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). 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 following differential expression tests are currently supported: "wilcox" : Wilcoxon rank sum test (default) "bimod" : Likelihood-ratio test for single cell feature expression, (McDavid et al., Bioinformatics, 2013) "roc" : Standard AUC classifier. (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. ## [58] deldir_1.0-6 utf8_1.2.3 tidyselect_1.2.0 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. 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. When only 1% of genes were differentially expressed (pDE = 0.01), all methods had NPV values near 1. I prefer to apply a threshold when showing Volcano plots, displaying any points with extreme / impossible p-values (e.g. RNA-Seq Data Heatmap: Is it necessary to do a log2 . Volcano plots in R: complete script. True positives were identified as those genes in the bulk RNA-seq analysis with FDR<0.05 and |log2(CD66+/CD66)|>1. 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). 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. ## [13] magrittr_2.0.3 memoise_2.0.1 tensor_1.5 (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. Search for other works by this author on: Iowa Institute of Human Genetics, Roy J. and Lucille A. ## [7] crosstalk_1.2.0 listenv_0.9.0 scattermore_0.8 ## Matrix products: default For full access to this pdf, sign in to an existing account, or purchase an annual subscription. Marker detection methods were found to have unacceptable FDR due to pseudoreplication bias, in which cells from the same individual are correlated but treated as independent replicates, and pseudobulk methods were found to be too conservative, in the sense that too many differentially expressed genes were undiscovered. Supplementary Figure S13 shows concordance between adjusted P-values for each method. Next, I'm looking to visualize this using a volcano plot using the EnhancedVolcano package: The study by Zimmerman et al. ## [22] spatstat.sparse_3.0-1 colorspace_2.1-0 rappdirs_0.3.3 So, If I change the assay to "RNA", how we can trust that the DEGs are not due . First, a random proportion of genes, pDE, were flagged as differentially expressed. 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. 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. First, we present a statistical model linking differences in gene counts at the cellular level to four sources: (i) subject-specific factors (e.g. 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. In addition to the inference reports and the associated Volcano plot views that allow users to visualize the distribution of fold change of all genes from say, one cluster to another, or one cluster to all cells, users can also visualize the normalized read . "t" : Student's t-test. ## [11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C Infinite p-values are set defined value of the highest -log(p) + 100. 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 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. healthy versus disease), an additional layer of variability is introduced. Raw gene-by-cell count matrices for pig scRNA-seq data are available as GEO accession GSE150211. (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. 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. 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. Tried. 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. For each method, the computed P-values for all genes were adjusted to control the FDR using the BenjaminiHochberg procedure (Benjamini and Hochberg, 1995). The subject and mixed methods are composed of genes that have high inter-group (CF versus non-CF) and low intra-group (between subject) variability, whereas the wilcox, NB, MAST, DESeq2 and Monocle methods tend to be sensitive to a highly variable gene expression pattern from the third CF pig. 6a) and plotting well-known markers of these two cell types (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). However, in studies with biological replication, gene expression is influenced by both cell-specific and subject-specific effects. ## [91] tibble_3.2.1 bslib_0.4.2 stringi_1.7.12 Supplementary data are available at Bioinformatics online. The expression parameter for the difference between groups 1 and 2, i2, was varied in order to evaluate the properties of DS analysis under a number of different scenarios. Pseudobulking has been tested in real scRNA-seq studies (Kang et al., 2018) and benchmarked extensively via simulation (Crowell et al., 2020). The data from pig airway epithelia underlying this article are available in GEO and can be accessed with GEO accession GSE150211. The top 50 genes for each method were defined to be the 50 genes with smallest adjusted P-values. 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. It furthers the University's objective of excellence in research, scholarship, and education by publishing worldwide, This PDF is available to Subscribers Only. ## [61] labeling_0.4.2 rlang_1.1.0 reshape2_1.4.4 Along with new functions add interactive functionality to plots, Seurat provides new accessory functions for manipulating and combining plots. ## [9] LC_ADDRESS=C LC_TELEPHONE=C 5a). Under normal circumstances, the DS analysis should remain valid because the pseudobulk method accounts for this imbalance via different size factors for each subject. I have been following the Satija lab tutorials and have found them intuitive and useful so far. The regression component of the model took the form logqij=i1+xj2i2, where xj2 is an indicator that subject j is in group 2. The difference between these formulas is in the mean calculation. The intra-cluster correlations are between 0.9 and 1, whereas the inter-cluster correlations are between 0.51 and 0.62. ## [52] ellipsis_0.3.2 ica_1.0-3 farver_2.1.1 All of the other methods compute P-values that are much smaller than those computed by the permutation tests. The null and alternative hypotheses for the i-th gene are H0i:i2=0 and H0i:i20, respectively. Visualizing FindMarkers result in Seurat using Heatmap 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). Hi, I am having difficulty in plotting the volcano plot. Help with Volcano plot - Biostar: S (Zimmerman et al., 2021). To avoid confounding the results by disease, this analysis is confined to data from six healthy subjects in the dataset. ADD REPLY link 18 months ago by Kevin Blighe 84k 0. Beta 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. Supplementary Figure S10 shows concordance between adjusted P-values for each method. ## Platform: x86_64-pc-linux-gnu (64-bit) In stage ii, we assume that we have not measured cell-level covariates, so that variation in expression between cells of the same type occurs only through the dispersion parameter ij2. provides an argument for using mixed models over pseudobulk methods because pseudobulk methods discovered fewer differentially expressed genes. Marker detection methods allow quantification of variation between cells and exploration of expression heterogeneity within tissues. . In stage iii, technical variation in counts is generated from a Poisson distribution. It is helpful to inspect the proposed model under a simplifying assumption. 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. When samples correspond to different experimental subjects, the first stage characterizes biological variation in gene expression between subjects. Four of the methods were applications of the FindMarkers function in the R package Seurat (Butler et al., 2018; Satija et al., 2015; Stuart et al., 2019) with different options for the type of test performed: for the method wilcox, cell counts were normalized, log-transformed and a Wilcoxon rank sum test was performed for each gene; for the method NB, cell counts were modeled using a negative binomial generalized linear model; for the method MAST, cell counts were modeled using a hurdle model based on the MAST software (Finak et al., 2015) and for the method DESeq2, cell counts were modeled using the DESeq2 software (Love et al., 2014). 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. The lists of genes detected by the other six methods likely contain many false discoveries. Supplementary Figure S12a shows volcano plots for the results of the seven DS methods described. The marker genes list can be a list or a dictionary. NCF = non-CF. Overall, mixed seems to have the best performance, with a good tradeoff between false positive and TPRs. ## [55] pkgconfig_2.0.3 sass_0.4.5 uwot_0.1.14 Figure 2 shows precision-recall (PR) curves averaged over 100 simulated datasets for each simulation setting and method. The implemented methods are subject (red), wilcox (blue), NB (green), MAST (purple), DESeq2 (orange), monocle (gold) and mixed (brown). Each panel shows results for 100 simulated datasets in 1 simulation setting. The computations for each method were performed on the high-performance computing cluster at the University of Iowa. ## [109] R6_2.5.1 promises_1.2.0.1 KernSmooth_2.23-20 Figure 4a shows volcano plots summarizing the DS results for the seven methods. Further, the cell-level variance and subject-level variance parameters were matched to the pig data. 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. 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. It sounds like you want to compare within a cell cluster, between cells from before and after treatment. 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). Applying the assumptions Cj-1csjck1 and Cj-1csjc2k2 completes the proof. 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. Suppose that cell-level variance ij20. The wilcox, MAST and Monocle methods had intermediate performance in these nine settings. If a gene was differentially expressed, i2 was simulated from a normal distribution with mean 0 and standard deviation (SD) . In contrast, single-cell experiments contain an additional source of biological variation between cells. ## Running under: Ubuntu 20.04.5 LTS 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). As you can see, there are four major groups of genes: - Genes that surpass our p-value and logFC cutoffs (blue). The following equations are identical: . The subject method had the highest PPV, and the NB method had the lowest PPV in all nine simulation settings. Volcano plots represent a useful way to visualise the results of differential expression analyses. 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. The analyses presented here have illustrated how different results could be obtained when data were analysed using different units of analysis. 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. . Visualization of RNA-Seq results with Volcano Plot in R Well demonstrate visualization techniques in Seurat using our previously computed Seurat object from the 2,700 PBMC tutorial. Single-cell RNA-seq: Marker identification 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. 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*. ## [94] highr_0.10 desc_1.4.2 lattice_0.20-45 Data visualization methods in Seurat Seurat - Satija Lab # search for positive markers monocyte.de.markers <- FindMarkers (pbmc, ident.1 = "CD14+ Mono", ident.2 = NULL, only.pos = TRUE) head (monocyte.de.markers) ## attached base packages: For each subject, gene counts are summed for all cells. CellSelector() will return a vector with the names of the points selected, so that you can then set them to a new identity class and perform differential expression. Figure 5d shows ROC and PR curves for the three scRNA-seq methods using the bulk RNA-seq as a gold standard. The number of genes detected by wilcox, NB, MAST, DESeq2, Monocle and mixed were 6928, 7943, 7368, 4512, 5982 and 821, respectively. ## [49] htmlwidgets_1.6.2 httr_1.4.5 RColorBrewer_1.1-3 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). Volcano plots in R: easy step-by-step tutorial - biostatsquid.com 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. 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. ## [118] sctransform_0.3.5 parallel_4.2.0 grid_4.2.0 "poisson" : Likelihood ratio test assuming an . The value of pDE describes the relative number of differentially expressed genes in a simulated dataset, and the value of controls the signal-to-noise ratio. ## [43] miniUI_0.1.1.1 Rcpp_1.0.10 viridisLite_0.4.1 < 10e-20) with a different symbol at the top of the graph. Rows correspond to different proportions of differentially expressed genes, pDE and columns correspond to different SDs of (natural) log fold change, . This issue is most likely to arise with rare cell types, in which few or no cells are profiled for any subject. The cluster contains hundreds of computation nodes with varying numbers of processor cores and memory, but all jobs were submitted to the same job queue, ensuring that the relative computation times for these jobs were comparable. The use of the dotplot is only meaningful when the counts matrix contains zeros representing no gene counts. 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). I keep receiving an error that says: "data must be a
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