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. As 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 healthy and IPF are considered true positives and all others are considered true negatives. Differential gene expression analysis for multi-subject single-cell RNA As an example, were going to select the same set of cells as before, and set their identity class to selected. . ## other attached packages: Results for analysis of CF and non-CF pig small airway secretory cells. https://satijalab.org/seurat/articles/de_vignette.html. As a gold standard, results from bulk RNA-seq of isolated AT2 cells and AM comparing IPF and healthy lungs (bulk). 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). Supplementary Figure S13 shows concordance between adjusted P-values for each method. 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. 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. We can then change the identity of these cells to turn them into their own mini-cluster. Finally, we discuss potential shortcomings and future work. Help! Data for the analysis of human skin biopsies were obtained from GEO accession GSE130973. These methods provide interpretable results that generalize to a population of research subjects, account for important sources of biological and technical variability and provide adequate FDR control. 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). For the AT2 cells (Fig. We will create a volcano plot colouring all significant genes. 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). ## [100] lifecycle_1.0.3 spatstat.geom_3.1-0 lmtest_0.9-40 In stage iii, technical variation in counts is generated from a Poisson distribution. In another study, mixed models were found to be superior alternatives to both pseudobulk and marker detection methods (Zimmerman et al., 2021). 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. 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). 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. 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. The results of our comparisons are shown in Figure 6. As an example, consider a simple design in which we compare gene expression for control and treated 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. sessionInfo()## R version 4.2.0 (2022-04-22) RNA-seqR "Seurat" FindMarkers() FindMarkers() Volcano plotMA plot Supplementary Figure S12a shows volcano plots for the results of the seven DS methods described. Yes, you can use the second one for volcano plots, but it might help to understand what it's implying. 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. Data visualization methods in Seurat Seurat - Satija Lab ## [112] gridExtra_2.3 parallelly_1.35.0 codetools_0.2-18 ## [64] later_1.3.0 munsell_0.5.0 tools_4.2.0 However, the plot does not look well volcanic. ", 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. 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. EnhancedVolcano and scRNAseq differential gene expression - Biostar: S Given the similar performances of wilcox, NB, MAST, DESeq2 and Monocle, in the simulations and animal model analysis, we only show the results for subject, wilcox and mixed. 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). ## [82] pbapply_1.7-0 future_1.32.0 nlme_3.1-157 (d) ROC and PR curves for subject, wilcox and mixed methods using bulk RNA-seq as a gold standard. In addition, it will plot either 'umap', 'tsne', or, # DoHeatmap now shows a grouping bar, splitting the heatmap into groups or clusters. ## 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. To avoid confounding the results by disease, this analysis is confined to data from six healthy subjects in the dataset. Four of the methods were applications of the FindMarkers function in the R package Seurat (Butler et al., 2018; . (a) t-SNE plot shows CD66+ (turquoise) and CD66- (salmon) basal cells from single-cell RNA-seq profiling of human trachea. 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. Crowell et al. Introduction to Single-cell RNA-seq - ARCHIVED - GitHub Pages ## [3] thp1.eccite.SeuratData_3.1.5 stxBrain.SeuratData_0.1.1 (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. Supplementary Figure S14 shows the results of marker detection for T cells and macrophages. Then, for each method, we defined the permutation test statistic to be the unadjusted P-value generated by the method. This can, # be changed with the `group.by` parameter, # Use community-created themes, overwriting the default Seurat-applied theme Install ggmin, # with remotes::install_github('sjessa/ggmin'), # Seurat also provides several built-in themes, such as DarkTheme; for more details see, # Include additional data to display alongside cell names by passing in a data frame of, # information Works well when using FetchData, ## [1] "AAGATTACCGCCTT" "AAGCCATGAACTGC" "AATTACGAATTCCT" "ACCCGTTGCTTCTA", # Now, we find markers that are specific to the new cells, and find clear DC markers, ## p_val avg_log2FC pct.1 pct.2 p_val_adj, ## FCER1A 3.239004e-69 3.7008561 0.800 0.017 4.441970e-65, ## SERPINF1 7.761413e-36 1.5737896 0.457 0.013 1.064400e-31, ## HLA-DQB2 1.721094e-34 0.9685974 0.429 0.010 2.360309e-30, ## CD1C 2.304106e-33 1.7785158 0.514 0.025 3.159851e-29, ## ENHO 5.099765e-32 1.3734708 0.400 0.010 6.993818e-28, ## ITM2C 4.299994e-29 1.5590007 0.371 0.010 5.897012e-25, ## [1] "selected" "Naive CD4 T" "Memory CD4 T" "CD14+ Mono" "B", ## [6] "CD8 T" "FCGR3A+ Mono" "NK" "Platelet", # LabelClusters and LabelPoints will label clusters (a coloring variable) or individual points, # Both functions support `repel`, which will intelligently stagger labels and draw connecting, # lines from the labels to the points or clusters, ## Platform: x86_64-pc-linux-gnu (64-bit), ## BLAS: /usr/lib/x86_64-linux-gnu/openblas-pthread/libblas.so.3, ## LAPACK: /usr/lib/x86_64-linux-gnu/openblas-pthread/liblapack.so.3, ## [1] LC_CTYPE=en_US.UTF-8 LC_NUMERIC=C, ## [3] LC_TIME=en_US.UTF-8 LC_COLLATE=en_US.UTF-8, ## [5] LC_MONETARY=en_US.UTF-8 LC_MESSAGES=en_US.UTF-8, ## [7] LC_PAPER=en_US.UTF-8 LC_NAME=C, ## [9] LC_ADDRESS=C LC_TELEPHONE=C, ## [11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C, ## [1] stats graphics grDevices utils datasets methods base, ## [1] patchwork_1.1.2 ggplot2_3.4.1, ## [3] thp1.eccite.SeuratData_3.1.5 stxBrain.SeuratData_0.1.1, ## [5] ssHippo.SeuratData_3.1.4 pbmcsca.SeuratData_3.0.0, ## [7] pbmcMultiome.SeuratData_0.1.2 pbmc3k.SeuratData_3.1.4, ## [9] panc8.SeuratData_3.0.2 ifnb.SeuratData_3.1.0, ## [11] hcabm40k.SeuratData_3.0.0 bmcite.SeuratData_0.3.0, ## [13] SeuratData_0.2.2 SeuratObject_4.1.3. For this study, there were 35 distinct permutations of CF and non-CF labels between the 7 pigs. Another interactive feature provided by Seurat is being able to manually select cells for further investigation. ## [22] spatstat.sparse_3.0-1 colorspace_2.1-0 rappdirs_0.3.3 For each subject, the number of cells and numbers of UMIs per cell were matched to the pig data. ## [5] ssHippo.SeuratData_3.1.4 pbmcsca.SeuratData_3.0.0 ADD REPLY link 18 months ago by Kevin Blighe 84k 0. 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. healthy versus disease), an additional layer of variability is introduced. Third, the proposed model also ignores many aspects of the gene expression distribution in favor of simplicity. ## [37] gtable_0.3.3 leiden_0.4.3 future.apply_1.10.0 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. I prefer to apply a threshold when showing Volcano plots, displaying any points with extreme / impossible p-values (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. ## [118] sctransform_0.3.5 parallel_4.2.0 grid_4.2.0 FindMarkers: Finds markers (differentially expressed genes) for identified clusters. Data for the analysis of human trachea were obtained from GEO accessions GSE143705 (bulk RNA-seq) and GSE143706 (scRNA-seq). ## [7] pbmcMultiome.SeuratData_0.1.2 pbmc3k.SeuratData_3.1.4 Among the other five methods, when the number of differentially expressed genes was small (pDE = 0.01), the mixed method had the highest PPV values, whereas for higher numbers of differentially expressed genes (pDE > 0.01), the DESeq2 method had the highest PPV values. For each setting, 100 datasets were simulated, and we compared seven different DS methods. Subject-level gene expression scores were computed as the average counts per million for all cells from each subject. Visualizing marker genes Scanpy documentation - Read the Docs Along with new functions add interactive functionality to plots, Seurat provides new accessory functions for manipulating and combining plots. First, we present a statistical model linking differences in gene counts at the cellular level to four sources: (i) subject-specific factors (e.g. Further, the cell-level variance and subject-level variance parameters were matched to the pig data. ## Matrix products: default For the T cells, (Supplementary Fig. We evaluated the performance of our tested approaches for human multi-subject DS analysis in health and disease. ## Analysis of AT2 cells and AMs from healthy and IPF lungs. 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). Basic volcano plot. 1. SeuratFindMarkers() Volcano plot - We performed DS analysis using the same seven methods as Section 3.1. Infinite p-values are set defined value of the highest -log(p) + 100. ## For each subject, gene counts are summed for all cells. The null and alternative hypotheses for the i-th gene are H0i:i2=0 and H0i:i20, respectively. Oxford University Press is a department of the University of Oxford. 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. Comparisons of characteristics of the simulated and real data are shown in Supplementary Figures S1S6. Specifically, we considered a setting in which there were two groups of subjects to compare, containing four and three subjects, respectively with 21 731 genes. I would like to create a volcano plot to compare differentially expressed genes (DEGs) across two samples- a "before" and "after" treatment. Red and blue dots represent genes with a log 2 FC (fold . # 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. 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. ## Running under: Ubuntu 20.04.5 LTS Simply add the splitting variable to object, # metadata and pass it to the split.by argument, # SplitDotPlotGG has been replaced with the `split.by` parameter for DotPlot, # DimPlot replaces TSNEPlot, PCAPlot, etc. . Default is set to Inf. 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. This interactive plotting feature works with any ggplot2-based scatter plots (requires a geom_point layer). provides an argument for using mixed models over pseudobulk methods because pseudobulk methods discovered fewer differentially expressed genes. As a gold standard, results from bulk RNA-seq comparing CD66+ and CD66- basal cells (bulk). It furthers the University's objective of excellence in research, scholarship, and education by publishing worldwide, This PDF is available to Subscribers Only. For example, a simple definition of sjc is the number of unique molecular identifiers (UMIs) collected from cell c of subject j. This is the model used in DESeq2 (Love et al., 2014). Supplementary Figure S12b shows the top 50 genes for each method, defined as the genes with the 50 smallest adjusted P-values. As you can see, there are four major groups of genes: - Genes that surpass our p-value and logFC cutoffs (blue). Gene counts were simulated from the model in Section 2.1. If a gene was not differentially expressed, the value of i2 was set to 0. dotplot visualization does not work for scaled or corrected matrices in which cero counts had been replaced by other values. ## [16] cluster_2.1.3 ROCR_1.0-11 limma_3.54.1 In (a), vertical axes are negative log10-transformed adjusted P-values, and horizontal axes are log2-transformed fold changes. NCF = non-CF. 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. ## [19] globals_0.16.2 matrixStats_0.63.0 pkgdown_2.0.7 For the AM cells (Fig. First, the CF and non-CF labels were permuted between subjects. Supplementary Figure S9 contains computation times for each method and simulation setting for the 100 simulated datasets. 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. Supplementary Figure S10 shows concordance between adjusted P-values for each method. ## [121] tidyr_1.3.0 rmarkdown_2.21 Rtsne_0.16 . 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. This work was supported by the National Institutes of Health [NHLBI K01HL140261]; the Parker B. Francis Fellowship Program; the Cystic Fibrosis Foundation University of Iowa Research Development Program (Bioinformatics Core); a Pilot Grant from the University of Iowa Center for Gene Therapy [NIH NIDDK DK54759] and a Pilot Grant from the University of Iowa Environmental Health Sciences Research Center [NIH NIEHS ES005605]. 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. 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. ## [43] miniUI_0.1.1.1 Rcpp_1.0.10 viridisLite_0.4.1 All seven methods identify two distinct groups of genes: those with higher average expression in large airways and those with higher average expression in small airways. With this data you can now make a volcano plot; Repeat for all cell clusters/types of interest, depending on your research questions. First, a random proportion of genes, pDE, were flagged as differentially expressed. A richer model might assume cell-level expression is drawn from a non-parametric family of distributions in the second stage of the proposed model rather than a gamma family. Overall, mixed seems to have the best performance, with a good tradeoff between false positive and TPRs. ## [1] LC_CTYPE=en_US.UTF-8 LC_NUMERIC=C A common use of DGE analysis for scRNA-seq data is to perform comparisons between pre-defined subsets of cells (referred to here as marker detection methods); many methods have been developed to perform this analysis (Butler et al., 2018; Delmans and Hemberg, 2016; Finak et al., 2015; Guo et al., 2015; Kharchenko et al., 2014; Korthauer et al., 2016; Miao et al., 2018; Qiu et al., 2017a, b; Wang et al., 2019; Wang and Nabavi, 2018). The implemented methods are subject (red), wilcox (blue), NB (green), MAST (purple), DESeq2 (orange), monocle (gold) and mixed (brown). One such subtype, defined by expression of CD66, was further processed by sorting basal cells according to detection of CD66 and profiling by bulk RNA-seq. 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. Next, we matched the empirical moments of the distributions of Eijc and Eij to the population moments. Here, we present a highly-configurable function that produces publication-ready volcano plots. Figure 4a shows volcano plots summarizing the DS results for the seven methods. In scRNA-seq studies, where cells are collected from multiple subjects (e.g. # S3 method for default FindMarkers( object, slot = "data", counts = numeric (), cells.1 = NULL, cells.2 = NULL, features = NULL, logfc.threshold = 0.25, test.use = "wilcox", min.pct = 0.1, min.diff.pct = -Inf, verbose = TRUE, only.pos = FALSE, max.cells.per.ident = Inf, random.seed = 1, latent.vars = NULL, min.cells.feature = 3, min.cells.group The wilcox, MAST and Monocle methods had intermediate performance in these nine settings. We then compare multiple differential expression testing methods on scRNA-seq datasets from human samples and from animal models. 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 number of UMIs for cell c was taken to be the size factor sjc in stage 3 of the proposed model. ## [70] ggridges_0.5.4 evaluate_0.20 stringr_1.5.0 ## [61] labeling_0.4.2 rlang_1.1.0 reshape2_1.4.4 For macrophages (Supplementary Fig. ## [103] jquerylib_0.1.4 RcppAnnoy_0.0.20 data.table_1.14.8 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. Flexible wrapper for GEX volcano plots GEX_volcano ## [124] spatstat.explore_3.1-0 shiny_1.7.4. In your DoHeatmap () call, you do not provide features so the function does not know which genes/features to use for the heatmap. Two of the methods had much longer computation times with DESeq2 running for 186min and mixed running for 334min. In each panel, PR curves are plotted for each of seven DS analysis methods: subject (red), wilcox (blue), NB (green), MAST (purple), DESeq2 (orange), Monocle (gold) and mixed (brown). Default is 0.25. Applying themes to plots. Figure 5d shows ROC and PR curves for the three scRNA-seq methods using the bulk RNA-seq as a gold standard. 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. 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. 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. A volcano plot is a type of scatterplot that shows statistical significance (P value) versus magnitude of change (fold change). When only 1% of genes were differentially expressed (pDE = 0.01), all methods had NPV values near 1. 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. S14f), wilcox produces better ranked gene lists of known markers than both subject and wilcox and again, the mixed method has the worst performance. 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. Rows correspond to different proportions of differentially expressed genes, pDE and columns correspond to different SDs of (natural) log fold change, . Gene expression markers of identity classes FindMarkers ## [79] fitdistrplus_1.1-8 purrr_1.0.1 RANN_2.6.1 Step 3: Create a basic volcano plot. ## [58] deldir_1.0-6 utf8_1.2.3 tidyselect_1.2.0 ## [7] LC_PAPER=en_US.UTF-8 LC_NAME=C All of the other methods compute P-values that are much smaller than those computed by the permutation tests. To use, simply make a ggplot2-based scatter plot (such as DimPlot() or FeaturePlot()) and pass the resulting plot to HoverLocator(). < 10e-20) with a different symbol at the top of the graph. In practice, often only one cutoff value for the adjusted P-value will be chosen to detect genes. 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. ## [5] LC_MONETARY=en_US.UTF-8 LC_MESSAGES=en_US.UTF-8 I understand a little bit more now. The difference between these formulas is in the mean calculation. 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. To whom correspondence should be addressed. Volcano plots in R: easy step-by-step tutorial - biostatsquid.com 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. Step 5: Export and save it. Step 4: Customise it! For example, consider a hypothetical gene having heterogeneous expression in CF pigs, where cells were either low expressors or high expressors versus homogeneous expression in non-CF pigs, where cells were moderate expressors. 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. 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. I prefer to apply a threshold when showing Volcano plots, displaying any points with extreme / impossible p-values (e.g. 5c). Let Gammaa,b denote the gamma distribution with shape parameter a and scale parameter b, Poissonm denote the Poisson distribution with mean m and XY denote the conditional distribution of random variable X given random variable Y. Platypus source: R/GEX_volcano.R - rdrr.io
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