Augment to Interpret: Unsupervised and Inherently Interpretable Graph Embeddings

Unsupervised learning allows us to leverage unlabelled data, which has become abundantly available, and to create embeddings that are usable on a variety of downstream tasks. However, the typical lack of interpretability of unsupervised representation learning has become a limiting factor with regard to recent transparent-AI regulations. In this paper, we study graph representation learning and we show that data augmentation that preserves semantics can be learned and used to produce interpretations. Our framework, which we named INGENIOUS, creates inherently interpretable embeddings and eliminates the need for costly additional post-hoc analysis. We also introduce additional metrics addressing the lack of formalism and metrics in the understudied area of unsupervised-representation learning interpretability. Our results are supported by an experimental study applied to both graph-level and node-level tasks and show that interpretable embeddings provide state-of-the-art performance on subsequent downstream tasks

Unsupervised analysis of scRNA-seq data with machine learning models

The advancements in cell sequencing techniques over the last decade encouraged increasing adoption rates and lead to the release of numerous publicly available datasets.Single-cell RNA sequencing (scRNA-seq) is a sequencing technique providing transcriptomic profilings of individual cells and which became the dominant technology for the study of gene-expression. Due to the typical absence of annotations, clustering algorithms are routinely employed in the underlying computational analysis. Several technical challenges (i.e. high dimensionality and sparsity, dropout) motivated the pro-posal of numerous techniques adapted to the specificities of scRNA-seq data. However, despite these efforts, there is no consensus on the best performing method.We propose a suite of three novel methods for the unsupervised analysis (i.e. clustering) of scRNA-seq data. contrastive-sc applies the self-supervised contrastive learning techniques, originally proposed for image processing, to scRNA-seq data in order to create and cluster cell embeddings. graph-sc clusters scRNA-seq data with a graph convolutional auto-encoder model and offers the possibility to seamlessly in-tegrate various types of external data (i.e. gene correlations) under the same optimization task. discover performs bottom-up subspace clustering on scRNA-seq, bulk RNA-seq and microarray data with a hybrid genetic algorithm. An extensive experimental study is performed to assess each of the proposed methods on simulated and real-world datasets. Our methods compared favorably with state-of-the-art techniques when compared with over 10 competing algorithms.The clustering analysis is typically followed by differential expression (DE) analysis, which identifies the genes expressed differently across the identified clusters – the entry point for the biological downstream validation experiments. Our final contribution demonstrates that employing gradient-based explainability techniques on neural network clustering methods can identify the DE genes and outperforms several state-of-the-art dedicated methods while being significantly faster.Doctorat en Sciencesinfo:eu-repo/semantics/nonPublishe

Contrastive self-supervised clustering of scRNA-seq data.

Single-cell RNA sequencing (scRNA-seq) has emerged has a main strategy to study transcriptional activity at the cellular level. Clustering analysis is routinely performed on scRNA-seq data to explore, recognize or discover underlying cell identities. The high dimensionality of scRNA-seq data and its significant sparsity accentuated by frequent dropout events, introducing false zero count observations, make the clustering analysis computationally challenging. Even though multiple scRNA-seq clustering techniques have been proposed, there is no consensus on the best performing approach. On a parallel research track, self-supervised contrastive learning recently achieved state-of-the-art results on images clustering and, subsequently, image classification.info:eu-repo/semantics/publishe

GNN-based embedding for clustering scRNA-seq data

Abstract Motivation Single-cell RNA sequencing (scRNA-seq) provides transcriptomic profiling for individual cells, allowing researchers to study the heterogeneity of tissues, recognize rare cell identities and discover new cellular subtypes. Clustering analysis is usually used to predict cell class assignments and infer cell identities. However, the high sparsity of scRNA-seq data, accentuated by dropout events generates challenges that have motivated the development of numerous dedicated clustering methods. Nevertheless, there is still no consensus on the best performing method. Results graph-sc is a new method leveraging a graph autoencoder network to create embeddings for scRNA-seq cell data. While this work analyzes the performance of clustering the embeddings with various clustering algorithms, other downstream tasks can also be performed. A broad experimental study has been performed on both simulated and scRNA-seq datasets. The results indicate that although there is no consistently best method across all the analyzed datasets, graph-sc compares favorably to competing techniques across all types of datasets. Furthermore, the proposed method is stable across consecutive runs, robust to input down-sampling, generally insensitive to changes in the network architecture or training parameters and more computationally efficient than other competing methods based on neural networks. Modeling the data as a graph provides increased flexibility to define custom features characterizing the genes, the cells and their interactions. Moreover, external data (e.g. gene network) can easily be integrated into the graph and used seamlessly under the same optimization task. Availability and implementation https://github.com/ciortanmadalina/graph-sc. Supplementary information Supplementary data are available at Bioinformatics online.info:eu-repo/semantics/publishe

Exploiting Genomic Features to Improve the Prediction of Transcription Factor-Binding Sites in Plants.

The identification of transcription factor (TF) target genes is central in biology. A popular approach is based on the location by pattern matching of potential cis-regulatory elements (CREs). During the last few years, tools integrating next-generation sequencing data have been developed to improve the performance of pattern matching. However, such tools have not yet been comprehensively evaluated in plants. Hence, we developed a new streamlined method aiming at predicting CREs and target genes of plant TFs in specific organs or conditions. Our approach implements a supervised machine learning strategy, which allows decision rule models to be learnt using TF ChIP-chip/seq experimental data. Different layers of genomic features were integrated in predictive models: the position on the gene, the DNA sequence conservation, the chromatin state and various CRE footprints. Among the tested features, the chromatin features were crucial for improving the accuracy of the method. Furthermore, we evaluated the transferability of predictive models across TFs, organs and species. Finally, we validated our method by correctly inferring the target genes of key TFs controlling metabolite biosynthesis at the organ level in Arabidopsis. We developed a tool-Wimtrap-to reproduce our approach in plant species and conditions/organs for which ChIP-chip/seq data are available. Wimtrap is a user-friendly R package that supports an R Shiny web interface and is provided with pre-built models that can be used to quickly get predictions of CREs and TF gene targets in different organs or conditions in Arabidopsis thaliana, Solanum lycopersicum, Oryza sativa and Zea mays.info:eu-repo/semantics/publishe

The Specific Molecular Changes Induced by Diabetic Conditions in Valvular Endothelial Cells and upon Their Interactions with Monocytes Contribute to Endothelial Dysfunction

Aortic valve disease (AVD) represents a global public health challenge. Research indicates a higher prevalence of diabetes in AVD patients, accelerating disease advancement. Although the specific mechanisms linking diabetes to valve dysfunction remain unclear, alterations of valvular endothelial cells (VECs) homeostasis due to high glucose (HG) or their crosstalk with monocytes play pivotal roles. The aim of this study was to determine the molecular signatures of VECs in HG and upon their interaction with monocytes in normal (NG) or high glucose conditions and to propose novel mechanisms underlying valvular dysfunction in diabetes. VECs and THP-1 monocytes cultured in NG/HG conditions were used. The RNAseq analysis revealed transcriptomic changes in VECs, in processes related to cytoskeleton regulation, focal adhesions, cellular junctions, and cell adhesion. Key molecules were validated by qPCR, Western blot, and immunofluorescence assays. The alterations in cytoskeleton and intercellular junctions impacted VEC function, leading to changes in VECs adherence to extracellular matrix, endothelial permeability, monocyte adhesion, and transmigration. The findings uncover new molecular mechanisms of VEC dysfunction in HG conditions and upon their interaction with monocytes in NG/HG conditions and may help to understand mechanisms of valvular dysfunction in diabetes and to develop novel therapeutic strategies in AVD

Chronic High Glucose Concentration Induces Inflammatory and Remodeling Changes in Valvular Endothelial Cells and Valvular Interstitial Cells in a Gelatin Methacrylate 3D Model of the Human Aortic Valve

Calcific aortic valve disease (CAVD), a degenerative disease characterized by inflammation, fibrosis and calcification, is accelerated in diabetes. Hyperglycemia contributes to this process by mechanisms that still need to be uncovered. We have recently developed a 3D model of the human aortic valve based on gelatin methacrylate and revealed that high glucose (HG) induced osteogenic molecules and increased calcium deposits in a pro-osteogenic environment. To further understand the events leading to calcification in diabetic conditions in CAVD, we analyzed here the inflammatory and remodeling mechanisms induced by HG in our 3D model. We exposed valvular endothelial cells (VEC) and interstitial cells (VIC) to normal glucose (NG) or HG for 7 and 14 days, then we isolated and separated the cells by anti-CD31 immunomagnetic beads. The changes induced by HG in the 3D model were investigated by real-time polymerase chain reaction (RT-PCR), Western blot, enzyme-linked immunosorbent assay (ELISA) and immunofluorescence. Our results showed that HG induced expression of different cytokines, cell adhesion molecules and matrix metalloproteinases in VEC and VIC. In addition, protein kinase C was increased in VEC and VIC, indicating molecular mechanisms associated with HG induced inflammation and remodeling in both valvular cells. These findings may indicate new biomarkers and targets for therapy in diabetes associated with CAVD

Supplementary_figure_1 – Supplemental material for Diabetes-induced early molecular and functional changes in aortic heart valves in a murine model of atherosclerosis

Supplemental material, Supplementary_figure_1 for Diabetes-induced early molecular and functional changes in aortic heart valves in a murine model of atherosclerosis by Monica Madalina Tucureanu, Alexandru Filippi, Nicoleta Alexandru, Cristina Ana Constantinescu, Letitia Ciortan, Razvan Macarie, Mihaela Vadana, Geanina Voicu, Sabina Frunza, Dan Nistor, Agneta Simionescu, Dan Teodor Simionescu, Adriana Georgescu and Ileana Manduteanu in Diabetes & Vascular Disease Researc

Supplementary_Table_1 – Supplemental material for Diabetes-induced early molecular and functional changes in aortic heart valves in a murine model of atherosclerosis

Supplemental material, Supplementary_Table_1 for Diabetes-induced early molecular and functional changes in aortic heart valves in a murine model of atherosclerosis by Monica Madalina Tucureanu, Alexandru Filippi, Nicoleta Alexandru, Cristina Ana Constantinescu, Letitia Ciortan, Razvan Macarie, Mihaela Vadana, Geanina Voicu, Sabina Frunza, Dan Nistor, Agneta Simionescu, Dan Teodor Simionescu, Adriana Georgescu and Ileana Manduteanu in Diabetes & Vascular Disease Researc

Supplementary_Table_1 – Supplemental material for Diabetes-induced early molecular and functional changes in aortic heart valves in a murine model of atherosclerosis

Supplemental material, Supplementary_Table_1 for Diabetes-induced early molecular and functional changes in aortic heart valves in a murine model of atherosclerosis by Monica Madalina Tucureanu, Alexandru Filippi, Nicoleta Alexandru, Cristina Ana Constantinescu, Letitia Ciortan, Razvan Macarie, Mihaela Vadana, Geanina Voicu, Sabina Frunza, Dan Nistor, Agneta Simionescu, Dan Teodor Simionescu, Adriana Georgescu and Ileana Manduteanu in Diabetes & Vascular Disease Researc