Appropriate laboratory practices can reduce contamination, but do not eliminate it. Contaminants come from various sources, including reagents. 2021īACKGROUND: The accuracy of microbial community surveys based on marker-gene and metagenomic sequencing (MGS) suffers from the presence of contaminants-DNA sequences not truly present in the sample. Modeling the heterogeneity in COVID-19's reproductive number and its impact on predictive scenarios JOURNAL OF APPLIED STATISTICS Donnat, C., Holmes, S.Our approach represents a broadly applicable strategy to leverage single-cell resolution data maximally toward uncovering CCC circuitry and rich niche-phenotype relationships in health and disease. Finally, we utilize single-cell communication networks calculated using Scriabin to follow communication pathways that operate between timepoints in longitudinal datasets, highlighting bystander cells as important initiators of inflammatory reactions in acute SARS-CoV-2 infection. We then apply Scriabin to uncover co-expressed programs of CCC from atlas-scale datasets, validating known communication pathways required for maintaining the intestinal stem cell niche as well as previously unappreciated modes of intercellular communication. We leverage multiple published datasets to show that Scriabin recovers expected CCC edges and use spatial transcriptomic data to validate that the recovered edges are biologically meaningful. Here we present Scriabin a" a flexible and scalable framework for comparative analysis of CCC at single-cell resolution. Inference of cell-cell communication (CCC) from single-cell RNA-sequencing data is a powerful technique to uncover putative axes of multicellular coordination, yet existing methods perform this analysis at the level of the cell type or cluster, discarding single-cell level information. The tools developed in this project are all open source packages developed in R and provide an example of reproducible research in action. In a long term collaboration with Professor David Relman (Stanford Medical School) we are developing a multi-table toolbox of non parametric methods that enable users to normalize and visualize the multiple facets of the microbiota in the human body under different classes of perturbations. More general manifolds have also proved useful in one of our current projects, joint with Xavier Pennec of INRIA-SophiaAntiolis which focuses on the uses of differential geometry in computational anatomy and image processing. The statistical bases for these nonparametric methods are computer intensive methods using optimization and Kernels and we often find useful embeddings of high dimensional data in low dimensional structures, the extreme case being finding a natural ordering in high dimensional data. This has proved useful in the study of drug resistant mutations in HIV and in the study of the dynamics of bacterial communities in the Human Microbiome. We have generalized methods such as Principal Components Analysis (PCA) to more diverse data incorporating spatial information as well as tree dependency structures. Whether using image analysis and segmentation for the study of cancer and immune cell interactions, or brain imaging and DNA sequence analyses for the study of dependencies between genetic and neurological dynamics, all these statistical studies have involved large complex datasets of different types where dynamics of interactions between different components of a system are the key to understanding the underlying biology. Our work focuses on large heterogeneous multi-layer data analyses.
0 Comments
Leave a Reply. |
AuthorWrite something about yourself. No need to be fancy, just an overview. ArchivesCategories |