An introduction to representation learning for single-cell data analysis

Article Computer Science, Artificial Intelligence

Fotomics: fourier transform-based omics imagification for deep learning-based cell-identity mapping using single-cell omics profiles

Seid Miad Zandavi et al.

Summary: This study proposes a method to transform high-dimensional molecular measurements into two-dimensional images, improving the representation and classification of biological samples using automated image recognition methods.

ARTIFICIAL INTELLIGENCE REVIEW (2023)

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Letter Biotechnology & Applied Microbiology

A Python library for probabilistic analysis of single-cell omics data

Adam Gayoso et al.

NATURE BIOTECHNOLOGY (2022)

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Article Biochemical Research Methods

Benchmarking atlas-level data integration in single-cell genomics

Malte D. Luecken et al.

Summary: This study benchmarked 68 method and preprocessing combinations on 85 batches of gene expression data, highlighting the importance of highly variable gene selection in improving method performance. When dealing with complex integration tasks, scANVI, Scanorama, scVI, and scGen consistently performed well, while the performance of single-cell ATAC-sequencing integration was strongly influenced by the choice of feature space.

NATURE METHODS (2022)

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Article Multidisciplinary Sciences

C-type lectin receptor CLEC4A2 promotes tissue adaptation of macrophages and protects against atherosclerosis

Inhye Park et al.

Summary: CLEC4A2 is a distinguishing feature of vascular resident macrophages that have athero-protective properties. It promotes monocyte-to-macrophage differentiation and is critical for maintaining vascular homeostasis. CLEC4A2 deficiency leads to dysfunctional cholesterol metabolism and worsens the development of atherosclerosis.

NATURE COMMUNICATIONS (2022)

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Article Biochemical Research Methods

Integration, exploration, and analysis of high-dimensional single-cell cytometry data using Spectre

Thomas Myles Ashhurst et al.

Summary: The Spectre R package enables comprehensive integration and analysis of high-dimensional cytometry data from different batches or experiments. It streamlines the analytical stages of raw data pre-processing, batch alignment, integration, clustering, visualization, and statistical analysis, making it suitable for bioinformaticians and laboratory scientists. Spectre's modular design allows for scalable analysis of very large datasets generated by flow cytometry, mass cytometry, or spectral cytometry.

CYTOMETRY PART A (2022)

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Review Biochemistry & Molecular Biology

Computational Methods for Single-Cell Imaging and Omics Data Integration

Ebony Rose Watson et al.

Summary: Integrating single cell omics and imaging allows for a comprehensive understanding of the mechanisms driving tissue-level phenotypes. This review discusses the current technologies and methods for generating, processing, and analyzing single-cell omics and imaging data, and how they can be integrated to enhance our understanding of complex biological phenomena.

FRONTIERS IN MOLECULAR BIOSCIENCES (2022)

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Article Oncology

The Spatial Landscape of Progression and Immunoediting in Primary Melanoma at Single-Cell Resolution

Ajit J. Nirmal et al.

Summary: This study investigates immune evasion and immunoediting in primary melanoma using high-plex imaging, 3D high-resolution microscopy, and spatially resolved microregion transcriptomics. The results show significant changes in cellular neighborhoods involving tumor, immune, and stromal cells during the progression of precursor states, melanoma in situ, and invasive tumor. Immunossuppression is already detectable in precursor regions, and a suppressive environment is formed along the tumor-stromal boundary during invasion, involving cytokine gradients, MHC-II expression, and PD1-PDL1-mediated cell contacts.

CANCER DISCOVERY (2022)

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Article Biochemical Research Methods

Self-supervised deep learning encodes high-resolution features of protein subcellular localization

Hirofumi Kobayashi et al.

Summary: Cytoself is a self-supervised deep learning-based approach that accurately predicts and clusters protein localization from fluorescence images, providing insights into the diversity and complexity of cellular architecture.

NATURE METHODS (2022)

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Article Biochemistry & Molecular Biology

Disentangling single-cell omics representation with a power spectral density-based feature extraction

Seid Miad Zandavi et al.

Summary: Emerging single-cell technologies provide high-resolution measurements of distinct cellular modalities, but the complexity and inaccuracies in single-cell sequencing measurements can hinder data analysis. This study presents a novel preprocessing method that reduces data complexity, enhances cell-type separation, and enables the identification of rare cells.

NUCLEIC ACIDS RESEARCH (2022)

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Editorial Material Multidisciplinary Sciences

Diagonal integration of multimodal single-cell data: potential pitfalls and paths forward

Yang Xu et al.

Summary: Diagonal integration of multimodal single-cell data is a trending topic, but bridging gaps is essential to enable novel biological discoveries using diagonal methods.

NATURE COMMUNICATIONS (2022)

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Review Oncology

Mapping Phenotypic Plasticity upon the Cancer Cell State Landscape Using Manifold Learning

Daniel B. Burkhardt et al.

Summary: Phenotypic plasticity refers to the ability of cancer cells to undergo dynamic non-genetic changes in cell state, which amplifies cancer heterogeneity and promotes metastasis and therapy evasion. With the advancement of technologies to record molecular mechanisms at single-cell resolution, manifold learning techniques can effectively model cell state dynamics, resembling our understanding of the cell state landscape. State-gating therapies targeting phenotypic plasticity are anticipated to limit cancer heterogeneity, metastasis, and therapy resistance.

CANCER DISCOVERY (2022)

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Article Multidisciplinary Sciences

A strategy to quantify myofibroblast activation on a continuous spectrum

Alexander Hillsley et al.

Summary: The study developed a strategy based on microscopy imaging and machine learning methods to quantify the activation of myofibroblasts. The results demonstrated the existence of a continuous spectrum of myofibroblast activation and provided an imaging-based strategy to determine the position of a cell on that spectrum.

SCIENTIFIC REPORTS (2022)

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Article Biochemical Research Methods

scSemiGAN: a single-cell semi-supervised annotation and dimensionality reduction framework based on generative adversarial network

Zhongyuan Xu et al.

Summary: The article proposes a single-cell semi-supervised cell-type annotation and dimensionality reduction framework based on a generative adversarial network, called scSemiGAN, which is capable of performing deep latent representation learning and cell-type label prediction simultaneously. scSemiGAN achieves competitive or superior performance in multiple downstream tasks including cell-type annotation, latent representation visualization, confounding factor removal, and enrichment analysis.

BIOINFORMATICS (2022)

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Article Computer Science, Artificial Intelligence

Simultaneous dimensionality reduction and integration for single-cell ATAC-seq data using deep learning

Wolfgang Kopp et al.

Summary: Advancements in single-cell technologies have allowed for the study of chromatin accessibility at a more detailed level, but the complexity and heterogeneity of the data pose challenges for computational analysis. To address this, a dedicated computational approach using a variational auto-encoder has been developed, enabling both dimensionality reduction and batch correction for single-cell ATAC-seq data.

NATURE MACHINE INTELLIGENCE (2022)

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Article Biotechnology & Applied Microbiology

Benchmarking clustering algorithms on estimating the number of cell types from single-cell RNA-sequencing data

Lijia Yu et al.

Summary: This study systematically benchmarks a range of clustering algorithms for single-cell RNA-seq data and summarizes the strengths and weaknesses of each method. The authors evaluate the performance of the algorithms using a large number of datasets and provide a multi-aspect recommendation to users.

GENOME BIOLOGY (2022)

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Review Biotechnology & Applied Microbiology

Statistics or biology: the zero-inflation controversy about scRNA-seq data

Ruochen Jiang et al.

Summary: Researchers have differing views on the interpretation of zeros in single-cell RNA-seq data, with some considering them as biological signals while others see them as missing data; factors contributing to biological and non-biological zeros were discussed, as well as their impact on data analysis; emphasis was placed on the importance of transparent analysis.

GENOME BIOLOGY (2022)

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Article Multidisciplinary Sciences

Multi-domain translation between single-cell imaging and sequencing data using autoencoders

Karren Dai Yang et al.

Summary: The authors use autoencoders to learn a probabilistic coupling and map different data modalities to a shared latent space, presenting an approach for integrating vastly different modalities. The integration of imaging and transcriptomics is still an open challenge, but this method provides a framework for diverse applications in biomedical discovery.

NATURE COMMUNICATIONS (2021)

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Article Biochemistry & Molecular Biology

Semisupervised adversarial neural networks for single-cell classification

Jacob C. Kimmel et al.

Summary: scNym is a semi-supervised adversarial neural network that can transfer cell identity annotations between different experiments by learning rich representations of cell identities from both labeled and unlabeled datasets. It shows superior performance in transferring annotations across experiments and can synthesize information from multiple datasets to improve accuracy. Additionally, scNym models are well calibrated, interpretable, and can be enhanced with saliency methods.

GENOME RESEARCH (2021)

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Article Multidisciplinary Sciences

Single-cell epigenomics reveals mechanisms of human cortical development

Ryan S. Ziffra et al.

Summary: During mammalian development, differences in chromatin state play a crucial role in cell fate specification and defining cell identity in the developing brain. Through studying chromatin accessibility patterns, it was found that cerebral organoids recapitulate most cell-type-specific enhancer accessibility patterns but lack some open chromatin regions found in vivo. Systematic comparison of chromatin accessibility across brain regions revealed unexpected diversity among neural progenitor cells and highlighted the role of retinoic acid signaling in neuronal lineage specification in the prefrontal cortex.

NATURE (2021)

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Article Genetics & Heredity

ArchR is a scalable software package for integrative single-cell chromatin accessibility analysis

Jeffrey M. Granja et al.

Summary: ArchR is a software suite for single-cell chromatin accessibility analysis in R, allowing fast and comprehensive analysis and processing of single-cell chromatin accessibility data, providing multiple analysis functions, and capable of handling large amounts of single-cell data in a short period of time.

NATURE GENETICS (2021)

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Review Biotechnology & Applied Microbiology

Computational principles and challenges in single-cell data integration

Ricard Argelaguet et al.

Summary: The development of single-cell multimodal assays has provided a powerful tool for investigating cellular heterogeneity in multiple dimensions. Data integration is a key challenge in analyzing single-cell multimodal data, with existing strategies utilizing similar mathematical ideas but having distinct goals and principles.

NATURE BIOTECHNOLOGY (2021)

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Review Biochemistry & Molecular Biology

From imaging a single cell to implementing precision medicine: an exciting new era

Loukia G. Karacosta

Summary: In the age of high-throughput, single-cell biology, single-cell imaging has shown significant advancements both technologically and in translational applications. The synchronous progress of imaging and computational biology has opened up opportunities for merging the two, providing tools for observing, understanding, and predicting cellular and tissue phenotypes and behaviors. The use of multiplexed single-cell imaging and machine learning algorithms enables patient stratification and predictive diagnostics of clinical specimens. By harnessing computational tools and systems biology approaches, the exciting applications and future implementation of single-cell imaging in precision medicine can be further strengthened.

EMERGING TOPICS IN LIFE SCIENCES (2021)

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Article Genetics & Heredity

A Comparison for Dimensionality Reduction Methods of Single-Cell RNA-seq Data

Ruizhi Xiang et al.

Summary: The study compared the performance of different dimensionality reduction methods in scRNA-seq data analysis. t-SNE showed the best accuracy and computing cost, while UMAP demonstrated high stability and preserved the cohesion and separation of cell populations.

FRONTIERS IN GENETICS (2021)

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Review Biochemical Research Methods

Supervised application of internal validation measures to benchmark dimensionality reduction methods in scRNA-seq data

Forrest C. Koch et al.

Summary: The study conducted a comprehensive benchmark of dimensionality reduction methods in scRNA-seq data, evaluating the performance of over 25000 low-dimensional embeddings across 33 methods and 55 datasets using IVMs. The findings suggest that hyperparameter optimization with IVMs can lead to near-optimal clustering results.

BRIEFINGS IN BIOINFORMATICS (2021)

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Article Biochemistry & Molecular Biology

Representation learning applications in biological sequence analysis

Hitoshi Iuchi et al.

Summary: Representation learning in biological sequence analysis is a critical method for converting biological sequences into vectors for function and structure estimation. By treating biological sequences as sentences and applying natural language processing techniques, we can better analyze and utilize large amounts of sequencing data.

COMPUTATIONAL AND STRUCTURAL BIOTECHNOLOGY JOURNAL (2021)

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Article Mathematical & Computational Biology

Penalized Latent Dirichlet Allocation Model in Single-Cell RNA Sequencing

Xiaotian Wu et al.

Summary: Single-cell RNA sequencing quantifies gene expression variation at individual cell level, and the penalized Latent Dirichlet Allocation (pLDA) model is developed to reduce the data dimension and extract robust and interpretable biological information from scRNA-seq data. The pLDA model considers genes as words, cells as documents, and latent biological functions as topics, showing improved performances in cell-type classification and providing interpretable topics with biological functions.

STATISTICS IN BIOSCIENCES (2021)

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Article Biochemistry & Molecular Biology

Dimension Reduction and Clustering Models for Single-Cell RNA Sequencing Data: A Comparative Study

Chao Feng et al.

INTERNATIONAL JOURNAL OF MOLECULAR SCIENCES (2020)

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Article Cell Biology

A Quantitative Framework for Evaluating Single-Cell Data Structure Preservation by Dimensionality Reduction Techniques

Cody N. Heiser et al.

CELL REPORTS (2020)

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Article Cell Biology

Single-cell RNA sequencing reveals the heterogeneity of liver-resident immune cells in human

Juanjuan Zhao et al.

CELL DISCOVERY (2020)

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Review Urology & Nephrology

Tools for the analysis of high-dimensional single-cell RNA sequencing data

Yan Wu et al.

NATURE REVIEWS NEPHROLOGY (2020)

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Review Biochemistry & Molecular Biology

Single-cell multiomics: technologies and data analysis methods

Jeongwoo Lee et al.

EXPERIMENTAL AND MOLECULAR MEDICINE (2020)

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Label-free detection of rare circulating tumor cells by image analysis and machine learning

Shen Wang et al.

SCIENTIFIC REPORTS (2020)

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Editorial Material Multidisciplinary Sciences

Single cell transcriptomics comes of age

Sarah Aldridge et al.

NATURE COMMUNICATIONS (2020)

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On hyperparameter optimization of machine learning algorithms: Theory and practice

Li Yang et al.

NEUROCOMPUTING (2020)

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Article Biotechnology & Applied Microbiology

Benchmarking principal component analysis for large-scale single-cell RNA-sequencing

Koki Tsuyuzaki et al.

GENOME BIOLOGY (2020)

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Article Genetics & Heredity

ComBat-seq: batch effect adjustment for RNA-seq count data

Yuqing Zhang et al.

NAR GENOMICS AND BIOINFORMATICS (2020)

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A comparison of single-cell trajectory inference methods

Wouter Saelens et al.

NATURE BIOTECHNOLOGY (2019)

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Article Biochemical Research Methods

cisTopic: cis-regulatory topic modeling on single-cell ATAC-seq data

Carmen Bravo Gonzalez-Blas et al.

NATURE METHODS (2019)

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Review Engineering, Biomedical

Integrating imaging and omics data: A review

Laura Antonelli et al.

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Structure-preserving visualisation of high dimensional single-cell datasets

Benjamin Szubert et al.

SCIENTIFIC REPORTS (2019)

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Review Biochemical Research Methods

Beyond bulk: a review of single cell transcriptomics methodologies and applications

Ashwinikumar Kulkarni et al.

CURRENT OPINION IN BIOTECHNOLOGY (2019)

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Massively parallel single-cell chromatin landscapes of human immune cell development and intratumoral T cell exhaustion

Ansuman T. Satpathy et al.

NATURE BIOTECHNOLOGY (2019)

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Editorial Material Biochemical Research Methods

A dream of single-cell proteomics

Vivien Marx

NATURE METHODS (2019)

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Article Biochemical Research Methods

Data denoising with transfer learning in single-cell transcriptomics

Jingshu Wang et al.

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Learning unsupervised feature representations for single cell microscopy images with paired cell inpainting

Alex X. Lu et al.

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Exploring single-cell data with deep multitasking neural networks

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The art of using t-SNE for single-cell transcriptomics

Dmitry Kobak et al.

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High-Content Imaging of Unbiased Chemical Perturbations Reveals that the Phenotypic Plasticity of the Actin Cytoskeleton Is Constrained

Nicole S. Bryce et al.

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Dimensionality reduction for visualizing single-cell data using UMAP

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Semisoft clustering of single-cell data

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Visualizing structure and transitions in high-dimensional biological data

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Single-cell RNA-seq denoising using a deep count autoencoder

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Advances in mass spectrometry based single-cell metabolomics

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scPred: accurate supervised method for cell-type classification from single-cell RNA-seq data

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Accuracy, robustness and scalability of dimensionality reduction methods for single-cell RNA-seq analysis

Shiquan Sun et al.

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Recovering Gene Interactions from Single-Cell Data Using Data Diffusion

David van Dijk et al.

CELL (2018)

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Single cell transcriptomics in neuroscience: cell classification and beyond

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Interpretable dimensionality reduction of single cell transcriptome data with deep generative models

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Visualizing and Interpreting Single-Cell Gene Expression Datasets with Similarity Weighted Nonnegative Embedding

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Robust classification of single-cell transcriptome data by nonnegative matrix factorization

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Single-cell RNA-seq enables comprehensive tumour and immune cell profiling in primary breast cancer

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The Human Cell Atlas

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MATCHER: manifold alignment reveals correspondence between single cell transcriptome and epigenome dynamics

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Single-cell RNA-seq and computational analysis using temporal mixture modeling resolves TH1/TFH fate bifurcation in malaria

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SLICER: inferring branched, nonlinear cellular trajectories from single cell RNA-seq data

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Diffusion maps for high-dimensional single-cell analysis of differentiation data

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Data-Driven Phenotypic Dissection of AML Reveals Progenitor-like Cells that Correlate with Prognosis

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Spatial reconstruction of single-cell gene expression data

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CIDRE: an illumination-correction method for optical microscopy

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Disentangling Membrane Dynamics and Cell Migration; Differential Influences of F-actin and Cell-Matrix Adhesions

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ZIFA: Dimensionality reduction for zero-inflated single-cell gene expression analysis

Emma Pierson et al.

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Probabilistic PCA of censored data: accounting for uncertainties in the visualization of high-throughput single-cell qPCR data

Florian Buettner et al.

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The dynamics and regulators of cell fate decisions are revealed by pseudotemporal ordering of single cells

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Single-cell technologies for monitoring immune systems

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Nonnegative Matrix Factorization: A Comprehensive Review

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Comparison of Methods for Image-Based Profiling of Cellular Morphological Responses to Small-Molecule Treatment

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Cellular Heterogeneity: Do Differences Make a Difference?

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Systems microscopy: An emerging strategy for the life sciences

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CellProfiler: image analysis software for identifying and quantifying cell phenotypes

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Module networks: identifying regulatory modules and their condition-specific regulators from gene expression data

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Laplacian eigenmaps for dimensionality reduction and data representation

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An introduction to representation learning for single-cell data analysis

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