d, Percentages of Ki-67+ S+ Bm cells are provided in paired blood and tonsil samples of SARS-CoV-2-vaccinated and recovered individuals (n=16). select from data frame rows with a condition in r, Split data in R with two specific values of column, Subset a dataframe based on numerical values of a string inside a variable, How to filter based on a specific criteria in R. How to subset data in R: participant only needs to meet one of five criteria? Sci. 8d,e). However there are a few times that i found some genes that are primary markers for one certain subtype of the cells i want to sub clustering do not exist in the integration assay, which may lead to some problems. ## [103] stringi_1.7.12 highr_0.10 desc_1.4.2 Samples in cf were compared using KruskalWallis test with Dunns multiple comparison, showing adjusted P values. These observations in circulating Bm cells were paralleled by the appearance of resting Bm cells in tonsils, where they showed high expression of CD69 and CD21 and comparable SHM counts to circulating Bm cells. Is this workflow indeed the best? ## [106] lattice_0.20-45 Matrix_1.5-3 multtest_2.54.0 Numbers indicate percentages of parent population. They were also enriched in gene transcripts involved in interferon (IFN)- and BCR signaling and showed high expression of integrins ITGAX, ITGB2 and ITGB7 (Fig. The pro of this approach is that I use this method to solve the problem in the previous approach and now i have the genes that are primary markers for the cell sub types. Making statements based on opinion; back them up with references or personal experience. Below, we demonstrate how to modify the Seurat integration workflow for datasets that have been normalized with the sctransform workflow. However I did the following: Next I perform FindConservedMarkers on each of the cell clusters to identify conserved gene markers for each cell cluster. Thank you for the wonderful package. max.cells.per.ident = Inf, Asterisks indicate significantly different segment usage between S and the respective S+ Bm cell subsets. | WhichCells(object = object, ident = "ident.keep") | WhichCells(object = object, idents = "ident.keep") | subsetting seurat object with multiple samples. 6h). At months 6 and 12 post-infection, CD21+ resting Bm cells were the major Bm cell subset in the circulation and were also detected in peripheral lymphoid organs, where they carried tissue residency markers. Human memory B cells show plasticity and adopt multiple fates upon Med. Naradikian, M. S., Hao, Y. Atypical B cells up-regulate costimulatory molecules during malaria and secrete antibodies with T follicular helper cell support. 6a and Extended Data Fig. SCT_integrated <- IntegrateData(anchorset = SCT_Integrated.anchors, normalization.method = "SCT", features.to.integrate = rownames(SCT_Integrated)) Different batches were aligned using Batchelor (v.1.10.0) (ref. UMAP and clustering grouped Bm cells by IgG (clusters 15), IgM (clusters 6 and 7) and IgA (clusters 8 and 9) expression and revealed a phenotypical shift from acute infection to months 6 and 12 post-infection characterized by increased expression of CD21 on S+ Bm cells, whereas expression of Blimp-1, Ki-67, CD11c, CD71 and FcRL5 diminished (Extended Data Fig. and M.B.S. Annu. operators sufficient to make every possible logical expression? Glad to find out so many of you thinking about the same problem here, sad to realize there is indeed no pratical guide about how to do this properly yet. I simply used the FindNeighbors and FindClusters command in order to create the 'seurat_clusters' list in the meta.data. . Zurbuchen, Y., Michler, J., Taeschler, P. et al. Learn R. Search all packages and functions. A recent question here gets into that particular problem a bit. IFI6 and ISG15, on the other hand, are core interferon response genes and are upregulated accordingly in all cell types. a, Heatmap compares V heavy (VH; left) and VL (right) gene usage in indicated S+ Bm cell subsets and S Bm cells (non-binders) from scRNA-seq data of SARS-CoV-2-infected patients at months 6 and 12 post-infection. Chang, L. Y., Li, Y. If material is not included in the articles Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. "~/Downloads/pbmc3k/filtered_gene_bc_matrices/hg19/", # Get cell and feature names, and total numbers, # Set identity classes to an existing column in meta data, # Subset Seurat object based on identity class, also see ?SubsetData, # Subset on the expression level of a gene/feature, # Subset on a value in the object meta data, # Downsample the number of cells per identity class, # View metadata data frame, stored in object@meta.data, # Retrieve specific values from the metadata, # Retrieve or set data in an expression matrix ('counts', 'data', and 'scale.data'), # Get cell embeddings and feature loadings, # FetchData can pull anything from expression matrices, cell embeddings, or metadata, # Dimensional reduction plot for PCA or tSNE, # Dimensional reduction plot, with cells colored by a quantitative feature, # Scatter plot across single cells, replaces GenePlot, # Scatter plot across individual features, repleaces CellPlot, # New things to try! 1 Overview of SARS-CoV-2 cohorts analyzed in this study. At the moment you are getting index from row comparison, then using that index to subset columns. Why do men's bikes have high bars where you can hit your testicles while women's bikes have the bar much lower? a, Gating strategy is provided for identification of SARS-CoV-2 S+ and nucleocapsid (N+) germinal center (GC) and Bm cells in tonsil from a SARS-CoV-2-recovered and vaccinated individual (CoV-T2). Immunol. Seurat has a vast, ggplot2-based plotting library. d, Frequency of S+ Bm cells was measured by flow cytometry and separated by mild (acute, n=40; month 6, n=39; month 12, n=11) and severe COVID-19 (acute, n=19; month 6, n=22; month 12, n=6). While functions exist within Seurat to perform DE analysis, the p-values from these analyses are often inflated as each cell is treated as an independent . | object@scale.data | GetAssayData(object = object, slot = "scale.data") | | object@data | GetAssayData(object = object) | Nat Immunol (2023). | NoLegend | Remove all legend elements | The integrated assay consists of 3000 features comings from the original integration analysis (so choosed from the whole dataset, and not only from cells of the subset). Thank you. ## 3i). Cells were sorted on a FACS Aria III 4L sorter using the FACS Diva software. rev2023.4.21.43403. Browse other questions tagged, Start here for a quick overview of the site, Detailed answers to any questions you might have, Discuss the workings and policies of this site. T-bet+ B cells are induced by human viral infections and dominate the HIV gp140 response. In g, two-sided Wilcoxon test was used with Holm multiple comparison correction. ## Platform: x86_64-pc-linux-gnu (64-bit) Lines connect shared clones. The heterogeneity of Bm cells could be explained by several models38,39. To learn more, see our tips on writing great answers. How can I find help page about "%in%"? For full details, please read our tutorial. SCT_integrated <- RunPCA(SCT_integrated) BMC Bioinformatics 14, 7 (2013). However, this brings the cost of flexibility. O.B. Creates a Seurat object containing only a subset of the cells in the 1g and Extended Data Fig. 6b). f,g, WNN UMAP of Bm cells was derived from scRNA-seq analysis of blood and tonsillar B cells (n=4). B cell clonality analysis was performed mainly with the changeo-10x pipeline from the Immcantation suite65 using the singularity image provided by Immcantation developers. Weiss, G. E. et al. (by re-cluster I mean the entire subsetted dataset is treated as an independent body of cells and re-analyzed similar to what you allude to. Already on GitHub? First, we create a column in the meta.data slot to hold both the cell type and stimulation information and switch the current ident to that column. Adamo, S. et al. Robbiani, D. F. et al. Developed by Paul Hoffman, Satija Lab and Collaborators. 23, 10081020 (2022). control_subset <- RunPCA(control_subset, npcs = 30, verbose = FALSE) to Shown are 30 most frequently used VH segments, sorted by hierarchical clustering, with colors indicating frequencies. Distinct effector B cells induced by unregulated Toll-like receptor 7 contribute to pathogenic responses in systemic lupus erythematosus. Cell 184, 12011213.e14 (2021). 7d). Using multiple criteria in subset function and logical operators Jordan. Hnzelmann, S., Castelo, R. & Guinney, J. GSVA: gene set variation analysis for microarray and RNA-seq data. Very few S+ tonsillar Bm cells expressed FcRL4 in both vaccinated and recovered individuals (Extended Data Fig. Antigen-specific Bm cells were dominated by CD21CD27+ Bm cells (around 55% of S+ Bm cells) and, to a lesser extent, by CD21CD27 Bm cells (515%) at week 2 post-second dose and post-third dose compared to month 6 post-second dose. SHM counts were low in unswitched S+ CD21+ Bm cells, slightly higher in CD21+CD27 resting Bm cells, and high by comparison in CD21+CD27+ resting, CD21CD27+CD71+ activated and CD21CD27 Bm cells (Fig. After discussing with colleagues and reading other articles I decided to go for option b). b, Violin plots of frequencies of CD21CD27+, CD21CD27, CD21+CD27+ and CD21+CD27 cells within S+ Bm cells are shown at acute infection (n=23) and months 6 (n=52) and 12 post-infection (n=16). d, Exemplary dendrograms (IgPhyML B cell trees) display different persistent Bm cell clones at months 6 (triangles) and 12 (dots) post-infection. My assumption was that it would start with 1 and if it does evaluate to "false" it would go on to 2 and than to 3, and if none matches the statement after == is "false" and if one of them matches, it is "true". Segment usage between Bm cell subsets was compared using edgeR (v3.36). ## [139] Biobase_2.58.0 numDeriv_2016.8-1.1 shiny_1.7.4. We then identify anchors using the FindIntegrationAnchors() function, which takes a list of Seurat objects as input, and use these anchors to integrate the two datasets together with IntegrateData(). @timoast , how can we finally tackle this issue? r rna-seq single-cell seurat Share The probes were mixed in 1:1 Brilliant Buffer (BD Bioscience) and FACS buffer (PBS with 2% FBS and 2mM EDTA) with 5M of free d-biotin. seurat_object <- subset (seurat_object, subset = DF.classifications_0.25_0.03_252 == 'Singlet') #this approach works I would like to automate this process but the _0.25_0.03_252 of DF.classifications_0.25_0.03_252 is based on values that are calculated and will not be known in advance. Seurat is great for scRNAseq analysis and it provides many easy-to-use ggplot2 wrappers for visualization. ## [100] spatstat.utils_3.0-1 tibble_3.1.8 bslib_0.4.2 ## [22] matrixStats_0.63.0 sandwich_3.0-2 pkgdown_2.0.7 Bioinformatics 32, 28472849 (2016). accept.value = NULL, The most common way is using the objects Idents: Idents (skin) <- "predicted_cell_type" skin_subset <- subset (skin, idents = "0:CD8 T cell") For the code you provided, I believe using quotations around the column name will work: Antibody affinity shapes the choice between memory and germinal center B cell fates. Nature 595, 426431 (2021). 30 most frequently used segments among RBD+ Bm cells are shown. http://creativecommons.org/licenses/by/4.0/. Cao, J. et al. As far as heterogeneity goes, if you keep sub-sampling till you reach 2 cells you will find differences between even them. White areas represent BCR sequences found in single cells only. f, Violin plots of percentages of Ki-67+ S+ Bm cells are shown at indicated timepoints. Poon, M. M. L. et al. 1a and Supplementary Table 1). As suggested by #2042, you can change the set of features to be integrated by using the features.to.integrate argument in IntegrateData. Immunol. *P<0.05, **P<0.01, ***P<0.001, ****P<0.0001. a, Uniform manifold approximation and projection (UMAP) plots of S+ Bm cells are provided during acute SARS-CoV-2 infection and at months 6 and 12, showing samples of nonvaccinated individuals from the SARS-CoV-2 Infection Cohort, subsampled to maximally 25 cells per sample (Acute, n=44; month 6, n=59; month 12, n=17). It would be nice if Satija lab could give more clear instruction on how to proceed in case of high versus low heterogeneity after subsettting. Analysis of V heavy and light chain frequencies identified several chains enriched in RBD+ Bm cells compared with RBD Bm cells described to encode RBD-binding antibodies, including IGHV3-30, IGHV3-53, IGHV3-66, IGKV1-9 and IGKV1-33 (refs. To learn more, see our tips on writing great answers. Commun. 5f,g). Single-cell RNA-seq: Pseudobulk differential expression analysis