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. Rows correspond to different proportions of differentially expressed genes, pDE and columns correspond to different SDs of (natural) log fold change, . Future work with mixed models for scRNA-seq data should focus on maintaining scalable and computationally efficient implementation in software. 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). ## [7] LC_PAPER=en_US.UTF-8 LC_NAME=C provides an argument for using mixed models over pseudobulk methods because pseudobulk methods discovered fewer differentially expressed genes. Single-cell RNA-sequencing (scRNA-seq) enables analysis of the effects of different conditions or perturbations on specific cell types or cellular states. These approaches will likely yield better type I and type II error rate control, but as we saw for the mixed method in our simulation, the computation times can be substantially longer and the computational burden of these methods scale with the number of cells, whereas the pseudobulk method scales with the number of subjects. 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. It sounds like you want to compare within a cell cluster, between cells from before and after treatment. ## [28] dplyr_1.1.1 crayon_1.5.2 jsonlite_1.8.4 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 each setting, 100 datasets were simulated, and we compared seven different DS methods. When only 1% of genes were differentially expressed (pDE = 0.01), all methods had NPV values near 1. ## [1] systemfonts_1.0.4 plyr_1.8.8 igraph_1.4.1 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). Raw gene-by-cell count matrices for pig scRNA-seq data are available as GEO accession GSE150211. ## [121] tidyr_1.3.0 rmarkdown_2.21 Rtsne_0.16 Figure 4a shows volcano plots summarizing the DS results for the seven 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. True positives were identified as those genes in the bulk RNA-seq analysis with FDR<0.05 and |log2(CD66+/CD66)|>1. 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. These were the values used in the original paper for this dataset. The computations for each method were performed on the high-performance computing cluster at the University of Iowa. I have been following the Satija lab tutorials and have found them intuitive and useful so far. 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). For each method, the computed P-values for all genes were adjusted to control the FDR using the BenjaminiHochberg procedure (Benjamini and Hochberg, 1995). Applying the assumptions Cj-1csjck1 and Cj-1csjc2k2 completes the proof. 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. The intra-cluster correlations are between 0.9 and 1, whereas the inter-cluster correlations are between 0.51 and 0.62. It is helpful to inspect the proposed model under a simplifying assumption. 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. Help! ## See ?FindMarkers in the Seurat package for all options. 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. 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. baseplot <- DimPlot (pbmc3k.final, reduction = "umap") # Add custom labels and titles baseplot + labs (title = "Clustering of 2,700 PBMCs") d Volcano plots showing DE between T cells from random groups of unstimulated controls drawn . ## [7] pbmcMultiome.SeuratData_0.1.2 pbmc3k.SeuratData_3.1.4 ## [124] spatstat.explore_3.1-0 shiny_1.7.4. ## Matrix products: default 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]. A volcano plot is a type of scatterplot that shows statistical significance (P value) versus magnitude of change (fold change). ADD REPLY link 18 months ago by Kevin Blighe 84k 0. We performed DS analysis using the same seven methods as Section 3.1. 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. Infinite p-values are set defined value of the highest -log(p) + 100. Further, they used flow cytometry to isolate alveolar type II (AT2) cell and alveolar macrophage (AM) fractions from the lung samples and profiled these PCTs using bulk RNA-seq. #' @return 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. Supplementary Figure S12a shows volcano plots for the results of the seven DS methods described. 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. (d) ROC and PR curves for subject, wilcox and mixed methods using bulk RNA-seq as a gold standard. I would like to create a volcano plot to compare differentially expressed genes (DEGs) across two samples- a "before" and "after" treatment. We will also label the top 10 most significant genes with their . run FindMarkers on your processed data, setting ident.1 and ident.2 to correspond to before- and after- labelled cells; You will be returned a gene list of pvalues + logFc + other statistics. ## [5] ssHippo.SeuratData_3.1.4 pbmcsca.SeuratData_3.0.0 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). ## 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. If the ident.2 parameter is omitted or set to NULL, FindMarkers () will test for differentially expressed features between the group specified by ident.1 and all other cells. In contrast, single-cell experiments contain an additional source of biological variation between cells. As an example, consider a simple design in which we compare gene expression for control and treated subjects. 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. For macrophages (Supplementary Fig. Theorem 1: The expected value of Kij is ij=sjqij. ## [13] SeuratData_0.2.2 SeuratObject_4.1.3 Marker detection methods allow quantification of variation between cells and exploration of expression heterogeneity within tissues. ## [43] miniUI_0.1.1.1 Rcpp_1.0.10 viridisLite_0.4.1 The results of our comparisons are shown in Figure 6. 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. . (b) CD66+ basal cells were identified via detection of CEACAM5 or CEACAM6. ## [79] fitdistrplus_1.1-8 purrr_1.0.1 RANN_2.6.1 The subject method had the shortest average computation times, typically <1 min. NCF = non-CF. True positives were identified as those genes in the bulk RNA-seq analysis with FDR<0.05 and |log2(IPF/healthy)|>1. Infinite p-values are set defined value of the highest . 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. 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. 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. Supplementary data are available at Bioinformatics online. 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. 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 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. The use of the dotplot is only meaningful when the counts matrix contains zeros representing no gene counts. The volcano plot that is being produced after this analysis is wierd and seems not to be correct. I keep receiving an error that says: "data must be a , or an object coercible by fortify(), not an S4 object with class . ## Performance measures for DS analysis of simulated data. (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. Before you start. 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*. This is the model used in DESeq2 (Love et al., 2014). Nine simulation settings were considered. ## [22] spatstat.sparse_3.0-1 colorspace_2.1-0 rappdirs_0.3.3 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. This interactive plotting feature works with any ggplot2-based scatter plots (requires a geom_point layer). However, the plot does not look well volcanic. As a gold standard, results from bulk RNA-seq comparing CD66+ and CD66- basal cells (bulk). Basic volcano plot. # 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. Department of Internal Medicine, Roy J. and Lucille A. ## [37] gtable_0.3.3 leiden_0.4.3 future.apply_1.10.0 Generally, the NPV values were more similar across 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. (a) Volcano plots and (b) heatmaps of top 50 genes for 7 different DS analysis methods. In our simulation study, we also found that the pseudobulk method was conservative, but in some settings, mixed models had inflated FDR. 10e-20) with a different symbol at the top of the graph. Data for the analysis of human trachea were obtained from GEO accessions GSE143705 (bulk RNA-seq) and GSE143706 (scRNA-seq). Subject-level gene expression scores were computed as the average counts per million for all cells from each subject. Analysis of AT2 cells and AMs from healthy and IPF lungs. We propose an extension of the negative binomial model to scRNA-seq data by introducing an additional stage in the model hierarchy. 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. Tried. Under this assumption, ijij and the three-stage model reduces to a two-stage model. They also thank Paul A. Reyfman and Alexander V. Misharin for sharing bulk RNA-seq data used in this study. ## [1] patchwork_1.1.2 ggplot2_3.4.1 # Calculate feature-specific contrast levels based on quantiles of non-zero expression. . ## [19] globals_0.16.2 matrixStats_0.63.0 pkgdown_2.0.7 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). We designed a simulation study to examine characteristics of using subjects or cells as units of analysis for DS testing under data simulated from the proposed model. 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. . ## [103] jquerylib_0.1.4 RcppAnnoy_0.0.20 data.table_1.14.8 ## [9] panc8.SeuratData_3.0.2 ifnb.SeuratData_3.1.0 ## LAPACK: /usr/lib/x86_64-linux-gnu/openblas-pthread/liblapack.so.3 However, in studies with biological replication, gene expression is influenced by both cell-specific and subject-specific effects. We evaluated the performance of our tested approaches for human multi-subject DS analysis in health and disease. 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. This is done by passing the Seurat object used to make the plot into CellSelector(), as well as an identity class. We proceed as follows. Introduction. 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. dotplot visualization does not work for scaled or corrected matrices in which cero counts had been replaced by other values. 1 Answer. With this data you can now make a volcano plot. ## [94] highr_0.10 desc_1.4.2 lattice_0.20-45 Volcano plots are commonly used to display the results of RNA-seq or other omics experiments. Results for analysis of CF and non-CF pig small airway secretory cells. For example, a simple definition of sjc is the number of unique molecular identifiers (UMIs) collected from cell c of subject j. 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. Red and blue dots represent genes with a log 2 FC (fold . 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. 6b). 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. ## [40] abind_1.4-5 scales_1.2.1 spatstat.random_3.1-4 First, a random proportion of genes, pDE, were flagged as differentially expressed. The wilcox, MAST and Monocle methods had intermediate performance in these nine settings. When samples correspond to different experimental subjects, the first stage characterizes biological variation in gene expression between subjects. 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. ## other attached packages: With this data you can now make a volcano plot; Repeat for all cell clusters/types of interest, depending on your research questions. 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. disease and intervention), (ii) variation between subjects, (iii) variation between cells within subjects and (iv) technical variation introduced by sampling RNA molecules, library preparation and sequencing. ## [52] ellipsis_0.3.2 ica_1.0-3 farver_2.1.1 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. 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. 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 scRNA-seq data for the analysis of human lung tissue were obtained from GEO accession GSE122960, and the bulk RNA-seq of purified AT2 and AM fractions were shared by the authors immediately upon request. Beta 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. 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. So, If I change the assay to "RNA", how we can trust that the DEGs are not due . Infinite p-values are set defined value of the highest -log(p) + 100. The number of genes detected by wilcox, NB, MAST, DESeq2, Monocle and mixed were 6928, 7943, 7368, 4512, 5982 and 821, respectively. 14.1 Basic usage. 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. Then, for each method, we defined the permutation test statistic to be the unadjusted P-value generated by the method. Figure 2 shows precision-recall (PR) curves averaged over 100 simulated datasets for each simulation setting and method. If a gene was not differentially expressed, the value of i2 was set to 0. As increases, the width of the distribution of effect sizes increases, so that the signal-to-noise ratio for differentially expressed genes is larger. 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). Here, we present a highly-configurable function that produces publication-ready volcano plots. Default is set to Inf. It is important to emphasize that the aggregation of counts occurs within cell types or cell states, so that the advantages of single-cell sequencing are retained. The recall, also known as the true positive rate (TPR), is the fraction of differentially expressed genes that are detected. 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. (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. We also assume that cell types or states have been identified, DS analysis will be performed within each cell type of interest and henceforth, the notation corresponds to one cell type. Then the regression model from Section 2.1 simplifies to logqij=i1+i2xj2. For the T cells, (Supplementary Fig. Given the similar performances of wilcox, NB, MAST, DESeq2 and Monocle, in the simulations and animal model analysis, we only show the results for subject, wilcox and mixed. Oxford University Press is a department of the University of Oxford. We have found this particularly useful for small clusters that do not always separate using unbiased clustering, but which look tantalizingly distinct. The null and alternative hypotheses for the i-th gene are H0i:i2=0 and H0i:i20, respectively. 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. The Author(s) 2021. 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. 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. 5a). Each panel shows results for 100 simulated datasets in 1 simulation setting. ## [1] stats graphics grDevices utils datasets methods base 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. 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). Further, the cell-level variance and subject-level variance parameters were matched to the pig data. 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. If a gene was differentially expressed, i2 was simulated from a normal distribution with mean 0 and standard deviation (SD) . The implemented methods are subject (red), wilcox (blue), NB (green), MAST (purple), DESeq2 (orange), monocle (gold) and mixed (brown). For each of these two cell types, the expression profiles are compared to all other cells as in traditional marker detection analysis. The study by Zimmerman et al. The top 50 genes for each method were defined to be the 50 genes with smallest adjusted P-values. 1. For each subject, the number of cells and numbers of UMIs per cell were matched to the pig data. ## BLAS: /usr/lib/x86_64-linux-gnu/openblas-pthread/libblas.so.3 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 addition, it will plot either 'umap', 'tsne', or, # DoHeatmap now shows a grouping bar, splitting the heatmap into groups or clusters. ## Running under: Ubuntu 20.04.5 LTS Overall, mixed seems to have the best performance, with a good tradeoff between false positive and TPRs. ## [13] magrittr_2.0.3 memoise_2.0.1 tensor_1.5 Supplementary Figure S9 contains computation times for each method and simulation setting for the 100 simulated datasets. 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. The following equations are identical: . We are deprecating this functionality in favor of the patchwork system. Published by Oxford University Press. #' @param min_pct The minimum percentage of cells in either group to express a gene for it to be tested. In another study, mixed models were found to be superior alternatives to both pseudobulk and marker detection methods (Zimmerman et al., 2021). Supplementary Figure S10 shows concordance between adjusted P-values for each method. I change the test.use but did not work. See Supplementary Material for brief example code demonstrating the usage of aggregateBioVar. In addition to returning a vector of cell names, CellSelector() can also take the selected cells and assign a new identity to them, returning a Seurat object with the identity classes already set. RNA-Seq Data Heatmap: Is it necessary to do a log2 .

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