Biology subjects

Vidal, E.

Publications and source records attributed to Vidal, E..

5 recordsLinked to original sources

bioRxiv2018

Comparative Analysis of Linear and Nonlinear Dimension Reduction Techniques on Mass Cytometry Data

Mass cytometry, also known as CyTOF, is a newly developed technology for quantification and classification of immune cells that can allow for analysis of over three dozen protein markers per cell. The high dimensional data that is generated requires innovative methods for analysis and visualization. We conducted a comparative analysis of four dimension reduction techniques - principal component analysis (PCA), isometric feature mapping (Isomap), t-distributed stochastic neighbor embedding (t-SNE), and Diffusion Maps by implementing them on benchmark mass cytometry data sets. We compare the results of these reductions using computation time, residual variance, a newly developed comparison metric we term neighborhood proportion error (NPE), and two-dimensional visualizations. We find that t-SNE and Diffusion Maps are the two most effective methods for preserving relationships of interest among cells and providing informative visualizations. In low dimensional embeddings, t-SNE exhibits well-defined phenotypic clustering. Additionally, Diffusion Maps can represent cell differentiation pathways with long projections along each diffusion component. We thus recommend a complementary approach using t-SNE and Diffusion Maps in order to extract diverse and informative cell relationship information in a two-dimensional setting from CyTOF data.

bioinformatics↗

bioRxiv2017

Hormone Control Regions mediate opposing steroid receptor-dependent genome organizations

In breast cancer cells, topologically associating domains (TADs) behave as units of hormonal gene regulation with transcripts within hormone responsive TADs changing coordinately their expression in response to steroid hormones. Here we further described that responsive TADs contain 20-100 kb-long clusters of intermingled estrogen receptor (ER) and progesterone receptor (PR) binding sites, hereafter called Hormone-Control Regions (HCRs). We identified more than 200 HCRs, which are frequently bound by ER and PR even in the absence of hormones. These HCRs establish steady long-distance inter-TAD interactions between them and organize characteristic looping structures with promoters even in the absence of hormones. This organization is dependent on the expression of the receptors and is further dynamically modulated in response to steroid hormones. HCRs function as platforms integrating different signals resulting in some cases in opposite transcriptional responses to estrogens or progestins. Altogether, these results suggest that steroid hormone receptors act not only as hormone-regulated sequence-specific transcription factors, but also as local and global genome organizers.\n\nHighlightsHormone responsive TADs are organized around conserved large regulatory regions (HCRs) enriched in ER and PR.\nHCR contact promoters within their TADs and engaged long-range inter-TADs contacts between them.\nBinding of the receptors in absence of hormones maintains global HCR-HCR interactions and intra-TADs regulatory loops.\nHCRs can integrate the hormone signals in divergent ways leading to opposite restructuration of TADs in response to Estrogens or Progestins.

genomics↗

bioRxiv2017

OneD: increasing reproducibility of Hi-C Samples with abnormal karyotypes

The three-dimensional conformation of genomes is an essential component of their biological activity. The advent of the Hi-C technology enabled an unprecedented progress in our understanding of genome structures. However, Hi-C is subject to systematic biases that can compromise downstream analyses. Several strategies have been proposed to remove those biases, but the issue of abnormal karyotypes received little attention. Many experiments are performed in cancer cell lines, which typically harbor large-scale copy number variations that create visible defects on the raw Hi-C maps. The consequences of these widespread artifacts on the normalized maps are mostly unexplored. We observed that current normalization methods are not robust to the presence of large-scale copy number variations, potentially obscuring biological differences and enhancing batch effects. To address this issue, we developed an alternative approach designed to take into account chromosomal abnormalities. The method, called OneD, increases reproducibility among replicates of Hi-C samples with abnormal karyotype, outperforming previous methods significantly. On normal karyotypes, OneD fared equally well as state-of-the-art methods, making it a safe choice for Hi-C normalization. OneD is fast and scales well in terms of computing resources for resolutions up to 1 kbp. OneD is implemented as an R package available at http://www.github.com/qenvio/dryhic.

bioinformatics↗

bioRxiv2017

Managing The Analysis Of High-Throughput Sequencing Data

T47D_rep2 and b1913e6c1_51720e9cf were two Hi-C samples. They were born and processed at the same time, yet their fates were very different. The life of b1913e6c1_51720e9cf was simple and fruitful, while that of T47D_rep2 was full of accidents and sorrow. At the heart of these differences lies the fact that b1913e6c1_51720e9cf was born under a lab culture of Documentation, Automation, Traceability, Autonomy and compliance with the FAIR Principles. Their lives are a lesson for those who wish to embark on the journey of managing high throughput sequencing data.

bioinformatics↗

bioRxiv2017

Transcription Factors Orchestrate Dynamic Interplay Between Genome Topology And Gene Regulation During Cell Reprogramming

Chromosomal architecture is known to influence gene expression, yet its role in controlling cell fate remains poorly understood. Reprogramming of somatic cells into pluripotent stem cells by the transcription factors (TFs) Oct4, Sox2, Klf4 and Myc offers an opportunity to address this question but is severely limited by the low proportion of responding cells. We recently developed a highly efficient reprogramming protocol that synchronously converts somatic into pluripotent stem cells. Here, we employ this system to integrate time-resolved changes in genome topology with gene expression, TF binding and chromatin state dynamics. This revealed that TFs drive topological genome reorganization at multiple architectural levels, which often precedes changes in gene expression. Removal of locus-specific topological barriers can explain why pluripotency genes are activated sequentially, instead of simultaneously, during reprogramming. Taken together, our study implicates genome topology as an instructive force for implementing transcriptional programs and cell fate in mammals.

molecular biology↗