7g). | object@scale.data | GetAssayData(object = object, slot = "scale.data") | Pape, K. A. et al. Seurats centered log ratio transformation was applied across features, followed by a scaling of obtained values, resulting in final LIBRA scores. VH and V light (VL) genes are indicated on top of dendrograms. ## [31] xfun_0.37 dplyr_1.1.0 crayon_1.5.2 The various Bm cell subsets could comprise entirely separate lineages, with distinct BCR repertoires. Sci. We can explore these marker genes for each cluster and use them to annotate our clusters as specific cell types. Collectively, these data identify a durable, IgG1-dominated S+ Bm cell response forming upon SARS-CoV-2 infection. I have added them all together and created the VlnPlot to check for the quality of the samples. Antibody affinity shapes the choice between memory and germinal center B cell fates. Branch lengths represent mutation numbers per site between each node. 183, 21762182 (2009). A.E.M. J. All samples were analyzed by flow cytometry and paired blood and tonsil samples from four patients also by scRNA-seq. & Kaplan, D. E. Hepatitis C viraemia reversibly maintains subset of antigen-specific T-bet+ tissue-like memory B cells. a, Dot plots and medians of frequencies of S+ Bm cells are provided at baseline (n=10), week 2 post-second dose (n=10) and month 6 post-second dose (n=11). 7, 83848410 (2021). a, SARS-CoV-2-infected patients were analyzed by spectral flow cytometry and scRNA-seq at acute infection and months 6 and 12 post-infection. But I especially don't get why this one did not work: If anyone can tell me why the latter did not function I would appreciate it. PubMed ## [103] stringi_1.7.12 highr_0.10 desc_1.4.2 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. Immunol. I.E.A. & Shlomchik, M. J. Germinal center and extrafollicular B cell responses in vaccination, immunity, and autoimmunity. 2e), which correlated with an improved binding breadth, as measured by variant-binding ability of SWT+ Bm cells (Fig. Red line represents fitted second-order polynomial function (R2=0.1932). Making statements based on opinion; back them up with references or personal experience. The code generated during the current study is available at https://github.com/Moors-Code/MBC_Plasticity_Moor_Boyman_Collaboration. EDIT: | object@hvg.info | HVFInfo(object = object) | First, we focused on samples from nonvaccinated individuals at acute infection (n=59, day 14 on average after symptom onset), month 6 (n=61, day 202 after symptom onset) and month 12 (n=17, day 374) (Fig. J.M. Why does Acts not mention the deaths of Peter and Paul? I am worried that the top variable features of the original Seurat Object are not the same variable features of the new subset. 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. PubMed Central Single-cell RNA-seq: Clustering Analysis - In-depth-NGS-Data-Analysis Nat. ## loaded via a namespace (and not attached): ## [1] systemfonts_1.0.4 sn_2.1.0 plyr_1.8.8, ## [4] igraph_1.4.1 lazyeval_0.2.2 sp_1.6-0, ## [7] splines_4.2.0 listenv_0.9.0 scattermore_0.8, ## [10] qqconf_1.3.1 TH.data_1.1-1 digest_0.6.31, ## [13] htmltools_0.5.4 fansi_1.0.4 magrittr_2.0.3, ## [16] memoise_2.0.1 tensor_1.5 cluster_2.1.3, ## [19] ROCR_1.0-11 limma_3.54.1 globals_0.16.2, ## [22] matrixStats_0.63.0 sandwich_3.0-2 pkgdown_2.0.7, ## [25] spatstat.sparse_3.0-0 colorspace_2.1-0 rappdirs_0.3.3, ## [28] ggrepel_0.9.3 rbibutils_2.2.13 textshaping_0.3.6, ## [31] xfun_0.37 dplyr_1.1.0 crayon_1.5.2, ## [34] jsonlite_1.8.4 progressr_0.13.0 spatstat.data_3.0-0, ## [37] survival_3.3-1 zoo_1.8-11 glue_1.6.2, ## [40] polyclip_1.10-4 gtable_0.3.1 leiden_0.4.3, ## [43] future.apply_1.10.0 BiocGenerics_0.44.0 abind_1.4-5, ## [46] scales_1.2.1 mvtnorm_1.1-3 spatstat.random_3.1-3, ## [49] miniUI_0.1.1.1 Rcpp_1.0.10 plotrix_3.8-2, ## [52] metap_1.8 viridisLite_0.4.1 xtable_1.8-4, ## [55] reticulate_1.28 stats4_4.2.0 htmlwidgets_1.6.1, ## [58] httr_1.4.5 RColorBrewer_1.1-3 TFisher_0.2.0, ## [61] ellipsis_0.3.2 ica_1.0-3 farver_2.1.1, ## [64] pkgconfig_2.0.3 sass_0.4.5 uwot_0.1.14, ## [67] deldir_1.0-6 utf8_1.2.3 tidyselect_1.2.0, ## [70] labeling_0.4.2 rlang_1.0.6 reshape2_1.4.4, ## [73] later_1.3.0 munsell_0.5.0 tools_4.2.0, ## [76] cachem_1.0.7 cli_3.6.0 generics_0.1.3, ## [79] mathjaxr_1.6-0 ggridges_0.5.4 evaluate_0.20, ## [82] stringr_1.5.0 fastmap_1.1.1 yaml_2.3.7, ## [85] ragg_1.2.5 goftest_1.2-3 knitr_1.42, ## [88] fs_1.6.1 fitdistrplus_1.1-8 purrr_1.0.1, ## [91] RANN_2.6.1 pbapply_1.7-0 future_1.31.0, ## [94] nlme_3.1-157 mime_0.12 formatR_1.14, ## [97] compiler_4.2.0 plotly_4.10.1 png_0.1-8, ## [100] spatstat.utils_3.0-1 tibble_3.1.8 bslib_0.4.2, ## [103] stringi_1.7.12 highr_0.10 desc_1.4.2, ## [106] lattice_0.20-45 Matrix_1.5-3 multtest_2.54.0, ## [109] vctrs_0.5.2 mutoss_0.1-12 pillar_1.8.1, ## [112] lifecycle_1.0.3 Rdpack_2.4 spatstat.geom_3.0-6, ## [115] lmtest_0.9-40 jquerylib_0.1.4 RcppAnnoy_0.0.20, ## [118] data.table_1.14.8 irlba_2.3.5.1 httpuv_1.6.9, ## [121] R6_2.5.1 promises_1.2.0.1 KernSmooth_2.23-20, ## [124] gridExtra_2.3 parallelly_1.34.0 codetools_0.2-18, ## [127] MASS_7.3-56 rprojroot_2.0.3 withr_2.5.0, ## [130] mnormt_2.1.1 sctransform_0.3.5 multcomp_1.4-22, ## [133] parallel_4.2.0 grid_4.2.0 tidyr_1.3.0, ## [136] rmarkdown_2.20 Rtsne_0.16 spatstat.explore_3.0-6, ## [139] Biobase_2.58.0 numDeriv_2016.8-1.1 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, Create an integrated data assay for downstream analysis, Identify cell types that are present in both datasets, Obtain cell type markers that are conserved in both control and stimulated cells, Compare the datasets to find cell-type specific responses to stimulation, When running sctransform-based workflows, including integration, do not run the.