Guillaume Huguet

Zohra Saci

Leila Kushan

Ana I. Silva

Marianne B.M. van den Bree

David E.J. Linden … (see 16 more)

Michael J. Owen

Jeremy Hall

Sarah Lippé

Bogdan Draganski

Laura Almasy

Sophia I. Thomopoulos

Neda Jahanshad

Ida E. Sønderby

Ole A. Andreassen

David C. Glahn

Armin Raznahan

Carrie Bearden

Tomas Paus

Paul M. Thompson

Sébastien Jacquemont

2025-04-19

medRxiv (preprint)

Geometry-Aware Generative Autoencoder for Warped Riemannian Metric Learning and Generative Modeling on Data Manifolds

Xingzhi Sun

Danqi Liao

Kincaid MacDonald

Yanlei Zhang

Guy Wolf

Ian Adelstein

Tim G. J. Rudner

Smita Krishnaswamy

Rapid growth of high-dimensional datasets in fields such as single-cell RNA sequencing and spatial genomics has led to unprecedented opportu… (see more)nities for scientific discovery, but it also presents unique computational and statistical challenges. Traditional methods struggle with geometry-aware data generation, interpolation along meaningful trajectories, and transporting populations via feasible paths. To address these issues, we introduce Geometry-Aware Generative Autoencoder (GAGA), a novel framework that combines extensible manifold learning with generative modeling. GAGA constructs a neural network embedding space that respects the intrinsic geometries discovered by manifold learning and learns a novel warped Riemannian metric on the data space. This warped metric is derived from both the points on the data manifold and negative samples off the manifold, allowing it to characterize a meaningful geometry across the entire latent space. Using this metric, GAGA can uniformly sample points on the manifold, generate points along geodesics, and interpolate between populations across the learned manifold. GAGA shows competitive performance in simulated and real-world datasets, including a 30% improvement over SOTA in single-cell population-level trajectory inference.

2025-01-22

aistats.org/AISTATS/2025/Conference (poster)

Geometry-Aware Generative Autoencoders for Warped Riemannian Metric Learning and Generative Modeling on Data Manifolds

Xingzhi Sun

Danqi Liao

Kincaid MacDonald

Yanlei Zhang

Chen Liu

Guy Wolf

Ian Adelstein

Tim G. J. Rudner

Smita Krishnaswamy

2025-01-22

aistats.org/AISTATS/2025/Conference (poster)

Mirror effect of genomic deletions and duplications on cognitive ability across the human cerebral cortex

Kuldeep Kumar

Sayeh Kazem

Thomas Renne

Worrawat Engchuan

Martineau Jean-Louis

Jakub Kopal

Zohra Saci

Omar Shanta

Bhooma Thiruvahindrapuram

Jeffrey R. MacDonald

Josephine Mollon

Laura Schultz

Emma E M Knowles

David Porteous

Gail Davies

Paul Redmond

Sarah E. Harris

Simon R. Cox

Gunter Schumann … (see 9 more)

Zdenka Pausova

Celia M. T. Greenwood

Tomas Paus

Stephen W Scherer

Laura Almasy

Jonathan Sebat

David C. Glahn

Sébastien Jacquemont

Regulation of gene expression shapes the interaction between brain networks which in-turn supports psychological processes such as cognitive… (see more) ability. How changes in level of gene expression across the cerebral cortex influence cognitive ability remains unknown. Here, we tackle this by leveraging genomic deletions and duplications - copy number variants (CNVs) that fully encompass one or more genes expressed in the human cortex - which lead to large effects on gene-expression levels. We assigned genes to 180 regions of the human cerebral cortex based on their preferential expression across the cortex computed using data from the Allen Human Brain Atlas. We aggregated CNVs in cortical regions, and ran a burden association analysis to compute the mean effect size of genes on general cognitive ability for each of the 180 regions. When affected by CNVs, most of the regional gene-sets were associated with lower cognitive ability. The spatial patterns of effect sizes across the cortex were correlated negatively between deletions and duplications. The largest effect sizes for deletions and duplications were observed for gene-sets with high expression in sensorimotor and association regions, respectively. These two opposing patterns of effect sizes were not influenced by intolerance to loss of function, demonstrating orthogonality to dosage-sensitivity scores. The same mirror patterns were also observed after stratifying genes based on cell types and developmental epochs markers. These results suggest that the effect size of gene dosage on cognitive ability follows a cortical gradient. The same brain region and corresponding gene-set may show different effects on cognition depending on whether variants increase or decrease transcription. The latter has major implications for the association of brain networks with phenotypes

2025-01-07

bioRxiv (preprint)

Mirror effect of genomic deletions and duplications on cognitive ability across the human cerebral cortex

Kuldeep Kumar

Sayeh Kazem

Thomas Renne

Worrawat Engchuan

Martineau Jean-Louis

Jakub Kopal

Zohra Saci

Omar Shanta

Bhooma Thiruvahindrapuram

Jeffrey R. MacDonald

Josephine Mollon

Laura Schultz

Emma E M Knowles

David Porteous

Gail Davies

Paul Redmond

Sarah E. Harris

Simon R. Cox

Gunter Schumann … (see 9 more)

Zdenka Pausova

Celia M. T. Greenwood

Tomas Paus

Stephen W Scherer

Laura Almasy

Jonathan Sebat

David C. Glahn

Sébastien Jacquemont

Regulation of gene expression shapes the interaction between brain networks which in-turn supports psychological processes such as cognitive… (see more) ability. How changes in level of gene expression across the cerebral cortex influence cognitive ability remains unknown. Here, we tackle this by leveraging genomic deletions and duplications - copy number variants (CNVs) that fully encompass one or more genes expressed in the human cortex - which lead to large effects on gene-expression levels. We assigned genes to 180 regions of the human cerebral cortex based on their preferential expression across the cortex computed using data from the Allen Human Brain Atlas. We aggregated CNVs in cortical regions, and ran a burden association analysis to compute the mean effect size of genes on general cognitive ability for each of the 180 regions. When affected by CNVs, most of the regional gene-sets were associated with lower cognitive ability. The spatial patterns of effect sizes across the cortex were correlated negatively between deletions and duplications. The largest effect sizes for deletions and duplications were observed for gene-sets with high expression in sensorimotor and association regions, respectively. These two opposing patterns of effect sizes were not influenced by intolerance to loss of function, demonstrating orthogonality to dosage-sensitivity scores. The same mirror patterns were also observed after stratifying genes based on cell types and developmental epochs markers. These results suggest that the effect size of gene dosage on cognitive ability follows a cortical gradient. The same brain region and corresponding gene-set may show different effects on cognition depending on whether variants increase or decrease transcription. The latter has major implications for the association of brain networks with phenotypes

2025-01-07

bioRxiv (preprint)

Effects of gene dosage on cognitive ability: A function-based association study across brain and non-brain processes

Thomas Renne

Cécile Poulain

Alma Dubuc

Kuldeep Kumar

Sayeh Kazem

Worrawat Engchuan

Omar Shanta

Elise Douard

Catherine Proulx

Martineau Jean-Louis

Zohra Saci

Josephine Mollon

Laura Schultz

Emma E M Knowles

Simon R. Cox

David Porteous

Gail Davies

Paul Redmond

Sarah E. Harris … (see 10 more)

Gunter Schumann

Aurélie Labbe

Zdenka Pausova

Tomas Paus

Stephen W Scherer

Jonathan Sebat

Laura Almasy

David C. Glahn

Sébastien Jacquemont

2024-12-11

Cell Genomics (published)

Geometry-Aware Generative Autoencoders for Metric Learning and Generative Modeling on Data Manifolds

Xingzhi Sun

Danqi Liao

Kincaid MacDonald

Yanlei Zhang

Guy Wolf

Ian Adelstein

Tim G. J. Rudner

Smita Krishnaswamy

Non-linear dimensionality reduction methods have proven successful at learning low-dimensional representations of high-dimensional point clo… (see more)uds on or near data manifolds. However, existing methods are not easily extensible—that is, for large datasets, it is prohibitively expensive to add new points to these embeddings. As a result, it is very difficult to use existing embeddings generatively, to sample new points on and along these manifolds. In this paper, we propose GAGA (geometry-aware generative autoencoders) a framework which merges the power of generative deep learning with non-linear manifold learning by: 1) learning generalizable geometry-aware neural network embeddings based on non-linear dimensionality reduction methods like PHATE and diffusion maps, 2) deriving a non-euclidean pullback metric on the embedded space to generate points faithfully along manifold geodesics, and 3) learning a flow on the manifold that allows us to transport populations. We provide illustration on easily-interpretable synthetic datasets and showcase results on simulated and real single cell datasets. In particular, we show that the geodesic-based generation can be especially important for scientific datasets where the manifold represents a state space and geodesics can represent dynamics of entities over this space.

2024-06-17

ICML.cc/2024/Workshop/GRaM (published)

295. Rare Variant Genetic Architecture of the Human Cortical MRI Phenotypes in General Population

Kuldeep Kumar

Sayeh Kazem

Zhijie Liao

Jakub Kopal

Thomas Renne

Martineau Jean-Louis

Zhe Xie

Zohra Saci

Laura Almasy

David C. Glahn

Tomas Paus

Carrie Bearden

Paul Thompson

Richard A.I. Bethlehem

Varun Warrier

Sébastien Jacquemont

2024-05-01

Biological Psychiatry (published)

Effects of gene dosage on cognitive ability: A function-based association study across brain and non-brain processes

Thomas Renne

Cécile Poulain

Alma Dubuc

Kuldeep Kumar

Sayeh Kazem

Worrawat Engchuan

Omar Shanta

Elise Douard

Catherine Proulx

Martineau Jean-Louis

Zohra Saci

Josephine Mollon

Laura Schultz

Emma E M Knowles

Simon R. Cox

David Porteous

Gail Davies

Paul Redmond

Sarah E. Harris … (see 10 more)

Gunter Schumann

Aurélie Labbe

Zdenka Pausova

Tomas Paus

Stephen W Scherer

Jonathan Sebat

Laura Almasy

David C. Glahn

Sébastien Jacquemont

Genomic Copy Number Variants (CNVs) that increase risk for neurodevelopmental disorders are also associated with lower cognitive ability in … (see more)general population cohorts. Studies have focussed on a small set of recurrent CNVs, but burden analyses suggested that the vast majority of CNVs affecting cognitive ability are too rare to reach variant-level association. As a result, the full range of gene-dosage-sensitive biological processes linked to cognitive ability remains unknown. To investigate this issue, we identified all CNVs >50 kilobases in 258k individuals from 6 general population cohorts with assessments of general cognitive abilities. We performed a CNV-GWAS and functional burden analyses, which tested 6502 gene-sets defined by tissue and cell-type transcriptomics as well as gene ontology disrupted by all rare coding CNVs. CNV-GWAS identified a novel duplication at 2q12.3 associated with higher performance in cognitive ability. Among the 864 gene-sets associated with cognitive ability, only 11% showed significant effects for both deletions and duplication. Accordingly, we systematically observed negative correlations between deletion and duplication effect sizes across all levels of biological observations. We quantified the preferential effects of deletions versus duplication using tagDS, a new normalized metric. Cognitive ability was preferentially affected by cortical, presynaptic, and negative-regulation gene-sets when duplicated. In contrast, preferential effects of deletions were observed for subcortical, post-synaptic, and positive-regulation gene-sets. A large proportion of gene-sets assigned to non-brain organs were associated with cognitive ability due to low tissue specificity genes, which were associated with higher sensitive to haploinsufficiency. Overall, most biological functions associated with cognitive ability are divided into those sensitive to either deletion or duplications.

2024-04-17

bioRxiv (preprint)

Effects of gene dosage on cognitive ability: A function-based association study across brain and non-brain processes

Thomas Renne

Cécile Poulain

Alma Dubuc

Kuldeep Kumar

Sayeh Kazem

Worrawat Engchuan

Omar Shanta

Elise Douard

Catherine Proulx

Martineau Jean-Louis

Zohra Saci

Josephine Mollon

Laura Schultz

Emma E M Knowles

Simon R. Cox

David Porteous

Gail Davies

Paul Redmond

Sarah E. Harris … (see 10 more)

Gunter Schumann

Aurélie Labbe

Zdenka Pausova

Tomas Paus

Stephen W Scherer

Jonathan Sebat

Laura Almasy

David C. Glahn

Sébastien Jacquemont

Genomic Copy Number Variants (CNVs) that increase risk for neurodevelopmental disorders are also associated with lower cognitive ability in … (see more)general population cohorts. Studies have focussed on a small set of recurrent CNVs, but burden analyses suggested that the vast majority of CNVs affecting cognitive ability are too rare to reach variant-level association. As a result, the full range of gene-dosage-sensitive biological processes linked to cognitive ability remains unknown. To investigate this issue, we identified all CNVs >50 kilobases in 258k individuals from 6 general population cohorts with assessments of general cognitive abilities. We performed a CNV-GWAS and functional burden analyses, which tested 6502 gene-sets defined by tissue and cell-type transcriptomics as well as gene ontology disrupted by all rare coding CNVs. CNV-GWAS identified a novel duplication at 2q12.3 associated with higher performance in cognitive ability. Among the 864 gene-sets associated with cognitive ability, only 11% showed significant effects for both deletions and duplication. Accordingly, we systematically observed negative correlations between deletion and duplication effect sizes across all levels of biological observations. We quantified the preferential effects of deletions versus duplication using tagDS, a new normalized metric. Cognitive ability was preferentially affected by cortical, presynaptic, and negative-regulation gene-sets when duplicated. In contrast, preferential effects of deletions were observed for subcortical, post-synaptic, and positive-regulation gene-sets. A large proportion of gene-sets assigned to non-brain organs were associated with cognitive ability due to low tissue specificity genes, which were associated with higher sensitive to haploinsufficiency. Overall, most biological functions associated with cognitive ability are divided into those sensitive to either deletion or duplications.

2024-04-17

bioRxiv (preprint)

Effects of gene dosage on cognitive ability: A function-based association study across brain and non-brain processes

Thomas Renne

Cécile Poulain

Alma Dubuc

Kuldeep Kumar

Sayeh Kazem

Worrawat Engchuan

Omar Shanta

Elise Douard

Catherine Proulx

Martineau Jean-Louis

Zohra Saci

Josephine Mollon

Laura Schultz

Emma E M Knowles

Simon R. Cox

David Porteous

Gail Davies

Paul Redmond

Sarah E. Harris … (see 10 more)

Gunter Schumann

Aurélie Labbe

Zdenka Pausova

Tomas Paus

Stephen W Scherer

Jonathan Sebat

Laura Almasy

David C. Glahn

Sébastien Jacquemont

Genomic Copy Number Variants (CNVs) that increase risk for neurodevelopmental disorders are also associated with lower cognitive ability in … (see more)general population cohorts. Studies have focussed on a small set of recurrent CNVs, but burden analyses suggested that the vast majority of CNVs affecting cognitive ability are too rare to reach variant-level association. As a result, the full range of gene-dosage-sensitive biological processes linked to cognitive ability remains unknown. To investigate this issue, we identified all CNVs >50 kilobases in 258k individuals from 6 general population cohorts with assessments of general cognitive abilities. We performed a CNV-GWAS and functional burden analyses, which tested 6502 gene-sets defined by tissue and cell-type transcriptomics as well as gene ontology disrupted by all rare coding CNVs. CNV-GWAS identified a novel duplication at 2q12.3 associated with higher performance in cognitive ability. Among the 864 gene-sets associated with cognitive ability, only 11% showed significant effects for both deletions and duplication. Accordingly, we systematically observed negative correlations between deletion and duplication effect sizes across all levels of biological observations. We quantified the preferential effects of deletions versus duplication using tagDS, a new normalized metric. Cognitive ability was preferentially affected by cortical, presynaptic, and negative-regulation gene-sets when duplicated. In contrast, preferential effects of deletions were observed for subcortical, post-synaptic, and positive-regulation gene-sets. A large proportion of gene-sets assigned to non-brain organs were associated with cognitive ability due to low tissue specificity genes, which were associated with higher sensitive to haploinsufficiency. Overall, most biological functions associated with cognitive ability are divided into those sensitive to either deletion or duplications.

2024-04-17

bioRxiv (preprint)

Improving and Generalizing Flow-Based Generative Models with Minibatch Optimal Transport

Alexander Tong

Yanlei Zhang

Kilian FATRAS

Continuous normalizing flows (CNFs) are an attractive generative modeling technique, but they have been held back by limitations in their si… (see more)mulation-based maximum likelihood training. We introduce the generalized \textit{conditional flow matching} (CFM) technique, a family of simulation-free training objectives for CNFs. CFM features a stable regression objective like that used to train the stochastic flow in diffusion models but enjoys the efficient inference of deterministic flow models. In contrast to both diffusion models and prior CNF training algorithms, CFM does not require the source distribution to be Gaussian or require evaluation of its density. A variant of our objective is optimal transport CFM (OT-CFM), which creates simpler flows that are more stable to train and lead to faster inference, as evaluated in our experiments. Furthermore, OT-CFM is the first method to compute dynamic OT in a simulation-free way. Training CNFs with CFM improves results on a variety of conditional and unconditional generation tasks, such as inferring single cell dynamics, unsupervised image translation, and Schrödinger bridge inference.

2024-03-11

TMLR (accepted)