Flow cytometry using the multimer probe approach (Extended Data Fig. Subsequent reclustering of Bm cells resolved six clusters (Fig. To learn more, see our tips on writing great answers. subsetting seurat object with multiple samples. I then change DefaultAssay to RNA, run SCTransform() again setting the do.scale = TRUE, and do.center = TRUE. Viant, C. et al. 212, 20412056 (2015). Johnson, J. L. et al. Samples in cf were compared using KruskalWallis test with Dunns multiple comparison, showing adjusted P values. subsetting seurat object with multiple samples, Traffic: 1812 users visited in the last hour, User Agreement and Privacy Natl Acad. c, Frequency (median interquartile range) of S+ (left) and N+ (right) GC B cells within total B cells are given in tonsils of SARS-CoV-2-vaccinated and in recovered individuals. The same positive control from a SARS-CoV-2-vaccinated healthy control was included in every experiment to ensure consistent results. Also, cells previously occurring as cluster outliers from cl7 found their way to the corresponding clusters. After discussing with colleagues and reading other articles I decided to go for option b). Replies here and in some other GitHub issues have slightly different approaches but they all make general sense. We thank the patients for their participation in our study, S. Hasler for assistance with patient recruitment, L. Brgi and R. Masek for help with sample processing, the Departments of Otorhinolaryngology and Anesthesiology, the Transplantation Immunology Laboratory of University Hospital Zurich, E. Baechli, A. Rudiger, M. Stssi-Helbling and L. Huber for help with patient recruitment, the Functional Genomics Center Zurich and Genomics Facility Basel for help with sample preparation and next-generation sequencing, and S. Chevrier, D. Pinschewer, L. Ceglarek, D. Caspar and the members of the Boyman and Moor Laboratories for helpful discussions. 25,26,27,28,29). Maturation and persistence of the anti-SARS-CoV-2 memory B cell response. 7, eabq3277 (2022). Nave B cell (n=1462 cells), served as reference and are the same as in Fig. It did always just select values that matched the first of the criteria, here 1. For full details, please read our tutorial. I have a seurat object with 10 samples (5 in duplicates). I was able to achieve this in the following way: Would be interesting to know if Seurat provides such functionality out of the box. after integration, I subsetted my cells of interest using the integrated assay, and I still see apparent batch effects. 3d). Is it necessary to run FindVariableFeatures on the RNA assay of the subset and get new variables to use in PCA in order to properly cluster the subset? a, Scatter plot comparing binding scores (LIBRA-Score) was determined from scRNA-seq for SWT and RBD binding, with every dot representing a cell. Tonsils were processed according to established protocols47,53. Subsets and markers of antigen-specific B cells and antigen-specific B cell subsets were evaluated only if more than nine or three specific cells per sample were detected, respectively. An AUC value of 1 means that expression values for this gene alone can perfectly classify the two groupings (i.e. Red dashed lines indicate minimal and maximal cumulative enrichment values. The num_dim parameter of Monocles preprocess_cds() function was set to 20. 10, eaan8405 (2018). Nature 602, 148155 (2021). Does anyone has found a better solution to re-project a cluster of the dataset? the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Nucleic Acids Res. Knight and colleagues report altered granulopoiesis and increased frequency of immature neutrophil subsets with immunosuppressive properties in a subset of patients with sepsis with poor outcome. 17, 12261234 (2016). Change-O: a toolkit for analyzing large-scale B cell immunoglobulin repertoire sequencing data. Now that weve aligned the stimulated and control cells, we can start to do comparative analyses and look at the differences induced by stimulation. Now I understand that batch variation is a pain in the a** but honestly one has to assume this will occur naturally in a PCR as well. By clicking Accept all cookies, you agree Stack Exchange can store cookies on your device and disclose information in accordance with our Cookie Policy. object, At the moment you are getting index from row comparison, then using that index to subset columns. 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). 128, 45884603 (2018). How to convert a sequence of integers into a monomial. Is there a way to do that? Flow cytometry analysis of S+ Bm cells showed an upregulation of Blimp-1 at week 2 post-second dose compared with month 6, and increased expression of T-bet, FcRL5, CD71 and Ki-67 at week 2 post-second dose and post-third dose (Extended Data Fig. ## [91] RANN_2.6.1 pbapply_1.7-0 future_1.31.0 Then we use FindMarkers() to find the genes that are different between stimulated and control B cells. i, SHM counts are provided for nave B cells (n=1,607), blood (n=170) and tonsillar SWT+ Bm cells (n=1,128). Flow cytometry data were analyzed with FlowJo (version 10.8.0), with gating strategies shown in Extended Data Figs. Bm cells can be subdivided into phenotypically and functionally distinct subsets10. Functional groups of genes were ordered by hierarchical clustering. AutoPointSize: Automagically calculate a point size for ggplot2-based. 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. Pseudotime-based trajectory analysis using Monocle 3 in our scRNA-seq dataset (Extended Data Fig. What you say "got the right result" probably misses several cases where bf11 is indeed 1, 2 or 3. The standard Seurat workflow takes raw single-cell expression data and aims to find clusters within the data. To learn more, see our tips on writing great answers. 2e, as are preVac and nonVac SHM counts. ## loaded via a namespace (and not attached): Since the data I am analyzing comes from different diets as well as different batches, will batch-correction make me unable to determine differences in gene expression of cells from different diets? Commun. control_subset <- subset(SCT_not_integrated, orig.ident = 'Chow') Invest. 2d and 6a. Thanks for contributing an answer to Stack Overflow! Jenks, S. A. et al. Poon, M. M. L. et al. I am worried that the top variable features of the original Seurat Object are not the same variable features of the new subset. 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. #2812 (comment). I did integration with SCTransform. Gene set variation and enrichment analysis revealed a strong enrichment of a previously described B cell signature of IgDCD27CXCR5 atypical Bm cells from patients with systemic lupus erythematosus (SLE)36, in our SARS-CoV-2-specific CD21CD27FcRL5+ Bm cell subset (Fig. 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). d. Should ScaleData be run on the subset prior to PCA even though the subset comes from an integrated object prepped from SCT? 23, 10081020 (2022). | FilterCells(object = object, subset.names = "name", low.threshold = low, high.threshold = high) | subset(x = object, subset = name > low & name < high) | Cervia, C. et al. For the SARS-CoV-2 Tonsil Cohort, we used a cutoff of 7.5% detected mitochondrial genes. ## [61] ellipsis_0.3.2 ica_1.0-3 farver_2.1.1 to your account. The antibodies used are listed in Supplementary Tables 5 and 7. 43, e47 (2015). I have also been working on the single cell dataset and there are several times that i need to subcluster a proportion cell type. ## [58] httr_1.4.5 RColorBrewer_1.1-3 TFisher_0.2.0 What woodwind & brass instruments are most air efficient? ## [64] pkgconfig_2.0.3 sass_0.4.5 uwot_0.1.14 269, 118129 (2016). PubMed I have a seurat object with 10 samples (5 in duplicates). ## [70] labeling_0.4.2 rlang_1.0.6 reshape2_1.4.4 We also introduce simple functions for common tasks, like subsetting and merging, that mirror standard R functions. Nature Immunology thanks Stuart Tangye and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. Frauke Muecksch, Zijun Wang, Michel C. Nussenzweig, R. Camille Brewer, Nitya S. Ramadoss, Tobias V. Lanz, Laila Shehata, Wendy F. Wieland-Alter, Laura M. Walker, Alice Cho, Frauke Muecksch, Michel C. Nussenzweig, Marios Koutsakos, Patricia T. Illing, Katherine Kedzierska, Anastasia A. Minervina, Mikhail V. Pogorelyy, Paul G. Thomas, Nature Immunology control_subset <- RunPCA(control_subset, npcs = 30, verbose = FALSE, features = Variable Features(control_subset)) "~/Downloads/GSE100866_CBMC_8K_13AB_10X-RNA_umi.csv.gz", # To make life a bit easier going forward, we're going to discard all but the top 100 most highly expressed mouse genes, and remove the "HUMAN_" from the CITE-seq prefix, "~/Downloads/GSE100866_CBMC_8K_13AB_10X-ADT_umi.csv.gz". Weighted-nearest neighbor (WNN) clustering identified nave B cells (IgMhiIgDhiFCER2hi), nave/activated B cells (IgMhiIgDhiFCER2hiFCRL5hi), GC B cells (CD27hiCD38hiAICDAhi) and Bm cells (IgMloIgDloCD27int) (Extended Data Fig. In g, two-sided Wilcoxon test was used with Holm multiple comparison correction. ident.remove = NULL, Did the Golden Gate Bridge 'flatten' under the weight of 300,000 people in 1987? control_subset <- FindClusters(control_subset). J. CD21+ resting Bm cells became prevalent at 612months post-infection. Below, we demonstrate how to modify the Seurat integration workflow for datasets that have been normalized with the sctransform workflow. 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. If split.by is not NULL, the ncol is ignored so you can not arrange the grid. ## [88] fs_1.6.1 fitdistrplus_1.1-8 purrr_1.0.1 Is short-circuiting logical operators mandated? T-bet+ B cells have a protective role in mouse models of acute and chronic viral infections38,42. e, Shown are gating strategy (left) and stacked bar plots (mean+standard deviation; right) of IgG+, IgM+ and IgA+ S+ Bm cells at indicated timepoints (acute, n=23; month 6, n=52; month 12, n=16). The SWT+ Bm cells in the IgG+CD27hiCD45RBhi cluster (cluster 5) were mainly from blood, in the IgG+CD21hi cluster (cluster 2) predominantly tonsillar, while the IgG+CD27lo cluster (cluster 4) contained SWT+ Bm cells from both compartments. 351 2 15. VH/VL were clustered hierarchically, with colors indicating frequencies. Sci. Lines connect samples of same individual. 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). # To pull data from an assay that isn't the default, you can specify a key that's linked to an assay for feature pulling. object, cells = NULL, Nowicka, M. et al. Niessl, J. et al. ## [94] nlme_3.1-157 mime_0.12 formatR_1.14 | NoAxes | Remove axes and axis text | P.T. & Cancro, M. P. Age-associated B cells: key mediators of both protective and autoreactive humoral responses. 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. Preprocessing of raw scRNA-seq data was done as described51. CyTOF workflow: differential discovery in high-throughput high-dimensional cytometry datasets. Finally, we use a t-SNE to visualize our clusters in a two-dimensional space. # split the dataset into a list of two seurat objects (stim and CTRL), # normalize and identify variable features for each dataset independently, # select features that are repeatedly variable across datasets for integration, # this command creates an 'integrated' data assay, # specify that we will perform downstream analysis on the corrected data note that the, # original unmodified data still resides in the 'RNA' assay, # Run the standard workflow for visualization and clustering, # For performing differential expression after integration, we switch back to the original, ## CTRL_p_val CTRL_avg_log2FC CTRL_pct.1 CTRL_pct.2 CTRL_p_val_adj, ## GNLY 0 6.006173 0.944 0.045 0, ## FGFBP2 0 3.243588 0.505 0.020 0, ## CLIC3 0 3.461957 0.597 0.024 0, ## PRF1 0 2.650548 0.422 0.017 0, ## CTSW 0 2.987507 0.531 0.029 0, ## KLRD1 0 2.777231 0.495 0.019 0, ## STIM_p_val STIM_avg_log2FC STIM_pct.1 STIM_pct.2 STIM_p_val_adj, ## GNLY 0.000000e+00 5.858634 0.954 0.059 0.000000e+00, ## FGFBP2 3.408448e-165 2.191113 0.261 0.015 4.789892e-161, ## CLIC3 0.000000e+00 3.536367 0.623 0.030 0.000000e+00, ## PRF1 0.000000e+00 4.094579 0.862 0.057 0.000000e+00, ## CTSW 0.000000e+00 3.128054 0.592 0.035 0.000000e+00, ## KLRD1 0.000000e+00 2.863797 0.552 0.027 0.000000e+00, ## p_val avg_log2FC pct.1 pct.2 p_val_adj, ## ISG15 1.212995e-155 4.5997247 0.998 0.239 1.704622e-151, ## IFIT3 4.743486e-151 4.5017769 0.964 0.052 6.666020e-147, ## IFI6 1.680324e-150 4.2361116 0.969 0.080 2.361359e-146, ## ISG20 1.595574e-146 2.9452675 1.000 0.671 2.242260e-142, ## IFIT1 3.499460e-137 4.1278656 0.910 0.032 4.917791e-133, ## MX1 8.571983e-121 3.2876616 0.904 0.115 1.204621e-116, ## LY6E 1.359842e-117 3.1251242 0.895 0.152 1.910986e-113, ## TNFSF10 4.454596e-110 3.7816677 0.790 0.025 6.260044e-106, ## IFIT2 1.290640e-106 3.6584511 0.787 0.035 1.813736e-102, ## B2M 2.019314e-95 0.6073495 1.000 1.000 2.837741e-91, ## PLSCR1 1.464429e-93 2.8195675 0.794 0.117 2.057961e-89, ## IRF7 3.893097e-92 2.5867694 0.837 0.190 5.470969e-88, ## CXCL10 1.624151e-82 5.2608266 0.640 0.010 2.282419e-78, ## UBE2L6 2.482113e-81 2.1450306 0.852 0.299 3.488114e-77, ## PSMB9 5.977328e-77 1.6457686 0.940 0.571 8.399938e-73, ## 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] cowplot_1.1.1 ggplot2_3.4.1, ## [3] patchwork_1.1.2 thp1.eccite.SeuratData_3.1.5, ## [5] stxBrain.SeuratData_0.1.1 ssHippo.SeuratData_3.1.4, ## [7] pbmcsca.SeuratData_3.0.0 pbmcMultiome.SeuratData_0.1.2, ## [9] pbmc3k.SeuratData_3.1.4 panc8.SeuratData_3.0.2, ## [11] ifnb.SeuratData_3.1.0 hcabm40k.SeuratData_3.0.0, ## [13] bmcite.SeuratData_0.3.0 SeuratData_0.2.2, ## [15] SeuratObject_4.1.3 Seurat_4.3.0. To make the results reproducible, seed value was set (set.seed(42) in R) before execution. At the transcriptional level, S+ Bm cells at month 6 post-infection upregulated genes associated with B cell activation and recent GC emigration35, such as NKFBIA, JUND, MAP3K8, CXCR4 and CD83, compared with S+ Bm cells at month 12 (Extended Data Fig. Immunity 54, 12901303.e7 (2021). Sorted B cells were analyzed by scRNA-seq using the commercial 5 Single Cell GEX and VDJ v1.1 platform (10x Genomics). Transl. Primary Handling Editor: Ioana Visan in collaboration with the Nature Immunology team. Get the most important science stories of the day, free in your inbox. Takes either a list of cells to use as a subset, or a Systemic and mucosal antibody responses specific to SARS-CoV-2 during mild versus severe COVID-19. | object@cell.names | colnames(x = object) | 2, eaai8153 (2017). We obtained paired tonsil and peripheral blood mononuclear cell and serum samples. Seurat has a vast, ggplot2-based plotting library. Whereas subdivision of labor in terms of tissue homing and effector functions has been well characterized for memory T cells, functionally different subsets also exist for memory B (Bm) cells. Integrated analysis of multimodal single-cell data. *P<0.05, **P<0.01, ***P<0.001, ****P<0.0001. a, Sorting strategy for SARS-CoV-2 S+ Bm cells and S B cells, gated on CD19+ non-PB, for scRNA-seq is provided. I integrated samples across multiple batch conditions and diets after performing SCTransform (according to your most recent vignette for integration with SCTransform - Compiled: 2019-07-16). A minor scale definition: am I missing something? Phenotype, chemokine receptor expression and clonal connections suggested these cells formed from CD21+ resting Bm cells, although we cannot exclude that some might have arisen directly in the tonsils. 38 patients received SARS-CoV-2 mRNA vaccination during their recovery phase (three between acute infection and month 6, and 35 between month 6 and month 12). r rna-seq single-cell seurat Share # To see all keys for all objects, use the Key function. designed experiments and interpreted data. Samples in f were compared using a Kruskal-Wallis test with Dunns multiple comparison correction, with adjusted P values shown. PhenoGraph clustering identified an IgG+CD21CD27 cluster (cluster 2), which was TbethiCD11c+FcRL5+, and CD21CD27+ clusters characterized by high expression of CD71, Blimp-1 and Ki-67 (clusters 1, 7 and 8) (Extended Data Fig. Cell 179, 16361646.e15 (2019). J.N. 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. The transient occurrence of vaccine-specific CD21CD27 Bm cells has been described during responses to the influenza vaccine12,20, with one study reporting this Bm cell subset in de novo rather than recall responses20. original object. d, Violin plots of frequencies of Bm cell subsets of S+ Bm cells at the indicated time points. These authors contributed equally: Yves Zurbuchen, Jan Michler. | object@data | GetAssayData(object = object) | Some memory cells circulate between blood, secondary lymphoid organs and bone marrow, while others migrate to peripheral tissues and mucosal sites where they can become tissue resident3. Profiling B cell immunodominance after SARS-CoV-2 infection reveals antibody evolution to non-neutralizing viral targets. max per cell ident. Colors indicate frequency within RBD+ and RBD Bm cells. Generate points along line, specifying the origin of point generation in QGIS. They donated blood before vaccination, at days 813 (week 2) post-second dose, 6months after the second dose and days 1114 post-third dose. 2a) of patient CoV-P1 pre-exposure to SARS-CoV-2, at days 33 and 152 post-symptom onset and at day 12 post-first dose of SARS-CoV-2 mRNA vaccination (that is, day 166 post-symptom onset). 124, 10171030 (1966). The interrelatedness between these Bm cell subsets remains unknown. SCT_integrated <- FindClusters(SCT_integrated), control_subset <- subset(SCT_integrated, orig.ident = 'Chow') Here is an example with dummy data: The subset of dat where bf11 equals any of the set 1,2,3 is taken as follows using %in%: As to why your original didn't work, break it down to see the problem. During acute infection S+ Bm cells were mainly immunoglobulin (Ig)M+ and IgG+, whereas IgG+ Bm cells predominated (8590%) at months 6 and 12 post-infection (Fig. Can I general this code to draw a regular polyhedron? Hi all, I'm also interested in this issue, and wonder what is the best way to subset and reclustering data starting from an integrating dataset? Hi All, The text was updated successfully, but these errors were encountered: @attal-kush I hope its okay to piggyback of your question. SCT_integrated <- IntegrateData(anchorset = SCT_Integrated.anchors, normalization.method = "SCT", features.to.integrate = rownames(SCT_Integrated)) 131, e145516 (2021). Weiss, G. E. et al. Haga, C. L., Ehrhardt, G. R. A., Boohaker, R. J., Davis, R. S. & Cooper, M. D. Fc receptor-like 5 inhibits B cell activation via SHP-1 tyrosine phosphatase recruitment. high.threshold = Inf, ## [25] spatstat.sparse_3.0-0 colorspace_2.1-0 rappdirs_0.3.3 ## [112] lifecycle_1.0.3 Rdpack_2.4 spatstat.geom_3.0-6 SubsetData( 6, eabh0891 (2021). I have similar questions as @attal-kush with regards to reclustering of a subset of an integrated object. https://doi.org/10.1038/s41590-023-01497-y, DOI: https://doi.org/10.1038/s41590-023-01497-y. Front Immunol. 59). f,g, GSEA of CD21CD27FcRL5+ S+ Bm cells versus CD21+ resting S+ Bm cells are shown for indicated gene sets. Immunol. Thank you @satijalab for this amazing tool and the amazing tutorials !!!! 6b). S+ Bm cells continued to show lower but still significantly increased proliferation at month 6, and only returned to background levels at month 12 post-infection (Fig. A, scRNA-seq subcohort of SARS-CoV-2 Infection Cohort. Immunological memory to SARS-CoV-2 assessed for up to 8 months after infection. P values in e and g are shown if significant. Find centralized, trusted content and collaborate around the technologies you use most. control_subset <- FindNeighbors(control_subset, dims = 1:15) 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? This study was approved by the Cantonal Ethics Committee of Zurich (BASEC #2016-01440). This process consists of data normalization and variable feature selection, data scaling, a PCA on variable features, construction of a shared-nearest-neighbors graph, and clustering using a modularity optimizer. Cell 177, 524540 (2019). 7 Phenotypic and functional characterization of circulating S, Extended Data Fig. Alternatively, single B cell clones could give rise to different Bm cell subsets, with stably imprinted phenotypes or show plasticity. Raw counts obtained from the cellranger gene expression matrix were used to create cell datasets, which were preprocessed using the Monocle 3 pipeline. c, UMAP as in a was colored by normalized expression of indicated markers. The DotPlot() function with the split.by parameter can be useful for viewing conserved cell type markers across conditions, showing both the expression level and the percentage of cells in a cluster expressing any given gene. 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. 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: Here we showed that single severe acute respiratory syndrome coronavirus 2-specific Bm cell clones showed plasticity upon antigen rechallenge in previously exposed individuals. 2f). 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. Adjusted P values are shown if significant (p<0.05). Many thanks in advance. VL segments were sorted by a hierarchical clustering. CAS Note that @timoast from the Seurat team recommended otherwise, although I never seen an explanation why would this not best way to go. I want to subset a specific cell type (cluster) and examine subtypes in this cell type. Transcriptomes of individual cells were used as inputs for the gsva() function with default parameters. All samples were analyzed by flow cytometry and paired blood and tonsil samples from four patients also by scRNA-seq. using FetchData, Low cutoff for the parameter (default is -Inf), High cutoff for the parameter (default is Inf), Returns cells with the subset name equal to this value, Create a cell subset based on the provided identity classes, Subtract out cells from these identity classes (used for 9a). Analysis of SARS-CoV-2-specific GC Bcl-6+Ki-67+ B cells detected a trend towards elevated frequencies of S+ and N+ GC cells in recovered compared with vaccinated subjects (Extended Data Fig. Subsetting the before integrating data to interested cells and then do the whole integration, followed by PCA, umap, findneighbors and findclusters seemed reasonale to me.

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