Trajectory-based differential expression analysis for single-cell sequencing data

Trajectory inference has radically enhanced single-cell RNA-seq research by enabling the study of dynamic changes in gene expression. Downstream of trajectory inference, it is vital to discover genes that are (i) associated with the lineages in the trajectory, or (ii) differentially expressed between lineages, to illuminate the underlying biological processes. Current data analysis procedures, however, either fail to exploit the continuous resolution provided by trajectory inference, or fail to pinpoint the exact types of differential expression. We introduce tradeSeq, a powerful generalized additive model framework based on the negative binomial distribution that allows flexible inference of both within-lineage and between-lineage differential expression. By incorporating observation-level weights, the model additionally allows to account for zero inflation. We evaluate the method on simulated datasets and on real datasets from droplet-based and full-length protocols, and show that it yields biological insights through a clear interpretation of the data. Downstream of trajectory inference for cell lineages based on scRNA-seq data, differential expression analysis yields insight into biological processes. Here, Van den Berge et al. develop tradeSeq, a framework for the inference of within and between-lineage differential expression, based on negative binomial generalized additive models

Improving replicability in single-cell RNA-Seq cell type discovery with Dune

Single-cell transcriptome sequencing (scRNA-Seq) has allowed many new types of investigations at unprecedented and unique levels of resolution. Among the primary goals of scRNA-Seq is the classification of cells into potentially novel cell types. Many approaches build on the existing clustering literature to develop tools specific to single-cell applications. However, almost all of these methods rely on heuristics or user-supplied parameters to control the number of clusters identified. This affects both the resolution of the clusters within the original dataset as well as their replicability across datasets. While many recommendations exist to select these tuning parameters, most of them are quite ad hoc. In general, there is little assurance that any given set of parameters will represent an optimal choice in the ever-present trade-off between cluster resolution and replicability. For instance, it may be the case that another set of parameters will result in more clusters that are also more replicable, or in fewer clusters that are also less replicable. Here, we propose a new method called Dune for optimizing the trade-off between the resolution of the clusters and their replicability across datasets. Our method takes as input a set of clustering results on a single dataset, derived from any set of clustering algorithms and associated tuning parameters, and iteratively merges clusters within partitions in order to maximize their concordance between partitions. As demonstrated on a variety of scRNA-Seq datasets from different platforms, Dune outperforms existing techniques, that rely on hierarchical merging for reducing the number of clusters, in terms of replicability of the resultant merged clusters. It provides an objective approach for identifying replicable consensus clusters most likely to represent common biological features across multiple datasets

A transcriptomic and epigenomic cell atlas of the mouse primary motor cortex.

Single-cell transcriptomics can provide quantitative molecular signatures for large, unbiased samples of the diverse cell types in the brain1-3. With the proliferation of multi-omics datasets, a major challenge is to validate and integrate results into a biological understanding of cell-type organization. Here we generated transcriptomes and epigenomes from more than 500,000 individual cells in the mouse primary motor cortex, a structure that has an evolutionarily conserved role in locomotion. We developed computational and statistical methods to integrate multimodal data and quantitatively validate cell-type reproducibility. The resulting reference atlas-containing over 56 neuronal cell types that are highly replicable across analysis methods, sequencing technologies and modalities-is a comprehensive molecular and genomic account of the diverse neuronal and non-neuronal cell types in the mouse primary motor cortex. The atlas includes a population of excitatory neurons that resemble pyramidal cells in layer 4 in other cortical regions4. We further discovered thousands of concordant marker genes and gene regulatory elements for these cell types. Our results highlight the complex molecular regulation of cell types in the brain and will directly enable the design of reagents to target specific cell types in the mouse primary motor cortex for functional analysis

A multimodal cell census and atlas of the mammalian primary motor cortex

ABSTRACT We report the generation of a multimodal cell census and atlas of the mammalian primary motor cortex (MOp or M1) as the initial product of the BRAIN Initiative Cell Census Network (BICCN). This was achieved by coordinated large-scale analyses of single-cell transcriptomes, chromatin accessibility, DNA methylomes, spatially resolved single-cell transcriptomes, morphological and electrophysiological properties, and cellular resolution input-output mapping, integrated through cross-modal computational analysis. Together, our results advance the collective knowledge and understanding of brain cell type organization: First, our study reveals a unified molecular genetic landscape of cortical cell types that congruently integrates their transcriptome, open chromatin and DNA methylation maps. Second, cross-species analysis achieves a unified taxonomy of transcriptomic types and their hierarchical organization that are conserved from mouse to marmoset and human. Third, cross-modal analysis provides compelling evidence for the epigenomic, transcriptomic, and gene regulatory basis of neuronal phenotypes such as their physiological and anatomical properties, demonstrating the biological validity and genomic underpinning of neuron types and subtypes. Fourth, in situ single-cell transcriptomics provides a spatially-resolved cell type atlas of the motor cortex. Fifth, integrated transcriptomic, epigenomic and anatomical analyses reveal the correspondence between neural circuits and transcriptomic cell types. We further present an extensive genetic toolset for targeting and fate mapping glutamatergic projection neuron types toward linking their developmental trajectory to their circuit function. Together, our results establish a unified and mechanistic framework of neuronal cell type organization that integrates multi-layered molecular genetic and spatial information with multi-faceted phenotypic properties

CALDERA: Finding all significant de Bruijn subgraphs for bacterial GWAS

International audienceGenome wide association studies (GWAS), aiming to find genetic variants associated with a trait, have widely been used on bacteria to identify genetic determinants of drug resistance or hypervirulence. Recent bacterial GWAS methods usually rely on k -mers, whose presence in a genome can denote variants ranging from single nucleotide polymorphisms to mobile genetic elements. Since many bacterial species include genes that are not shared among all strains, this approach avoids the reliance on a common reference genome. However, the same gene can exist in slightly different versions across different strains, leading to diluted effects when trying to detect its association to a phenotype through k -mer based GWAS. Here we propose to overcome this by testing covariates built from closed connected subgraphs of the De Bruijn graph defined over genomic k -mers. These covariates are able to capture polymorphic genes as a single entity, improving k -mer based GWAS in terms of power and interpretability. As the number of subgraphs is exponential in the number of nodes in the DBG, a method naively testing all possible subgraphs would result in very low statistical power due to multiple testing corrections, and the mere exploration of these subgraphs would quickly become computationally intractable. The concept of testable hypothesis has successfully been used to address both problems in similar contexts. We leverage this concept to test all closed connected subgraphs by proposing a novel enumeration scheme for these objects which fully exploits the pruning opportunity offered by testability, resulting in drastic improvements in computational efficiency. We illustrate this on both real and simulated datasets and also demonstrate how considering subgraphs leads to a more powerful and interpretable method. Our method integrates with existing visual tools to facilitate interpretation. We also provide an implementation of our method, as well as code to reproduce all results at https://github.com/HectorRDB/Caldera_Recomb

Normalization benchmark of ATAC-seq datasets shows the importance of accounting for GC-content effects

The assay for transposase-accessible chromatin using sequencing (ATAC-seq) allows the study of epigenetic regulation of gene expression by assessing chromatin configuration for an entire genome. Despite its popularity, there have been limited studies investigating the analytical challenges related to ATAC-seq data, with most studies leveraging tools developed for bulk transcriptome sequencing. Here, we show that GC-content effects are omnipresent in ATAC-seq datasets. Since the GC-content effects are sample specific, they can bias downstream analyses such as clustering and differential accessibility analysis. We introduce a normalization method based on smooth-quantile normalization within GC-content bins and evaluate it together with 11 different normalization procedures on 8 public ATAC-seq datasets. Accounting for GC-content effects in the normalization is crucial for common downstream ATAC-seq data analyses, improving accuracy and interpretability. Through case studies, we show that exploratory data analysis is essential to guide the choice of an appropriate normalization method for a given dataset

Normalization benchmark of ATAC-seq datasets shows the importance of accounting for GC-content effects

The assay for transposase-accessible chromatin using sequencing (ATAC-seq) allows the study of epigenetic regulation of gene expression by assessing chromatin configuration for an entire genome. Despite its popularity, there have been limited studies investigating the analytical challenges related to ATAC-seq data, with most studies leveraging tools developed for bulk transcriptome sequencing. Here, we show that GC-content effects are omnipresent in ATAC-seq datasets. Since the GC-content effects are sample specific, they can bias downstream analyses such as clustering and differential accessibility analysis. We introduce a normalization method based on smooth-quantile normalization within GC-content bins and evaluate it together with 11 different normalization procedures on 8 public ATAC-seq datasets. Accounting for GC-content effects in the normalization is crucial for common downstream ATAC-seq data analyses, improving accuracy and interpretability. Through case studies, we show that exploratory data analysis is essential to guide the choice of an appropriate normalization method for a given dataset

A multimodal cell census and atlas of the mammalian primary motor cortex

none258Here we report the generation of a multimodal cell census and atlas of the mammalian primary motor cortex as the initial product of the BRAIN Initiative Cell Census Network (BICCN). This was achieved by coordinated large-scale analyses of single-cell transcriptomes, chromatin accessibility, DNA methylomes, spatially resolved single-cell transcriptomes, morphological and electrophysiological properties and cellular resolution input-output mapping, integrated through cross-modal computational analysis. Our results advance the collective knowledge and understanding of brain cell-type organization1-5. First, our study reveals a unified molecular genetic landscape of cortical cell types that integrates their transcriptome, open chromatin and DNA methylation maps. Second, cross-species analysis achieves a consensus taxonomy of transcriptomic types and their hierarchical organization that is conserved from mouse to marmoset and human. Third, in situ single-cell transcriptomics provides a spatially resolved cell-type atlas of the motor cortex. Fourth, cross-modal analysis provides compelling evidence for the transcriptomic, epigenomic and gene regulatory basis of neuronal phenotypes such as their physiological and anatomical properties, demonstrating the biological validity and genomic underpinning of neuron types. We further present an extensive genetic toolset for targeting glutamatergic neuron types towards linking their molecular and developmental identity to their circuit function. Together, our results establish a unifying and mechanistic framework of neuronal cell-type organization that integrates multi-layered molecular genetic and spatial information with multi-faceted phenotypic properties.openCallaway, Edward M.; Dong, Hong-Wei; Ecker, Joseph R.; Hawrylycz, Michael J.; Huang, Z. Josh; Lein, Ed S.; Ngai, John; Osten, Pavel; Ren, Bing; Tolias, Andreas Savas; White, Owen; Zeng, Hongkui; Zhuang, Xiaowei; Ascoli, Giorgio A.; Behrens, M. Margarita; Chun, Jerold; Feng, Guoping; Gee, James C.; Ghosh, Satrajit S.; Halchenko, Yaroslav O.; Hertzano, Ronna; Lim, Byung Kook; Martone, Maryann E.; Ng, Lydia; Pachter, Lior; Ropelewski, Alexander J.; Tickle, Timothy L.; Yang, X. William; Zhang, Kun; Bakken, Trygve E.; Berens, Philipp; Daigle, Tanya L.; Harris, Julie A.; Jorstad, Nikolas L.; Kalmbach, Brian E.; Kobak, Dmitry; Li, Yang Eric; Liu, Hanqing; Matho, Katherine S.; Mukamel, Eran A.; Naeemi, Maitham; Scala, Federico; Tan, Pengcheng; Ting, Jonathan T.; Xie, Fangming; Zhang, Meng; Zhang, Zhuzhu; Zhou, Jingtian; Zingg, Brian; Armand, Ethan; Yao, Zizhen; Bertagnolli, Darren; Casper, Tamara; Crichton, Kirsten; Dee, Nick; Diep, Dinh; Ding, Song-Lin; Dong, Weixiu; Dougherty, Elizabeth L.; Fong, Olivia; Goldman, Melissa; Goldy, Jeff; Hodge, Rebecca D.; Hu, Lijuan; Keene, C. Dirk; Krienen, Fenna M.; Kroll, Matthew; Lake, Blue B.; Lathia, Kanan; Linnarsson, Sten; Liu, Christine S.; Macosko, Evan Z.; McCarroll, Steven A.; McMillen, Delissa; Nadaf, Naeem M.; Nguyen, Thuc Nghi; Palmer, Carter R.; Pham, Thanh; Plongthongkum, Nongluk; Reed, Nora M.; Regev, Aviv; Rimorin, Christine; Romanow, William J.; Savoia, Steven; Siletti, Kimberly; Smith, Kimberly; Sulc, Josef; Tasic, Bosiljka; Tieu, Michael; Torkelson, Amy; Tung, Herman; van Velthoven, Cindy T. J.; Vanderburg, Charles R.; Yanny, Anna Marie; Fang, Rongxin; Hou, Xiaomeng; Lucero, Jacinta D.; Osteen, Julia K.; Pinto-Duarte, Antonio; Poirion, Olivier; Preissl, Sebastian; Wang, Xinxin; Aldridge, Andrew I.; Bartlett, Anna; Boggeman, Lara; O’Connor, Carolyn; Castanon, Rosa G.; Chen, Huaming; Fitzpatrick, Conor; Luo, Chongyuan; Nery, Joseph R.; Nunn, Michael; Rivkin, Angeline C.; Tian, Wei; Dominguez, Bertha; Ito-Cole, Tony; Jacobs, Matthew; Jin, Xin; Lee, Cheng-Ta; Lee, Kuo-Fen; Miyazaki, Paula Assakura; Pang, Yan; Rashid, Mohammad; Smith, Jared B.; Vu, Minh; Williams, Elora; Biancalani, Tommaso; Booeshaghi, A. Sina; Crow, Megan; Dudoit, Sandrine; Fischer, Stephan; Gillis, Jesse; Hu, Qiwen; Kharchenko, Peter V.; Niu, Sheng-Yong; Ntranos, Vasilis; Purdom, Elizabeth; Risso, Davide; de Bézieux, Hector Roux; Somasundaram, Saroja; Street, Kelly; Svensson, Valentine; Vaishnav, Eeshit Dhaval; Van den Berge, Koen; Welch, Joshua D.; An, Xu; Bateup, Helen S.; Bowman, Ian; Chance, Rebecca K.; Foster, Nicholas N.; Galbavy, William; Gong, Hui; Gou, Lin; Hatfield, Joshua T.; Hintiryan, Houri; Hirokawa, Karla E.; Kim, Gukhan; Kramer, Daniel J.; Li, Anan; Li, Xiangning; Luo, Qingming; Muñoz-Castañeda, Rodrigo; Stafford, David A.; Feng, Zhao; Jia, Xueyan; Jiang, Shengdian; Jiang, Tao; Kuang, Xiuli; Larsen, Rachael; Lesnar, Phil; Li, Yaoyao; Li, Yuanyuan; Liu, Lijuan; Peng, Hanchuan; Qu, Lei; Ren, Miao; Ruan, Zongcai; Shen, Elise; Song, Yuanyuan; Wakeman, Wayne; Wang, Peng; Wang, Yimin; Wang, Yun; Yin, Lulu; Yuan, Jing; Zhao, Sujun; Zhao, Xuan; Narasimhan, Arun; Palaniswamy, Ramesh; Banerjee, Samik; Ding, Liya; Huilgol, Dhananjay; Huo, Bingxing; Kuo, Hsien-Chi; Laturnus, Sophie; Li, Xu; Mitra, Partha P.; Mizrachi, Judith; Wang, Quanxin; Xie, Peng; Xiong, Feng; Yu, Yang; Eichhorn, Stephen W.; Berg, Jim; Bernabucci, Matteo; Bernaerts, Yves; Cadwell, Cathryn René; Castro, Jesus Ramon; Dalley, Rachel; Hartmanis, Leonard; Horwitz, Gregory D.; Jiang, Xiaolong; Ko, Andrew L.; Miranda, Elanine; Mulherkar, Shalaka; Nicovich, Philip R.; Owen, Scott F.; Sandberg, Rickard; Sorensen, Staci A.; Tan, Zheng Huan; Allen, Shona; Hockemeyer, Dirk; Lee, Angus Y.; Veldman, Matthew B.; Adkins, Ricky S.; Ament, Seth A.; Bravo, Héctor Corrada; Carter, Robert; Chatterjee, Apaala; Colantuoni, Carlo; Crabtree, Jonathan; Creasy, Heather; Felix, Victor; Giglio, Michelle; Herb, Brian R.; Kancherla, Jayaram; Mahurkar, Anup; McCracken, Carrie; Nickel, Lance; Olley, Dustin; Orvis, Joshua; Schor, Michael; Hood, Greg; Dichter, Benjamin; Grauer, Michael; Helba, Brian; Bandrowski, Anita; Barkas, Nikolaos; Carlin, Benjamin; D’Orazi, Florence D.; Degatano, Kylee; Gillespie, Thomas H.; Khajouei, Farzaneh; Konwar, Kishori; Thompson, Carol; Kelly, Kathleen; Mok, Stephanie; Sunkin, SusanCallaway, Edward M.; Dong, Hong-Wei; Ecker, Joseph R.; Hawrylycz, Michael J.; Huang, Z. Josh; Lein, Ed S.; Ngai, John; Osten, Pavel; Ren, Bing; Tolias, Andreas Savas; White, Owen; Zeng, Hongkui; Zhuang, Xiaowei; Ascoli, Giorgio A.; Behrens, M. Margarita; Chun, Jerold; Feng, Guoping; Gee, James C.; Ghosh, Satrajit S.; Halchenko, Yaroslav O.; Hertzano, Ronna; Lim, Byung Kook; Martone, Maryann E.; Ng, Lydia; Pachter, Lior; Ropelewski, Alexander J.; Tickle, Timothy L.; Yang, X. William; Zhang, Kun; Bakken, Trygve E.; Berens, Philipp; Daigle, Tanya L.; Harris, Julie A.; Jorstad, Nikolas L.; Kalmbach, Brian E.; Kobak, Dmitry; Li, Yang Eric; Liu, Hanqing; Matho, Katherine S.; Mukamel, Eran A.; Naeemi, Maitham; Scala, Federico; Tan, Pengcheng; Ting, Jonathan T.; Xie, Fangming; Zhang, Meng; Zhang, Zhuzhu; Zhou, Jingtian; Zingg, Brian; Armand, Ethan; Yao, Zizhen; Bertagnolli, Darren; Casper, Tamara; Crichton, Kirsten; Dee, Nick; Diep, Dinh; Ding, Song-Lin; Dong, Weixiu; Dougherty, Elizabeth L.; Fong, Olivia; Goldman, Melissa; Goldy, Jeff; Hodge, Rebecca D.; Hu, Lijuan; Keene, C. Dirk; Krienen, Fenna M.; Kroll, Matthew; Lake, Blue B.; Lathia, Kanan; Linnarsson, Sten; Liu, Christine S.; Macosko, Evan Z.; Mccarroll, Steven A.; Mcmillen, Delissa; Nadaf, Naeem M.; Nguyen, Thuc Nghi; Palmer, Carter R.; Pham, Thanh; Plongthongkum, Nongluk; Reed, Nora M.; Regev, Aviv; Rimorin, Christine; Romanow, William J.; Savoia, Steven; Siletti, Kimberly; Smith, Kimberly; Sulc, Josef; Tasic, Bosiljka; Tieu, Michael; Torkelson, Amy; Tung, Herman; van Velthoven, Cindy T. J.; Vanderburg, Charles R.; Yanny, Anna Marie; Fang, Rongxin; Hou, Xiaomeng; Lucero, Jacinta D.; Osteen, Julia K.; Pinto-Duarte, Antonio; Poirion, Olivier; Preissl, Sebastian; Wang, Xinxin; Aldridge, Andrew I.; Bartlett, Anna; Boggeman, Lara; O’Connor, Carolyn; Castanon, Rosa G.; Chen, Huaming; Fitzpatrick, Conor; Luo, Chongyuan; Nery, Joseph R.; Nunn, Michael; Rivkin, Angeline C.; Tian, Wei; Dominguez, Bertha; Ito-Cole, Tony; Jacobs, Matthew; Jin, Xin; Lee, Cheng-Ta; Lee, Kuo-Fen; Miyazaki, Paula Assakura; Pang, Yan; Rashid, Mohammad; Smith, Jared B.; Vu, Minh; Williams, Elora; Biancalani, Tommaso; Booeshaghi, A. Sina; Crow, Megan; Dudoit, Sandrine; Fischer, Stephan; Gillis, Jesse; Hu, Qiwen; Kharchenko, Peter V.; Niu, Sheng-Yong; Ntranos, Vasilis; Purdom, Elizabeth; Risso, Davide; de Bézieux, Hector Roux; Somasundaram, Saroja; Street, Kelly; Svensson, Valentine; Vaishnav, Eeshit Dhaval; Van den Berge, Koen; Welch, Joshua D.; An, Xu; Bateup, Helen S.; Bowman, Ian; Chance, Rebecca K.; Foster, Nicholas N.; Galbavy, William; Gong, Hui; Gou, Lin; Hatfield, Joshua T.; Hintiryan, Houri; Hirokawa, Karla E.; Kim, Gukhan; Kramer, Daniel J.; Li, Anan; Li, Xiangning; Luo, Qingming; Muñoz-Castañeda, Rodrigo; Stafford, David A.; Feng, Zhao; Jia, Xueyan; Jiang, Shengdian; Jiang, Tao; Kuang, Xiuli; Larsen, Rachael; Lesnar, Phil; Li, Yaoyao; Li, Yuanyuan; Liu, Lijuan; Peng, Hanchuan; Qu, Lei; Ren, Miao; Ruan, Zongcai; Shen, Elise; Song, Yuanyuan; Wakeman, Wayne; Wang, Peng; Wang, Yimin; Wang, Yun; Yin, Lulu; Yuan, Jing; Zhao, Sujun; Zhao, Xuan; Narasimhan, Arun; Palaniswamy, Ramesh; Banerjee, Samik; Ding, Liya; Huilgol, Dhananjay; Huo, Bingxing; Kuo, Hsien-Chi; Laturnus, Sophie; Li, Xu; Mitra, Partha P.; Mizrachi, Judith; Wang, Quanxin; Xie, Peng; Xiong, Feng; Yu, Yang; Eichhorn, Stephen W.; Berg, Jim; Bernabucci, Matteo; Bernaerts, Yves; Cadwell, Cathryn René; Castro, Jesus Ramon; Dalley, Rachel; Hartmanis, Leonard; Horwitz, Gregory D.; Jiang, Xiaolong; Ko, Andrew L.; Miranda, Elanine; Mulherkar, Shalaka; Nicovich, Philip R.; Owen, Scott F.; Sandberg, Rickard; Sorensen, Staci A.; Tan, Zheng Huan; Allen, Shona; Hockemeyer, Dirk; Lee, Angus Y.; Veldman, Matthew B.; Adkins, Ricky S.; Ament, Seth A.; Bravo, Héctor Corrada; Carter, Robert; Chatterjee, Apaala; Colantuoni, Carlo; Crabtree, Jonathan; Creasy, Heather; Felix, Victor; Giglio, Michelle; Herb, Brian R.; Kancherla, Jayaram; Mahurkar, Anup; Mccracken, Carrie; Nickel, Lance; Olley, Dustin; Orvis, Joshua; Schor, Michael; Hood, Greg; Dichter, Benjamin; Grauer, Michael; Helba, Brian; Bandrowski, Anita; Barkas, Nikolaos; Carlin, Benjamin; D’Orazi, Florence D.; Degatano, Kylee; Gillespie, Thomas H.; Khajouei, Farzaneh; Konwar, Kishori; Thompson, Carol; Kelly, Kathleen; Mok, Stephanie; Sunkin, Susa