seurat subset multiple conditions
Allergy Clin. Hoehn, K. B., Pybus, O. G. & Kleinstein, S. H. Phylogenetic analysis of migration, differentiation, and class switching in B cells. Seurats centered log ratio transformation was applied across features, followed by a scaling of obtained values, resulting in final LIBRA scores. How to perform subclustering and DE analysis on a subset of an - Github High-throughput mapping of B cell receptor sequences to antigen specificity. Sci. Creates a Seurat object containing only a subset of the cells in the Frozen mononuclear cells were stained in 96-well U-bottom plates using ZombieUV Live-Dead staining (BioLegend) and TruStain FcX (1:200, BioLegend) in PBS for 30min, followed by staining with the above-mentioned antigen-specific staining mix (200ng S, 50ng RBD, 100ng nucleocapsid, 100ng hemagglutinin and 20ng SAV-decoy per color per 50l) at 4C for 1h. Subsequently, cells were stained for 30min with surface markers, followed by fixation and permeabilization with transcription factor staining buffer (eBioscience) at room temperature for 1h and intracellular staining at room temperature for 30min, before washing and acquisition. Sci. How to convert a sequence of integers into a monomial, How to create a virtual ISO file from /dev/sr0. I have added them all together and created the VlnPlot to check for the quality of the samples. The method is named sctransform, and avoids some of the pitfalls of standard normalization workflows, including the addition of a pseudocount, and log-transformation. Proc. Bioinformatics 31, 33563358 (2015). 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. The point is that you need a series of single comparisons, not a comparison of a series of options. Nature 584, 437442 (2020). Immunity 51, 398410.e5 (2019). Samples in a and cf were compared using a Kruskal-Wallis test with Dunns multiple comparison correction. I followed a similar approach to @attal-kush. Thank you! 6c). ## [11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C In e, two-sided Wilcoxon test was used with Holm multiple comparison correction. Nave B cell (n=1462 cells), served as reference and are the same as in Fig. random.seed = 1, Samples in f were compared using a Kruskal-Wallis test with Dunns multiple comparison correction, with adjusted P values shown. and S.A. contributed to flow cytometry experiments, patient recruitment and data collection. And evaluation order? ), Digitalization Initiative of the Zurich Higher Education Institutions Rapid-Action Call #2021.1_RAC_ID_34 (to C.C. Seurats WNN analysis was used to take advantage of our multimodal approach during clustering and visualization59. New blog post from our CEO Prashanth: Community is the future of AI, Improving the copy in the close modal and post notices - 2023 edition, Manually define clusters in Seurat and determine marker genes, Trim Seurat object to contain expression info only for selected genes, Seurat VlnPlot presenting expression of multiple genes in a single cluster. 63). English version of Russian proverb "The hedgehogs got pricked, cried, but continued to eat the cactus", Effect of a "bad grade" in grad school applications. | object@cell.names | colnames(x = object) | Generally, you'll want use different parameters for each sample. That would be great if someone can confirm or deny :). between condition A cluster 1 vs. condition B cluster 1 cells). ## GOPB, Gene Ontology Biological Process. Hi Seurat team, Thank you for developing Seurat. | SetIdent(object = object, ident.use = "new.idents") | Idents(object = object) <- "new.idents" | Does anyone have an idea how I can automate the subset process? Durable SARS-CoV-2 B cell immunity after mild or severe disease. Y.Z. ident.remove = NULL, ## [118] data.table_1.14.8 irlba_2.3.5.1 httpuv_1.6.9 *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). 4 Unsupervised analysis of circulating S, Extended Data Fig. Lines connect samples of same individual. The antigen presenting potential of CD21low B Cells. object, accept.value = NULL, Then we use FindMarkers() to find the genes that are different between stimulated and control B cells. SubsetData function - RDocumentation rev2023.4.21.43403. Can be used to downsample the data to a certain P values for different comparisons are given below. This function performs differential gene expression testing for each dataset/group and combines the p-values using meta-analysis methods from the MetaDE R package. Differential gene expression identified higher expression of CR2, CD44, CCR6 and CD69 in tonsillar SWT+ Bm cells compared with blood SWT+ Bm cells, whereas the activation-related genes FGR and CD52 were higher in blood SWT+ Bm cells compared with their tonsillar counterparts (Extended Data Fig. Compared with their circulating counterparts, tonsillar S+ and N+ Bm cells expressed, on average, more CD69, less Ki-67, reduced T-bet and several chemokine receptors differently (Fig. Anti-SARS-CoV-2 antibodies were measured by a commercially available enzyme-linked immunosorbent assay specific for S1 of SARS-CoV-2 (Euroimmun SARS-CoV-2 IgG and IgA)57 or by a bead-based multiplexed immunoassay58. ## other attached packages: In d, severities were compared between the same timepoint using a Kruskal-Wallis test with a Dunns multiple comparison correction, with adjusted P values shown. AutoPointSize: Automagically calculate a point size for ggplot2-based. I have a few questions and was hoping you can help me address them; But as you can see, %in% is far more useful and less verbose in such circumstances. 11, eaax0904 (2019). CD21CD27 Bm cells were reported to be able to secrete antibodies when receiving T cell help and to act as antigen-presenting cells24. Choose a subset of cells, and then split by samples and then re-run the integration steps (select integration features, find anchors and integrate data). Cell Rep. 34, 108684 (2021). In short: I found that the first and second approaches lead to a nice integration while the third and fourth lead to an uncorrected batch effect (see the image below). We found that SARS-CoV-2-specific CD21CD27+ activated Bm cells and CD21CD27 Bm cells were the predominant subsets in circulation during acute infection and upon vaccination. 3a,b). 1b and Supplementary Table 3) comprised subjects seen at University Hospital Zurich between November 2021 and April 2022 that underwent tonsillectomy for recurrent and chronic tonsillitis or obstructive sleep apnea and were exposed to SARS-CoV-2 by infection and/or vaccination. Functions reduce_dimension(), order_cells() and graph_test() were executed with default parameters. Kurosaki, T., Kometani, K. & Ise, W. Memory B cells. As suggested by #2042, you can change the set of features to be integrated by using the features.to.integrate argument in IntegrateData. In b, frequencies were compared using a two-tailed Wilcoxon matched-pairs signed rank test. Here we plot 2-3 strong marker genes for each of our 14 clusters. ## loaded via a namespace (and not attached): Adding EV Charger (100A) in secondary panel (100A) fed off main (200A). # S3 method for Assay Sample assignment of cells was done using TotalSeq-based cell hashing and Seurats HTODemux() function. a, Flow cytometry plots show decoy S+ (top) and nucleocapsid (N)+ Bm cells (bottom) in paired tonsil and blood samples of a SARS-CoV-2-vaccinated (CoV-T1; left) and SARS-CoV-2-recovered patient (CoV-T2; right). contributed to patient recruitment and data collection. The flow cytometry and scRNA-seq subcohort characteristics are presented in Supplementary Tables 1 and 2, respectively. J. Clin. (palm-face-impact)@MariaKwhere were you 3 months ago?! 2d and 6a. j, WNNUMAP was derived as in f and colored by tissue origin. control_subset <- FindClusters(control_subset). Natl Acad. Atypical B cells are part of an alternative lineage of B cells that participates in responses to vaccination and infection in humans. 8d,e). 10, eaan8405 (2018). 2a). filtered_contig_annotations.csv files obtained from the cellranger multipipeline were used as input for the changeo-10x pipeline. BCR diversity was slightly reduced in S+ CD21CD27FcRL5+ compared with S+ CD21+ resting Bm cells (Extended Data Fig. 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. Just to demonstrate, a more complicated logical subset would be: data (airquality) dat <- subset (airquality, subset = (Temp > 80 & Month > 5) | Ozone < 40) And as Chase points out, %in% would be more efficient in your example: myNewDataFrame <- subset (bigfive, subset = bf11 %in% c (1, 2, 3)) Chang, L. Y., Li, Y. 2b). Our work also provides insight into the CD21CD27 Bm cells, which made up a sizeable portion of Bm cells following acute viral infection and vaccination in humans. Immunol. We performed scRNA-seq combined with feature barcoding, which allowed us to assess surface phenotype and to perform BCR-seq in sorted S+ Bm cells and S B cells from paired blood and tonsil samples of four patients (two SARS-CoV-2-recovered and two SARS-CoV-2-vaccinated). To see help pages for operators, use ? ## [40] polyclip_1.10-4 gtable_0.3.1 leiden_0.4.3 9e). Also, instead of changing the default assay to "RNA", finding the variable features, and changing the default assay back to "integrated", would it be make more sense to just delete those lines of code and just change: 12, 6703 (2021). Atypical B cells up-regulate costimulatory molecules during malaria and secrete antibodies with T follicular helper cell support. Another cohort (Extended Data Fig. Developed by Paul Hoffman, Satija Lab and Collaborators. Subsetting from seurat object based on orig.ident? 6, eabh0891 (2021). Asking for help, clarification, or responding to other answers. Honestly now I'm very stringent on what my definition of a DE is because minor gene fluctuations in scRNAseq data are very unreliable and reside within the realm of false-positive dropouts. ## [133] parallel_4.2.0 grid_4.2.0 tidyr_1.3.0 Is there a way to do that? How to set the 'features.to.integrate' as all the features? Conversely, the frequency of S+ CD21CD27 Bm cells rose quickly and remained stable over 150days post-vaccination, accounting for about 20% of S+ Bm cells (Fig. conceived the project, designed experiments and interpreted data. Cells are colored by timepoint (left) and by clusters identified by PhenoGraph algorithm (right). Low CD21 expression defines a population of recent germinal center graduates primed for plasma cell differentiation. d, Exemplary dendrograms (IgPhyML B cell trees) display different persistent Bm cell clones at months 6 (triangles) and 12 (dots) post-infection. # To see all keys for all objects, use the Key function. Maturation and persistence of the anti-SARS-CoV-2 memory B cell response. Hello, F1000Res. BMC Bioinformatics 14, 7 (2013). Many, many thanks for the great package and continued support! VH and V light (VL) genes are indicated on top of dendrograms. 4d). Gene set enrichment analysis (GSEA) was done as described51. I am trying to subset the object based on cells being classified as a 'Singlet' under seurat_object@meta.data[["DF.classifications_0.25_0.03_252"]] and can achieve this by doing the following: 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. The inclusion of patients with severe COVID-19 will have increased the average age of our cohort, whereas the individuals from which the tonsil samples were obtained were younger on average. But even then, using a blanket threshold for all clusters in a sample may remove populations of biological interest. What was the actual cockpit layout and crew of the Mi-24A? But I am not sure which assay should be used for FindVariableFeatures of the subset cells, RNA, SCT, or Integrated? | object@idents | Idents(object = object) | Following 20min staining with fixable viability dye eFluor 780 (eBioscience) and TruStain FcX and subsequently 1h antigen-specific staining mix, cells were incubated at 4C for 30min with a surface staining mix containing fluorescently labeled and barcoded antibodies, and each sample was marked with a hashtag antibody that allowed multiplexing (Supplementary Table 6). Red line represents fitted second-order polynomial function (R2=0.1932). Andrews, S. F. et al. | RestoreLegend | Restores a legend after removal | Look at what 1||2||3 evaluates to: and you'd get the same using | instead. Note that plotting functions now return ggplot2 objects, so you can add themes, titles, and, "2,700 PBMCs clustered using Seurat and viewed\non a two-dimensional tSNE", # Plotting helper functions work with ggplot2-based scatter plots, such as DimPlot, FeaturePlot, CellScatter, and. 5a,b and Extended Data Fig. 4c). Identified Bm cells (SARS-CoV-2 S B cells, n=2258; SWT+ Bm cells, n=1298) were subsequently reclustered as indicated in the box. To obtain sessionInfo()## R version 4.2.0 (2022-04-22) Numbers indicate percentages of parent population. b, N+ (left) and S+ (right) Bm cell frequencies were determined in paired blood and tonsils of SARS-CoV-2-vaccinated (n=8) and SARS-CoV-2-recovered individuals (n=8). | DarkTheme | Set a black background with white text | Therefore, I assume I cannot use Pearson residuals for DE analysis. T-bet+ B cells have a protective role in mouse models of acute and chronic viral infections38,42. Sci. Comprehensive analyses of B-cell compartments across the human body reveal novel subsets and a gut-resident memory phenotype. 5 Flow cytometry analysis of tonsillar and circulating SARS-CoV-2-specific B. Box plots show medians, box limits and interquartile ranges (IQRs), with whiskers representing 1.5 IQR and outliers (also applies to subsequent figures). Immunol. 30 most frequently used segments in resting Bm cells are displayed. In d, frequencies were compared using a two-tailed, two-proportions z-test with a Bonferroni-based multiple testing correction. a, CD21 and CD27 expression on S+ Bm cells during acute infection (top) and month 6 post-infection (bottom) of patient CoV-P2 was determined by flow cytometry. IFI6 and ISG15, on the other hand, are core interferon response genes and are upregulated accordingly in all cell types. I think the proper way is to subset before integration as in Smillie et al. Article HolmBonferroni method was used for P value adjustment of multiple comparisons. I am also stuck on this issue too. Seurat provides many prebuilt themes that can be added to ggplot2 plots for quick customization. ## [5] stxBrain.SeuratData_0.1.1 ssHippo.SeuratData_3.1.4 Defining antigen-specific plasmablast and memory B cell subsets in human blood after viral infection or vaccination. BCR and IFN- signaling appears to be a defining feature of CD21CD27 Bm cells, and probably induces and governs the T-bet-dependent transcriptional program in these cells32. Finally, we use a t-SNE to visualize our clusters in a two-dimensional space. SplitObject : Splits object into a list of subsetted objects. What were the most popular text editors for MS-DOS in the 1980s? Has the cause of a rocket failure ever been mis-identified, such that another launch failed due to the same problem? Eight were vaccinated by SARS-CoV-2 mRNA vaccination only, whereas another eight had recovered from SARS-CoV-2 infection with some of them additionally vaccinated.