## 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. #' @param min_pct The minimum percentage of cells in either group to express a gene for it to be tested. True positives were identified as those genes in the bulk RNA-seq analysis with FDR<0.05 and |log2(CD66+/CD66)|>1. Supplementary Figure S10 shows concordance between adjusted P-values for each method. RNA-Seq Data Heatmap: Is it necessary to do a log2 . Published by Oxford University Press. The negative binomial distribution has a convenient interpretation as a hierarchical model, which is particularly useful for sequencing studies. Comparison of methods for detection of CD66+ and CD66- basal cell markers from human trachea. However, in studies with biological replication, gene expression is influenced by both cell-specific and subject-specific effects. ## Platform: x86_64-pc-linux-gnu (64-bit) Entering edit mode. The other two methods were Monocle, which utilized a negative binomial generalized additive model to test for differences in gene expression using the R package Monocle (Qiu et al., 2017a, b; Trapnell et al., 2014) and mixed, which modeled counts using a negative binomial generalized linear mixed model with a random effect to account for differences in gene expression between subjects and DS testing was performed using a Wald test. #' @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. ## LAPACK: /usr/lib/x86_64-linux-gnu/openblas-pthread/liblapack.so.3 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. Here, we present a highly-configurable function that produces publication-ready volcano plots. 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. Nine simulation settings were considered. ## [55] pkgconfig_2.0.3 sass_0.4.5 uwot_0.1.14 # 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. 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. 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). 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). ## [52] ellipsis_0.3.2 ica_1.0-3 farver_2.1.1 The lists of genes detected by the other six methods likely contain many false discoveries. For macrophages (Supplementary Fig. Introduction. 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. The difference between these formulas is in the mean calculation. 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). Each panel shows results for 100 simulated datasets in 1 simulation setting. In addition to simulated data, we analysed an animal model dataset containing large and small airway epithelia from CF and non-CF pigs (Rogers et al., 2008). ## [22] spatstat.sparse_3.0-1 colorspace_2.1-0 rappdirs_0.3.3 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 number of genes detected by wilcox, NB, MAST, DESeq2, Monocle and mixed were 6928, 7943, 7368, 4512, 5982 and 821, respectively. Consider a purified cell type (PCT) study design, in which many cells from a cell type of interest could be isolated and profiled using bulk RNA-seq. 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*. ## [40] abind_1.4-5 scales_1.2.1 spatstat.random_3.1-4 Oxford University Press is a department of the University of Oxford. EnhancedVolcano (Blighe, Rana, and Lewis 2018) will attempt to fit as many labels in the plot window as possible, thus avoiding 'clogging' up the . Next, we applied our approach for marker detection and DS analysis to published human datasets. ## [1] systemfonts_1.0.4 plyr_1.8.8 igraph_1.4.1 It is helpful to inspect the proposed model under a simplifying assumption. 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. Second, there may be imbalances in the numbers of cells collected from different subjects. First, we identified the AT2 and AM cells via clustering (Fig. Performance measures for DS analysis of simulated data. Figure 4a shows volcano plots summarizing the DS results for the seven methods. Under this assumption, ijij and the three-stage model reduces to a two-stage model. 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. Figure 3a shows the area under the PR curve (AUPR) for each method and simulation setting. baseplot <- DimPlot (pbmc3k.final, reduction = "umap") # Add custom labels and titles baseplot + labs (title = "Clustering of 2,700 PBMCs") The vertical axis gives the precision (PPV) and the horizontal axis gives recall (TPR). Step 4: Customise it! 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. Each panel shows results for 100 simulated datasets in one simulation setting. The use of the dotplot is only meaningful when the counts matrix contains zeros representing no gene counts. In another study, mixed models were found to be superior alternatives to both pseudobulk and marker detection methods (Zimmerman et al., 2021). 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). A more powerful statistical test that yields well-controlled FDR could be constructed by considering techniques that estimate all parameters of the hierarchical model. Figure 5d shows ROC and PR curves for the three scRNA-seq methods using the bulk RNA-seq as a gold standard. Whereas the pseudobulk method is a simple approach to DS analysis, it has limitations. 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. The expression level of gene i for group 1, i1, was matched to the pig data by setting ei1=jcKijc/i'jcKi'jc. 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). Supplementary Figure S11 shows cumulative distribution functions (CDFs) of permutation P-values and method P-values. 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. Give feedback. 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. Four of the methods were applications of the FindMarkers function in the R package Seurat (Butler et al., 2018; . ## [61] labeling_0.4.2 rlang_1.1.0 reshape2_1.4.4 Because pseudobulk methods operate on gene-by-cell count matrices, they are broadly applicable to various single-cell technologies. The computations for each method were performed on the high-performance computing cluster at the University of Iowa. I understand a little bit more now. 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. In (b), rows correspond to different genes, and columns correspond to different pigs. Figure 2 shows precision-recall (PR) curves averaged over 100 simulated datasets for each simulation setting and method. 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. In that case, the number of modes in the expression distribution in the CF group (bimodal) and the non-CF group (unimodal) would be different, but the pseudobulk method may not detect a difference, because it is only able to detect differences in mean expression. However, the plot does not look well volcanic. 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. We proceed as follows. ## [49] htmlwidgets_1.6.2 httr_1.4.5 RColorBrewer_1.1-3 Supplementary data are available at Bioinformatics online. (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. 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. With this data you can now make a volcano plot. 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. The wilcox, MAST and Monocle methods had intermediate performance in these nine settings. ## [73] fastmap_1.1.1 yaml_2.3.7 ragg_1.2.5 Data for the analysis of human skin biopsies were obtained from GEO accession GSE130973.
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