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. 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. If a gene was not differentially expressed, the value of i2 was set to 0. Further, the cell-level variance and subject-level variance parameters were matched to the pig data. If a gene was differentially expressed, i2 was simulated from a normal distribution with mean 0 and standard deviation (SD) . S14f), wilcox produces better ranked gene lists of known markers than both subject and wilcox and again, the mixed method has the worst performance. 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. Specifically, the CDFs are in high agreement for the subject method in the range of P-values from 0 to 0.2, whereas the mixed method has a slight inflation of small P-values in the same range compared to the permutation test. This creates a data.frame with gene names as rows, and includes avg_log2FC, and adjusted p-values. Next, we applied our approach for marker detection and DS analysis to published human datasets. Results for analysis of CF and non-CF pig small airway secretory cells. For this study, there were 35 distinct permutations of CF and non-CF labels between the 7 pigs. This suggests that methods that fail to account for between subject differences in gene expression are more sensitive to biological variation between subjects, leading to more false discoveries. 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. When samples correspond to different experimental subjects, the first stage characterizes biological variation in gene expression between subjects. For the AM cells (Fig. 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. See ?FindMarkers in the Seurat package for all options. The observed counts for the PCT study are analogous to the aggregated counts for one cell type in a scRNA-seq study. ## Matrix products: default The expression level of gene i for group 1, i1, was matched to the pig data by setting ei1=jcKijc/i'jcKi'jc. In scRNA-seq studies, where cells are collected from multiple subjects (e.g. To whom correspondence should be addressed. Give feedback. ## [13] magrittr_2.0.3 memoise_2.0.1 tensor_1.5 ## [85] mime_0.12 formatR_1.14 compiler_4.2.0 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. See Supplementary Material for brief example code demonstrating the usage of aggregateBioVar. ## [28] dplyr_1.1.1 crayon_1.5.2 jsonlite_1.8.4 I would like to create a volcano plot to compare differentially expressed genes (DEGs) across two samples- a "before" and "after" treatment. Supplementary Figure S12a shows volcano plots for the results of the seven DS methods described. Well demonstrate visualization techniques in Seurat using our previously computed Seurat object from the 2,700 PBMC tutorial. Red and blue dots represent genes with a log 2 FC (fold . First, the CF and non-CF labels were permuted between subjects. I have been following the Satija lab tutorials and have found them intuitive and useful so far. Pseudobulking has been tested in real scRNA-seq studies (Kang et al., 2018) and benchmarked extensively via simulation (Crowell et al., 2020). ## [11] hcabm40k.SeuratData_3.0.0 bmcite.SeuratData_0.3.0 Figure 4b shows the top 50 genes for each method, defined by the smallest 50 adjusted P-values. (Zimmerman et al., 2021). . 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). provides an argument for using mixed models over pseudobulk methods because pseudobulk methods discovered fewer differentially expressed genes. 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. 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. ## other attached packages: For clarity of exposition, we adopt and extend notations similar to (Love et al., 2014). Each panel shows results for 100 simulated datasets in one simulation setting. Crowell et al. The authors thank Michael J. Welsh, Joseph Zabner, Kai Wang and Keyan Zarei for careful reading of the manuscript and helpful feedback that improved the clarity and content in the final draft. A more powerful statistical test that yields well-controlled FDR could be constructed by considering techniques that estimate all parameters of the hierarchical model. 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. PR curves for DS analysis methods. This interactive plotting feature works with any ggplot2-based scatter plots (requires a geom_point layer). 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. ## 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 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*. The regression component of the model took the form logqij=i1+xj2i2, where xj2 is an indicator that subject j is in group 2. In another study, mixed models were found to be superior alternatives to both pseudobulk and marker detection methods (Zimmerman et al., 2021). We set xj1=1 for all j and define xj2 as a dummy variable indicating that subject j belongs to the treated group. I have scoured the web but I still cannot figure out how to do this. True positives were identified as those genes in the bulk RNA-seq analysis with FDR<0.05 and |log2(IPF/healthy)|>1. First, the adjusted P-values for each method are sorted from smallest to largest. The number of UMIs for cell c was taken to be the size factor sjc in stage 3 of the proposed model. NCF = non-CF. 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 . I prefer to apply a threshold when showing Volcano plots, displaying any points with extreme / impossible p-values (e.g. 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. According to this criterion, the subject method had the best performance, and the degree to which subject outperformed the other methods improved with larger values of the signal-to-noise ratio parameter . 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. ## [73] fastmap_1.1.1 yaml_2.3.7 ragg_1.2.5 ## [58] deldir_1.0-6 utf8_1.2.3 tidyselect_1.2.0 S14e), we find that the subject and wilcox methods produce ranked gene lists with higher frequencies of marker genes than the mixed method, with subject having a slightly higher detection of known markers than wilcox. "t" : Student's t-test. 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. 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. Gene counts were simulated from the model in Section 2.1. 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. . ## [121] tidyr_1.3.0 rmarkdown_2.21 Rtsne_0.16 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 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). Figure 6(e and f) shows ROC and PR curves for the three scRNA-seq methods using the bulk RNA-seq as a gold standard. The other six methods involved DS testing with cells as the units of analysis. Step 4: Customise it! #' @param de_groups The two group labels to use for differential expression, supplied as a vector. For each method, we compared the permutation P-values to the P-values directly computed by each method, which we define as the method P-values. With this data you can now make a volcano plot. ## [55] pkgconfig_2.0.3 sass_0.4.5 uwot_0.1.14 ADD REPLY link 18 months ago by Kevin Blighe 84k 0. https://satijalab.org/seurat/articles/de_vignette.html. 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. Search for other works by this author on: Iowa Institute of Human Genetics, Roy J. and Lucille A. The lists of genes detected by the other six methods likely contain many false discoveries. 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. ## [25] ggrepel_0.9.3 textshaping_0.3.6 xfun_0.38 In this case, Cj-1csjc=sj* and Cj-1csjc2=sj*2, and the theorem holds. ## 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. Infinite p-values are set defined value of the highest -log(p) + 100. First, a random proportion of genes, pDE, were flagged as differentially expressed. 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. ## BLAS: /usr/lib/x86_64-linux-gnu/openblas-pthread/libblas.so.3
Region Iv Head Start Conference 2022,
Caroline Collins Height,
Police Incident In Truro Today,
Why Did The American Want To Exterminate Moros,
Where Did Nick Turani Go To College,
Articles F
