To avoid confounding the results by disease, this analysis is confined to data from six healthy subjects in the dataset. ## [76] goftest_1.2-3 knitr_1.42 fs_1.6.1 They also thank Paul A. Reyfman and Alexander V. Misharin for sharing bulk RNA-seq data used in this study. 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. a, Volcano plot of RNA-seq data from bulk hippocampal tissue from 8- to 9-month-old P301S transgenic and non-transgenic mice (Wald test). 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. The regression component of the model took the form logqij=i1+xj2i2, where xj2 is an indicator that subject j is in group 2. 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. 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. (Lahnemann et al., 2020). Default is set to Inf. # Calculate feature-specific contrast levels based on quantiles of non-zero expression. Our analysis of CF and non-CF pigs showed that the subject method better controlled the FPR of DS analysis when the expected rate of true positives is small; here, using the same animal model, we compare large and small airway ciliated cells which are expected to vary largely. ## ## [64] later_1.3.0 munsell_0.5.0 tools_4.2.0 In practice, often only one cutoff value for the adjusted P-value will be chosen to detect genes. The other six methods involved DS testing with cells as the units of analysis. Figure 5 shows the results of the marker detection analysis. Another interactive feature provided by Seurat is being able to manually select cells for further investigation. Department of Internal Medicine, Roy J. and Lucille A. 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). Supplementary Table S1 shows performance measures derived from these curves. Step 3: Create a basic volcano plot. 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. 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. In your last function call, you are trying to group based on a continuous variable pct.1 whereas group_by expects a categorical variable. I have successfully installed ggplot, normalized my datasets, merged the datasets, etc., but what I do not understand is how to transfer the sequencing data to the ggplot function. For full access to this pdf, sign in to an existing account, or purchase an annual subscription. ## [67] cachem_1.0.7 cli_3.6.1 generics_0.1.3 In another study, mixed models were found to be superior alternatives to both pseudobulk and marker detection methods (Zimmerman et al., 2021). ## [52] ellipsis_0.3.2 ica_1.0-3 farver_2.1.1 With this data you can now make a volcano plot. This work was supported by the National Institutes of Health [NHLBI K01HL140261]; the Parker B. Francis Fellowship Program; the Cystic Fibrosis Foundation University of Iowa Research Development Program (Bioinformatics Core); a Pilot Grant from the University of Iowa Center for Gene Therapy [NIH NIDDK DK54759] and a Pilot Grant from the University of Iowa Environmental Health Sciences Research Center [NIH NIEHS ES005605]. Specifically, we considered a setting in which there were two groups of subjects to compare, containing four and three subjects, respectively with 21 731 genes. EnhancedVolcano (Blighe, Rana, and Lewis 2018) will attempt to fit as many labels in the plot window as possible, thus avoiding 'clogging' up the . ## BLAS: /usr/lib/x86_64-linux-gnu/openblas-pthread/libblas.so.3 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. 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. Define the aggregated countsKij=cKijc, and let sj=csjc. Under this assumption, ijij and the three-stage model reduces to a two-stage model. Comparison of methods for detection of CD66+ and CD66- basal cell markers from human trachea. The resulting matrix contains counts of each genefor each subject and can be analyzed using software for bulk RNA-seq data. The authors thank Michael J. Welsh, Joseph Zabner, Kai Wang and Keyan Zarei for careful reading of the manuscript and helpful feedback that improved the clarity and content in the final draft. If a gene was not differentially expressed, the value of i2 was set to 0. The implemented methods are subject (red), wilcox (blue), NB (green), MAST (purple), DESeq2 (orange), monocle (gold) and mixed (brown). As scRNA-seq costs have decreased, collecting data from more than one biological replicate has become more feasible, but careful modeling of different layers of biological variation remains challenging for many users. ## [11] hcabm40k.SeuratData_3.0.0 bmcite.SeuratData_0.3.0 In general, the method subject had lower area under the ROC curve and lower TPR but with lower FPR. As a counterexample, suppose cells were misclassified, such that cells classified as type A are in reality, composed of a mixture of cells of types A and B. I keep receiving an error that says: "data must be a , or an object coercible by fortify(), not an S4 object with class . Next, we matched the empirical moments of the distributions of Eijc and Eij to the population moments. In our simulation study, we also found that the pseudobulk method was conservative, but in some settings, mixed models had inflated FDR. Volcano plots represent a useful way to visualise the results of differential expression analyses. make sure label exists on your cells in the metadata corresponding to treatment (before- and after-), You will be returned a gene list of pvalues + logFc + other statistics. The Author(s) 2021. To better illustrate the assumptions of the theorem, consider the case when the size factor sjcis the same for all cells in a sample j and denote the common size factor as sj*. 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. FindMarkers from Seurat returns p values as 0 for highly significant genes. ## [7] pbmcMultiome.SeuratData_0.1.2 pbmc3k.SeuratData_3.1.4 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. ## [124] spatstat.explore_3.1-0 shiny_1.7.4. Before you start. Pseudobulking has been tested in real scRNA-seq studies (Kang et al., 2018) and benchmarked extensively via simulation (Crowell et al., 2020). 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). I prefer to apply a threshold when showing Volcano plots, displaying any points with extreme / impossible p-values (e.g. (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. We can then change the identity of these cells to turn them into their own mini-cluster. The data from pig airway epithelia underlying this article are available in GEO and can be accessed with GEO accession GSE150211. As increases, the width of the distribution of effect sizes increases, so that the signal-to-noise ratio for differentially expressed genes is larger. 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. To consider characteristics of a real dataset, we matched fixed quantities and parameters of the model to empirical values from a small airway secretory cell subset from the newborn pig data we present again in Section 3.2. ## [58] deldir_1.0-6 utf8_1.2.3 tidyselect_1.2.0 As you can see, there are four major groups of genes: - Genes that surpass our p-value and logFC cutoffs (blue). Third, the proposed model also ignores many aspects of the gene expression distribution in favor of simplicity. The null and alternative hypotheses for the i-th gene are H0i:i2=0 and H0i:i20, respectively. (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. 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. For a sequence of cutoff values between 0 and 1, precision, also known as positive predictive value (PPV), is the fraction of genes with adjusted P-values less than a cutoff (detected genes) that are differentially expressed. Generally, the NPV values were more similar across methods. The following equations are identical: . "t" : Student's t-test. Overall, the subject and mixed methods had the highest concordance between permutation and method P-values. 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. 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. First, the CF and non-CF labels were permuted between subjects. Our study highlights user-friendly approaches for analysis of scRNA-seq data from multiple biological replicates. The subject method had the shortest average computation times, typically <1 min. Applying themes to plots. 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). Step 2: Get the data ready. Hi, I am having difficulty in plotting the volcano plot. 6f), the results are similar to AT2 cells with subject having the highest areas under the ROC and PR curves (0.88 and 0.15, respectively), followed by mixed (0.86 and 0.05, respectively) and wilcox (0.83 and 0.01, respectively). Figure 4a shows volcano plots summarizing the DS results for the seven methods. ## [46] xtable_1.8-4 reticulate_1.28 ggmin_0.0.0.9000 ## [19] globals_0.16.2 matrixStats_0.63.0 pkgdown_2.0.7 This will mean, however, that FindMarkers() takes longer to complete. If zjc1,zjc2,,zjcL are L cell-level covariates, then a log-linear regression model could take the form logijc=lzjclijl. Supplementary Figure S10 shows concordance between adjusted P-values for each method. 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). Theorem 1: The expected value of Kij is ij=sjqij. ## [37] gtable_0.3.3 leiden_0.4.3 future.apply_1.10.0 Whereas the pseudobulk method is a simple approach to DS analysis, it has limitations. 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. ## other attached packages: 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). To characterize these sources of variation, we consider the following three-stage model: In stage i, variation in expression between subjects is due to differences in covariates via the regression function qij and residual subject-to-subject variation via the dispersion parameter i. We will call genes significant here if they have FDR < 0.01 and a log2 fold change of 0.58 (equivalent to a fold-change of 1.5). # 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 Performance measures for DS analysis of simulated data. The vertical axes give the performance measures, and the horizontal axes label each method. ## locale: We set xj1=1 for all j and define xj2 as a dummy variable indicating that subject j belongs to the treated group. ## [94] highr_0.10 desc_1.4.2 lattice_0.20-45 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. 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. In contrast, single-cell experiments contain an additional source of biological variation between cells. Volcano plot in R with seurat and ggplot. 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. Entering edit mode. Therefore, as experiments that include biological replication become more common, statistical frameworks to account for multiple sources of biological variability will be critical, as recently described by Lhnemann et al. 10e-20) with a different symbol at the top of the graph. 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). 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. Along with new functions add interactive functionality to plots, Seurat provides new accessory functions for manipulating and combining plots. 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(). Rows correspond to different proportions of differentially expressed genes, pDE and columns correspond to different SDs of (natural) log fold change, . (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). 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. 14.1 Basic usage. In stage iii, technical variation in counts is generated from a Poisson distribution. I used ggplot to plot the graph, but my graph is blank at the center across Log2Fc=0. Infinite p-values are set defined value of the highest -log(p) + 100. 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. It sounds like you want to compare within a cell cluster, between cells from before and after treatment. Give feedback. 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. You signed in with another tab or window. Aggregation technique accounting for subject-level variation in DS analysis. To whom correspondence should be addressed. Single-cell RNA-sequencing (scRNA-seq) enables analysis of the effects of different conditions or perturbations on specific cell types or cellular states. 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. Supplementary data are available at Bioinformatics online. The recall, also known as the true positive rate (TPR), is the fraction of differentially expressed genes that are detected. 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. 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 . 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). Here is the Volcano plot: I read before that we are not allowed to do the differential gene expression using the integrated data. Supplementary Figure S12b shows the top 50 genes for each method, defined as the genes with the 50 smallest adjusted P-values. ## [1] LC_CTYPE=en_US.UTF-8 LC_NUMERIC=C In scRNA-seq studies, where cells are collected from multiple subjects (e.g. Seurat utilizes Rs plotly graphing library to create interactive plots. ## [11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C The color represents the average expression level, # Single cell heatmap of feature expression, # Plot a legend to map colors to expression levels. As we observed in Figure 2, the subject method had a larger area under the curve than the other six methods in all simulation settings, with larger differences for higher signal-to-noise ratios. For example, a simple definition of sjc is the number of unique molecular identifiers (UMIs) collected from cell c of subject j. 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. Nine simulation settings were considered. If a gene was differentially expressed, i2 was simulated from a normal distribution with mean 0 and standard deviation (SD) . ## [61] labeling_0.4.2 rlang_1.1.0 reshape2_1.4.4 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). d Volcano plots showing DE between T cells from random groups of unstimulated controls drawn . ## [25] ggrepel_0.9.3 textshaping_0.3.6 xfun_0.38 The observed counts for the PCT study are analogous to the aggregated counts for one cell type in a scRNA-seq study. ## [55] pkgconfig_2.0.3 sass_0.4.5 uwot_0.1.14 For each setting, 100 datasets were simulated, and we compared seven different DS methods. Visualize single cell expression distributions in each cluster, # Violin plot - Visualize single cell expression distributions in each cluster, # Feature plot - visualize feature expression in low-dimensional space, # Dot plots - the size of the dot corresponds to the percentage of cells expressing the, # feature in each cluster. We have found this particularly useful for small clusters that do not always separate using unbiased clustering, but which look tantalizingly distinct. 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). The expression level of gene i for group 1, i1, was matched to the pig data by setting ei1=jcKijc/i'jcKi'jc. < 10e-20) with a different symbol at the top of the graph. 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) t-SNE plot shows CD66+ (turquoise) and CD66- (salmon) basal cells from single-cell RNA-seq profiling of human trachea. Standard normalization, scaling, clustering and dimension reduction were performed using the R package Seurat version 3.1.1 (Butler et al., 2018; Satija et al., 2015; Stuart et al., 2019). FindMarkers: Finds markers (differentially expressed genes) for identified clusters. ## [1] patchwork_1.1.2 ggplot2_3.4.1 To generate such a plot, one can use SCpubr::do_VolcanoPlot (), which needs as input the Seurat object and the result of running Seurat::FindMarkers () choosing two groups. ## [70] ggridges_0.5.4 evaluate_0.20 stringr_1.5.0 Introduction. This model implicitly assumes that the only systematic variation in expression is due to subject-level covariates, and for a fixed level of covariates, any additional variation between subjects or cells is due to chance. 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. ## Platform: x86_64-pc-linux-gnu (64-bit) 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. We proceed as follows. NCF = non-CF. 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. ## ## [121] tidyr_1.3.0 rmarkdown_2.21 Rtsne_0.16 (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. This is done using the Seurat FindMarkers function default parameters, which to my understanding uses a wilcox.test with a Bonferroni correction. 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. In this comparison, many genes were detected by all seven methods. S14f), wilcox produces better ranked gene lists of known markers than both subject and wilcox and again, the mixed method has the worst performance. 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. Supplementary Figure S12a shows volcano plots for the results of the seven DS methods described. We then compare multiple differential expression testing methods on scRNA-seq datasets from human samples and from animal models. 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. . To illustrate scalability and performance of various methods in real-world conditions, we show results in a porcine model of cystic fibrosis and analyses of skin, trachea and lung tissues in human sample datasets. Hi, I am a novice in analyzing scRNAseq data. 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 . ## Matrix products: default ## [43] miniUI_0.1.1.1 Rcpp_1.0.10 viridisLite_0.4.1 ## [9] panc8.SeuratData_3.0.2 ifnb.SeuratData_3.1.0 Published by Oxford University Press.
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