A more powerful statistical test that yields well-controlled FDR could be constructed by considering techniques that estimate all parameters of the hierarchical model. Here, we present a highly-configurable function that produces publication-ready volcano plots. 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. (a) Volcano plots and (b) heatmaps of top 50 genes for 7 different DS analysis methods. 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. 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. For macrophages (Supplementary Fig. Overall, the subject and mixed methods had the highest concordance between permutation and method P-values. See Supplementary Material for brief example code demonstrating the usage of aggregateBioVar. One such subtype, defined by expression of CD66, was further processed by sorting basal cells according to detection of CD66 and profiling by bulk RNA-seq. ## [7] crosstalk_1.2.0 listenv_0.9.0 scattermore_0.8 (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). Supplementary data are available at Bioinformatics online. I would like to create a volcano plot to compare differentially expressed genes (DEGs) across two samples- a "before" and "after" treatment. We have found this particularly useful for small clusters that do not always separate using unbiased clustering, but which look tantalizingly distinct. ## [73] fastmap_1.1.1 yaml_2.3.7 ragg_1.2.5 ## [1] stats graphics grDevices utils datasets methods base Volcano plots are commonly used to display the results of RNA-seq or other omics experiments. Session Info First, a random proportion of genes, pDE, were flagged as differentially expressed. 5c). This is the model used in DESeq2 (Love et al., 2014). A volcano plot is a type of scatterplot that shows statistical significance (P value) versus magnitude of change (fold change). ## [7] LC_PAPER=en_US.UTF-8 LC_NAME=C FindMarkers : Gene expression markers of identity classes Supplementary Table S2 contains performance measures derived from the ROC and PR curves. ## [46] xtable_1.8-4 reticulate_1.28 ggmin_0.0.0.9000 As a gold standard, results from bulk RNA-seq of isolated AT2 cells and AM comparing IPF and healthy lungs (bulk). provides an argument for using mixed models over pseudobulk methods because pseudobulk methods discovered fewer differentially expressed genes. Was this translation helpful? 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. 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. ## [37] gtable_0.3.3 leiden_0.4.3 future.apply_1.10.0 Platypus source: R/GEX_volcano.R - rdrr.io ## [91] tibble_3.2.1 bslib_0.4.2 stringi_1.7.12 SeuratFindMarkers() Volcano plot - Analysis of AT2 cells and AMs from healthy and IPF lungs. ## [3] LC_TIME=en_US.UTF-8 LC_COLLATE=en_US.UTF-8 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. ## Matrix products: default Increasing sequencing depth can reduce technical variation and achieve more precise expression estimates, and collecting samples from more subjects can increase power to detect differentially expressed genes. Because these assumptions are difficult to validate in practice, we suggest following the guidelines for library complexity in bulk RNA-seq studies. Under this assumption, ijij and the three-stage model reduces to a two-stage model. ## Platform: x86_64-pc-linux-gnu (64-bit) Second, there may be imbalances in the numbers of cells collected from different subjects. I have scoured the web but I still cannot figure out how to do this. (b) AT2 cells and AM express SFTPC and MARCO, respectively. Generally, the NPV values were more similar across methods. Below is a brief demonstration but please see the patchwork package website here for more details and examples. 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. Downstream Analyses of SC Data - omicsoft doc - GitHub Pages ## [58] deldir_1.0-6 utf8_1.2.3 tidyselect_1.2.0 ## [70] ggridges_0.5.4 evaluate_0.20 stringr_1.5.0 ## [13] SeuratData_0.2.2 SeuratObject_4.1.3 This is done using the Seurat FindMarkers function default parameters, which to my understanding uses a wilcox.test with a Bonferroni correction. For full access to this pdf, sign in to an existing account, or purchase an annual subscription. For clarity of exposition, we adopt and extend notations similar to (Love et al., 2014). I have been following the Satija lab tutorials and have found them intuitive and useful so far. The marker genes list can be a list or a dictionary. Generally, tests for marker detection, such as the wilcox method, are sufficient if type I error rate control is less of a concern than type II error rate and in circumstances where type I error rate is most important, methods like subject and mixed can be used. As a gold standard, results from bulk RNA-seq comparing CD66+ and CD66- basal cells (bulk). EnhancedVolcano and scRNAseq differential gene expression - Biostar: S In contrast, single-cell experiments contain an additional source of biological variation between cells. Let Gammaa,b denote the gamma distribution with shape parameter a and scale parameter b, Poissonm denote the Poisson distribution with mean m and XY denote the conditional distribution of random variable X given random variable Y. (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. baseplot <- DimPlot (pbmc3k.final, reduction = "umap") # Add custom labels and titles baseplot + labs (title = "Clustering of 2,700 PBMCs") #' @param de_groups The two group labels to use for differential expression, supplied as a vector. For example, a simple definition of sjc is the number of unique molecular identifiers (UMIs) collected from cell c of subject j. # S3 method for default FindMarkers( object, slot = "data", counts = numeric (), cells.1 = NULL, cells.2 = NULL, features = NULL, logfc.threshold = 0.25, test.use = "wilcox", min.pct = 0.1, min.diff.pct = -Inf, verbose = TRUE, only.pos = FALSE, max.cells.per.ident = Inf, random.seed = 1, latent.vars = NULL, min.cells.feature = 3, min.cells.group It sounds like you want to compare within a cell cluster, between cells from before and after treatment. 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). CellSelector() will return a vector with the names of the points selected, so that you can then set them to a new identity class and perform differential expression. Yes, you can use the second one for volcano plots, but it might help to understand what it's implying. 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. In (b), rows correspond to different genes, and columns correspond to different pigs. 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. Infinite p-values are set defined value of the highest . FindMarkers function - RDocumentation Plots a volcano plot from the output of the FindMarkers function from the Seurat package or the GEX_cluster_genes function alternatively. 6e), subject and mixed have the same area under the ROC curve (0.82) while the wilcox method has slightly smaller area (0.78). Differential gene expression analysis for multi-subject single-cell RNA 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. To obtain permutation P-values, we measured the proportion of permutation test statistics less than or equal to the observed test statistic, which is the permutation test statistic under the observed labels. ## [15] Seurat_4.2.1.9001 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. Third, the proposed model also ignores many aspects of the gene expression distribution in favor of simplicity. d Volcano plots showing DE between T cells from random groups of unstimulated controls drawn . With this data you can now make a volcano plot; Repeat for all cell clusters/types of interest, depending on your research questions. In (a), vertical axes are negative log10-transformed adjusted P-values, and horizontal axes are log2-transformed fold changes. 5a). 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. The negative binomial distribution has a convenient interpretation as a hierarchical model, which is particularly useful for sequencing studies. Help with Volcano plot - Biostar: S The study by Zimmerman et al. ## 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. Single-cell RNA-sequencing (scRNA-seq) provides more granular biological information than bulk RNA-sequencing; bulk RNA sequencing remains popular due to lower costs which allows processing more biological replicates and design more powerful studies. Infinite p-values are set defined value of the highest -log(p) + 100. 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. When samples correspond to different experimental subjects, the first stage characterizes biological variation in gene expression between subjects. NCF = non-CF. RNA-Seq Data Heatmap: Is it necessary to do a log2 . In this case, Cj-1csjc=sj* and Cj-1csjc2=sj*2, and the theorem holds. ## [1] patchwork_1.1.2 ggplot2_3.4.1 ## [5] LC_MONETARY=en_US.UTF-8 LC_MESSAGES=en_US.UTF-8 The analyses presented here have illustrated how different results could be obtained when data were analysed using different units of analysis. S14f), wilcox produces better ranked gene lists of known markers than both subject and wilcox and again, the mixed method has the worst performance. Hi, I am a novice in analyzing scRNAseq data. I used ggplot to plot the graph, but my graph is blank at the center across Log2Fc=0. Tried. ## We performed DS analysis using the same seven methods as Section 3.1. FindMarkers: Finds markers (differentially expressed genes) for identified clusters. 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. R: Flexible wrapper for GEX volcano 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. Figure 5d shows ROC and PR curves for the three scRNA-seq methods using the bulk RNA-seq as a gold standard. This creates a data.frame with gene names as rows, and includes avg_log2FC, and adjusted p-values.