Supplementary Figure S14 shows the results of marker detection for T cells and macrophages. For each of these two cell types, the expression profiles are compared to all other cells as in traditional marker detection analysis. Volcano plots in R: complete script. (Zimmerman et al., 2021). Returns a volcano plot from the output of the FindMarkers function from the Seurat package, which is a ggplot object that can be modified or plotted. Results for alternative performance measures, including receiver operating characteristic (ROC) curves, TPRs and false positive rates (FPRs) can be found in Supplementary Figures S7 and S8. provides an argument for using mixed models over pseudobulk methods because pseudobulk methods discovered fewer differentially expressed genes. The expression level of gene i for group 1, i1, was matched to the pig data by setting ei1=jcKijc/i'jcKi'jc. In practice, we have omitted comparisons of gene expression in rare cell types because the gene expression profiles had high variation, and the reliability of the comparisons was questionable. With Seurat, all plotting functions return ggplot2-based plots by default, allowing one to easily capture and manipulate plots just like any other ggplot2-based plot. Next, I'm looking to visualize this using a volcano plot using the EnhancedVolcano package: ## [11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C 10e-20) with a different symbol at the top of the graph. https://satijalab.org/seurat/articles/de_vignette.html. The vertical axis gives the precision (PPV) and the horizontal axis gives recall (TPR). ## [82] pbapply_1.7-0 future_1.32.0 nlme_3.1-157 (a) t-SNE plot shows CD66+ (turquoise) and CD66- (salmon) basal cells from single-cell RNA-seq profiling of human trachea. The difference between these formulas is in the mean calculation. Under normal circumstances, the DS analysis should remain valid because the pseudobulk method accounts for this imbalance via different size factors for each subject. 1 Answer. (Lahnemann et al., 2020). RNA-seqR "Seurat" FindMarkers() FindMarkers() Volcano plotMA plot Figure 3(b and c) show the PPV and negative predictive value (NPV) for each method and simulation setting under an adjusted P-value cutoff of 0.05. DGE methods to address this additional complexity, which have been referred to as differential state (DS) analysis are just being explored in the scRNA-seq field (Crowell et al., 2020; Lun et al., 2016; McCarthy et al., 2017; Van den Berge et al., 2019; Zimmerman et al., 2021). The volcano plots for subject and mixed show a stronger association between effect size (absolute log2-transformed fold change) and statistical significance (negative log10-transformed adjusted P-value). Under this assumption, ijij and the three-stage model reduces to a two-stage model. If the ident.2 parameter is omitted or set to NULL, FindMarkers () will test for differentially expressed features between the group specified by ident.1 and all other cells. We performed marker detection analysis of cells obtained from a study of five human skin punch biopsies (Sole-Boldo et al., 2020). ## [3] thp1.eccite.SeuratData_3.1.5 stxBrain.SeuratData_0.1.1 Further, if we assume that, for some constants k1 and k2, Cj-1csjck1 and Cj-1csjc2k2 as Cj, then the variance of Kij is ij+i+o1ij2. The wilcox, MAST and Monocle methods had intermediate performance in these nine settings. Because we are comparing different cells from the same subjects, the subject and mixed methods can also account for the matching of cells by subject in the regression models. We compared the performances of subject, wilcox and mixed for DS analysis of the scRNA-seq from healthy and IPF subjects within AT2 and AM cells using bulk RNA-seq of purified AT2 and AM cell type fractions as a gold standard, similar to the method used in Section 3.5. ## [1] LC_CTYPE=en_US.UTF-8 LC_NUMERIC=C These analyses suggest that a nave approach to differential expression testing could lead to many false discoveries; in contrast, an approach based on pseudobulk counts has better FDR control. Infinite p-values are set defined value of the highest -log(p) + 100. As an example, were going to select the same set of cells as before, and set their identity class to selected. However, in studies with biological replication, gene expression is influenced by both cell-specific and subject-specific effects. We evaluated the performance of our tested approaches for human multi-subject DS analysis in health and disease. The other two methods were Monocle, which utilized a negative binomial generalized additive model to test for differences in gene expression using the R package Monocle (Qiu et al., 2017a, b; Trapnell et al., 2014) and mixed, which modeled counts using a negative binomial generalized linear mixed model with a random effect to account for differences in gene expression between subjects and DS testing was performed using a Wald test. ## [1] patchwork_1.1.2 ggplot2_3.4.1 First, in a simulation study, we show that when the gene expression distribution of a population of cells varies between subjects, a nave approach to differential expression analysis will inflate the FDR. In contrast, single-cell experiments contain an additional source of biological variation between cells. In your DoHeatmap () call, you do not provide features so the function does not know which genes/features to use for the heatmap. With this data you can now make a volcano plot; Repeat for all cell clusters/types of interest, depending on your research questions. ## 13714 features across 2638 samples within 1 assay, ## Active assay: RNA (13714 features, 2000 variable features), ## 2 dimensional reductions calculated: pca, umap, # Ridge plots - from ggridges. It enables quick visual identification of genes with large fold changes that are also statistically significant. Cons: (c) Volcano plots show results of three methods (subject, wilcox and mixed) used to identify CD66+ and CD66- basal cell marker genes. For the AM cells (Fig. ## [9] panc8.SeuratData_3.0.2 ifnb.SeuratData_3.1.0 If a gene was differentially expressed, i2 was simulated from a normal distribution with mean 0 and standard deviation (SD) . As increases, the width of the distribution of effect sizes increases, so that the signal-to-noise ratio for differentially expressed genes is larger. In terms of identifying the true positives, wilcox and mixed had better performance (TPR = 0.62 and 0.56, respectively) than subject (TPR = 0.34). Beta baseplot <- DimPlot (pbmc3k.final, reduction = "umap") # Add custom labels and titles baseplot + labs (title = "Clustering of 2,700 PBMCs") A software package, aggregateBioVar, is freely available on Bioconductor (https://www.bioconductor.org/packages/release/bioc/html/aggregateBioVar.html) to accommodate compatibility with upstream and downstream methods in scRNA-seq data analysis pipelines. To avoid confounding the results by disease, this analysis is confined to data from six healthy subjects in the dataset. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (, https://doi.org/10.1093/bioinformatics/btab337, https://www.bioconductor.org/packages/release/bioc/html/aggregateBioVar.html, https://creativecommons.org/licenses/by/4.0/, Receive exclusive offers and updates from Oxford Academic, Academic Pulmonary Sleep Medicine Physician Opportunity in Scenic Central Pennsylvania, MEDICAL MICROBIOLOGY AND CLINICAL LABORATORY MEDICINE PHYSICIAN, CLINICAL CHEMISTRY LABORATORY MEDICINE PHYSICIAN. Hi, I am having difficulty in plotting the volcano plot. In practice, often only one cutoff value for the adjusted P-value will be chosen to detect genes. The number of genes detected by wilcox, NB, MAST, DESeq2, Monocle and mixed were 6928, 7943, 7368, 4512, 5982 and 821, respectively. Define Kijc to be the count for gene i in cell ccollected from subject j, and a size factorsjc related to the amount of information collected from cell c in subject j (i=1,G; c=1,,Cj;j=1,,n). (b) CD66+ basal cells were identified via detection of CEACAM5 or CEACAM6. The resulting matrix contains counts of each genefor each subject and can be analyzed using software for bulk RNA-seq data. In summary, here we (i) suggested a modeling framework for scRNA-seq data from multiple biological sources, (ii) showed how failing to account for biological variation could inflate the FDR of DS analysis and (iii) provided a formal justification for the validity of pseudobulking to allow DS analysis to be performed on scRNA-seq data using software designed for DS analysis of bulk RNA-seq data (Crowell et al., 2020; Lun et al., 2016; McCarthy et al., 2017). As a gold standard, results from bulk RNA-seq of isolated AT2 cells and AM comparing IPF and healthy lungs (bulk). Subject-level gene expression scores were computed as the average counts per million for all cells from each subject. However, the plot does not look well volcanic. #' @param de_groups The two group labels to use for differential expression, supplied as a vector. Supplementary Figure S14(cd) show that generally the shapes of the volcano plots are more similar between the subject and mixed methods than the wilcox method. ## [55] pkgconfig_2.0.3 sass_0.4.5 uwot_0.1.14 In our simulation study, we also found that the pseudobulk method was conservative, but in some settings, mixed models had inflated FDR. Supplementary Figure S10 shows concordance between adjusted P-values for each method. ## loaded via a namespace (and not attached): ## [1] systemfonts_1.0.4 plyr_1.8.8 igraph_1.4.1, ## [4] lazyeval_0.2.2 sp_1.6-0 splines_4.2.0, ## [7] crosstalk_1.2.0 listenv_0.9.0 scattermore_0.8, ## [10] digest_0.6.31 htmltools_0.5.5 fansi_1.0.4, ## [13] magrittr_2.0.3 memoise_2.0.1 tensor_1.5, ## [16] cluster_2.1.3 ROCR_1.0-11 limma_3.54.1, ## [19] globals_0.16.2 matrixStats_0.63.0 pkgdown_2.0.7, ## [22] spatstat.sparse_3.0-1 colorspace_2.1-0 rappdirs_0.3.3, ## [25] ggrepel_0.9.3 textshaping_0.3.6 xfun_0.38, ## [28] dplyr_1.1.1 crayon_1.5.2 jsonlite_1.8.4, ## [31] progressr_0.13.0 spatstat.data_3.0-1 survival_3.3-1, ## [34] zoo_1.8-11 glue_1.6.2 polyclip_1.10-4, ## [37] gtable_0.3.3 leiden_0.4.3 future.apply_1.10.0, ## [40] abind_1.4-5 scales_1.2.1 spatstat.random_3.1-4, ## [43] miniUI_0.1.1.1 Rcpp_1.0.10 viridisLite_0.4.1, ## [46] xtable_1.8-4 reticulate_1.28 ggmin_0.0.0.9000, ## [49] htmlwidgets_1.6.2 httr_1.4.5 RColorBrewer_1.1-3, ## [52] ellipsis_0.3.2 ica_1.0-3 farver_2.1.1, ## [55] pkgconfig_2.0.3 sass_0.4.5 uwot_0.1.14, ## [58] deldir_1.0-6 utf8_1.2.3 tidyselect_1.2.0, ## [61] labeling_0.4.2 rlang_1.1.0 reshape2_1.4.4, ## [64] later_1.3.0 munsell_0.5.0 tools_4.2.0, ## [67] cachem_1.0.7 cli_3.6.1 generics_0.1.3, ## [70] ggridges_0.5.4 evaluate_0.20 stringr_1.5.0, ## [73] fastmap_1.1.1 yaml_2.3.7 ragg_1.2.5, ## [76] goftest_1.2-3 knitr_1.42 fs_1.6.1, ## [79] fitdistrplus_1.1-8 purrr_1.0.1 RANN_2.6.1, ## [82] pbapply_1.7-0 future_1.32.0 nlme_3.1-157, ## [85] mime_0.12 formatR_1.14 compiler_4.2.0, ## [88] plotly_4.10.1 png_0.1-8 spatstat.utils_3.0-2, ## [91] tibble_3.2.1 bslib_0.4.2 stringi_1.7.12, ## [94] highr_0.10 desc_1.4.2 lattice_0.20-45, ## [97] Matrix_1.5-3 vctrs_0.6.1 pillar_1.9.0, ## [100] lifecycle_1.0.3 spatstat.geom_3.1-0 lmtest_0.9-40, ## [103] jquerylib_0.1.4 RcppAnnoy_0.0.20 data.table_1.14.8, ## [106] cowplot_1.1.1 irlba_2.3.5.1 httpuv_1.6.9, ## [109] R6_2.5.1 promises_1.2.0.1 KernSmooth_2.23-20, ## [112] gridExtra_2.3 parallelly_1.35.0 codetools_0.2-18, ## [115] MASS_7.3-56 rprojroot_2.0.3 withr_2.5.0, ## [118] sctransform_0.3.5 parallel_4.2.0 grid_4.2.0, ## [121] tidyr_1.3.0 rmarkdown_2.21 Rtsne_0.16, ## [124] spatstat.explore_3.1-0 shiny_1.7.4, Fast integration using reciprocal PCA (RPCA), Integrating scRNA-seq and scATAC-seq data, Demultiplexing with hashtag oligos (HTOs), Interoperability between single-cell object formats. Here, we present the DS results comparing CF and non-CF pigs only in secretory cells from the small airways. If we omit DESeq2, which seems to be an outlier, the other six methods form two distinct clusters, with cluster 1 composed of wilcox, NB, MAST and Monocle, and cluster 2 composed of subject and mixed. ## [7] crosstalk_1.2.0 listenv_0.9.0 scattermore_0.8 Second, we make a formal argument for the validity of a DS test with subjects as the units of analysis and discuss our development of a Bioconductor package that can be incorporated into scRNA-seq analysis workflows. When samples correspond to different experimental subjects, the first stage characterizes biological variation in gene expression between subjects. Our study highlights user-friendly approaches for analysis of scRNA-seq data from multiple biological replicates. Although, in this work, we only consider the simple model presented above, the model could be extended to allow for systematic variation between cells by imposing a regression model in stage ii. Two of the methods had much longer computation times with DESeq2 running for 186min and mixed running for 334min. Until computationally efficient methods exist to fit hierarchical models incorporating all sources of biological variation inherent to scRNA-seq, we believe that pseudobulk methods are useful tools for obtaining time-efficient DS results with well-controlled FDR. The FindAllMarkers () function has three important arguments which provide thresholds for determining whether a gene is a marker: logfc.threshold: minimum log2 fold change for average expression of gene in cluster relative to the average expression in all other clusters combined. Crowell et al. ## [15] Seurat_4.2.1.9001 The scRNA-seq data for the analysis of human lung tissue were obtained from GEO accession GSE122960, and the bulk RNA-seq of purified AT2 and AM fractions were shared by the authors immediately upon request. Default is set to Inf. ## [79] fitdistrplus_1.1-8 purrr_1.0.1 RANN_2.6.1 Marker detection methods allow quantification of variation between cells and exploration of expression heterogeneity within tissues. # Calculate feature-specific contrast levels based on quantiles of non-zero expression. In this comparison, many genes were detected by all seven methods. Introduction. As an example, consider a simple design in which we compare gene expression for control and treated subjects. The subject method has the strongest type I error rate control and highest PPVs, wilcox has the highest TPRs and mixed has intermediate performance with better TPRs than subject yet lower FPRs than wilcox (Supplementary Table S2). For each subject, gene counts are summed for all cells. I prefer to apply a threshold when showing Volcano plots, displaying any points with extreme / impossible p-values (e.g. The recall, also known as the true positive rate (TPR), is the fraction of differentially expressed genes that are detected. When only 1% of genes were differentially expressed, the mixed method had a larger area under the curve than the other five methods. ## [5] LC_MONETARY=en_US.UTF-8 LC_MESSAGES=en_US.UTF-8 Theorem 1 provides a straightforward approach to estimating regression coefficients i1,,iR, testing hypotheses and constructing confidence intervals that properly account for variation in gene expression between subjects. I would like to create a volcano plot to compare differentially expressed genes (DEGs) across two samples- a "before" and "after" treatment. ## [76] goftest_1.2-3 knitr_1.42 fs_1.6.1 We proceed as follows. You can now select these cells by creating a ggplot2-based scatter plot (such as with DimPlot() or FeaturePlot(), and passing the returned plot to CellSelector(). For this study, there were 35 distinct permutations of CF and non-CF labels between the 7 pigs. In order to determine the reliability of the unadjusted P-values computed by each method, we compared them to the unadjusted P-values obtained from a permutation test. ## [73] fastmap_1.1.1 yaml_2.3.7 ragg_1.2.5 This will mean, however, that FindMarkers() takes longer to complete. Tried. Raw gene-by-cell count matrices for pig scRNA-seq data are available as GEO accession GSE150211. The value of pDE describes the relative number of differentially expressed genes in a simulated dataset, and the value of controls the signal-to-noise ratio. Rows correspond to different proportions of differentially expressed genes, pDE and columns correspond to different SDs of (natural) log fold change, . ## [124] spatstat.explore_3.1-0 shiny_1.7.4. I prefer to apply a threshold when showing Volcano plots, displaying any points with extreme / impossible p-values (e.g. Then, for each method, we defined the permutation test statistic to be the unadjusted P-value generated by the method. True positives were identified as those genes in the bulk RNA-seq analysis with FDR<0.05 and |log2(CD66+/CD66)|>1. For each method, we compared the permutation P-values to the P-values directly computed by each method, which we define as the method P-values. This research was supported in part through computational resources provided by The University of Iowa, Iowa City, Iowa. In this case, Cj-1csjc=sj* and Cj-1csjc2=sj*2, and the theorem holds. The vertical axes give the performance measures, and the horizontal axes label each method. Analysis of AT2 cells and AMs from healthy and IPF lungs. A common use of DGE analysis for scRNA-seq data is to perform comparisons between pre-defined subsets of cells (referred to here as marker detection methods); many methods have been developed to perform this analysis (Butler et al., 2018; Delmans and Hemberg, 2016; Finak et al., 2015; Guo et al., 2015; Kharchenko et al., 2014; Korthauer et al., 2016; Miao et al., 2018; Qiu et al., 2017a, b; Wang et al., 2019; Wang and Nabavi, 2018). ## [121] tidyr_1.3.0 rmarkdown_2.21 Rtsne_0.16 ## [16] cluster_2.1.3 ROCR_1.0-11 limma_3.54.1 ## [34] zoo_1.8-11 glue_1.6.2 polyclip_1.10-4 In addition to returning a vector of cell names, CellSelector() can also take the selected cells and assign a new identity to them, returning a Seurat object with the identity classes already set. Comparison of methods for detection of CD66+ and CD66- basal cell markers from human trachea. Below is a brief demonstration but please see the patchwork package website here for more details and examples. ## [94] highr_0.10 desc_1.4.2 lattice_0.20-45 We will also label the top 10 most significant genes with their . (a) Volcano plots and (b) heatmaps of top 50 genes for 7 different DS analysis methods. 5a). In the first stage of the hierarchy, gene expression for each sample is assumed to follow a gamma distribution with mean expression modeled as a function of sample-specific covariates. If subjects are composed of different proportions of types A and B, DS results could be due to different cell compositions rather than different mean expression levels. Here, we present a highly-configurable function that produces publication-ready volcano plots. This interactive plotting feature works with any ggplot2-based scatter plots (requires a geom_point layer). Theorem 1: The expected value of Kij is ij=sjqij. I keep receiving an error that says: "data must be a , or an object coercible by fortify(), not an S4 object with class . The observed counts for the PCT study are analogous to the aggregated counts for one cell type in a scRNA-seq study. Among the three genes detected by subject, the genes CFTR and CD36 were detected by all methods, whereas only subject, wilcox, MAST and Monocle detected APOB. The analyses presented here have illustrated how different results could be obtained when data were analysed using different units of analysis. For the T cells, (Supplementary Fig. In stage ii, we assume that we have not measured cell-level covariates, so that variation in expression between cells of the same type occurs only through the dispersion parameter ij2. These approaches will likely yield better type I and type II error rate control, but as we saw for the mixed method in our simulation, the computation times can be substantially longer and the computational burden of these methods scale with the number of cells, whereas the pseudobulk method scales with the number of subjects. Future work with mixed models for scRNA-seq data should focus on maintaining scalable and computationally efficient implementation in software. Pseudobulking has been tested in real scRNA-seq studies (Kang et al., 2018) and benchmarked extensively via simulation (Crowell et al., 2020). First, a random proportion of genes, pDE, were flagged as differentially expressed. ## [97] Matrix_1.5-3 vctrs_0.6.1 pillar_1.9.0 First, we present a statistical model linking differences in gene counts at the cellular level to four sources: (i) subject-specific factors (e.g. The Author(s) 2021. In Supplementary Figure S14(ef), we quantify the ability of each method to correctly identify markers of T cells and macrophages from a database of known cell type markers (Franzen et al., 2019). Well demonstrate visualization techniques in Seurat using our previously computed Seurat object from the 2,700 PBMC tutorial. Returns a volcano plot from the output of the FindMarkers function from the Seurat package, which is a ggplot object that can be modified or plotted. . The use of the dotplot is only meaningful when the counts matrix contains zeros representing no gene counts. FindMarkers from Seurat returns p values as 0 for highly significant genes. Default is 0.25. ## [13] SeuratData_0.2.2 SeuratObject_4.1.3 Four of the methods were applications of the FindMarkers function in the R package Seurat (Butler et al., 2018; . ## Running under: Ubuntu 20.04.5 LTS They also thank Paul A. Reyfman and Alexander V. Misharin for sharing bulk RNA-seq data used in this study. ## [13] magrittr_2.0.3 memoise_2.0.1 tensor_1.5 Carver College of Medicine, University of Iowa. In a scRNA-seq experiment with multiple subjects, we assume that the observed data consist of gene counts for G genes drawn from multiple cells among n subjects. This suggests that methods that fail to account for between subject differences in gene expression are more sensitive to biological variation between subjects, leading to more false discoveries. For each setting, 100 datasets were simulated, and we compared seven different DS methods. Consider a purified cell type (PCT) study design, in which many cells from a cell type of interest could be isolated and profiled using bulk RNA-seq. ## Step 4: Customise it! We designed a simulation study to examine characteristics of using subjects or cells as units of analysis for DS testing under data simulated from the proposed model. Along with new functions add interactive functionality to plots, Seurat provides new accessory functions for manipulating and combining plots. ## [3] LC_TIME=en_US.UTF-8 LC_COLLATE=en_US.UTF-8 In recent years, the reagent and effort costs of scRNA-seq have decreased dramatically as novel techniques have been developed (Aicher et al., 2019; Briggs et al., 2018; Cao et al., 2017; Chen et al., 2019; Gehring et al., 2020; Gierahn et al., 2017; Klein et al., 2015; Macosko et al., 2015; Natarajan et al., 2019; Rosenberg et al., 2018; Vitak et al., 2017; Zhang et al., 2019; Ziegenhain et al., 2017), so that biological replication, meaning data collected from multiple independent biological units such as different research animals or human subjects, is becoming more feasible; biological replication allows generalization of results to the population from which the sample was drawn. Compared to the T cell and macrophage marker detection analysis in Section 3.4, we note that the CD66+ and CD66-basal cells are not as transcriptionally distinct (Fig. This issue is most likely to arise with rare cell types, in which few or no cells are profiled for any subject. We also assume that cell types or states have been identified, DS analysis will be performed within each cell type of interest and henceforth, the notation corresponds to one cell type. Supplementary Figure S12b shows the top 50 genes for each method, defined as the genes with the 50 smallest adjusted P-values. Carver College of Medicine, University of Iowa, Seq-Well: a sample-efficient, portable picowell platform for massively parallel single-cell RNA sequencing, Newborn cystic fibrosis pigs have a blunted early response to an inflammatory stimulus, Controlling the false discovery rate: a practical and powerful approach to multiple testing, The dynamics of gene expression in vertebrate embryogenesis at single-cell resolution, Integrating single-cell transcriptomic data across different conditions, technologies, and species, Comprehensive single-cell transcriptional profiling of a multicellular organism, Single-cell reconstruction of human basal cell diversity in normal and idiopathic pulmonary fibrosis lungs, Single-cell RNA-seq technologies and related computational data analysis, Muscat detects subpopulation-specific state transitions from multi-sample multi-condition single-cell transcriptomics data, Discrete distributional differential expression (D3E)a tool for gene expression analysis of single-cell RNA-seq data, MAST: a flexible statistical framework for assessing transcriptional changes and characterizing heterogeneity in single-cell RNA sequencing data, PanglaoDB: a web server for exploration of mouse and human single-cell RNA sequencing data, Highly multiplexed single-cell RNA-seq by DNA oligonucleotide tagging of cellular proteins, Data Analysis Using Regression and Multilevel/Hierarchical Models, Seq-Well: portable, low-cost RNA sequencing of single cells at high throughput, SINCERA: a pipeline for single-cell RNA-seq profiling analysis, baySeq: empirical Bayesian methods for identifying differential expression in sequence count data, Single-cell RNA sequencing technologies and bioinformatics pipelines, Multiplexed droplet single-cell RNA-sequencing using natural genetic variation, Bayesian approach to single-cell differential expression analysis, Droplet barcoding for single-cell transcriptomics applied to embryonic stem cells, A statistical approach for identifying differential distributions in single-cell RNA-seq experiments, Eleven grand challenges in single-cell data science, EBSeq: an empirical Bayes hierarchical model for inference in RNA-seq experiments, Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2, Current best practices in single-cell RNA-seq analysis: a tutorial, A step-by-step workflow for low-level analysis of single-cell RNA-seq data with bioconductor, Highly parallel genome-wide expression profiling of individual cells using nanoliter droplets, Scater: pre-processing, quality control, normalization and visualization of single-cell RNA-seq data in R, DEsingle for detecting three types of differential expression in single-cell RNA-seq data, Comparative analysis of sequencing technologies for single-cell transcriptomics, Single-cell mRNA quantification and differential analysis with Census, Reversed graph embedding resolves complex single-cell trajectories, Single-cell transcriptomic analysis of human lung provides insights into the pathobiology of pulmonary fibrosis, edgeR: a Bioconductor package for differential expression analysis of digital gene expression data, Disruption of the CFTR gene produces a model of cystic fibrosis in newborn pigs, Single-cell profiling of the developing mouse brain and spinal cord with split-pool barcoding, Spatial reconstruction of single-cell gene expression data, Single-cell transcriptomes of the human skin reveal age-related loss of fibroblast priming, Cystic fibrosis pigs develop lung disease and exhibit defective bacterial eradication at birth, Comprehensive integration of single-cell data, The dynamics and regulators of cell fate decisions are revealed by pseudotemporal ordering of single cells, RNA sequencing data: Hitchhikers guide to expression analysis, A systematic evaluation of single cell RNA-seq analysis pipelines, Sequencing thousands of single-cell genomes with combinatorial indexing, Comparative analysis of differential gene expression analysis tools for single-cell RNA sequencing data, SigEMD: A powerful method for differential gene expression analysis in single-cell RNA sequencing data, Using single-cell RNA sequencing to unravel cell lineage relationships in the respiratory tract, Comparative analysis of droplet-based ultra-high-throughput single-cell RNA-seq systems, Comparative analysis of single-cell RNA sequencing methods, A practical solution to pseudoreplication bias in single-cell studies. ## loaded via a namespace (and not attached): Supplementary Table S1 shows performance measures derived from these curves. Figure 4b shows the top 50 genes for each method, defined by the smallest 50 adjusted P-values. ## locale: Further, they used flow cytometry to isolate alveolar type II (AT2) cell and alveolar macrophage (AM) fractions from the lung samples and profiled these PCTs using bulk RNA-seq.
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