The human body at cellular resolution: the NIH Human Biomolecular Atlas Program. Expression Atlas update: from tissues to single cells. Gene Expression Omnibus: NCBI gene expression and hybridization array data repository. CellMarker: a manually curated resource of cell markers in human and mouse. PanglaoDB: a web server for exploration of mouse and human single-cell RNA sequencing data. Archived: SCSig collection: Signatures of Single Cell Identities įranzén, O., Gan, L.-M. Comprehensive integration of single-cell data. Dimensionality reduction for visualizing single-cell data using UMAP. Clustering single cells: a review of approaches on high-and low-depth single-cell RNA-seq data. Challenges in unsupervised clustering of single-cell RNA-seq data. A systematic performance evaluation of clustering methods for single-cell RNA-seq data. SCANPY: large-scale single-cell gene expression data analysis. Integrating single-cell transcriptomic data across different conditions, technologies, and species. BICF Cellranger count analysis workflow (version publish_1.2.0). Current best practices in single-cell RNA-seq analysis: a tutorial. Single-cell RNA sequencing technologies and bioinformatics pipelines. Tutorial: guidelines for the experimental design of single-cell RNA sequencing studies. Comparative analysis of single-cell RNA sequencing methods. Optimal-transport analysis of single-cell gene expression identifies developmental trajectories in reprogramming. Single-cell messenger RNA sequencing reveals rare intestinal cell types. Single-cell transcriptomics of 20 mouse organs creates a Tabula Muris. Construction of a human cell landscape at single-cell level. Highly parallel genome-wide expression profiling of individual cells using nanoliter droplets. Droplet barcoding for single-cell transcriptomics applied to embryonic stem cells. Strategies for converting RNA to amplifiable cDNA for single-cell RNA sequencing methods. The Gene Matrix Transposed (GMT) file used for pathway analysis in the accompanying R code can be downloaded from. The supplementary data from the Diaz-Mejia et al. 37 with read data from NCBI Sequence Read Archive accession number SRX1723926. The collection of PBMC marker genes used in the accompanying R code is available from Diaz-Mejia et al. The query data set used in the accompanying R code is available from 10x Genomics and can be downloaded from. The human bulk RNA-seq data used to generate the reference data set in the accompanying R code ( and ) are available from the Database of Immune Cell Expression and can be downloaded in R through the package ‘celldex’ 43 by the command DatabaseImmuneCellExpressionData(). The analyzed data can be accessed interactively at. 6 are available through the NCBI GEO accession GSE129788, as reported by Ximerakis et al. 3 and 7 are available from 10x Genomics and can be downloaded from. The analyzed data from which the map was directly created can also be accessed interactively as the R package HumanLiver from. 100 through the NCBI GEO accession GSE115469. 1 and 4 are available from MacParland et al. The data used to generate this tutorial are openly available at the following sources. Basic familiarity with computer software is assumed, and basic knowledge of programming (e.g., in the R language) is recommended. Guiding principles and specific recommendations for software tools and resources that can be used for each step are covered, and an R notebook is included to help run the recommended workflow. Frequently encountered challenges are discussed, as well as strategies to address them. We recommend a three-step workflow including automatic cell annotation (wherever possible), manual cell annotation and verification. This tutorial focuses on how to interpret these data to identify cell types, states and other biologically relevant patterns with the objective of creating an annotated map of cells. Standard experimental protocols and analysis workflows have been developed to create single-cell transcriptomic maps from tissues. Single-cell transcriptomics can profile thousands of cells in a single experiment and identify novel cell types, states and dynamics in a wide variety of tissues and organisms.
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