findmarkers volcano plot

First, it is assumed that prerequisite steps in the bioinformatic pipeline produced cells that conform to the assumptions of the proposed model. 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 . It is helpful to inspect the proposed model under a simplifying assumption. FindMarkers function - RDocumentation Seurat has four tests for differential expression which can be set with the test.use parameter: ROC test ("roc"), t-test ("t"), LRT test based on zero-inflated data ("bimod", default), LRT test based on tobit-censoring models ("tobit") The ROC test returns the 'classification power' for any individual marker (ranging from 0 . Each panel shows results for 100 simulated datasets in one simulation setting. 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). In (b), rows correspond to different genes, and columns correspond to different pigs. Infinite p-values are set defined value of the highest -log(p) + 100. SCpubr - 14 Volcano plots 5a). 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. It sounds like you want to compare within a cell cluster, between cells from before and after treatment. If a gene was differentially expressed, i2 was simulated from a normal distribution with mean 0 and standard deviation (SD) . ## [115] MASS_7.3-56 rprojroot_2.0.3 withr_2.5.0 Before you start. 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). 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. 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. 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). Further, subject has the highest AUPR (0.21) followed by mixed (0.14) and wilcox (0.08). In contrast, single-cell experiments contain an additional source of biological variation between cells. ## Running under: Ubuntu 20.04.5 LTS Step 3: Create a basic volcano plot. 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. ## [5] ssHippo.SeuratData_3.1.4 pbmcsca.SeuratData_3.0.0 1 Answer. Step 4: Customise it! Under normal circumstances, the DS analysis should remain valid because the pseudobulk method accounts for this imbalance via different size factors for each subject. I used ggplot to plot the graph, but my graph is blank at the center across Log2Fc=0. Next, we used subject, wilcox and mixed to test for differences in expression between healthy and IPF subjects within the AT2 and AM cell populations. ## [49] htmlwidgets_1.6.2 httr_1.4.5 RColorBrewer_1.1-3 For the AM cells (Fig. ## [22] spatstat.sparse_3.0-1 colorspace_2.1-0 rappdirs_0.3.3 ## [31] progressr_0.13.0 spatstat.data_3.0-1 survival_3.3-1 On the other hand, subject had the smallest FPR (0.03) compared to wilcox and mixed (0.26 and 0.08, respectively) and had a higher PPV (0.38 compared to 0.10 and 0.23). Four of the methods were applications of the FindMarkers function in the R package Seurat (Butler et al., 2018; . R: Flexible wrapper for GEX volcano plots We propose an extension of the negative binomial model to scRNA-seq data by introducing an additional stage in the model hierarchy. ## [7] crosstalk_1.2.0 listenv_0.9.0 scattermore_0.8 Because these assumptions are difficult to validate in practice, we suggest following the guidelines for library complexity in bulk RNA-seq studies. ## Applying themes to plots. 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. Performance measures for DS analysis of simulated data. These were the values used in the original paper for this dataset. Next, I'm looking to visualize this using a volcano plot using the EnhancedVolcano package: Crowell et al. We will create a volcano plot colouring all significant genes. They also thank Paul A. Reyfman and Alexander V. Misharin for sharing bulk RNA-seq data used in this study. When samples correspond to different experimental subjects, the first stage characterizes biological variation in gene expression between subjects. For clarity of exposition, we adopt and extend notations similar to (Love et al., 2014). Below is a brief demonstration but please see the patchwork package website here for more details and examples. The vertical axes give the performance measures, and the horizontal axes label each method. 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. Figure 6(e and f) shows ROC and PR curves for the three scRNA-seq methods using the bulk RNA-seq as a gold standard. Results for analysis of CF and non-CF pig small airway secretory cells. 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. (2019) used scRNA-seq to profile cells from the lungs of healthy subjects and those with pulmonary fibrosis disease subtypes, including hypersensitivity pneumonitis, systemic sclerosis-associated and myositis-associated interstitial lung diseases and IPF (Reyfman et al., 2019). EnhancedVolcano (Blighe, Rana, and Lewis 2018) will attempt to fit as many labels in the plot window as possible, thus avoiding 'clogging' up the . Tried. Overall, mixed seems to have the best performance, with a good tradeoff between false positive and TPRs. 6a) and plotting well-known markers of these two cell types (Fig. ## Platform: x86_64-pc-linux-gnu (64-bit) Search for other works by this author on: Iowa Institute of Human Genetics, Roy J. and Lucille A. Here, we propose a statistical model for scRNA-seq gene counts, describe a simple method for estimating model parameters and show that failing to account for additional biological variation in scRNA-seq studies can inflate false discovery rates (FDRs) of statistical tests. As a gold standard, results from bulk RNA-seq of isolated AT2 cells and AM comparing IPF and healthy lungs (bulk). (Lahnemann et al., 2020). 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. The implemented methods are subject (red), wilcox (blue), NB (green), MAST (purple), DESeq2 (orange), monocle (gold) and mixed (brown). 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. Volcano plots are commonly used to display the results of RNA-seq or other omics experiments. 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. Supplementary Figure S12a shows volcano plots for the results of the seven DS methods described. Platypus source: R/GEX_volcano.R - rdrr.io Confronting false discoveries in single-cell differential expression If mi is the sample mean of {Eij} over j, vi is the sample variance of {Eij} over j, mij is the sample mean of {Eijc} over c, and vij is the sample variance of {Eijc} over c, we fixed the subject-level and cell-level variance parameters to be i=vi/mi2 and ij2=vij/mij2, respectively. Well demonstrate visualization techniques in Seurat using our previously computed Seurat object from the 2,700 PBMC tutorial. "poisson" : Likelihood ratio test assuming an . Visualization of RNA-Seq results with Volcano Plot 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). Rows correspond to different proportions of differentially expressed genes, pDE and columns correspond to different SDs of (natural) log fold change, . ## [121] tidyr_1.3.0 rmarkdown_2.21 Rtsne_0.16 The recall, also known as the true positive rate (TPR), is the fraction of differentially expressed genes that are detected. Second, there may be imbalances in the numbers of cells collected from different subjects. . ## [13] SeuratData_0.2.2 SeuratObject_4.1.3 Default is 0.25. 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. Introduction. We identified cell types, and our DS analyses focused on comparing expression profiles between large and small airways and CF and non-CF pigs. 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. For example, a simple definition of sjc is the number of unique molecular identifiers (UMIs) collected from cell c of subject j. healthy versus disease), an additional layer of variability is introduced. All of the other methods compute P-values that are much smaller than those computed by the permutation tests. To use, simply make a ggplot2-based scatter plot (such as DimPlot() or FeaturePlot()) and pass the resulting plot to HoverLocator(). Third, the proposed model also ignores many aspects of the gene expression distribution in favor of simplicity. The subject method had the highest PPV, and the NB method had the lowest PPV in all nine simulation settings. 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. We set xj1=1 for all j and define xj2 as a dummy variable indicating that subject j belongs to the treated group. 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. With this data you can now make a volcano plot; Repeat for all cell clusters/types of interest, depending on your research questions. More conventional statistical techniques for hierarchical models, such as maximum likelihood or Bayesian maximum a posteriori estimation, could produce less noisy parameter estimates and hence, lead to a more powerful DS test (Gelman and Hill, 2007). Our study highlights user-friendly approaches for analysis of scRNA-seq data from multiple biological replicates. Another interactive feature provided by Seurat is being able to manually select cells for further investigation. As scRNA-seq studies grow in scope, due to technological advances making these studies both less labor-intensive and less expensive, biological replication will become the norm. ## [124] spatstat.explore_3.1-0 shiny_1.7.4. In our simulation study, we also found that the pseudobulk method was conservative, but in some settings, mixed models had inflated FDR. Differential gene expression analysis for multi-subject single-cell RNA ## BLAS: /usr/lib/x86_64-linux-gnu/openblas-pthread/libblas.so.3 https://satijalab.org/seurat/articles/de_vignette.html. In this comparison, many genes were detected by all seven methods. A volcano plot is a type of scatterplot that shows statistical significance (P value) versus magnitude of change (fold change). 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. Session Info 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. Because pseudobulk methods operate on gene-by-cell count matrices, they are broadly applicable to various single-cell technologies. Because the permutation test is calibrated so that the permuted data represent sampling under the null distribution of no gene expression difference between CF and non-CF, agreement between the distributions of the permutation P-values and method P-values indicate appropriate calibration of type I error control for each method. #' @param de_groups The two group labels to use for differential expression, supplied as a vector. Applying the assumptions Cj-1csjck1 and Cj-1csjc2k2 completes the proof. The subject and mixed methods show the highest ratios of inter-group to intra-group variation in gene expression, whereas the other five methods have substantial intra-group variation. # Particularly useful when plotting multiple markers, # Visualize co-expression of two features simultaneously, # Split visualization to view expression by groups (replaces FeatureHeatmap), # Violin plots can also be split on some variable. 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. Supplementary Figure S10 shows concordance between adjusted P-values for each method. 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). 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. ## [76] goftest_1.2-3 knitr_1.42 fs_1.6.1 (a) AUPR, (b) PPV with adjusted P-value cutoff 0.05 and (c) NPV with adjusted P-value cutoff 0.05 for 7 DS analysis methods. RNA-Seq Data Heatmap: Is it necessary to do a log2 . The general process for detecting genes then would be: Repeat for all cell clusters/types of interest, depending on your research questions. The study by Zimmerman et al. Step-by-step guide to create your volcano plot. ## [1] patchwork_1.1.2 ggplot2_3.4.1 Supplementary Figure S9 contains computation times for each method and simulation setting for the 100 simulated datasets. The resulting matrix contains counts of each genefor each subject and can be analyzed using software for bulk RNA-seq data. ## [55] pkgconfig_2.0.3 sass_0.4.5 uwot_0.1.14 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. 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. In practice, often only one cutoff value for the adjusted P-value will be chosen to detect genes. The FindAllMarkers () function has three important arguments which provide thresholds for determining whether a gene is a marker: logfc.threshold: minimum log2 foldchange for average expression of gene in cluster relative to the average expression in all other clusters combined. For each method, the computed P-values for all genes were adjusted to control the FDR using the BenjaminiHochberg procedure (Benjamini and Hochberg, 1995). 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 . 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). The Author(s) 2021. ## [11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C The marginal distribution of Kij is approximately negative binomial with mean ij=sjqij and variance ij+iij2. I have been following the Satija lab tutorials and have found them intuitive and useful so far. All seven methods identify two distinct groups of genes: those with higher average expression in large airways and those with higher average expression in small airways. Volcano plots represent a useful way to visualise the results of differential expression analyses. First, the adjusted P-values for each method are sorted from smallest to largest. can I use FindMarkers in an integrated data #5881 - Github NPV is the fraction of undetected genes that were not differentially expressed. FindMarkers: Finds markers (differentially expressed genes) for identified clusters. With this data you can now make a volcano plot. 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. For each subject, the number of cells and numbers of UMIs per cell were matched to the pig data. Data for the analysis of human skin biopsies were obtained from GEO accession GSE130973. Whereas the pseudobulk method is a simple approach to DS analysis, it has limitations. ## [3] thp1.eccite.SeuratData_3.1.5 stxBrain.SeuratData_0.1.1 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. Supplementary Figure S14 shows the results of marker detection for T cells and macrophages. (b) AT2 cells and AM express SFTPC and MARCO, respectively. Here, we introduce a mathematical framework for modeling different sources of biological variation introduced in scRNA-seq data, and we provide a mathematical justification for the use of pseudobulk methods for DS analysis. To whom correspondence should be addressed. r - FindMarkers from Seurat returns p values as 0 for highly Overall, the volcano plots for subject and mixed look similar with a higher number of genes upregulated in the IPF group, while the wilcox method exhibits a much different shape with more genes highly downregulated in the IPF group. (Crowell et al., 2020) provides a thorough comparison of a variety of DGE methods for scRNA-seq with biological replicates including: (i) marker detection methods, (ii) pseudobulk methods, where gene counts are aggregated between cells from different biological samples and (iii) mixed models, where models for gene expression are adjusted for sample-specific or batch effects. 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. ADD REPLY link 18 months ago by Kevin Blighe 84k 0. (a) t-SNE plot shows AT2 cells (red) and AM (green) from single-cell RNA-seq profiling of human lung from healthy subjects and subjects with IPF. To avoid confounding the results by disease, this analysis is confined to data from six healthy subjects in the dataset. The volcano plot for the subject method shows three genes with adjusted P-value <0.05 (log10(FDR) > 1.3), whereas the other six methods detected a much larger number of genes. PR curves for DS analysis methods. Next, we applied our approach for marker detection and DS analysis to published human datasets. 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. 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. In addition to the inference reports and the associated Volcano plot views that allow users to visualize the distribution of fold change of all genes from say, one cluster to another, or one cluster to all cells, users can also visualize the normalized read . However, the plot does not look well volcanic. 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 lists of genes detected by the other six methods likely contain many false discoveries. 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. FloWuenne/scFunctions source: R/DE_Seurat.R - rdrr.io The top 50 genes for each method were defined to be the 50 genes with smallest adjusted P-values. Supplementary data are available at Bioinformatics online. RNA-seqR "Seurat" FindMarkers() FindMarkers() Volcano plotMA plot This is done by passing the Seurat object used to make the plot into CellSelector(), as well as an identity class. The volcano plots for the three scRNA-seq methods have similar shapes, but the wilcox and mixed methods have inflated adjusted P-values relative to subject (Fig. 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. ## [25] ggrepel_0.9.3 textshaping_0.3.6 xfun_0.38 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. The following equations are identical: . 6e), subject and mixed have the same area under the ROC curve (0.82) while the wilcox method has slightly smaller area (0.78). Specifically, if Kijc is the count of gene i in cell c from pig j, we defined Eijc=Kijc/i'Ki'jc to be the normalized expression for cell c from subject j and Eij=cKijc/i'cKi'jc to be the normalized expression for subject j. Marker detection methods allow quantification of variation between cells and exploration of expression heterogeneity within tissues. 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). As an example, consider a simple design in which we compare gene expression for control and treated subjects. The expression parameter for the difference between groups 1 and 2, i2, was varied in order to evaluate the properties of DS analysis under a number of different scenarios. When only 1% of genes were differentially expressed, the mixed method had a larger area under the curve than the other five methods.