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1. 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
FOS: Computer and information sciences, Computer Vision and Pattern Recognition (cs.CV), 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
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3. Single-cell RNA-sequencing of differentiating iPS cells reveals dynamic genetic effects on gene expression

Author

Cuomo, Anna S. E., Seaton, Daniel D., McCarthy, Davis J., Martinez, Iker, Bonder, Marc Jan, Garcia-Bernardo, Jose, Amatya, Shradha, Madrigal, Pedro, Isaacson, Abigail, Buettner, Florian, Knights, Andrew, Natarajan, Kedar Nath, Vallier, Ludovic, Marioni, John C., Chhatriwala, Mariya, Stegle, Oliver, Agu, Chukwuma A., Alderton, Alex, Danecek, Petr, Denton, Rachel, Durbin, Richard, Gaffney, Daniel J., Goncalves, Angela, Halai, Reena, Harper, Sarah, Kirton, Christopher M., Kolb-Kokocinski, Anja, Leha, Andreas, McCarthy, Shane A., Memari, Yasin, Patel, Minal, Birney, Ewan, Casale, Francesco Paolo, Clarke, Laura, Harrison, Peter W., Kilpinen, Helena, Streeter, Ian, Denovi, Davide, Meleckyte, Ruta, Moens, Natalie, Watt, Fiona M., Ouwehand, Willem H., Lamond, Angus I., Bensaddek, Dalila, Beales, Philip, Seaton, Daniel D. [0000-0002-5222-3893], McCarthy, Davis J. [0000-0002-2218-6833], Madrigal, Pedro [0000-0003-1959-8199], Knights, Andrew [0000-0003-2107-4175], Natarajan, Kedar Nath [0000-0002-9264-1280], Vallier, Ludovic [0000-0002-3848-2602], Marioni, John C. [0000-0001-9092-0852], and Apollo - University of Cambridge Repository

Subjects
13/31, 45/91, 631/532/2064/2158, 631/208/729/743, 45/43, article, 631/208/191, 631/208/205, 45/100, 45/15, 38/91
Abstract

Recent developments in stem cell biology have enabled the study of cell fate decisions in early human development that are impossible to study in vivo. However, understanding how development varies across individuals and, in particular, the influence of common genetic variants during this process has not been characterised. Here, we exploit human iPS cell lines from 125 donors, a pooled experimental design, and single-cell RNA-sequencing to study population variation of endoderm differentiation. We identify molecular markers that are predictive of differentiation efficiency of individual lines, and utilise heterogeneity in the genetic background across individuals to map hundreds of expression quantitative trait loci that influence expression dynamically during differentiation and across cellular contexts.

Published
2020

10. 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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11. 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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12. 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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14. 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

15. 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
European Continental Ancestry Group, Diabetes, Human Genome, Diabetes Mellitus, Genetics, Humans, Genetic Variation, 2.1 Biological and endogenous factors, Aetiology, White People, Type 2, 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

18. Additional file 6: Figure S3. of Classification of low quality cells from single-cell RNA-seq data

Author

Ilicic, Tomislav, Kim, Jong, Kolodziejczyk, Aleksandra, Bagger, Frederik, McCarthy, Davis, Marioni, John, and Teichmann, Sarah

Subjects
ComputingMethodologies_PATTERNRECOGNITION
Abstract

Post-QC outliers and SVM performance evaluation. (A) Visualization of low and high quality cells after outlier detection with traditional and with our PCA feature-based methods (B) Schematic of nested cross-validation. The training set was split twice into 10 folds. The inner folds were important to estimate optimal hyperparameters, whereas the outer folds served to measure accuracy. Optimal hyperparameters were saved for later use. (C) Sensitivity and specificity of feature-based PCA and SVM using TPM values. (PDF 558Â kb)

Published
2016
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19. Additional file 6: Figure S3. of Classification of low quality cells from single-cell RNA-seq data

Author

Ilicic, Tomislav, Kim, Jong, Kolodziejczyk, Aleksandra, Bagger, Frederik, McCarthy, Davis, Marioni, John, and Teichmann, Sarah

Subjects
ComputingMethodologies_PATTERNRECOGNITION
Abstract

Post-QC outliers and SVM performance evaluation. (A) Visualization of low and high quality cells after outlier detection with traditional and with our PCA feature-based methods (B) Schematic of nested cross-validation. The training set was split twice into 10 folds. The inner folds were important to estimate optimal hyperparameters, whereas the outer folds served to measure accuracy. Optimal hyperparameters were saved for later use. (C) Sensitivity and specificity of feature-based PCA and SVM using TPM values. (PDF 558Â kb)

Published
2016
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20. Additional file 5: Figure S2. of Classification of low quality cells from single-cell RNA-seq data

Author

Ilicic, Tomislav, Kim, Jong, Kolodziejczyk, Aleksandra, Bagger, Frederik, McCarthy, Davis, Marioni, John, and Teichmann, Sarah

Abstract

Additional technical features and subsets of data. Boxplots comparing (A) ratio of of duplicated reads/exonic (B) ratio spike-in/exonic expression between high quality and multiple, broken, empty cells. (C) PCA of features using only 25Â % of data shows identical results compared to using all data. (D) Comparison of two microscopic images of a single C1 capturing site containing one intact and one deceptive cell, respectively. (PDF 1026Â kb)

Published
2016
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21. Additional file 7: Figure S4. of Classification of low quality cells from single-cell RNA-seq data

Author

Ilicic, Tomislav, Kim, Jong, Kolodziejczyk, Aleksandra, Bagger, Frederik, McCarthy, Davis, Marioni, John, and Teichmann, Sarah

Abstract

Datasets distant from mES training data. (A) Comparing log normalized UMI counts (y-axis) and log normalized read counts (x-axis) for each gene in 960 mESCs. (B) PCA of first two principal components of all features. Low quality cells separate from high quality cells. (C, D) PCA plot of features of two published human cancer cell datasets [28, 53]. Boxplots on the left and bottom show the top three features separating low from high quality cells for PC1 and PC2, respectively. They align with our previous findings that the mtDNA and ERCC to mapped reads ratios are upregulated in low quality cells. (E) Feature-based PCA combining mouse ES training set and two published human cancer datasets. â Cytoplasmâ separates not only the human from the mouse but also the two different cancer samples from each other, meaning that the features trained on mouse cells are not directly transferrable to human cancer cells. (PDF 591Â kb)

Published
2016
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22. Additional file 1: Figure S1. of Classification of low quality cells from single-cell RNA-seq data

Author

Ilicic, Tomislav, Kim, Jong, Kolodziejczyk, Aleksandra, Bagger, Frederik, McCarthy, Davis, Marioni, John, and Teichmann, Sarah

Subjects
endocrine system, embryonic structures, hormones, hormone substitutes, and hormone antagonists, reproductive and urinary physiology
Abstract

Overview of single cell RNA sequencing datasets. (A) Total number of cells per dataset. (B) Number of high quality and low quality cells per dataset. (C) Proportion of each type of low quality cells (broken, empty, multiple). (D) Number of cells for 2i/LIF, alternative 2i/LIF, and serum/LIF condition for the training dataset (960 mESCs). (PDF 441Â kb)

Published
2016
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23. Additional file 5: Figure S2. of Classification of low quality cells from single-cell RNA-seq data

Author

Ilicic, Tomislav, Kim, Jong, Kolodziejczyk, Aleksandra, Bagger, Frederik, McCarthy, Davis, Marioni, John, and Teichmann, Sarah

Abstract

Additional technical features and subsets of data. Boxplots comparing (A) ratio of of duplicated reads/exonic (B) ratio spike-in/exonic expression between high quality and multiple, broken, empty cells. (C) PCA of features using only 25Â % of data shows identical results compared to using all data. (D) Comparison of two microscopic images of a single C1 capturing site containing one intact and one deceptive cell, respectively. (PDF 1026Â kb)

Published
2016
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24. Additional file 7: Figure S4. of Classification of low quality cells from single-cell RNA-seq data

Author

Ilicic, Tomislav, Kim, Jong, Kolodziejczyk, Aleksandra, Bagger, Frederik, McCarthy, Davis, Marioni, John, and Teichmann, Sarah

Abstract

Datasets distant from mES training data. (A) Comparing log normalized UMI counts (y-axis) and log normalized read counts (x-axis) for each gene in 960 mESCs. (B) PCA of first two principal components of all features. Low quality cells separate from high quality cells. (C, D) PCA plot of features of two published human cancer cell datasets [28, 53]. Boxplots on the left and bottom show the top three features separating low from high quality cells for PC1 and PC2, respectively. They align with our previous findings that the mtDNA and ERCC to mapped reads ratios are upregulated in low quality cells. (E) Feature-based PCA combining mouse ES training set and two published human cancer datasets. â Cytoplasmâ separates not only the human from the mouse but also the two different cancer samples from each other, meaning that the features trained on mouse cells are not directly transferrable to human cancer cells. (PDF 591Â kb)

Published
2016
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26. 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
Quality Control, DNA Copy Number Variations, Genotype, Induced Pluripotent Stem Cells, Quantitative Trait Loci, Genetic Variation, Cellular Reprogramming, 3. Good health, Phenotype, Gene Expression Regulation, Organ Specificity, Humans, Transcriptome, Cells, Cultured
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 5-46% 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.

27. Method to Synchronize Cell Cycle of Human Pluripotent Stem Cells without Affecting Their Fundamental Characteristics

Author

Yiangou, Loukia, Grandy, Rodrigo A, Morell, Carola M, Tomaz, Rute A, Osnato, Anna, Kadiwala, Juned, Muraro, Daniele, Garcia-Bernardo, Jose, Nakanoh, Shota, Bernard, William G, Ortmann, Daniel, McCarthy, Davis J, Simonic, Ingrid, Sinha, Sanjay, and Vallier, Ludovic

Subjects
single-cell RNA-seq, Nocodazole, cell cycle synchronization, Cell Cycle, Endoderm, Human Embryonic Stem Cells, Karyotype, Cell Differentiation, Tubulin Modulators, 3. Good health, Cell Line, ectoderm, mesoderm, Humans, Cellular Reprogramming Techniques, Transcriptome, hPSCs
Abstract

Cell cycle progression and cell fate decisions are closely linked in human pluripotent stem cells (hPSCs). However, the study of these interplays at the molecular level remains challenging due to the lack of efficient methods allowing cell cycle synchronization of large quantities of cells. Here, we screened inhibitors of cell cycle progression and identified nocodazole as the most efficient small molecule to synchronize hPSCs in the G2/M phase. Following nocodazole treatment, hPSCs remain pluripotent, retain a normal karyotype and can successfully differentiate into the three germ layers and functional cell types. Moreover, genome-wide transcriptomic analyses on single cells synchronized for their cell cycle and differentiated toward the endoderm lineage validated our findings and showed that nocodazole treatment has no effect on gene expression during the differentiation process. Thus, our synchronization method provides a robust approach to study cell cycle mechanisms in hPSCs.

28. 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, 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

Subjects
13. Climate action
Abstract

The recent boom in microfluidics and combinatorial indexing strategies, combined with low sequencing costs, has empowered single-cell sequencing technology. Thousands-or even millions-of cells analyzed in a single experiment amount to a data revolution in single-cell biology and pose unique data science problems. Here, we outline eleven challenges that will be central to bringing this emerging field of single-cell data science forward. For each challenge, we highlight motivating research questions, review prior work, and formulate open problems. This compendium is for established researchers, newcomers, and students alike, highlighting interesting and rewarding problems for the coming years.

30. 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, Lam, Vincent KL, Park, Kyong Soo, Saleheen, Danish, So, Wing Yee, Tam, Claudia HT, Afzal, Uzma, Aguilar, David, Arya, Rector, Aung, Tin, Chan, Edmund, Navarro, Carmen, Cheng, Ching-Yu, Palli, Domenico, Correa, Adolfo, Curran, Joanne E, Rybin, Dennis, 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, Loh, Marie, Musani, Solomon K, Puppala, Sobha, Scott, William R, Yengo, Loïc, Tan, Sian-Tsung, Taylor, 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 SF, Kinnunen, Leena, Esko, Tõnu, Farmer, Andrew J, Hakaste, Liisa, Hodgkiss, Dylan, Kravic, Jasmina, Lyssenko, Valeri, 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, De Angelis, Martin Hrabé, 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 NA, 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 Cn, Chia, Kee Seng, Daly, Mark J, Ebrahim, Shah B, Langenberg, Claudia, Elliott, Paul, Jablonski, Kathleen A, Lehman, Donna M, Jia, Weiping, Ma, Ronald CW, Pollin, Toni I, Sandhu, Manjinder, Tandon, Nikhil, Froguel, Philippe, Barroso, Inês, Teo, Yik Ying, Zeggini, Eleftheria, Loos, Ruth JF, 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, Mohlke, Karen L, Meitinger, Thomas, Groop, Leif, Abecasis, Goncalo, Scott, Laura J, Morris, Andrew P, Kang, Hyun Min, Altshuler, David, Burtt, Noël P, Florez, Jose C, Boehnke, Michael, and McCarthy, Mark I

Subjects
Diabetes Mellitus, Type 2, Genetic Variation, Humans, White People, 3. Good health
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.

33. Single-cell RNA-sequencing of differentiating iPS cells reveals dynamic genetic effects on gene expression

Author

Marc Jan Bonder, Oliver Stegle, Andrew J Knights, Davis J. McCarthy, Abigail Isaacson, Kedar Nath Natarajan, John C. Marioni, Daniel D Seaton, Jose Garcia-Bernardo, Mariya Chhatriwala, Shradha Amatya, Anna S E Cuomo, Pedro Madrigal, Iker Martinez, Ludovic Vallier, Florian Buettner, Seaton, Daniel D [0000-0002-5222-3893], McCarthy, Davis J [0000-0002-2218-6833], Madrigal, Pedro [0000-0003-1959-8199], Knights, Andrew [0000-0003-2107-4175], Natarajan, Kedar Nath [0000-0002-9264-1280], Vallier, Ludovic [0000-0002-3848-2602], Marioni, John C [0000-0001-9092-0852], Stegle, Oliver [0000-0002-8818-7193], and Apollo - University of Cambridge Repository

Subjects
0301 basic medicine, Male, Science, Induced Pluripotent Stem Cells, Quantitative Trait Loci, Cell, General Physics and Astronomy, Gene Expression, Computational biology, Quantitative trait locus, Cell fate determination, Biology, General Biochemistry, Genetics and Molecular Biology, Article, Cell Line, Genetic Heterogeneity, 03 medical and health sciences, 0302 clinical medicine, Single-cell analysis, Gene expression, medicine, Humans, lcsh:Science, Induced pluripotent stem cell, Genetic Association Studies, Genetic association study, 030304 developmental biology, 0303 health sciences, Multidisciplinary, Genetic heterogeneity, Gene Expression Profiling, Endoderm, RNA, Functional genomics, Cell Differentiation, General Chemistry, Cell biology, Gene expression profiling, 030104 developmental biology, medicine.anatomical_structure, Expression quantitative trait loci, lcsh:Q, Female, Gene-Environment Interaction, Single-Cell Analysis, 030217 neurology & neurosurgery, Definitive endoderm
Abstract

Recent developments in stem cell biology have enabled the study of cell fate decisions in early human development that are impossible to study in vivo. However, understanding how development varies across individuals and, in particular, the influence of common genetic variants during this process has not been characterised. Here, we exploit human iPS cell lines from 125 donors, a pooled experimental design, and single-cell RNA-sequencing to study population variation of endoderm differentiation. We identify molecular markers that are predictive of differentiation efficiency of individual lines, and utilise heterogeneity in the genetic background across individuals to map hundreds of expression quantitative trait loci that influence expression dynamically during differentiation and across cellular contexts., Studying the genetic effects on early stages of human development is challenging due to a scarcity of biological material. Here, the authors utilise induced pluripotent stem cells from 125 donors to track gene expression changes and expression quantitative trait loci at single cell resolution during in vitro endoderm differentiation.

Published
2020
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34. Eleven grand challenges in single-cell data science

Author

Catalina A. Vallejos, Samuel Aparicio, Emma M. Keizer, Ion I. Mandoiu, Luca Pinello, Huan Yang, Maria Florescu, Camille Stephan Otto Attolini, Marcel J. T. Reinders, Ewa Szczurek, Ahmed Mahfouz, Alexandros Stamatakis, Jasmijn A. Baaijens, Amir Niknejad, Rens Holmer, Tzu Hao Kuo, Benjamin J. Raphael, Felix Mölder, Alexey M. Kozlov, Giacomo Corleone, Alexander Zelikovsky, Bas E. Dutilh, Alexander Schönhuth, Antoine-Emmanuel Saliba, Davis J. McCarthy, Sohrab P. Shah, Mark D. Robinson, Johannes Köster, David Lähnemann, Pavel Skums, Boudewijn P. F. Lelieveldt, Antonio Cappuccio, Jeroen de Ridder, Niko Beerenwinkel, Antonios Somarakis, Stephanie C. Hicks, Lukasz Raczkowski, Kieran R Campbell, John C. Marioni, Thamar Jessurun Lobo, Marleen Balvert, Oliver Stegle, Katharina Jahn, Indu Khatri, Jan O. Korbel, Tobias Marschall, Alice C. McHardy, Szymon M. Kielbasa, Fabian J. Theis, Victor Guryev, Buys de Barbanson, Robinson, Mark D [0000-0002-3048-5518], Apollo - University of Cambridge Repository, McCarthy, Davis J [0000-0002-2218-6833], Vallejos, Catalina A [0000-0003-3638-1960], Mölder, Felix [0000-0002-3976-9701], Stegle, Oliver [0000-0002-8818-7193], Zelikovsky, Alex [0000-0003-4424-4691], Theoretical Biology and Bioinformatics, Sub Bioinformatics, Robinson, Mark D. [0000-0002-3048-5518], BRICS, Braunschweiger Zentrum für Systembiologie, Rebenring 56,38106 Braunschweig, Germany., and HIRI, Helmholtz-Institut für RNA-basierte Infektionsforschung, Josef-Shneider Strasse 2, 97080 Würzburg, Germany.

Subjects
RNA-SEQUENCING DATA, CHROMATIN ACCESSIBILITY, lcsh:QH426-470, Bioinformatics, Maximum likelihood, Medizin, Review, Biology, Boom, Wiskundige en Statistische Methoden - Biometris, Field (computer science), 03 medical and health sciences, Spatial reconstruction, 0302 clinical medicine, ANALYSIS REVEALS, Bioinformatica, Tumours of the digestive tract Radboud Institute for Molecular Life Sciences [Radboudumc 14], Life Science, Animals, Humans, RNA-Seq, MAXIMUM-LIKELIHOOD, Mathematical and Statistical Methods - Biometris, lcsh:QH301-705.5, 030304 developmental biology, Grand Challenges, GENE-EXPRESSION, 0303 health sciences, Extramural, DATA processing & computer science, Data Science, WHOLE-GENOME AMPLIFICATION, Genomics, Data science, Compendium, lcsh:Genetics, lcsh:Biology (General), WIDE EXPRESSION, Research questions, TREE INFERENCE, ddc:004, Single-Cell Analysis, SPATIAL RECONSTRUCTION, TUMOR MICROENVIRONMENT, 030217 neurology & neurosurgery
Abstract

Contains fulltext : 218166.pdf (Publisher’s version ) (Open Access) The recent boom in microfluidics and combinatorial indexing strategies, combined with low sequencing costs, has empowered single-cell sequencing technology. Thousands-or even millions-of cells analyzed in a single experiment amount to a data revolution in single-cell biology and pose unique data science problems. Here, we outline eleven challenges that will be central to bringing this emerging field of single-cell data science forward. For each challenge, we highlight motivating research questions, review prior work, and formulate open problems. This compendium is for established researchers, newcomers, and students alike, highlighting interesting and rewarding problems for the coming years.

Published
2020

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