seurat subset multiple conditions

J. Clin. contributed to patient recruitment. Returns a Seurat object containing only the relevant subset of cells, Run the code above in your browser using DataCamp Workspace, SubsetData: Return a subset of the Seurat object, pbmc1 <- SubsetData(object = pbmc_small, cells = colnames(x = pbmc_small)[. Best wishes & Warnatz, K. Naive- and memory-like CD21 low B cell subsets share core phenotypic and signaling characteristics in systemic autoimmune disorders. When comparing dataset quality, we noticed a markedly lower median gene detection and unique molecular identifier count per cell in one of our datasets of the SARS-CoV-2 Infection Cohort. Asking for help, clarification, or responding to other answers. Immunity 33, 451463 (2010). ## BLAS: /usr/lib/x86_64-linux-gnu/openblas-pthread/libblas.so.3 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. max per cell ident. ## Not the answer you're looking for? Single-cell RNA sequencing (scRNA-seq) indicated that single Bm cell clones adopted different fates upon antigen reexposure. r - Subset on multiple genes in Seurat - Bioinformatics Stack Exchange ISSN 1529-2916 (online) Subsequently, we analyzed S+ Bm cells in the blood of SARS-CoV-2-nave individuals (all seronegative for S-specific antibodies) by flow cytometry (n=11, five females and six males) and scRNA-seq (n=3) sampled before their SARS-CoV-2 mRNA vaccination, at days 813 (week 2) post-second dose, 6months after the second dose and days 1114 post-third dose (Extended Data Fig. 7g). 6d,e). At the moment you are getting index from row comparison, then using that index to subset columns. Nat. Or should we go directly onto integrated dataset and RunPCA? Also, please provide a reproducible example data for testing, dput (myData). Subsequently, the mononuclear cells were frozen in FBS with 10% dimethyl sulfoxide and stored in liquid nitrogen until use. Could you please let me know if the steps below are the correct way to go about identifying clusters and markers? I can figure out what it is by doing the following: Where meta_data = 'DF.classifications_0.25_0.03_252' and is a character class. Upon antigen reencounter, Bm cells differentiate into antibody-secreting plasma cells or reenter GCs where they undergo additional SHM9. Thank you @satijalab for this amazing tool and the amazing tutorials !!!! Compare: For your example, I believe the following should work: See the examples in ?subset for more. Nat. & Cancro, M. P. Age-associated B cells: key mediators of both protective and autoreactive humoral responses. | WhichCells(object = object, subset.name = "name", low.threshold = low, high.threshold = high) | WhichCells(object = object, expression = name > low & name < high) | J. Immunol. Activation dynamics and immunoglobulin evolution of pre-existing and newly generated human memory B cell responses to influenza hemagglutinin. g, Heat map represents V heavy (VH) gene usage, in RBD+ and RBD Bm cells in scRNA-seq dataset from months 6 and 12. | RestoreLegend | Restores a legend after removal | | Seurat v2.X | Seurat v3.X | So I have a couple of questions regarding my workflow: For downstream DE analysis, the scale.data slot in the SCT assay has disappeared after integration. Lines connect paired samples. I have also been working on the single cell dataset and there are several times that i need to subcluster a proportion cell type. I followed a similar approach to @attal-kush. Immunity 49, 725739.e6 (2018). Google Scholar. ## [52] metap_1.8 viridisLite_0.4.1 xtable_1.8-4 Making statements based on opinion; back them up with references or personal experience. Shared transcriptional profiles of atypical B cells suggest common drivers of expansion and function in malaria, HIV, and autoimmunity. | object@hvg.info | HVFInfo(object = object) | | object@raw.data | GetAssayData(object = object, slot = "counts") | Sakharkar, M. et al. j, WNNUMAP was derived as in f and colored by tissue origin. subsetting cells to find sub clusters - Github 3e and Extended Data Fig. Overexpression of T-bet in HIV infection is associated with accumulation of B cells outside germinal centers and poor affinity maturation. c, Cohort overview of SARS-CoV-2 Vaccination Cohort. B cells that differentiate in the GC undergo affinity maturation through somatic hypermutation (SHM) of the B cell receptor (BCR) following which B cells can become long-lived plasma cells or Bm cells4,5,6. control_subset <- RunPCA(control_subset, npcs = 30, verbose = FALSE, features = Variable Features(control_subset)), SARS-CoV-2 spike-specific memory B cells express higher levels of T-bet and FcRL5 after non-severe COVID-19 as compared to severe disease. Single-cell RNA-seq: Pseudobulk differential expression analysis Immunity 55, 945964 (2022). Here we plot 2-3 strong marker genes for each of our 14 clusters. | RotatedAxis | Rotates x-axis labels |. Learn R. Search all packages and functions. 43, e47 (2015). h, Expression of selected genes (left) and surface protein markers (right) are shown in Bm cell clusters. Subsequent reclustering of Bm cells resolved six clusters (Fig. a) My approach would be to just run FindClusters() with a higher resolution on the whole dataset until the desired subclustering is reached. All plotting functions will return a ggplot2 plot by default, allowing easy customization with ggplot2. For UMAP representations and PhenoGraph clustering (Rphenograph package, version 0.99.1) (ref. Immunol. ## [49] miniUI_0.1.1.1 Rcpp_1.0.10 plotrix_3.8-2 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). I'm also interested in understanding better how to do this. It only takes a minute to sign up. Black lines indicate trajectory. Very few S+ tonsillar Bm cells expressed FcRL4 in both vaccinated and recovered individuals (Extended Data Fig. Biotechnol. b, Scatter plots as in a display binding scores for SWT, RBD, Sbeta and Sdelta antigen constructs against each other. J. Exp. As an aside, your middle two samples with a majority portion of cells with %mitochondrial reads > 10% are rather worrying, as they may largely be dead/dying. 8e,f). You are using a browser version with limited support for CSS. This study was approved by the Cantonal Ethics Committee of Zurich (BASEC #2016-01440). For the same reasons, I felt this was the most intuitive way. Whether CD21CD27 Bm cells contribute to protective immunity during infection in humans remains controversial41. Nature Immunology thanks Stuart Tangye and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. How to perform subclustering and DE analysis on a subset of an integrated object, Supervised clustering on a subset of integrated object (best practices?). A. et al. 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. But I especially don't get why this one did not work: Fourteen cycles (in one case 17) of initial cDNA amplification were used for all sample batches, and single-cell sequencing libraries for whole-transcriptome analysis (GEX), BCR profiling (VDJ) and TotalSeq (BioLegend) barcode detection (ADT) were generated. We observed a strong increase in the frequency of S+ and RBD+ Bm cells in SARS-CoV-2-infected individuals at months 6 (median 0.14% and 0.033%, respectively) and 12 post-infection (median 0.068% and 0.02%) compared with acute infection (median 0.016% and 0.0023%) (Fig. Eg, the name of a gene, PC_1, a I did see batch effects here (cells from different batches did not share clusters). Well occasionally send you account related emails. I just want to make sure the Seurat Team agrees with my workflow for identifying the cell clusters and conserved markers for the integrated and sctransform analysis. Numbers indicate percentages of parent population. Austin, J. W. et al. We can explore these marker genes for each cluster and use them to annotate our clusters as specific cell types. Victora, G. D. & Nussenzweig, M. C. Germinal centers. | object@var.genes | VariableFeatures(object = object) | Gene expression data and TotalSeq surface proteome data were integrated separately. I did SCTransform() workflow, then subset a cluster of interest. I just do not want to do manual subsetting on 10 genes, then manually getting @data matrix from each subset, and recreating seurat object afterwards. CAS 4e). 3a,b). Thank you! 8b,c). Samples in d were compared using KruskalWallis test with Dunns multiple comparison correction, showing adjusted P values if significant. In the SARS-CoV-2 Tonsil Cohort and SARS-CoV-2 Vaccination Cohort, cells with fewer than 200 or more than 4,000 detected genes were excluded from the analysis. Of these, 35 received SARS-CoV-2 mRNA vaccination between month 6 and month 12, and 3 subjects between acute infection and month 6. Cutting edge: B cellintrinsic T-bet expression is required to control chronic viral infection. We included a total of 65 patients of the full cohort51,52 on the basis of a power calculation from pre-experiments and according to sample availability of at least paired samples from two timepoints. SCT_integrated <- FindNeighbors(SCT_integrated, dims = 1:15) | RenameIdent(object = object, old.ident.name = "old.ident", new.ident.name = "new.ident") | RenameIdents(object = object, "old.ident" = "new.ident") | Look at what 1||2||3 evaluates to: and you'd get the same using | instead. Cell 185, 15881601.e14 (2022). Germinal centre-driven maturation of B cell response to mRNA vaccination. rowSums () determines how many non-zero counts you have. Samples in cf were compared using KruskalWallis test with Dunns multiple comparison, showing adjusted P values. i, SHM counts are provided for nave B cells (n=1,607), blood (n=170) and tonsillar SWT+ Bm cells (n=1,128). parameter (for example, a gene), to subset on. Box plots show medians, box limits and interquartile ranges (IQRs), with whiskers representing 1.5 IQR and outliers (also applies to subsequent figures). I hope it is useful. By using uniform manifold approximation and projection (UMAP) we visualized S+ Bm cells from the flow cytometry dataset obtained in nonvaccinated post-infection samples and performed a PhenoGraph clustering (Extended Data Fig. Seurats WNN analysis was used to take advantage of our multimodal approach during clustering and visualization59. 5d). Google Scholar. g, UMAPs represent Monocle 3 analysis of all Bm cells (left) and S+ Bm cells (right). Still in the same situation. ), Deutsche Forschungsgemeinschaft (WA 1597/6-1 and WA 1597/7-1 to K.W. J. J. Immunol. High-affinity memory B cells induced by SARS-CoV-2 infection produce more plasmablasts and atypical memory B cells than those primed by mRNA vaccines. 208, 25992606 (2011). ## [10] qqconf_1.3.1 TH.data_1.1-1 digest_0.6.31 Antigen-specific cells per sample were sorted with 1,5002,000 nonspecific B cells, as shown in Extended Data Figs. WNN clustering of all sequenced Bm cells identified ten clusters that, on the basis of the expression of cell surface markers and Ig isotype, were merged into five subsets annotated as CD21CD27+CD71+ activated Bm cells, CD21CD27FcRL5+ Bm cells, CD21+CD27 resting Bm cells, CD21+CD27+ resting Bm cells and unswitched CD21+ Bm cells (Fig. | object@dr$pca | object[["pca"]] | Convergent antibody responses to SARS-CoV-2 in convalescent individuals. Flow cytometry using the multimer probe approach (Extended Data Fig. I am also wondering if there is an official recommendation for this task. and O.B. Gene sets were obtained from the Molecular Signatures Database (v7.5.1, collections H and C5) and loaded in R by the package msigdbr (v.7.5.1). Lines connect shared clones. ## [118] data.table_1.14.8 irlba_2.3.5.1 httpuv_1.6.9 ## [115] lmtest_0.9-40 jquerylib_0.1.4 RcppAnnoy_0.0.20 Commun. Human memory B cells show plasticity and adopt multiple fates upon recall response to SARS-CoV-2, https://doi.org/10.1038/s41590-023-01497-y. A minor scale definition: am I missing something? In c and g, all P values are shown, in the other graphs adjusted P values are shown if significant (p<0.05). Robbiani, D. F. et al. How to retrieve multidimensional data from CSV file? 24, 389396 (2017). subset.name = NULL, This is because the RNA slot is a true representative of biological variation, when someone tries to reproduce your findings they won't perform a negative binomial regression on their PCR. AutoPointSize: Automagically calculate a point size for ggplot2-based. The flow cytometry and scRNA-seq subcohort characteristics are presented in Supplementary Tables 1 and 2, respectively. conceived the project, designed experiments and interpreted data. ), Filling the Gap Program of UZH (to M.E.R. after integration, I subsetted my cells of interest using the integrated assay, and I still see apparent batch effects. ## [70] labeling_0.4.2 rlang_1.0.6 reshape2_1.4.4 Using multiple criteria in subset function and logical operators ISSN 1529-2908 (print). Looking for job perks? Is short-circuiting logical operators mandated? Google Scholar. Statistical analysis was performed with GraphPad Prism (version 9.4.1, GraphPad Software, USA) and R (version 4.1.0). I want to subset a specific cell type (cluster) and examine subtypes in this cell type. 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. 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. rev2023.4.21.43403. 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. The commands are largely similar, with a few key differences: Normalize datasets individually by SCTransform (), instead of NormalizeData () prior to integration 12, 6703 (2021). Takes either a list of cells to use as a subset, or a To subscribe to this RSS feed, copy and paste this URL into your RSS reader. accept.value = NULL, 1 Answer Sorted by: 1 With a little bit of workaround: i) Add a new column to the data slot (only because your original subset () call does so but it can be raw counts or any other data matrix in your Seurat object). Alice. Severe deficiency of switched memory B cells (CD27+IgMIgD) in subgroups of patients with common variable immunodeficiency: a new approach to classify a heterogeneous disease. Each set of modal data (eg. It did always just select values that matched the first of the criteria, here 1. ## [97] compiler_4.2.0 plotly_4.10.1 png_0.1-8 The code generated during the current study is available at https://github.com/Moors-Code/MBC_Plasticity_Moor_Boyman_Collaboration. and A.E.M. c, Heat map shows selected, significantly differentially expressed genes in indicated S+ Bm cell subsets. This will display FeaturePlots of the list of given genes, split by a grouping variable (stimulation condition here). Data-driven phenotypic dissection of AML reveals progenitor-like cells that correlate with prognosis. Anyone you share the following link with will be able to read this content: Sorry, a shareable link is not currently available for this article. Box plots show median, box limits, and interquartile ranges (IQR), with whiskers representing 1.5x IQR and outliers. Gu, Z., Eils, R. & Schlesner, M. Complex heatmaps reveal patterns and correlations in multidimensional genomic data. 3fh and Extended Data Fig. Cells with LIBRA scores >0 for the respective antigens were defined as antigen-specific, and in the SARS-CoV-2 infection, cohort cells were considered S+ if any of the antigens used for baiting (SWT, Sbeta, Sdelta, RBD) were defined as specific. Goel, R. R. et al. c. Should FindVariableFeatures be run on the RNA assay, the integrated assay, or the SCT assay? 1a). However, antibody responses to several previously applied vaccines were normal in T-bet-deficient patients30. ident.use = NULL, Now, I have a Seurat object with 3 assays: RNA, SCT, and Integrated. A longitudinal cohort (Extended Data Fig. Hi @vertesy , Connect and share knowledge within a single location that is structured and easy to search. | object@cell.names | colnames(x = object) | Hi @attal-kush , In the SARS-CoV-2 Infection Cohort, cells with fewer than 200 or more than 2,500 detected genes and cells with more than 10% detected mitochondrial genes were excluded from the analysis. This scRNA-seq approach detected frequencies of about 30% of RBD+ Bm cells within S+ Bm cells that were comparable to flow cytometry (Extended Data Figs. How to create a virtual ISO file from /dev/sr0, enjoy another stunning sunset 'over' a glass of assyrtiko. Nucleic Acids Res. Can the game be left in an invalid state if all state-based actions are replaced? RDocumentation. SplitObject : Splits object into a list of subsetted objects. scRNA-seq was performed on samples from nine patients of the SARS-CoV-2 Infection Cohort (Supplementary Table 2), three of the SARS-CoV-2 Vaccination Cohort, and paired blood and tonsil samples of four patients of the SARS-CoV-2 Tonsil Cohort (two recovered and two only vaccinated). DefaultAssay(control_subset) <- "RNA" White areas represent BCR sequences found in single cells only. Zurbuchen, Y., Michler, J., Taeschler, P. et al. Unique combinations of bases were appended to cell barcodes per batch before combining the data from different batches of sequencing to prevent cell barcode collisions. 3g,h and Extended Data Fig. Article and JavaScript. The pro of this approach is that it is fast and easy. 5c). Immunity 51, 398410.e5 (2019). Time-resolved analysis identified a peak in the frequency of S+ Bm cells in the first days post-vaccination, reaching 3% of total B cells on average, followed by a slow decrease in frequency over day 150 post-vaccination (Fig. How to merge clusters and what steps needed after merging in SCTransform workflow? Sokal, A. et al. Immunol. Btw, regarding DE analysis in your question 1, according to #1836 (comment), it says that both RNA and SCT assay could be used for DE analysis if my understanding is correct. Clonal diversity between Bm cell subsets was investigated using the alphaDiversity function of Immcantations package Alakazam (v1.2.0) (ref. e, Violin plots of geometric mean fluorescence intensities (gMFI) or percentages of indicated markers in S+ Bm cells at indicated time points. Thank you for the wonderful package. Barnett, B. E. et al. In the meantime, to ensure continued support, we are displaying the site without styles Finally, we use a t-SNE to visualize our clusters in a two-dimensional space. Dot plots and medians (right) of frequencies of RBD+ Bm cells at acute infection (n=59) and month 6 (n=61) and 12 post-infection (n=17). 6ac). Sci. ## [7] LC_PAPER=en_US.UTF-8 LC_NAME=C In d, frequencies were compared using a two-tailed, two-proportions z-test with a Bonferroni-based multiple testing correction. 10, eaan8405 (2018). Viral Hepat. ## [1] LC_CTYPE=en_US.UTF-8 LC_NUMERIC=C ## [112] lifecycle_1.0.3 Rdpack_2.4 spatstat.geom_3.0-6 b) Running FindVariableGenes() and RunPCA() again on the integrated dataset does not seem helpful to me because the limited feature space of 3000 is not changed. AverageExpression: Averaged feature expression by identity class | AddMetaData(object = object, metadata = vector, col.name = "name") | object$name <- vector | Preprocessing of raw scRNA-seq data was done as described51. Allergy Clin. Google Scholar. Making statements based on opinion; back them up with references or personal experience. # HoverLocator replaces the former `do.hover` argument It can also show extra data throught the `information` argument, # designed to work smoothly with FetchData, # FeatureLocator replaces the former `do.identify`, # Run analyses by specifying the assay to use, # Pull feature expression from both assays by using keys, # Plot data from multiple assays using keys, Fast integration using reciprocal PCA (RPCA), Integrating scRNA-seq and scATAC-seq data, Demultiplexing with hashtag oligos (HTOs), Interoperability between single-cell object formats, Set font sizes for various elements of a plot. In addition, reconstruction of clonal lineage trees and visualizing persistent S+ Bm cell clones in a circos plot indicated that individual Bm cell clones acquired different Bm cell fates; for example, a given clone was of a CD21+CD27 resting phenotype at month 6 and adopted CD21+CD27+ resting, CD21CD27+CD71+ or CD21CD27FcRL5+ Bm cell phenotype at month 12 post-infection (post-vaccination) (Fig. Jordan. Generally, you'll want use different parameters for each sample. Cells are colored by timepoint (left) and by clusters identified by PhenoGraph algorithm (right). 8d,e). 9, 47 (2020). The cohort size was based on sample availability. Sign up for a free GitHub account to open an issue and contact its maintainers and the community. e and f, UMAP represents Monocle 3 analysis on all Bm cells in scRNA-seq dataset, colored by clusters identified (e) or pseudotime annotation (f). max.cells.per.ident = Inf, 11, 2664 (2020). Thank you. Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. Moreover, our multimer staining approach might miss low-affinity antigen binders50. Numbers indicate percentages of parent population. ), Swiss Academy of Medical Sciences (SAMW) fellowships (#323530-191230 to Y.Z. | DarkTheme | Set a black background with white text | r - Conditional subsetting of Seurat object - Stack Overflow 5e,g). Circulating and intrahepatic antiviral B cells are defective in hepatitis B. J. Clin. Warnatz, K. et al. Is it valid to set features.to.integrate to all the genes in the original Seurat object if I want run subclustering on the subset using its integrated assay? In this study, we demonstrated that individual clones of SARS-CoV-2-specific Bm cells harbored the capacity to follow phenotypically and functionally different trajectories after antigen reexposure, becoming CD21CD27+, CD21CD27 or CD21+CD27+/ Bm cells. Transl. d, Heatmap displays V light (VL) gene usage in RBD+ and RBD Bm cells from scRNA-seq dataset of SARS-CoV-2-infected patients at month 6 and 12 post-infection. A recent question here gets into that particular problem a bit. privacy statement. 6g and Extended Data Fig. Short story about swapping bodies as a job; the person who hires the main character misuses his body, Generate points along line, specifying the origin of point generation in QGIS. Sci. Unswitched CD21+ Bm cells were IgM+, whereas the other Bm cell subsets expressed mainly IgG, with IgG1 being the dominant subclass (Extended Data Fig. Hao, Y. et al. | FontSize | Set font sizes for various elements of a plot | 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. Is it possible and valid instead to use values from the "data" slot of the SCT assay (log-normalized corrected values) for the MAST test? Seurat has a vast, ggplot2-based plotting library. Seurat provides many prebuilt themes that can be added to ggplot2 plots for quick customization. In humans, resting Bm cells are typically CD21hi, and express the tumor necrosis factor (TNF) receptor superfamily member CD27. *P<0.05, **P<0.01, ***P<0.001, ****P<0.0001. A multiple hypothesis correction procedure was applied to obtain adjusted P values. If they had a confirmed SARS-CoV-2 infection and/or SARS-CoV-2 nucleocapsid-specific antibodies, they were considered SARS-CoV-2-recovered. Then we use FindMarkers() to find the genes that are different between stimulated and control B cells. I want to know: Briefly, lists of differentially expressed genes were preranked in decreasing order by the negative logarithm of their P value, multiplied for the sign of their average log-fold change (in R, -log(P_val)*sign(avg_log2FC)). FindAllMarkers and FindMarkers functions were executed with logfc.thresholds set to 0.25 (0.1 for comparing resting Bm cells at month 6 versus month 12) and a min.pct cutoff at 0.1. If I decide that batch correction is not required for my samples, could I subset cells from my original Seurat Object (after running Quality Control and clustering on it), set the assay to "RNA", and and run the standard SCTransform pipeline. Annu. Site design / logo 2023 Stack Exchange Inc; user contributions licensed under CC BY-SA. Antibody affinity shapes the choice between memory and germinal center B cell fates. I did integration with SCTransform. ## [4] igraph_1.4.1 lazyeval_0.2.2 sp_1.6-0 I used the first way as @Zha0rong described for re-clustering of subset cells, choosing a subset and then use the integration assay to Run PCA, umap, findneighbors and findclusters to do subclustering. rev2023.4.21.43403. Annu. For scRNA-seq data, distribution was assumed to be normal, but this was not formally tested. ## [94] nlme_3.1-157 mime_0.12 formatR_1.14 Seurat (version 3.1.4) Identified Bm cells (SARS-CoV-2 S B cells, n=2258; SWT+ Bm cells, n=1298) were subsequently reclustered as indicated in the box. I wanted to base an analysis on data that that was matching one of a few criteria, e.g. ## [64] pkgconfig_2.0.3 sass_0.4.5 uwot_0.1.14 The following tutorial is designed to give you an overview of the kinds of comparative analyses on complex cell types that are possible using the Seurat integration procedure. 2b,c). | NoLegend | Remove all legend elements | Kurosaki, T., Kometani, K. & Ise, W. Memory B cells. But I would like to be able to select data via logical operators, so: why did the first approach not work? e, Heat map shows enrichment scores of selected gene sets that are significantly different between CD27lo/hiCD21+ resting and CD21CD27FcRL5+ S+ Bm cell subsets in a pseudobulk analysis (n=5 individuals). Cervia, C. et al. Generic Doubly-Linked-Lists C implementation. a, Representative flow cytometry plots of decoy S+ Bm cells are displayed at pre-vaccination (preVac; left; month 6) and day 78 post-vaccination (postVac; right; month 12 post-infection) in patient CoV-P3. For example, to only cluster cells using a single sample group, control, we could run the following: . 2b). 3c). With Seurat, you can easily switch between different assays at the single cell level (such as ADT counts from CITE-seq, or integrated/batch-corrected data). low.threshold = -Inf, @satijalab, could you please help us? Collectively, these observations indicated that individual S+ Bm cell clones could adopt different Bm fates post-vaccination in SARS-CoV-2-recovered individuals. All samples were analyzed by flow cytometry and paired blood and tonsil samples from four patients also by scRNA-seq. Adjusted P values are shown if significant (p<0.05). C.C. Our longitudinal analysis found that distinct Bm cell subsets were clonally related, suggesting plasticity of Bm cell subsets. r rna-seq single-cell seurat Share control_subset <- RunPCA(control_subset, npcs = 30, verbose = FALSE, features = Variable Features(control_subset))
Zachary Latham Wife, Hilda Wiesel Description, Larimer County Police Reports, Brittany Kellum And Ricky Stenhouse Jr, Articles S