Data for the analysis of human skin biopsies were obtained from GEO accession GSE130973. 10e-20) with a different symbol at the top of the graph. In stage iii, technical variation in counts is generated from a Poisson distribution. Each panel shows results for 100 simulated datasets in one simulation setting. The wilcox, MAST and Monocle methods had intermediate performance in these nine settings. Supplementary data are available at Bioinformatics online. (Lahnemann et al., 2020). This is the model used in DESeq2 (Love et al., 2014). FindMarkers from Seurat returns p values as 0 for highly significant genes. ## [16] cluster_2.1.3 ROCR_1.0-11 limma_3.54.1 Finally, we discuss potential shortcomings and future work. Red and blue dots represent genes with a log 2 FC (fold . Data visualization methods in Seurat Seurat - Satija Lab Improvements in type I and type II error rate control of the DS test could be considered by modeling cell-level gene expression adjusted for potential differences in gene expression between subjects, similar to the mixed method in Section 3. I would like to create a volcano plot to compare differentially expressed genes (DEGs) across two samples- a "before" and "after" treatment. ## BLAS: /usr/lib/x86_64-linux-gnu/openblas-pthread/libblas.so.3 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). See ?FindMarkers in the Seurat package for all options. 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. NPV is the fraction of undetected genes that were not differentially expressed. The intra-cluster correlations are between 0.9 and 1, whereas the inter-cluster correlations are between 0.51 and 0.62. In this comparison, many genes were detected by all seven methods. The color represents the average expression level, # Single cell heatmap of feature expression, # Plot a legend to map colors to expression levels. Downstream Analyses of SC Data - omicsoft doc - GitHub Pages For example, consider a hypothetical gene having heterogeneous expression in CF pigs, where cells were either low expressors or high expressors versus homogeneous expression in non-CF pigs, where cells were moderate expressors. The second stage represents technical variation introduced by the processes of sampling from a population of RNAs, building a cDNA library and sequencing. Default is 0.25. data("pbmc_small") # Find markers for cluster 2 markers <- FindMarkers(object = pbmc_small, ident.1 = 2) head(x = markers) # Take all cells in cluster 2, and find markers that separate cells in the 'g1' group (metadata # variable 'group') markers <- FindMarkers(pbmc_small, ident.1 = "g1", group.by = 'groups', subset.ident = "2") head(x = markers) # Pass 'clustertree' or an object of class . Figure 2 shows precision-recall (PR) curves averaged over 100 simulated datasets for each simulation setting and method. I understand a little bit more now. ## [70] ggridges_0.5.4 evaluate_0.20 stringr_1.5.0 Single-cell RNA-seq: Marker identification Then, we consider the top g genes for each method, which are the g genes with the smallest adjusted P-values, and find what percentage of these top genes are known markers. We have developed the software package aggregateBioVar (available on Bioconductor) to facilitate broad adoption of pseudobulk-based DE testing; aggregateBioVar includes a detailed vignette, has low code complexity and minimal dependencies and is highly interoperable with existing RNA-seq analysis software using Bioconductor core data structures (Fig. Theorem 1: The expected value of Kij is ij=sjqij. can I use FindMarkers in an integrated data #5881 - Github Here is the Volcano plot: I read before that we are not allowed to do the differential gene expression using the integrated data. ## locale: This figure suggests that the methods that account for between subject differences in gene expression (subject and mixed) will detect different sets of genes than the methods that treat cells as the units of analysis. ## Default is 0.25. 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(). Plotting multiple plots was previously achieved with the CombinePlot() function. ## [1] systemfonts_1.0.4 plyr_1.8.8 igraph_1.4.1 Yes, you can use the second one for volcano plots, but it might help to understand what it's implying. ## 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. Help! Volcano plot in R with seurat and ggplot #6674 - Github Cons: We will also label the top 10 most significant genes with their . These methods appear to form two clusters: the cell-level methods (wilcox, NB, MAST, DESeq2 and Monocle) and the subject-level method (subject), with mixed sharing modest concordance with both clusters. Tried. Volcano plots in R: easy step-by-step tutorial - biostatsquid.com In scRNA-seq studies, where cells are collected from multiple subjects (e.g. Published by Oxford University Press. 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. Furthermore, guidelines for library complexity in bulk RNA-seq studies apply to data with heterogeneity between cell types, so these recommendations should be sufficient for both PCT and scRNA-seq studies, in which data have been stratified by cell type. . (e and f) ROC and PR curves for subject, wilcox and mixed methods using bulk RNA-seq as a gold standard for (e) AT2 cells and (f) AM. For the AT2 cells (Fig. These methods provide interpretable results that generalize to a population of research subjects, account for important sources of biological and technical variability and provide adequate FDR control. Simply add the splitting variable to object, # metadata and pass it to the split.by argument, # SplitDotPlotGG has been replaced with the `split.by` parameter for DotPlot, # DimPlot replaces TSNEPlot, PCAPlot, etc. 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. You can download this dataset from SeuratData, In addition to changes to FeaturePlot(), several other plotting functions have been updated and expanded with new features and taking over the role of now-deprecated functions. 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. According to this criterion, the subject method had the best performance, and the degree to which subject outperformed the other methods improved with larger values of the signal-to-noise ratio parameter . For each method, the computed P-values for all genes were adjusted to control the FDR using the BenjaminiHochberg procedure (Benjamini and Hochberg, 1995). 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). The number of genes detected by wilcox, NB, MAST, DESeq2, Monocle and mixed were 6928, 7943, 7368, 4512, 5982 and 821, respectively. The other six methods involved DS testing with cells as the units of analysis. However, a better approach is to avoid using p-values as quantitative / rankable results in plots; they're not meant to be used in that way. Applying themes to plots. The analyses presented here have illustrated how different results could be obtained when data were analysed using different units of analysis. EnhancedVolcano (Blighe, Rana, and Lewis 2018) will attempt to fit as many labels in the plot window as possible, thus avoiding 'clogging' up the . Department of Internal Medicine, Roy J. and Lucille A. Visualization of RNA-Seq results with Volcano Plot in R #' @param de_groups The two group labels to use for differential expression, supplied as a vector. Visualizing FindMarkers result in Seurat using Heatmap Whereas the pseudobulk method is a simple approach to DS analysis, it has limitations. In the second stage, the observed data for each gene, measured as a count, is assumed to follow a Poisson distribution with mean equal to the product of a size factor, such as sequencing depth, and gene expression generated in the first stage. Volcano plots represent a useful way to visualise the results of differential expression analyses. This is done by passing the Seurat object used to make the plot into CellSelector(), as well as an identity class. RNA-Seq Data Heatmap: Is it necessary to do a log2 . Help! ## [11] hcabm40k.SeuratData_3.0.0 bmcite.SeuratData_0.3.0 You signed in with another tab or window. Among the other five methods, when the number of differentially expressed genes was small (pDE = 0.01), the mixed method had the highest PPV values, whereas for higher numbers of differentially expressed genes (pDE > 0.01), the DESeq2 method had the highest PPV values. The top 50 genes for each method were defined to be the 50 genes with smallest adjusted P-values. As you can see, there are four major groups of genes: - Genes that surpass our p-value and logFC cutoffs (blue). In that case, the number of modes in the expression distribution in the CF group (bimodal) and the non-CF group (unimodal) would be different, but the pseudobulk method may not detect a difference, because it is only able to detect differences in mean expression. Give feedback. As increases, the width of the distribution of effect sizes increases, so that the signal-to-noise ratio for differentially expressed genes is larger. 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). The main idea of the theorem is that if gene counts are summed across cells and the number of cells grows large for each subject, the influence of cell-level variation on the summed counts is negligible. Define the aggregated countsKij=cKijc, and let sj=csjc. ## [82] pbapply_1.7-0 future_1.32.0 nlme_3.1-157 In the bulk RNA-seq, genes with adjusted P-values less than 0.05 and at least a 2-fold difference in gene expression between CD66+ and CD66-basal cells are considered true positives and all others are considered true negatives. Third, we examine properties of DS testing in practice, comparing cells versus subjects as units of analysis in a simulation study and using available scRNA-seq data from humans and pigs. This will mean, however, that FindMarkers() takes longer to complete. ## Platform: x86_64-pc-linux-gnu (64-bit) 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. Differential gene expression analysis for multi-subject single-cell RNA Four of the cell-level methods had somewhat longer average computation times, with MAST running for 7min, wilcox and Monocle running for 9min and NB running for 18min. FindMarkers: Finds markers (differentially expressed genes) for identified clusters. #' @param plot.adj.pvalue logical specifying whether adjusted p-value should by plotted on the y-axis. Hi, I am a novice in analyzing scRNAseq data. Nine simulation settings were considered. ## [34] zoo_1.8-11 glue_1.6.2 polyclip_1.10-4 With this data you can now make a volcano plot; Repeat for all cell clusters/types of interest, depending on your research questions. In bulk RNA-seq studies, gene counts are often assumed to follow a negative binomial distribution (Hardcastle and Kelly, 2010; Leng et al., 2013; Love et al., 2014; Robinson et al., 2010). 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. Next, I'm looking to visualize this using a volcano plot using the EnhancedVolcano package: The following differential expression tests are currently supported: "wilcox" : Wilcoxon rank sum test (default) "bimod" : Likelihood-ratio test for single cell feature expression, (McDavid et al., Bioinformatics, 2013) "roc" : Standard AUC classifier. The lists of genes detected by the other six methods likely contain many false discoveries. Further, the cell-level variance and subject-level variance parameters were matched to the pig data. Then, for each method, we defined the permutation test statistic to be the unadjusted P-value generated by the method. As an example, consider a simple design in which we compare gene expression for control and treated subjects. I prefer to apply a threshold when showing Volcano plots, displaying any points with extreme / impossible p-values (e.g. ## [109] R6_2.5.1 promises_1.2.0.1 KernSmooth_2.23-20 In a study in which a treatment has the effect of altering the composition of cells, subjects in the treatment and control groups may have different numbers of cells of each cell type. ## [10] digest_0.6.31 htmltools_0.5.5 fansi_1.0.4 In general, the method subject had lower area under the ROC curve and lower TPR but with lower FPR. Step 1: Set up your script. ## [88] plotly_4.10.1 png_0.1-8 spatstat.utils_3.0-2 Overall, the subject and mixed methods had the highest concordance between permutation and method P-values. 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. ", I have seen tutorials on the web, but the data there is not processed the same as how I have been doing following the Satija lab method, and, my files are not .csv, but instead are .tsv. The implemented methods are subject (red), wilcox (blue), NB (green), MAST (purple), DESeq2 (orange), monocle (gold) and mixed (brown). ## LAPACK: /usr/lib/x86_64-linux-gnu/openblas-pthread/liblapack.so.3 Under this assumption, ijij and the three-stage model reduces to a two-stage model. For each setting, 100 datasets were simulated, and we compared seven different DS methods. 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. The method subject treated subjects as the units of analysis, and statistical tests were performed according to the procedure outlined in Sections 2.2 and 2.3. Further, subject has the highest AUPR (0.21) followed by mixed (0.14) and wilcox (0.08). d Volcano plots showing DE between T cells from random groups of unstimulated controls drawn . As in Section 3.5, in the bulk RNA-seq, genes with adjusted P-values less than 0.05 and at least a 2-fold difference in gene expression between healthy and IPF are considered true positives and all others are considered true negatives. In order to objectively measure the performance of our tested approaches in scRNA-seq DS analysis, we compared them to a gold standard consistent of bulk RNA-seq analysis of purified/sorted cell types. Platypus source: R/GEX_volcano.R - rdrr.io The null and alternative hypotheses for the i-th gene are H0i:i2=0 and H0i:i20, respectively. 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. To measure heterogeneity in expression among different groups, we assume that mean expression for gene iin subject j is influenced by R subject-specific covariates xj1,,xjR. In a scRNA-seq study of human tracheal epithelial cells from healthy subjects and subjects with idiopathic pulmonary fibrosis (IPF), the authors found that the basal cell population contained specialized subtypes (Carraro et al., 2020). For full access to this pdf, sign in to an existing account, or purchase an annual subscription. This study found that generally pseudobulk methods and mixed models had better statistical characteristics than marker detection methods, in terms of detecting differentially expressed genes with well-controlled false discovery rates (FDRs), and pseudobulk methods had fast computation times. 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. r - FindMarkers from Seurat returns p values as 0 for highly baseplot <- DimPlot (pbmc3k.final, reduction = "umap") # Add custom labels and titles baseplot + labs (title = "Clustering of 2,700 PBMCs") 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. 6e), subject and mixed have the same area under the ROC curve (0.82) while the wilcox method has slightly smaller area (0.78). ## [15] Seurat_4.2.1.9001 (c and d) Volcano plots show results of three methods (subject, wilcox and mixed) used to find differentially expressed genes between IPF and healthy lungs in (c) AT2 cells and (d) AM. Multiple methods and bioinformatic tools exist for initial scRNA-seq data processing, including normalization, dimensionality reduction, visualization, cell type identification, lineage relationships and differential gene expression (DGE) analysis (Chen et al., 2019; Hwang et al., 2018; Luecken and Theis, 2019; Vieth et al., 2019; Zaragosi et al., 2020). Crowell et al. (b) AT2 cells and AM express SFTPC and MARCO, respectively. First, the adjusted P-values for each method are sorted from smallest to largest. The expression level of gene i for group 1, i1, was matched to the pig data by setting ei1=jcKijc/i'jcKi'jc. For example, lets pretend that DCs had merged with monocytes in the clustering, but we wanted to see what was unique about them based on their position in the tSNE plot. Further, applying computational methods that account for all sources of variation will be necessary to gain better insights into biological systems, operating at the granular level of cells all the way up to the level of populations of subjects. ## [7] LC_PAPER=en_US.UTF-8 LC_NAME=C In each panel, PR curves are plotted for each of seven DS analysis methods: subject (red), wilcox (blue), NB (green), MAST (purple), DESeq2 (orange), Monocle (gold) and mixed (brown). The vertical axes give the performance measures, and the horizontal axes label each method. For higher numbers of differentially expressed genes (pDE > 0.01), the subject method had lower NPV values when = 0.5 and similar or higher NPV values when > 0.5. 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. 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. Comparisons of characteristics of the simulated and real data are shown in Supplementary Figures S1S6. Under normal circumstances, the DS analysis should remain valid because the pseudobulk method accounts for this imbalance via different size factors for each subject. In practice, this assumption is unlikely to be satisfied, but if we make modest assumptions about the growth rates of the size factors and numbers of cells per subject, we can obtain a useful approximation. ## [40] abind_1.4-5 scales_1.2.1 spatstat.random_3.1-4 Marker detection methods were found to have unacceptable FDR due to pseudoreplication bias, in which cells from the same individual are correlated but treated as independent replicates, and pseudobulk methods were found to be too conservative, in the sense that too many differentially expressed genes were undiscovered. #' @param min_pct The minimum percentage of cells in either group to express a gene for it to be tested. The volcano plot for the subject method shows three genes with adjusted P-value <0.05 (-log 10 (FDR) > 1.3), whereas the other six methods detected a much larger number of genes. provides an argument for using mixed models over pseudobulk methods because pseudobulk methods discovered fewer differentially expressed genes.