findmarkers volcano plot

## LAPACK: /usr/lib/x86_64-linux-gnu/openblas-pthread/liblapack.so.3 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. ## [4] lazyeval_0.2.2 sp_1.6-0 splines_4.2.0 The following equations are identical: . 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. Hi, I am having difficulty in plotting the volcano plot. 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*. SeuratFindMarkers() Volcano plot - ## [43] miniUI_0.1.1.1 Rcpp_1.0.10 viridisLite_0.4.1 Define the aggregated countsKij=cKijc, and let sj=csjc. As an example, consider a simple design in which we compare gene expression for control and treated subjects. Cons: 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. 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. We are deprecating this functionality in favor of the patchwork system. Platypus source: R/GEX_volcano.R - rdrr.io ## [79] fitdistrplus_1.1-8 purrr_1.0.1 RANN_2.6.1 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. 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. The subject method had the shortest average computation times, typically <1 min. Here, we present the DS results comparing CF and non-CF pigs only in secretory cells from the small airways. 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. NCF = non-CF. Next, I'm looking to visualize this using a volcano plot using the EnhancedVolcano package: However, the plot does not look well volcanic. (a) Volcano plots and (b) heatmaps of top 50 genes for 7 different DS analysis methods. 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. The lists of genes detected by the other six methods likely contain many false discoveries. To avoid confounding the results by disease, this analysis is confined to data from six healthy subjects in the dataset. Downstream Analyses of SC Data - omicsoft doc - GitHub Pages To use, simply make a ggplot2-based scatter plot (such as DimPlot() or FeaturePlot()) and pass the resulting plot to HoverLocator(). ## locale: FindMarkers from Seurat returns p values as 0 for highly significant genes. Generally, the NPV values were more similar across methods. Entering edit mode. The intra-cluster correlations are between 0.9 and 1, whereas the inter-cluster correlations are between 0.51 and 0.62. Subject-level gene expression scores were computed as the average counts per million for all cells from each subject. 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. You signed in with another tab or window. It enables quick visual identification of genes with large fold changes that are also statistically significant. R: Flexible wrapper for GEX volcano plots It is helpful to inspect the proposed model under a simplifying assumption. ## 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. ## [112] gridExtra_2.3 parallelly_1.35.0 codetools_0.2-18 Tau activation of microglial cGAS-IFN reduces MEF2C-mediated cognitive ## [7] LC_PAPER=en_US.UTF-8 LC_NAME=C For this study, there were 35 distinct permutations of CF and non-CF labels between the 7 pigs. First, the adjusted P-values for each method are sorted from smallest to largest. Single-cell RNA-seq: Marker identification Well demonstrate visualization techniques in Seurat using our previously computed Seurat object from the 2,700 PBMC tutorial. 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. Gene expression markers of identity classes FindMarkers 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). 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. Further, subject has the highest AUPR (0.21) followed by mixed (0.14) and wilcox (0.08). 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. For each subject, the number of cells and numbers of UMIs per cell were matched to the pig data. #' @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. ## [70] ggridges_0.5.4 evaluate_0.20 stringr_1.5.0 Introduction to Single-cell RNA-seq - ARCHIVED - GitHub Pages In (a), vertical axes are negative log10-transformed adjusted P-values, and horizontal axes are log2-transformed fold changes. 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. PR curves for DS analysis methods. In our simulation, the analysis focused on transcriptome-wide data simulated from the proposed model for scRNA-seq counts under different numbers of differentially expressed genes and different signal-to-noise ratios. The general process for detecting genes then would be: Repeat for all cell clusters/types of interest, depending on your research questions. 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). ## [100] lifecycle_1.0.3 spatstat.geom_3.1-0 lmtest_0.9-40 I change the test.use but did not work. 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). In (b), rows correspond to different genes, and columns correspond to different pigs. ## Infinite p-values are set defined value of the highest . (Lahnemann et al., 2020). 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. Seurat part 4 - Cell clustering - NGS Analysis 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). See ?FindMarkers in the Seurat package for all options. 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. 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 CD66+ and CD66-basal cells are considered true positives and all others are considered true negatives. We propose an extension of the negative binomial model to scRNA-seq data by introducing an additional stage in the model hierarchy. The implemented methods are subject (red), wilcox (blue), NB (green), MAST (purple), DESeq2 (orange), monocle (gold) and mixed (brown). 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). ## [67] cachem_1.0.7 cli_3.6.1 generics_0.1.3 ## Running under: Ubuntu 20.04.5 LTS can I use FindMarkers in an integrated data #5881 - Github SCpubr - 14 Volcano plots 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). 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 vertical axes give the performance measures, and the horizontal axes label each method. Rows correspond to different proportions of differentially expressed genes, pDE and columns correspond to different SDs of (natural) log fold change, . Was this translation helpful? ## [31] progressr_0.13.0 spatstat.data_3.0-1 survival_3.3-1 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. In extreme cases, where only a few cells have been collected for some subjects, interpretation of gene expression differences should be handled with caution. ## [3] thp1.eccite.SeuratData_3.1.5 stxBrain.SeuratData_0.1.1 Whereas the pseudobulk method is a simple approach to DS analysis, it has limitations. Confronting false discoveries in single-cell differential expression In your DoHeatmap () call, you do not provide features so the function does not know which genes/features to use for the heatmap. For each method, the computed P-values for all genes were adjusted to control the FDR using the BenjaminiHochberg procedure (Benjamini and Hochberg, 1995). (d) ROC and PR curves for subject, wilcox and mixed methods using bulk RNA-seq as a gold standard. Visualizing FindMarkers result in Seurat using Heatmap 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. 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. Hi, I am a novice in analyzing scRNAseq data. For the AM cells (Fig. 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. Finally, we discuss potential shortcomings and future work. 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 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. However, in studies with biological replication, gene expression is influenced by both cell-specific and subject-specific effects. As you can see, there are four major groups of genes: - Genes that surpass our p-value and logFC cutoffs (blue). #' @param plot.adj.pvalue logical specifying whether adjusted p-value should by plotted on the y-axis. Figure 3a shows the area under the PR curve (AUPR) for each method and simulation setting. . EnhancedVolcano (Blighe, Rana, and Lewis 2018) will attempt to fit as many labels in the plot window as possible, thus avoiding 'clogging' up the . ## ## [13] SeuratData_0.2.2 SeuratObject_4.1.3 Seurat utilizes Rs plotly graphing library to create interactive plots. (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. The second stage represents technical variation introduced by the processes of sampling from a population of RNAs, building a cDNA library and sequencing. (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. Volcano plot in R with seurat and ggplot. Session Info 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. ## 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. The expression level of gene i for group 1, i1, was matched to the pig data by setting ei1=jcKijc/i'jcKi'jc. 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. In scRNA-seq studies, where cells are collected from multiple subjects (e.g. 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.