# search for positive markers monocyte.de.markers <- FindMarkers (pbmc, ident.1 = "CD14+ Mono", ident.2 = NULL, only.pos = TRUE) head (monocyte.de.markers) See ?FindMarkers in the Seurat package for all options. provides an argument for using mixed models over pseudobulk methods because pseudobulk methods discovered fewer differentially expressed genes. 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. Downstream Analyses of SC Data - omicsoft doc - GitHub Pages In your DoHeatmap () call, you do not provide features so the function does not know which genes/features to use for the heatmap. 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. Because these assumptions are difficult to validate in practice, we suggest following the guidelines for library complexity in bulk RNA-seq studies. Supplementary Figure S12a shows volcano plots for the results of the seven DS methods described. ## [31] progressr_0.13.0 spatstat.data_3.0-1 survival_3.3-1 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. 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 general process for detecting genes then would be: Repeat for all cell clusters/types of interest, depending on your research questions. Figure 3a shows the area under the PR curve (AUPR) for each method and simulation setting. ## ## [25] ggrepel_0.9.3 textshaping_0.3.6 xfun_0.38 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. 6e), subject and mixed have the same area under the ROC curve (0.82) while the wilcox method has slightly smaller area (0.78). 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. . The following equations are identical: . (c and d) Volcano plots show results of three methods (subject, wilcox and mixed) used to find differentially expressed genes between IPF and healthy lungs in (c) AT2 cells and (d) AM. Raw gene-by-cell count matrices for pig scRNA-seq data are available as GEO accession GSE150211. # 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. To use, simply make a ggplot2-based scatter plot (such as DimPlot() or FeaturePlot()) and pass the resulting plot to HoverLocator(). 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 . Future work with mixed models for scRNA-seq data should focus on maintaining scalable and computationally efficient implementation in software. 1 Answer. Aggregation technique accounting for subject-level variation in DS analysis. This research was supported in part through computational resources provided by The University of Iowa, Iowa City, Iowa. 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. Supplementary Table S1 shows performance measures derived from these curves. For clarity of exposition, we adopt and extend notations similar to (Love et al., 2014). ## [11] hcabm40k.SeuratData_3.0.0 bmcite.SeuratData_0.3.0 Red and blue dots represent genes with a log 2 FC (fold . ## [40] abind_1.4-5 scales_1.2.1 spatstat.random_3.1-4 ## [121] tidyr_1.3.0 rmarkdown_2.21 Rtsne_0.16 Theorem 1 implies that when the number of cells per subject is large, the aggregated counts follow a distribution with the same mean and variance structure as the negative binomial model used in many software packages for DS analysis of bulk RNA-seq data. Next, we applied our approach for marker detection and DS analysis to published human 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). The color represents the average expression level, # Single cell heatmap of feature expression, # Plot a legend to map colors to expression levels. 14.1 Basic usage. The number of UMIs for cell c was taken to be the size factor sjc in stage 3 of the proposed model. It sounds like you want to compare within a cell cluster, between cells from before and after treatment. The study by Zimmerman et al. Developed by Paul Hoffman, Satija Lab and Collaborators. We proceed as follows. 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. ## [94] highr_0.10 desc_1.4.2 lattice_0.20-45 ## [100] lifecycle_1.0.3 spatstat.geom_3.1-0 lmtest_0.9-40 ## [52] ellipsis_0.3.2 ica_1.0-3 farver_2.1.1 ## [112] gridExtra_2.3 parallelly_1.35.0 codetools_0.2-18 ## Visualizing FindMarkers result in Seurat using Heatmap 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). ## [3] LC_TIME=en_US.UTF-8 LC_COLLATE=en_US.UTF-8 . can I use FindMarkers in an integrated data #5881 - Github 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. First, a random proportion of genes, pDE, were flagged as differentially expressed. 1. 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). Before you start. 5a). We have found this particularly useful for small clusters that do not always separate using unbiased clustering, but which look tantalizingly distinct. Volcano plots in R: easy step-by-step tutorial - biostatsquid.com As an example, were going to select the same set of cells as before, and set their identity class to selected. Tau activation of microglial cGAS-IFN reduces MEF2C-mediated cognitive Among the three genes detected by subject, the genes CFTR and CD36 were detected by all methods, whereas only subject, wilcox, MAST and Monocle detected APOB. Figure 2 shows precision-recall (PR) curves averaged over 100 simulated datasets for each simulation setting and method. Create volcano plot. ## [19] globals_0.16.2 matrixStats_0.63.0 pkgdown_2.0.7 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). Here is the Volcano plot: I read before that we are not allowed to do the differential gene expression using the integrated data. (a) t-SNE plot shows CD66+ (turquoise) and CD66- (salmon) basal cells from single-cell RNA-seq profiling of human trachea. First, we present a statistical model linking differences in gene counts at the cellular level to four sources: (i) subject-specific factors (e.g. 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. ## 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. (c) Volcano plots show results of three methods (subject, wilcox and mixed) used to identify CD66+ and CD66- basal cell marker genes. ## [115] MASS_7.3-56 rprojroot_2.0.3 withr_2.5.0 EnhancedVolcano: publication-ready volcano plots with enhanced 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. ## [9] panc8.SeuratData_3.0.2 ifnb.SeuratData_3.1.0 Further, the cell-level variance and subject-level variance parameters were matched to the pig data. This interactive plotting feature works with any ggplot2-based scatter plots (requires a geom_point layer). Further, if we assume that, for some constants k1 and k2, Cj-1csjck1 and Cj-1csjc2k2 as Cj, then the variance of Kij is ij+i+o1ij2. Step 3: Create a basic volcano plot. 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(). 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. ## [82] pbapply_1.7-0 future_1.32.0 nlme_3.1-157 With this data you can now make a volcano plot. 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. The resulting matrix contains counts of each genefor each subject and can be analyzed using software for bulk RNA-seq data. The null and alternative hypotheses for the i-th gene are H0i:i2=0 and H0i:i20, respectively. This is the model used in DESeq2 (Love et al., 2014). The observed counts for the PCT study are analogous to the aggregated counts for one cell type in a scRNA-seq study. 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. ## [70] ggridges_0.5.4 evaluate_0.20 stringr_1.5.0 We are deprecating this functionality in favor of the patchwork system. Figure 5 shows the results of the marker detection analysis. Gene counts were simulated from the model in Section 2.1. Importantly, although these results specifically target differences in small airway secretory cells and are not directly comparable with other transcriptome studies, previous bulk RNA-seq (Bartlett et al., 2016) and microarray (Stoltz et al., 2010) studies have suggested few gene expression differences in airway epithelial tissues between CF and non-CF pigs; true differential gene expression between genotypes at birth is therefore likely to be small, as detected by the subject method. Was this translation helpful? Supplementary Figure S12b shows the top 50 genes for each method, defined as the genes with the 50 smallest adjusted P-values. Then the regression model from Section 2.1 simplifies to logqij=i1+i2xj2. 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. FindMarkers from Seurat returns p values as 0 for highly - ECHEMI Another interactive feature provided by Seurat is being able to manually select cells for further investigation. ## [91] tibble_3.2.1 bslib_0.4.2 stringi_1.7.12 Alternatively, batch correction methods have been proposed to remove inter-individual differences prior to DS analysis, however, this increases type I error rates and disturbs the rank-order of results as explained in Zimmerman et al. ## [7] crosstalk_1.2.0 listenv_0.9.0 scattermore_0.8 We set xj1=1 for all j and define xj2 as a dummy variable indicating that subject j belongs to the treated group. First, we identified the AT2 and AM cells via clustering (Fig. d Volcano plots showing DE between T cells from random groups of unstimulated controls drawn . Below is a brief demonstration but please see the patchwork package website here for more details and examples. If a gene was not differentially expressed, the value of i2 was set to 0. ## [106] cowplot_1.1.1 irlba_2.3.5.1 httpuv_1.6.9 For each subject, the number of cells and numbers of UMIs per cell were matched to the pig data. ## ", I have seen tutorials on the web, but the data there is not processed the same as how I have been doing following the Satija lab method, and, my files are not .csv, but instead are .tsv. 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. Visualization of RNA-Seq results with Volcano Plot ## [5] LC_MONETARY=en_US.UTF-8 LC_MESSAGES=en_US.UTF-8 . (Zimmerman et al., 2021). 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. 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. Third, the proposed model also ignores many aspects of the gene expression distribution in favor of simplicity. The difference between these formulas is in the mean calculation. This is done using the Seurat FindMarkers function default parameters, which to my understanding uses a wilcox.test with a Bonferroni correction. ## 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 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. In this comparison, many genes were detected by all seven methods. 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 marginal distribution of Kij is approximately negative binomial with mean ij=sjqij and variance ij+iij2. ## [7] LC_PAPER=en_US.UTF-8 LC_NAME=C ## [34] zoo_1.8-11 glue_1.6.2 polyclip_1.10-4 Overall, these results suggest that the current marker detection analysis tools used in common practice, such as wilcox, will produce a reliable set of markers. 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. 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. Infinite p-values are set defined value of the highest -log(p) + 100. 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. Define Kijc to be the count for gene i in cell ccollected from subject j, and a size factorsjc related to the amount of information collected from cell c in subject j (i=1,G; c=1,,Cj;j=1,,n). A software package, aggregateBioVar, is freely available on Bioconductor (https://www.bioconductor.org/packages/release/bioc/html/aggregateBioVar.html) to accommodate compatibility with upstream and downstream methods in scRNA-seq data analysis pipelines. When only 1% of genes were differentially expressed, the mixed method had a larger area under the curve than the other five methods. Introduction to Single-cell RNA-seq - ARCHIVED - GitHub Pages 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. Then, for each method, we defined the permutation test statistic to be the unadjusted P-value generated by the method. Nine simulation settings were considered. Two of the methods had much longer computation times with DESeq2 running for 186min and mixed running for 334min. With this data you can now make a volcano plot; Repeat for all cell clusters/types of interest, depending on your research questions. A volcano plot is a type of scatterplot that shows statistical significance (P value) versus magnitude of change (fold change). Here, we compare the performance of subject, wilcox and mixed to detect cell subtype markers of CD66+ and CD66- basal cells with bulk RNA-seq data from corresponding PCTs. The implementation provided in the Seurat function 'FindMarkers' was used for all seven tests . 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.
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