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Coexpression enables multi-study cellular trajectories of development and disease
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What this work says
Outgoing claims: the papers this work cites, builds on, or argues with.
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Unsupervised Learning of Functional Network Dynamics in Resting State fMRI
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Expression Profile of Long Non-Coding RNAs in Serum of Patients with Multiple Sclerosis
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Disease-Associated Microglia: A Universal Immune Sensor of Neurodegeneration
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Molecular Architecture of the Mouse Nervous System
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Molecular Diversity and Specializations among the Cells of the Adult Mouse Brain
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Single-Cell Multi-omic Integration Compares and Contrasts Features of Brain Cell Identity
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Comprehensive Integration of Single-Cell Data
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Geometric Sketching Compactly Summarizes the Single-Cell Transcriptomic Landscape
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Stepwise estimation of common principal components
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Microglia and necroptosis: The culprits of neuronal cell death in multiple sclerosis
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The TREM2-APOE Pathway Drives the Transcriptional Phenotype of Dysfunctional Microglia in Neurodegenerative Diseases
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Single-Cell RNA Sequencing of Microglia throughout the Mouse Lifespan and in the Injured Brain Reveals Complex Cell-State Changes
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Single-Cell RNA Sequencing Resolves Spatiotemporal Development of Pre-thymic Lymphoid Progenitors and Thymus Organogenesis in Human Embryos
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A High-Resolution Spatiotemporal Atlas of Gene Expression of the Developing Mouse Brain
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Hematopoietic Hierarchy – An Updated Roadmap
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From ‘differential expression’ to ‘differential networking’ – identification of dysfunctional regulatory networks in diseases
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Co-expression in Single-Cell Analysis: Saving Grace or Original Sin?
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Developmental diversification of cortical inhibitory interneurons
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The dynamics and regulators of cell fate decisions are revealed by pseudotemporal ordering of single cells
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Batch effects in single-cell RNA-sequencing data are corrected by matching mutual nearest neighbors
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Integrating single-cell transcriptomic data across different conditions, technologies, and species
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Massively parallel digital transcriptional profiling of single cells
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Diffusion pseudotime robustly reconstructs lineage branching
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Characterizing the replicability of cell types defined by single cell RNA-sequencing data using MetaNeighbor
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The lncRNA Neat1 promotes activation of inflammasomes in macrophages
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Spatial and temporal heterogeneity of mouse and human microglia at single-cell resolution
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The single-cell transcriptional landscape of mammalian organogenesis
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A comparison of single-cell trajectory inference methods
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Efficient integration of heterogeneous single-cell transcriptomes using Scanorama
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Evaluating measures of association for single-cell transcriptomics
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Joint analysis of heterogeneous single-cell RNA-seq dataset collections
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scGen predicts single-cell perturbation responses
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Fast, sensitive and accurate integration of single-cell data with Harmony
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Fast unfolding of communities in large networks
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Evaluation of tools for highly variable gene discovery from single-cell RNA-seq data
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Differential coexpression analysis using microarray data and its application to human cancer
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Sparse inverse covariance estimation with the graphical lasso
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Probabilistic Harmonization and Annotation of Single-cell Transcriptomics Data with Deep Generative Models
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Metric learning enables synthesis of heterogeneous single-cell modalities
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The T-box transcription factor Eomes/Tbr2 regulates neurogenesis in the cortical subventricular zone
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The Higher-Order Singular Value Decomposition: Theory and an Application [Lecture Notes
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Riemannian Dictionary Learning and Sparse Coding for Positive Definite Matrices
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Random Walks, Markov Processes and the Multiscale Modular Organization of Complex Networks
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Controlling the False Discovery Rate: A Practical and Powerful Approach to Multiple Testing
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MCA: Multiresolution Correlation Analysis, a graphical tool for subpopulation identification in single-cell gene expression data
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Loss of the receptor tyrosine kinase Axl leads to enhanced inflammation in the CNS and delayed removal of myelin debris during Experimental Autoimmune Encephalomyelitis
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Exploiting single-cell expression to characterize co-expression replicability
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SCANPY: large-scale single-cell gene expression data analysis
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PAGA: graph abstraction reconciles clustering with trajectory inference through a topology preserving map of single cells
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MetaCell: analysis of single-cell RNA-seq data using K-nn graph partitions
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CRC Standard Probability and Statistics Tables and Formulae
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CRC Standard Probability and Statistics Tables and Formulae
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A Brief Introduction to the Transduction of Neural Activity into Fos Signal
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REVIGO Summarizes and Visualizes Long Lists of Gene Ontology Terms
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ForceAtlas2, a Continuous Graph Layout Algorithm for Handy Network Visualization Designed for the Gephi Software
inferredcites →https://doi.org/10.15252/msb.20188746openalex-snapshot
Current best practices in single‐cell RNA‐seq analysis: a tutorial
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The role of microglia in multiple sclerosis
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Exploring Network Structure, Dynamics, and Function using NetworkX
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Gene Expression Profiling of Multiple Sclerosis Pathology Identifies Early Patterns of Demyelination Surrounding Chronic Active Lesions
inferredcites →https://doi.org/10.7554/elife.42025openalex-snapshot
Microglial SIRPα regulates the emergence of CD11c+ microglia and demyelination damage in white matter
What points at this work
Incoming claims: where this work appears in other papers’ reference lists and arguments.
inferred← citeshttps://doi.org/10.1016/j.cels.2021.04.010openalex-snapshot
Single-cell co-expression analysis reveals that transcriptional modules are shared across cell types in the brain
inferred← citeshttps://doi.org/10.1016/j.cels.2024.04.005openalex-snapshot
Causal gene regulatory analysis with RNA velocity reveals an interplay between slow and fast transcription factors
inferred← citeshttps://doi.org/10.1016/j.coisb.2021.05.005openalex-snapshot
Recent advances in trajectory inference from single-cell omics data
inferred← citeshttps://doi.org/10.1093/bioinformatics/btag135openalex-snapshot
Local transcriptional covariation produces accurate estimates of cell phenotype
inferred← citeshttps://doi.org/10.1146/annurev-biodatasci-012220-100601openalex-snapshot
Computational Methods for Single-Cell RNA Sequencing
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