, Ludovic, Watt, Fiona M., Durbin, Richard, Stegle, Oliver, and Gaffney, Daniel J.

Subjects
Environmental issues, Science and technology, Zoology and wildlife conservation
Abstract

Author(s): Helena Kilpinen; Angela Goncalves; Andreas Leha; Vackar Afzal; Kaur Alasoo; Sofie Ashford; Sendu Bala; Dalila Bensaddek; Francesco Paolo Casale; Oliver J. Culley; Petr Danecek; Adam Faulconbridge; Peter W. Harrison; [...]

Published
2017
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42. Eleven grand challenges in single-cell data science

Author

Theoretical Biology and Bioinformatics, Sub Bioinformatics, Lähnemann, David, Köster, Johannes, Szczurek, Ewa, McCarthy, Davis J, Hicks, Stephanie C, Robinson, Mark D, Vallejos, Catalina A, Campbell, Kieran R, Beerenwinkel, Niko, Mahfouz, Ahmed, Pinello, Luca, Skums, Pavel, Stamatakis, Alexandros, Attolini, Camille Stephan-Otto, Aparicio, Samuel, Baaijens, Jasmijn, Balvert, Marleen, Barbanson, Buys de, Cappuccio, Antonio, Corleone, Giacomo, Dutilh, Bas E, Florescu, Maria, Guryev, Victor, Holmer, Rens, Jahn, Katharina, Lobo, Thamar Jessurun, Keizer, Emma M, Khatri, Indu, Kielbasa, Szymon M, Korbel, Jan O, Kozlov, Alexey M, Kuo, Tzu-Hao, Lelieveldt, Boudewijn P F, Mandoiu, Ion I, Marioni, John C, Marschall, Tobias, Mölder, Felix, Niknejad, Amir, Raczkowski, Lukasz, Reinders, Marcel, Ridder, Jeroen de, Saliba, Antoine-Emmanuel, Somarakis, Antonios, Stegle, Oliver, Theis, Fabian J, Yang, Huan, Zelikovsky, Alex, McHardy, Alice C, Raphael, Benjamin J, Shah, Sohrab P, Schönhuth, Alexander, Theoretical Biology and Bioinformatics, Sub Bioinformatics, Lähnemann, David, Köster, Johannes, Szczurek, Ewa, McCarthy, Davis J, Hicks, Stephanie C, Robinson, Mark D, Vallejos, Catalina A, Campbell, Kieran R, Beerenwinkel, Niko, Mahfouz, Ahmed, Pinello, Luca, Skums, Pavel, Stamatakis, Alexandros, Attolini, Camille Stephan-Otto, Aparicio, Samuel, Baaijens, Jasmijn, Balvert, Marleen, Barbanson, Buys de, Cappuccio, Antonio, Corleone, Giacomo, Dutilh, Bas E, Florescu, Maria, Guryev, Victor, Holmer, Rens, Jahn, Katharina, Lobo, Thamar Jessurun, Keizer, Emma M, Khatri, Indu, Kielbasa, Szymon M, Korbel, Jan O, Kozlov, Alexey M, Kuo, Tzu-Hao, Lelieveldt, Boudewijn P F, Mandoiu, Ion I, Marioni, John C, Marschall, Tobias, Mölder, Felix, Niknejad, Amir, Raczkowski, Lukasz, Reinders, Marcel, Ridder, Jeroen de, Saliba, Antoine-Emmanuel, Somarakis, Antonios, Stegle, Oliver, Theis, Fabian J, Yang, Huan, Zelikovsky, Alex, McHardy, Alice C, Raphael, Benjamin J, Shah, Sohrab P, and Schönhuth, Alexander

Published
2020

46. Additional file 3: of Combined single-cell profiling of expression and DNA methylation reveals splicing regulation and heterogeneity

Author

Linker, Stephanie, Urban, Lara, Clark, Stephen, Chhatriwala, Mariya, Shradha Amatya, McCarthy, Davis, Ebersberger, Ingo, Vallier, Ludovic, Reik, Wolf, Stegle, Oliver, and Bonder, Marc

Abstract

Supplementary Results & Figure S1-S11. Supplementary results on the deep modeling of the splicing states in single cells and the Figures S1 to S11. (PDF 2190 kb)

Published
2019
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49. Visualization of Biomedical Data

Author

ODonoghue, Seán I, Baldi, Bernadetta F, Clark, Susan J, Darling, Aaron E, Hogan, James M, Kaur, Sandeep, Maier-Hein, Lena, McCarthy, Davis J, Moore, William J, Stenau, Esther, Swedlow, Jason R, Vuong, Jenny, and Procter, James B

Abstract

The rapid increase in volume and complexity of biomedical data requires changes in research, communication, training, and clinical practices. This includes learning how to effectively integrate automated analysis with high-data-density visualizations that clearly express complex phenomena. In this review, we summarize key principles and resources from data visualization research that address this difficult challenge. We then survey how visualization is being used in a selection of emerging biomedical research areas, including: 3D genomics, single-cell RNA-seq, the protein structure universe, phosphoproteomics, augmented-reality surgery, and metagenomics. While specific areas need highly tailored visualization tools, there are common visualization challenges that can be addressed with general methods and strategies. Unfortunately, poor visualization practices are also common; however, there are good prospects for improvements and innovations that will revolutionize how we see and think about our data. We outline initiatives aimed at fostering these improvements via better tools, peer-to-peer learning, and interdisciplinary collaboration with computer scientists, science communicators, and graphic designers.

Published
2018
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