cyCombine allows for robust integration of single-cell cytometry datasets within and across technologies
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Søren H. Dam | Catherine J. Wu | L. Rassenti | T. Kipps | L. Olsen | J. Lederer | M. Leipold | M. Barnkob | S. Gohil | N. Purroy | C. B. Pedersen | Jennifer Nguyen | S. H. Dam | Michael D. Leipold
[1] Fabian J Theis,et al. Ultra‐high sensitivity mass spectrometry quantifies single‐cell proteome changes upon perturbation , 2020, bioRxiv.
[2] James L. Melville. The Uniform Manifold Approximation and Projection (UMAP) Method for Dimensionality Reduction [R package uwot version 0.1.10] , 2020 .
[3] T. Speed,et al. Removing unwanted variation with CytofRUV to integrate multiple CyTOF datasets , 2020, eLife.
[4] M. Jaimes,et al. OMIP‐069: Forty‐Color Full Spectrum Flow Cytometry Panel for Deep Immunophenotyping of Major Cell Subsets in Human Peripheral Blood , 2020, Cytometry. Part A : the journal of the International Society for Analytical Cytology.
[5] Rui Yang,et al. Multi-batch cytometry data integration for optimal immunophenotyping , 2020, bioRxiv.
[6] J. Mikes,et al. Systems-Level Immunomonitoring from Acute to Recovery Phase of Severe COVID-19 , 2020, Cell Reports Medicine.
[7] Jonathan A. Rebhahn,et al. SwiftReg cluster registration automatically reduces flow cytometry data variability including batch effects , 2020, Communications Biology.
[8] Marta E. Alarcón-Riquelme,et al. Key steps and methods in the experimental design and data analysis of highly multi-parametric flow and mass cytometry , 2020, Computational and structural biotechnology journal.
[9] Lars Rønn Olsen,et al. Algorithmic Clustering Of Single‐Cell Cytometry Data—How Unsupervised Are These Analyses Really? , 2019, Cytometry. Part A : the journal of the International Society for Analytical Cytology.
[10] R. Irizarry. ggplot2 , 2019, Introduction to Data Science.
[11] C. Fegan,et al. Increased frequency of CD4+PD‐1+HLA‐DR+ T cells is associated with disease progression in CLL , 2019, British journal of haematology.
[12] Yvan Saeys,et al. CytoNorm: A Normalization Algorithm for Cytometry Data , 2019, Cytometry. Part A : the journal of the International Society for Analytical Cytology.
[13] Debashis Ghosh,et al. Minimizing Batch Effects in Mass Cytometry Data , 2019, Front. Immunol..
[14] B. Porse,et al. Quantitative single-cell proteomics as a tool to characterize cellular hierarchies , 2019, Nature Communications.
[15] Paul J. Hoffman,et al. Comprehensive Integration of Single-Cell Data , 2018, Cell.
[16] Lars R Olsen,et al. The anatomy of single cell mass cytometry data. , 2019, Cytometry. Part A : the journal of the International Society for Analytical Cytology.
[17] Uri Shaham,et al. Batch Effect Removal via Batch-Free Encoding , 2018, bioRxiv.
[18] Mark D. Robinson,et al. diffcyt: Differential discovery in high-dimensional cytometry via high-resolution clustering , 2018, Communications Biology.
[19] Mark D. Robinson,et al. Compensation of Signal Spillover in Suspension and Imaging Mass Cytometry , 2018, Cell systems.
[20] Reinhard Dummer,et al. High-dimensional single-cell analysis predicts response to anti-PD-1 immunotherapy , 2018, Nature Network Boston.
[21] Kevin R. Moon,et al. Exploring single-cell data with deep multitasking neural networks , 2017, Nature Methods.
[22] R. Tibshirani,et al. An immune clock of human pregnancy , 2017, Science Immunology.
[23] H. Swerdlow,et al. Large-scale simultaneous measurement of epitopes and transcriptomes in single cells , 2017, Nature Methods.
[24] John C. Marioni,et al. Testing for differential abundance in mass cytometry data , 2017, Nature Methods.
[25] N. Slavov,et al. SCoPE-MS: mass spectrometry of single mammalian cells quantifies proteome heterogeneity during cell differentiation , 2017, Genome Biology.
[26] Jun Zhao,et al. Removal of batch effects using distribution‐matching residual networks , 2016, Bioinform..
[27] G. Nolan,et al. Mass Cytometry: Single Cells, Many Features , 2016, Cell.
[28] G. Walther,et al. Earth Mover’s Distance (EMD): A True Metric for Comparing Biomarker Expression Levels in Cell Populations , 2016, PloS one.
[29] Eli R. Zunder,et al. Palladium-based mass tag cell barcoding with a doublet-filtering scheme and single-cell deconvolution algorithm , 2015, Nature Protocols.
[30] Greg Finak,et al. High‐throughput flow cytometry data normalization for clinical trials , 2014, Cytometry. Part A : the journal of the International Society for Analytical Cytology.
[31] Sean C. Bendall,et al. Normalization of mass cytometry data with bead standards , 2013, Cytometry. Part A : the journal of the International Society for Analytical Cytology.
[32] J. Gribben,et al. T cells from CLL patients exhibit features of T-cell exhaustion but retain capacity for cytokine production. , 2013, Blood.
[33] E. Waller,et al. Translational Applications of Flow Cytometry in Clinical Practice , 2012, The Journal of Immunology.
[34] C. Fegan,et al. Expansion of a CD8+PD-1+ Replicative Senescence Phenotype in Early Stage CLL Patients Is Associated with Inverted CD4:CD8 Ratios and Disease Progression , 2011, Clinical Cancer Research.
[35] Matthew D. Wilkerson,et al. ConsensusClusterPlus: a class discovery tool with confidence assessments and item tracking , 2010, Bioinform..
[36] Ryan R Brinkman,et al. Per‐channel basis normalization methods for flow cytometry data , 2009, Cytometry. Part A : the journal of the International Society for Analytical Cytology.
[37] O. Ornatsky,et al. Mass cytometry: technique for real time single cell multitarget immunoassay based on inductively coupled plasma time-of-flight mass spectrometry. , 2009, Analytical chemistry.
[38] Cheng Li,et al. Adjusting batch effects in microarray expression data using empirical Bayes methods. , 2007, Biostatistics.
[39] Teuvo Kohonen,et al. Self-organized formation of topologically correct feature maps , 2004, Biological Cybernetics.