Search Results - "Fabian, J."

Current best practices in single‐cell RNA‐seq analysis: a tutorial by Luecken, Malte D, Theis, Fabian J

“…Single‐cell RNA‐seq has enabled gene expression to be studied at an unprecedented resolution. The promise of this technology is attracting a growing user base…”
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Over 1000 tools reveal trends in the single-cell RNA-seq analysis landscape by Zappia, Luke, Theis, Fabian J

“…Recent years have seen a revolution in single-cell RNA-sequencing (scRNA-seq) technologies, datasets, and analysis methods. Since 2016, the scRNA-tools…”
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SCANPY: large-scale single-cell gene expression data analysis by Wolf, F Alexander, Angerer, Philipp, Theis, Fabian J

“…SCANPY is a scalable toolkit for analyzing single-cell gene expression data. It includes methods for preprocessing, visualization, clustering, pseudotime and…”
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Diffusion maps for high-dimensional single-cell analysis of differentiation data by Haghverdi, Laleh, Buettner, Florian, Theis, Fabian J

“…Single-cell technologies have recently gained popularity in cellular differentiation studies regarding their ability to resolve potential heterogeneities in…”
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Generalizing RNA velocity to transient cell states through dynamical modeling by Bergen, Volker, Lange, Marius, Peidli, Stefan, Wolf, F. Alexander, Theis, Fabian J.

“…RNA velocity has opened up new ways of studying cellular differentiation in single-cell RNA-sequencing data. It describes the rate of gene expression change…”
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Single-cell RNA-seq denoising using a deep count autoencoder by Eraslan, Gökcen, Simon, Lukas M., Mircea, Maria, Mueller, Nikola S., Theis, Fabian J.

“…Single-cell RNA sequencing (scRNA-seq) has enabled researchers to study gene expression at a cellular resolution. However, noise due to amplification and…”
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scGen predicts single-cell perturbation responses by Lotfollahi, Mohammad, Wolf, F. Alexander, Theis, Fabian J.

“…Accurately modeling cellular response to perturbations is a central goal of computational biology. While such modeling has been based on statistical,…”
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Diffusion pseudotime robustly reconstructs lineage branching by Haghverdi, Laleh, Büttner, Maren, Wolf, F Alexander, Buettner, Florian, Theis, Fabian J

“…Diffusion pseudotime (DPT) enables robust and scalable inference of cellular trajectories, branching events, metastable states and underlying gene dynamics…”
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Spatial components of molecular tissue biology by Palla, Giovanni, Fischer, David S., Regev, Aviv, Theis, Fabian J.

“…Methods for profiling RNA and protein expression in a spatially resolved manner are rapidly evolving, making it possible to comprehensively characterize cells…”
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A test metric for assessing single-cell RNA-seq batch correction by Büttner, Maren, Miao, Zhichao, Wolf, F. Alexander, Teichmann, Sarah A., Theis, Fabian J.

“…Single-cell transcriptomics is a versatile tool for exploring heterogeneous cell populations, but as with all genomics experiments, batch effects can hamper…”
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destiny: diffusion maps for large-scale single-cell data in R by Angerer, Philipp, Haghverdi, Laleh, Büttner, Maren, Theis, Fabian J, Marr, Carsten, Buettner, Florian

“…: Diffusion maps are a spectral method for non-linear dimension reduction and have recently been adapted for the visualization of single-cell expression data…”
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Deep learning: new computational modelling techniques for genomics by Eraslan, Gökcen, Avsec, Žiga, Gagneur, Julien, Theis, Fabian J.

“…As a data-driven science, genomics largely utilizes machine learning to capture dependencies in data and derive novel biological hypotheses. However, the…”
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Scalable Parameter Estimation for Genome-Scale Biochemical Reaction Networks by Fröhlich, Fabian, Kaltenbacher, Barbara, Theis, Fabian J, Hasenauer, Jan

“…Mechanistic mathematical modeling of biochemical reaction networks using ordinary differential equation (ODE) models has improved our understanding of small-…”
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CellRank for directed single-cell fate mapping by Lange, Marius, Bergen, Volker, Klein, Michal, Setty, Manu, Reuter, Bernhard, Bakhti, Mostafa, Lickert, Heiko, Ansari, Meshal, Schniering, Janine, Schiller, Herbert B., Pe’er, Dana, Theis, Fabian J.

“…Computational trajectory inference enables the reconstruction of cell state dynamics from single-cell RNA sequencing experiments. However, trajectory inference…”
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Squidpy: a scalable framework for spatial omics analysis by Palla, Giovanni, Spitzer, Hannah, Klein, Michal, Fischer, David, Schaar, Anna Christina, Kuemmerle, Louis Benedikt, Rybakov, Sergei, Ibarra, Ignacio L., Holmberg, Olle, Virshup, Isaac, Lotfollahi, Mohammad, Richter, Sabrina, Theis, Fabian J.

“…Spatial omics data are advancing the study of tissue organization and cellular communication at an unprecedented scale. Flexible tools are required to store,…”
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Benchmarking atlas-level data integration in single-cell genomics by Luecken, Malte D., Büttner, M., Chaichoompu, K., Danese, A., Interlandi, M., Mueller, M. F., Strobl, D. C., Zappia, L., Dugas, M., Colomé-Tatché, M., Theis, Fabian J.

“…Single-cell atlases often include samples that span locations, laboratories and conditions, leading to complex, nested batch effects in data. Thus, joint…”
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RNA velocity—current challenges and future perspectives by Bergen, Volker, Soldatov, Ruslan A, Kharchenko, Peter V, Theis, Fabian J

“…RNA velocity has enabled the recovery of directed dynamic information from single‐cell transcriptomics by connecting measurements to the underlying kinetics of…”
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PAGA: graph abstraction reconciles clustering with trajectory inference through a topology preserving map of single cells by Wolf, F Alexander, Hamey, Fiona K, Plass, Mireya, Solana, Jordi, Dahlin, Joakim S, Göttgens, Berthold, Rajewsky, Nikolaus, Simon, Lukas, Theis, Fabian J

“…Single-cell RNA-seq quantifies biological heterogeneity across both discrete cell types and continuous cell transitions. Partition-based graph abstraction…”
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Computational analysis of cell-to-cell heterogeneity in single-cell RNA-sequencing data reveals hidden subpopulations of cells by Buettner, Florian, Natarajan, Kedar N, Casale, F Paolo, Proserpio, Valentina, Scialdone, Antonio, Theis, Fabian J, Teichmann, Sarah A, Marioni, John C, Stegle, Oliver

“…Hidden cell sub-populations are detected by accounting for confounding variation inthe analysis of single-cell RNA-seq data. Recent technical developments have…”
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A BaSiC tool for background and shading correction of optical microscopy images by Peng, Tingying, Thorn, Kurt, Schroeder, Timm, Wang, Lichao, Theis, Fabian J., Marr, Carsten, Navab, Nassir

“…Quantitative analysis of bioimaging data is often skewed by both shading in space and background variation in time. We introduce BaSiC, an image correction…”
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