Before you start. Crowell et al. 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. I keep receiving an error that says: "data must be a , or an object coercible by fortify(), not an S4 object with class . Visualizing FindMarkers result in Seurat using Heatmap #' @param output_dir The relative directory that will be used to save results. 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. 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 . 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. Comparisons of characteristics of the simulated and real data are shown in Supplementary Figures S1S6. This is the model used in DESeq2 (Love et al., 2014). 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. Four of the cell-level methods had somewhat longer average computation times, with MAST running for 7min, wilcox and Monocle running for 9min and NB running for 18min. Visualizing marker genes Scanpy documentation - Read the Docs Red and blue dots represent genes with a log 2 FC (fold . ## [7] LC_PAPER=en_US.UTF-8 LC_NAME=C I prefer to apply a threshold when showing Volcano plots, displaying any points with extreme / impossible p-values (e.g. ## [28] dplyr_1.1.1 crayon_1.5.2 jsonlite_1.8.4 The vertical axis gives the precision (PPV) and the horizontal axis gives recall (TPR). 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. 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. The data from pig airway epithelia underlying this article are available in GEO and can be accessed with GEO accession GSE150211. 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. These analyses provide guidance on strengths and weaknesses of different methods in practice. To measure heterogeneity in expression among different groups, we assume that mean expression for gene iin subject j is influenced by R subject-specific covariates xj1,,xjR. ## 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. Andrew L Thurman, Jason A Ratcliff, Michael S Chimenti, Alejandro A Pezzulo, Differential gene expression analysis for multi-subject single-cell RNA-sequencing studies with aggregateBioVar, Bioinformatics, Volume 37, Issue 19, 1 October 2021, Pages 32433251, https://doi.org/10.1093/bioinformatics/btab337. "poisson" : Likelihood ratio test assuming an . 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. ## [70] ggridges_0.5.4 evaluate_0.20 stringr_1.5.0 sessionInfo()## R version 4.2.0 (2022-04-22) In order to objectively measure the performance of our tested approaches in scRNA-seq DS analysis, we compared them to a gold standard consistent of bulk RNA-seq analysis of purified/sorted cell types. Help! Here, we present a highly-configurable function that produces publication-ready volcano plots. Analysis of AT2 cells and AMs from healthy and IPF lungs. 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). 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. As increases, the width of the distribution of effect sizes increases, so that the signal-to-noise ratio for differentially expressed genes is larger. Aggregation technique accounting for subject-level variation in DS analysis. Well demonstrate visualization techniques in Seurat using our previously computed Seurat object from the 2,700 PBMC tutorial. ## other attached packages: 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. Platypus source: R/GEX_volcano.R - rdrr.io NPV is the fraction of undetected genes that were not differentially expressed. The general process for detecting genes then would be: Repeat for all cell clusters/types of interest, depending on your research questions. FindMarkers : Gene expression markers of identity classes Supplementary Figure S12b shows the top 50 genes for each method, defined as the genes with the 50 smallest adjusted P-values. Hi, I am having difficulty in plotting the volcano plot. ## [22] spatstat.sparse_3.0-1 colorspace_2.1-0 rappdirs_0.3.3 The null and alternative hypotheses for the i-th gene are H0i:i2=0 and H0i:i20, respectively. For each setting, 100 datasets were simulated, and we compared seven different DS methods. 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 only 1% of genes were differentially expressed, the mixed method had a larger area under the curve than the other five methods. Step 3: Create a basic volcano plot. ## [13] SeuratData_0.2.2 SeuratObject_4.1.3 ## [7] pbmcMultiome.SeuratData_0.1.2 pbmc3k.SeuratData_3.1.4 Default is 0.25. We then compare multiple differential expression testing methods on scRNA-seq datasets from human samples and from animal models. 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. 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). < 10e-20) with a different symbol at the top of the graph. Applying the assumptions Cj-1csjck1 and Cj-1csjc2k2 completes the proof. (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 proceed as follows. ## [64] later_1.3.0 munsell_0.5.0 tools_4.2.0 To whom correspondence should be addressed. In our simulation study, we also found that the pseudobulk method was conservative, but in some settings, mixed models had inflated FDR. However, the plot does not look well volcanic. Supplementary Figure S11 shows cumulative distribution functions (CDFs) of permutation P-values and method P-values. Single-cell RNA-seq: Marker identification Basic volcano plot. ## [15] Seurat_4.2.1.9001 We are deprecating this functionality in favor of the patchwork system. Flexible wrapper for GEX volcano plots GEX_volcano This interactive plotting feature works with any ggplot2-based scatter plots (requires a geom_point layer). In your last function call, you are trying to group based on a continuous variable pct.1 whereas group_by expects a categorical variable. . https://satijalab.org/seurat/articles/de_vignette.html. In order to contrast DS analysis with cells as units of analysis versus subjects as units of analysis, we analysed both simulated and experimental data. 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. Further, subject has the highest AUPR (0.21) followed by mixed (0.14) and wilcox (0.08). . Help with Volcano plot - Biostar: S Tau activation of microglial cGAS-IFN reduces MEF2C-mediated cognitive 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. Each panel shows results for 100 simulated datasets in 1 simulation setting. 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. We performed DS analysis using the same seven methods as Section 3.1. Figure 5d shows ROC and PR curves for the three scRNA-seq methods using the bulk RNA-seq as a gold standard. In extreme cases, where only a few cells have been collected for some subjects, interpretation of gene expression differences should be handled with caution. 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. Comparison of methods for detection of CD66+ and CD66- basal cell markers from human trachea. 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. (b) AT2 cells and AM express SFTPC and MARCO, respectively. Differential expression testing Seurat - Satija Lab 1 Answer. 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. Volcano plots are commonly used to display the results of RNA-seq or other omics experiments. Step-by-step guide to create your volcano plot. 14.1 Basic usage. Seurat part 4 - Cell clustering - NGS Analysis In contrast, single-cell experiments contain an additional source of biological variation between cells. This creates a data.frame with gene names as rows, and includes avg_log2FC, and adjusted p-values. ## [106] cowplot_1.1.1 irlba_2.3.5.1 httpuv_1.6.9 ## [88] plotly_4.10.1 png_0.1-8 spatstat.utils_3.0-2 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. The observed counts for the PCT study are analogous to the aggregated counts for one cell type in a scRNA-seq study. As a gold standard, results from bulk RNA-seq comparing CD66+ and CD66- basal cells (bulk). 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. ## [46] xtable_1.8-4 reticulate_1.28 ggmin_0.0.0.9000 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. Under normal circumstances, the DS analysis should remain valid because the pseudobulk method accounts for this imbalance via different size factors for each subject. In recent years, the reagent and effort costs of scRNA-seq have decreased dramatically as novel techniques have been developed (Aicher et al., 2019; Briggs et al., 2018; Cao et al., 2017; Chen et al., 2019; Gehring et al., 2020; Gierahn et al., 2017; Klein et al., 2015; Macosko et al., 2015; Natarajan et al., 2019; Rosenberg et al., 2018; Vitak et al., 2017; Zhang et al., 2019; Ziegenhain et al., 2017), so that biological replication, meaning data collected from multiple independent biological units such as different research animals or human subjects, is becoming more feasible; biological replication allows generalization of results to the population from which the sample was drawn. 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. 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). 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. Oxford University Press is a department of the University of Oxford. This issue is most likely to arise with rare cell types, in which few or no cells are profiled for any subject. The Author(s) 2021. 6a) and plotting well-known markers of these two cell types (Fig. It furthers the University's objective of excellence in research, scholarship, and education by publishing worldwide, This PDF is available to Subscribers Only. 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. ## loaded via a namespace (and not attached): ## [1] systemfonts_1.0.4 plyr_1.8.8 igraph_1.4.1, ## [4] lazyeval_0.2.2 sp_1.6-0 splines_4.2.0, ## [7] crosstalk_1.2.0 listenv_0.9.0 scattermore_0.8, ## [10] digest_0.6.31 htmltools_0.5.5 fansi_1.0.4, ## [13] magrittr_2.0.3 memoise_2.0.1 tensor_1.5, ## [16] cluster_2.1.3 ROCR_1.0-11 limma_3.54.1, ## [19] globals_0.16.2 matrixStats_0.63.0 pkgdown_2.0.7, ## [22] spatstat.sparse_3.0-1 colorspace_2.1-0 rappdirs_0.3.3, ## [25] ggrepel_0.9.3 textshaping_0.3.6 xfun_0.38, ## [28] dplyr_1.1.1 crayon_1.5.2 jsonlite_1.8.4, ## [31] progressr_0.13.0 spatstat.data_3.0-1 survival_3.3-1, ## [34] zoo_1.8-11 glue_1.6.2 polyclip_1.10-4, ## [37] gtable_0.3.3 leiden_0.4.3 future.apply_1.10.0, ## [40] abind_1.4-5 scales_1.2.1 spatstat.random_3.1-4, ## [43] miniUI_0.1.1.1 Rcpp_1.0.10 viridisLite_0.4.1, ## [46] xtable_1.8-4 reticulate_1.28 ggmin_0.0.0.9000, ## [49] htmlwidgets_1.6.2 httr_1.4.5 RColorBrewer_1.1-3, ## [52] ellipsis_0.3.2 ica_1.0-3 farver_2.1.1, ## [55] pkgconfig_2.0.3 sass_0.4.5 uwot_0.1.14, ## [58] deldir_1.0-6 utf8_1.2.3 tidyselect_1.2.0, ## [61] labeling_0.4.2 rlang_1.1.0 reshape2_1.4.4, ## [64] later_1.3.0 munsell_0.5.0 tools_4.2.0, ## [67] cachem_1.0.7 cli_3.6.1 generics_0.1.3, ## [70] ggridges_0.5.4 evaluate_0.20 stringr_1.5.0, ## [73] fastmap_1.1.1 yaml_2.3.7 ragg_1.2.5, ## [76] goftest_1.2-3 knitr_1.42 fs_1.6.1, ## [79] fitdistrplus_1.1-8 purrr_1.0.1 RANN_2.6.1, ## [82] pbapply_1.7-0 future_1.32.0 nlme_3.1-157, ## [85] mime_0.12 formatR_1.14 compiler_4.2.0, ## [88] plotly_4.10.1 png_0.1-8 spatstat.utils_3.0-2, ## [91] tibble_3.2.1 bslib_0.4.2 stringi_1.7.12, ## [94] highr_0.10 desc_1.4.2 lattice_0.20-45, ## [97] Matrix_1.5-3 vctrs_0.6.1 pillar_1.9.0, ## [100] lifecycle_1.0.3 spatstat.geom_3.1-0 lmtest_0.9-40, ## [103] jquerylib_0.1.4 RcppAnnoy_0.0.20 data.table_1.14.8, ## [106] cowplot_1.1.1 irlba_2.3.5.1 httpuv_1.6.9, ## [109] R6_2.5.1 promises_1.2.0.1 KernSmooth_2.23-20, ## [112] gridExtra_2.3 parallelly_1.35.0 codetools_0.2-18, ## [115] MASS_7.3-56 rprojroot_2.0.3 withr_2.5.0, ## [118] sctransform_0.3.5 parallel_4.2.0 grid_4.2.0, ## [121] tidyr_1.3.0 rmarkdown_2.21 Rtsne_0.16, ## [124] spatstat.explore_3.1-0 shiny_1.7.4, Fast integration using reciprocal PCA (RPCA), Integrating scRNA-seq and scATAC-seq data, Demultiplexing with hashtag oligos (HTOs), Interoperability between single-cell object formats. For full access to this pdf, sign in to an existing account, or purchase an annual subscription. healthy versus disease), an additional layer of variability is introduced. 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. NCF = non-CF. 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. Each panel shows results for 100 simulated datasets in one simulation setting. ## [73] fastmap_1.1.1 yaml_2.3.7 ragg_1.2.5 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. I prefer to apply a threshold when showing Volcano plots, displaying any points with extreme / impossible p-values (e.g. First, we present a statistical model linking differences in gene counts at the cellular level to four sources: (i) subject-specific factors (e.g. GEX_volcano : Flexible wrapper for GEX volcano plots 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. ## [3] LC_TIME=en_US.UTF-8 LC_COLLATE=en_US.UTF-8 ## [19] globals_0.16.2 matrixStats_0.63.0 pkgdown_2.0.7 The marker genes list can be a list or a dictionary. Session Info The regression component of the model took the form logqij=i1+xj2i2, where xj2 is an indicator that subject j is in group 2. EnhancedVolcano and scRNAseq differential gene expression - Biostar: S The wilcox, MAST and Monocle methods had intermediate performance in these nine settings. This is done by passing the Seurat object used to make the plot into CellSelector(), as well as an identity class. 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. Supplementary Table S1 shows performance measures derived from these curves. First, we identified the AT2 and AM cells via clustering (Fig. #' @param plot.adj.pvalue logical specifying whether adjusted p-value should by plotted on the y-axis.