for each unique tissue site, we performed an initial integration across all samples by training a single - cell variational inference ( scvi ) model on the gene expression with following parameters: 10, 000 highly variable genes using the seurat _ v3 option in scanpy, early stopping enabled and 50 epochs, 10 epochs for n _ epochs _ kl _ warmup, two layers in encoder and decoder, nb gene likelihood and a mini - batch size of 256. to perform filtering of low - quality events, we used the following quality metrics: the probability of a doublet predicted by scrublet, the probability of a doublet from hashsolo, the percentage of erythrocyte genes as described above, whether a cell contained both tcr and bcr, whether the celltypist erythrocyte model predicted a cell to be an erythrocyte, as well as cells with a total count below 2, 000 unique molecular identifiers, 1, 200 unique genes or 200 protein counts. all scores were added to generate a per - cell quality metric. to perform filtering, we argued that cells that group together and have evidence of low quality should be removed from downstream analysis. we first used louvain clustering on the coordinates from scvi latent space using 15 nearest neighbors to cluster the per - tissue integrated data with a resolution of 5. 0. every cluster with a median low - quality score ( described above ) of at least one was removed from downstream analysis. although some low - quality events were retained with this filtering, their frequency was drastically reduced. we additionally established tissue - specific cut - offs to remove additional events and removed clusters with a mean low - quality score of 0. 3 from all tissues, except for the lung ln and jej, for which the threshold was manually increased to recover higher - quality cells. using a course cell - type annotation based on manual annotation of clusters, we identified cell types that were consistently filtered out, even though their quality did not appear to be spuriously low by manual inspection. we retained mast cells and hematopoietic stem cells from all tissues, all macrophages from lns and spleen, all erythrocytes and platelets from bm and all monocytes from liver. we concatenated cells from all tissues and computed the 10, 000 top highly variable genes using the seurat _ v3 option in scanpy and used the same parameters as described above, but with a mini - batch size of 1, 024 to accelerate the training process. we used this integrated latent space to assign initial cell types and removed all cell types that were not labeled as immune cells. additionally, we removed all cells for which manual labeling and automatic labeling using mmochi ( see below ) were inconclusive about coarse cell - type identity ( for example, b cell, myeloid, t cell ). these events were of low quality by manual inspection. we performed post hoc manual removal of these and other clusters of low - quality cells after integrating all cells. to identify canonical immune cell subsets, we used a recently reported, supervised machine learning algorithm, mmochi ) ( v0. 2. 1 ). we first normalized the gene expression ( gex ) count matrix using log ( 10, 000 c g, i / t g, i + 1 ), where c g, i represents the counts for gex feature g in cell i, and t g, i is the total counts for all gex features in cell i. similarly, we normalized the antibod y - derived tag ( adt ) count matrix using log ( 1, 000 c a, i / t a, i + 1 ), where c a, i represents the counts for adt feature a in cell i and t a, i is the total counts for all adt features in cell i. we applied landmark registration ( mmochi ) to batch - correct the adt expression across experimental batches. in brief, we applied automatic detection of landmarks ( peaks ) in the expression distributions for each adt feature in a given sample, applying manual adjustments as needed using the graphical user interface, then performed curve registration and warping to align the positive and negative peaks for each adt feature across batches. we provided mmochi with a hierarchy of immune cell subsets and their canonical surface protein - level and rna - level markers ( supplementary fig. 2 ) and used the markers to identify high - confidence members ( cells ) of each subset for training. for each classification level, automatic thresholds for high - confidence adt or gex marker - positive and marker - negative cells were manually adjusted as needed using the supplied gui ( supplementary table 3 ). following mmochi ’ s internal training data refinement, we applied an 80 – 20 train – test split and trained a random forest classifier, sklearn. ensemble. randomforestclassifier, on both gene and protein expression. for 2 of the 24 organ donors and a subset of samples from a third donor, we did not perform cite - seq and only had scrna - seq profiles. thus, these samples were excluded from the mmochi classification described here. however, we used a k - nearest neighbors approach to transfer the classifier labels to individual cells profiled from these two organ donors. specifically, we used the sklearn. neighbors. kneighborsclassifier with n _ neighbors = 10 to construct a k - nearest neighbors graph in the mrvi embedding of the dataset ( see below ) and classify the remaining cells. of the subsets, pdcs were identified using two separate nodes on the hierarchy ( supplementary fig. 1 ), as pdcs shared expression with both b cells and myeloid cells. once classified, the two subsets were merged into a single population of pdcs. the mmochi annotation was used at two separate levels throughout the paper, defined as either one of the 34 fine - grained subsets or grouped into cd4 t cells, cd8 and unconventional t cells ( including γδ t cells and cd8 mait cells ), b cells, nk cells and ilcs or myeloid cells ( including monocytes, macrophages, cdcs, migratory dcs and pdcs ). owing to the breadth of tissues and human subjects sampled in our dataset and high - resolution annotation of immune subsets, we anticipated that our immune atlas would be useful to the research community as a reference for performing cell - type label transfer. to facilitate this application, we trained a model using popv, a tool developed for cell - type label transfer that uses several annotation algorithms and consensus voting to determine annotations and evaluate their confidence. popv also calculates joint embeddings of the query and reference datasets, which can be used for visualization of the query data and other analysis tasks. a popv model was trained using the tissues and mmochi annotations ( fig. 2 ) as the reference dataset. label transfer performance was evaluated using the human lung cell atlas as a query dataset. to visualize the data, we computed umap embeddings as described above on joint scvi embeddings, which were calculated as part of the popv pipeline. to evaluate the importance of adt information in the mmochi classification performance, we additionally applied a pre - trained celltypist model ( immune _ all _ low; https: / / celltypist. cog. sanger. ac. uk / models / pan _ immune _ celltypist / v2 / immune _ all _ low. pkl ) using default settings to the tissue immune cells ( fig. 2 ). to integrate scrna - seq profiles of immune cells in our study, we first used scvi, which did not yield a fully integrated latent space and clustered by site of collection ( for example, us or uk ). we next leveraged mrvi, which uses a mixture - of - gaussian as a prior and enforces stronger separation of true cell state and effect of donors on gene expression, as has been recently demonstrated. mrvi takes advantage of a prior based on a multimodal variational mixture of posteriors ( similar to a vampprior ), which have been shown to outperform gaussian priors for scrna - seq integration in benchmarking studies. in brief, mrvi finds a sample - agnostic latent space, u, and computes a sample - specific embedding.