PMCID
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PMC10287567
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An integrated cell atlas of the lung in health and disease.
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For the semisupervised training, the following early-stopping parameter settings were used: early stopping metric: accuracy; save best state metric: accuracy; on: full dataset; patience: 10; threshold: 0.001; reduce lr on plateau: True; lr_patience: 8; and lr_factor: 0.1.
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PMC10287567
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An integrated cell atlas of the lung in health and disease.
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The integrated latent embedding generated by scANVI was used for downstream analysis (clustering and visualization).
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PMC10287567
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An integrated cell atlas of the lung in health and disease.
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For gene-level analyses (differential expression and covariate effect modeling), uncorrected counts were used.
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PMC10287567
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An integrated cell atlas of the lung in health and disease.
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To cluster the cells in the HLCA core, a nearest neighbor graph was calculated based on the 30 latent dimensions that were obtained from the scANVI output, with the number of neighbors set to k = 30.
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PMC10287567
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An integrated cell atlas of the lung in health and disease.
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This choice of k, while improving clustering robustness, could impair the detection of very rare cell types.
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PMC10287567
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An integrated cell atlas of the lung in health and disease.
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Coarse Leiden clustering was done on the graph with a resolution of r = 0.01.
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PMC10287567
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An integrated cell atlas of the lung in health and disease.
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For each of the resulting level 1 clusters, a new neighbor graph was calculated using scANVIs 30 latent dimensions, with the number of neighbors again set to k = 30.
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PMC10287567
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An integrated cell atlas of the lung in health and disease.
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Based on the new neighbor graph, each cluster was clustered into smaller level 2 clusters with Leiden clustering at a resolution of r = 0.2.
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PMC10287567
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An integrated cell atlas of the lung in health and disease.
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The same was done for levels 3 and 4 and (where needed) 5, with k set to 15, 10 and 10, respectively, and the resolution set to 0.2.
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PMC10287567
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An integrated cell atlas of the lung in health and disease.
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Clusters were named based on their parent clusters and sister clusters (for example, cluster 1.2 is the third biggest subcluster (starting at 0) of cluster 1).
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PMC10287567
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An integrated cell atlas of the lung in health and disease.
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For visualization, a 2D UMAP of the atlas was generated based on the 30 nearest neighbors graph.
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PMC10287567
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An integrated cell atlas of the lung in health and disease.
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To quantify cluster cell type label disagreement for a specific level of annotation, the label Shannon entropy was calculated on the cell type label distribution per cluster as[12pt] $$- _^k p( )}[ )} ],$$−∑i=1kpxilogpxi,where x1…xk are the set of labels at that annotation level and p(xi) is the fraction of cells in the cluster that was labeled as xi.
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PMC10287567
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An integrated cell atlas of the lung in health and disease.
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Cells without a label at the level under consideration were not included in the entropy calculation.
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PMC10287567
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An integrated cell atlas of the lung in health and disease.
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If <20% of cells were labeled at the level under consideration, the entropy was set to not available for the figures.
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PMC10287567
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An integrated cell atlas of the lung in health and disease.
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The entropy of donors per cluster (that is, diversity of donors in a cluster) was calculated in the same way.
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PMC10287567
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An integrated cell atlas of the lung in health and disease.
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To set a threshold for high label entropy, we calculated the label entropy of a hypothetical cluster with 75% of cells given one label and 25% of cells given another label, as a cluster with <75% of cells with the same label suggests substantial disagreement in terms of cell type labeling.
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PMC10287567
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An integrated cell atlas of the lung in health and disease.
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Clusters with a label entropy above that level (0.56) were considered to have high label entropy.
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PMC10287567
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An integrated cell atlas of the lung in health and disease.
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Six small clusters with high label entropy even at the coarsest level of annotation highlighted doublet populations (identified via simultaneous expression of lineage-specific marker genes; for example, expression of both epithelial (AT2) and stromal (smooth muscle) marker genes) not labeled as such in the original datasets.
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PMC10287567
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An integrated cell atlas of the lung in health and disease.
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These clusters were removed from the HLCA core, bringing the total number of clusters to 94.
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PMC10287567
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An integrated cell atlas of the lung in health and disease.
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To set a threshold for low donor entropy, we calculated the label entropy for a hypothetical cluster with 95% of cells from one donor and the remaining 5% of cells distributed over all other donors, as clusters with >95% of the cells from the same cluster could be considered single-donor clusters, possibly caused by remaining batch effects or by donor-specific biology that is difficult to interpret.
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PMC10287567
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An integrated cell atlas of the lung in health and disease.
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Clusters with a donor entropy below that level (0.43) were considered clusters with low donor entropy.
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PMC10287567
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An integrated cell atlas of the lung in health and disease.
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To determine how well rare cell types (ionocytes, neuroendocrine cells and tuft cells) were clustered together and separate from other cell types after integration, we calculated recall (the percentage of all cells annotated as a specific rare cell type that were grouped into the cluster) and precision (the percentage of cells from the cluster that were annotated as a specific rare cell type) for all level 3 clusters.
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PMC10287567
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An integrated cell atlas of the lung in health and disease.
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Nested clustering on Harmony and Seurat’s RPCA output was done based on PCA of the corrected gene counts, recalculating the principal components for every parent cluster when performing clustering into smaller children clusters, with clustering performed as described above under ‘UMAP embedding and clustering’.
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PMC10287567
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An integrated cell atlas of the lung in health and disease.
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The level 3 clusters with the highest sensitivity for each cell type are included in Supplementary Fig. 3b.
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PMC10287567
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An integrated cell atlas of the lung in health and disease.
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Re-annotation of cells in the HLCA core was done by six investigators with expertise in lung biology (E.M., M.C.N., A.V.M., L.-E.Z., N.E.B. and J.A.K.) based on original annotations and differentially expressed genes of the HLCA core clusters.
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PMC10287567
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An integrated cell atlas of the lung in health and disease.
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Annotation was done per cluster, using finer clusters where these represented specific known cell types or states rather than donor-specific variation.
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PMC10287567
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An integrated cell atlas of the lung in health and disease.
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Annotations of cell identities were hierarchical (as was the harmonized cell type reference) and each cluster was annotated at the finest known level, whereafter coarser levels could automatically be inferred (for example, a cell annotated as a CD8 T cell was then automatically annotated as of T cell lineage at level 3, lymphoid cell lineage at level 2 and immune cell lineage at level 1).
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PMC10287567
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An integrated cell atlas of the lung in health and disease.
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The number of cells per cell type is shown for all levels in Supplementary Table 5.
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PMC10287567
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An integrated cell atlas of the lung in health and disease.
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Mislabeling of original cells was identified by comparing final annotations with original harmonized labels and checking whether these were contradictory (and not only done at different levels of detail).
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PMC10287567
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An integrated cell atlas of the lung in health and disease.
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Out of 61 final cell types, 18 included mostly mislabeled cells, 12 of which were previously known cell types.
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PMC10287567
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An integrated cell atlas of the lung in health and disease.
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Despite consisting of mostly mislabeled cells from the original datasets, individual experts agreed on the annotation of these cell types: for all previously known cell types with a high proportion of mislabeled cells, the majority of experts agreed on the final annotation for the atlas, or only differed in the granularity of annotation.
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PMC10287567
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An integrated cell atlas of the lung in health and disease.
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Marker genes were calculated based on per-sample, per-cell-type pseudo-bulks, calculating the mean (normalized and log-transformed) expression per pseudo-bulk for every gene.
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PMC10287567
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An integrated cell atlas of the lung in health and disease.
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Pseudo-bulks were only calculated for a sample if it had at least ten cells of the cell type under consideration.
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PMC10287567
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An integrated cell atlas of the lung in health and disease.
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An exception was made for cell types with fewer than 100 cells in total, for which the minimum number of cells per sample was set to 3.
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PMC10287567
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An integrated cell atlas of the lung in health and disease.
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Pseudo-bulks rather than cell-level counts were used to ensure equal weighing of every sample in the differential expression test, thus statistically testing cell type-specific changes in expression that were significant across samples rather than cells.
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PMC10287567
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An integrated cell atlas of the lung in health and disease.
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As pseudo-bulks represent the mean of a repeated draw from a single distribution, based on the central limit theorem, we expect pseudo-bulk gene counts to be normally distributed, and a t-test was therefore used to test differential gene expression, comparing a single cell type with all other cell types in the atlas (marker iteration 1).
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PMC10287567
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An integrated cell atlas of the lung in health and disease.
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To further filter out differentially expressed genes that were not consistently expressed across samples, we applied a filtering step to remove genes expressed in <80% of the pseudo-bulks, or genes expressed in <50% of cells per pseudo-bulk (with the filtering based on the mean across pseudo-bulks).
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PMC10287567
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An integrated cell atlas of the lung in health and disease.
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Similarly, to ensure specificity of gene expression, additional filtering was done to remove genes expressed in >20% of other pseudo-bulks.
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PMC10287567
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An integrated cell atlas of the lung in health and disease.
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For many cell types, marker genes unique to a single cell type across the entire atlas could not be found.
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PMC10287567
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An integrated cell atlas of the lung in health and disease.
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To nonetheless collect a robust and unique set of marker genes for every cell type, a hierarchical approach was taken, subsetting the atlas to four compartments (epithelial, endothelial, immune and stromal, for each of which a marker set was calculated) before calculating cell type-specific marker genes and filtering on uniqueness only within the compartment (marker iteration 2).
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PMC10287567
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An integrated cell atlas of the lung in health and disease.
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When necessary, a second subsetting step was done, now subsetting to the next coarsest cell type set within the compartment (for example, lymphatic endothelial cells) and repeating the same procedure (marker iteration 3).
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PMC10287567
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An integrated cell atlas of the lung in health and disease.
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Finally, filtering criteria were loosened for the remaining cell types for which no unique markers could be found in any of the iterations (marker iterations 4 and 5).
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PMC10287567
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An integrated cell atlas of the lung in health and disease.
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Exact filtering parameters per iteration can be found in Supplementary Table 16.
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PMC10287567
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An integrated cell atlas of the lung in health and disease.
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For lymphatic endothelial cell subtypes, one subtype contained sufficient cells for only a single sample, hampering a pseudo-bulk-based approach.
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PMC10287567
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An integrated cell atlas of the lung in health and disease.
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Therefore, lymphatic endothelial cell subset markers (mature, differentiating and proliferating) were chosen based on known literature, after checking consistency with expression patterns observed in the HLCA lymphatic endothelial cells.
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PMC10287567
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An integrated cell atlas of the lung in health and disease.
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To quantify the extent to which different technical and biological covariates correlated with interindividual variation in the atlas, we calculated the variance explained by each covariate for each cell type.
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PMC10287567
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An integrated cell atlas of the lung in health and disease.
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We first split the data in the HLCA core by cell type annotation, merging substates of a single cell type into one (Supplementary Table 5; includes the number of cells per cell type).
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PMC10287567
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An integrated cell atlas of the lung in health and disease.
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For every cell type, we excluded samples that had fewer than ten cells of the sample.
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PMC10287567
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An integrated cell atlas of the lung in health and disease.
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We then summarized covariate values per sample for every cell type depending on the variable, taking the mean across cells from a sample for scANVI latent components (integration results), UMI counts per cell and fractions of mitochondrial UMIs, while for all other covariates (for example, BMI and tissue sampling method) each sample had only one value; therefore, these values were used.
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PMC10287567
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An integrated cell atlas of the lung in health and disease.
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Next, we performed principal component regression on every covariate, as described previously (see the section ‘Splitting of studies into datasets’), but now using scANVI latent component scores instead of principal component scores for the regression, yielding a fraction of latent component variance explained per covariate.
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PMC10287567
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An integrated cell atlas of the lung in health and disease.
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Samples that did not have a value for a given covariate (for example, where the BMI was not recorded for the donor) were excluded from the regression.
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PMC10287567
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An integrated cell atlas of the lung in health and disease.
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Categorical covariates were converted to dummy variables.
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PMC10287567
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An integrated cell atlas of the lung in health and disease.
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Cell type–covariate pairs for which only one value was observed for the covariate were excluded from the analysis.
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PMC10287567
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An integrated cell atlas of the lung in health and disease.
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Quantification of the correlation or dependence between variables within a cell type and identification of the minimum number of samples needed to control for spurious correlation are described below.
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PMC10287567
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An integrated cell atlas of the lung in health and disease.
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To check the extent to which covariates correlated with each other, thereby possibly acting as confounders in the principal component regression scores, we determined dependence between all covariate pairs for every cell type.
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PMC10287567
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An integrated cell atlas of the lung in health and disease.
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If at least one covariate was continuous, we calculated the fraction of variance in the continuous covariate that could be explained by the other covariate (dummying categorical covariates) and took the square root (equal to Pearson’s r for two continuous covariates).
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PMC10287567
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An integrated cell atlas of the lung in health and disease.
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For two categorical covariates, if both covariates had more than two unique values, we calculated normalized mutual information between the covariates using scikit-learn, since a linear regression between these two covariates is not possible.
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PMC10287567
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An integrated cell atlas of the lung in health and disease.
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To control for spurious correlations between interindividual cell type variation and covariates due to low sample numbers, we assessed the relationship between sample number and mean variance explained across all covariates for every cell type.
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PMC10287567
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An integrated cell atlas of the lung in health and disease.
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We found that for cell types sampled in fewer than 40 samples the mean variance explained across all covariates showed a high negative correlation with the number of samples (Supplementary Fig. 4a).
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PMC10287567
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An integrated cell atlas of the lung in health and disease.
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We reasoned that for these cell types correlations between interindividual variation and our covariates were inflated due to undersampling.
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PMC10287567
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An integrated cell atlas of the lung in health and disease.
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Moreover, we note that at lower sample numbers technical and biological covariates often strongly correlate with each other across donors (Supplementary Fig. 4c).
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PMC10287567
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An integrated cell atlas of the lung in health and disease.
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This might lead to the attribution of true biological variation to technical covariates, and vice versa, complicating the interpretation of observed interindividual cell type variation.
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PMC10287567
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An integrated cell atlas of the lung in health and disease.
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Consequently, we consider 40 a recommended minimum number of samples to avoid spurious correlations between observed interindividual variation and tested covariates, and excluded results from cell types with fewer samples.
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PMC10287567
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An integrated cell atlas of the lung in health and disease.
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To select cell types for which covariate effects could be confidently modeled at the gene level, we followed the same procedure for every cell type: we filtered out all genes that were expressed in fewer than 50 cells and all samples that had fewer than ten cells of the cell type.
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PMC10287567
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An integrated cell atlas of the lung in health and disease.
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We furthermore filtered out datasets with fewer than two donors and refrained from modeling categories in covariates that had fewer than three donors in their category for that cell type.
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PMC10287567
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An integrated cell atlas of the lung in health and disease.
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We encoded smoking status as a continuous covariate, setting never to 0, former to 0.5 and current to 1.
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PMC10287567
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An integrated cell atlas of the lung in health and disease.
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Anatomical region was encoded into anatomical region CCF scores as described earlier.
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PMC10287567
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An integrated cell atlas of the lung in health and disease.
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As we noted that changes from the nose to the rest of the airways and lungs were often independent from continuous changes observed in the lungs only, we encoded nasal as a separate covariate, setting samples from the nose to 1 and all others to 0.
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PMC10287567
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An integrated cell atlas of the lung in health and disease.
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BMI and age were rescaled, such that the 10th percentile of observed values across the atlas was set to 0 and the 90th percentile was set to 1 (25 and 64 years for age, respectively, and 21.32 and 36,86 for BMI).
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PMC10287567
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An integrated cell atlas of the lung in health and disease.
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To determine whether covariance between covariates was low enough for modeling, we calculated the variance inflation factor (VIF) between covariates at the donor level.
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PMC10287567
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An integrated cell atlas of the lung in health and disease.
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The VIF quantifies multicollinearity among covariates of an ordinary least squares regression and a high VIF indicates strong linear dependence between variables.
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PMC10287567
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An integrated cell atlas of the lung in health and disease.
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If the VIF was higher than 5 for any covariate for a specific cell type, we concluded that covariance was too high and excluded that cell type from the modeling.
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PMC10287567
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An integrated cell atlas of the lung in health and disease.
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As many cell types lacked sufficient representation of harmonized ethnicities other than European, including harmonized ethnicity in the analysis simultaneously decreased the samples that could be included in the analysis to only those with ethnicity annotations; hence, we excluded harmonized ethnicity from the modeling.
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PMC10287567
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An integrated cell atlas of the lung in health and disease.
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To model the effects of demographic and anatomical covariates (sex, age, BMI, harmonized ethnicity, smoking status and anatomical location of the sample) on gene expression, we employed a generalized linear mixed model.
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PMC10287567
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An integrated cell atlas of the lung in health and disease.
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We used sample-level pseudo-bulks (split by cell type), since the covariates modeled also varied at the sample or donor level and not at the cell level.
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PMC10287567
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An integrated cell atlas of the lung in health and disease.
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Modeling these covariates at the cell level (that is, treating single cells as independent samples even when they come from the same sample) has been shown to inflate P values.
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PMC10287567
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An integrated cell atlas of the lung in health and disease.
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First, we split the lung cell atlas by cell type annotation, pooling detailed annotations into one subtype (for example, grouping all lymphatic endothelial cell subtypes into one) (Supplementary Table 5; includes the number of cells per cell type).
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PMC10287567
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An integrated cell atlas of the lung in health and disease.
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Subsequent filtering, covariate encoding and exclusion of cell types due to covariate dependence are described above.
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PMC10287567
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An integrated cell atlas of the lung in health and disease.
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Gene counts were summed across cells for every sample, within cell type.
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PMC10287567
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An integrated cell atlas of the lung in health and disease.
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Sample-wise sums (that is, pseudo-bulks) were normalized using edgeR’s calcNormFactors function, using default parameter settings.
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PMC10287567
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An integrated cell atlas of the lung in health and disease.
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We then used voom, a method designed for bulk RNA-seq that estimates observation-specific gene variances and incorporates these into the modeling.
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PMC10287567
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An integrated cell atlas of the lung in health and disease.
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Specifically, we used a voom extension (differential expression testing with linear mixed models) that allows for mixed-effects modeling and modeled gene expression as:[12pt] $$}[ }} ] 1 + } + } + } + } + } + }\, } \\+ ( }} )$$~1+age+sex+BMI+smoking+nose+CCFscore+1∣datasetwhere dataset is treated as a random effect to correct for dataset-specific changes in expression and all other effects are modeled as fixed effects.
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PMC10287567
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An integrated cell atlas of the lung in health and disease.
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Resulting P values were corrected for multiple testing within every covariate using the Benjamini–Hochberg procedure.
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PMC10287567
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An integrated cell atlas of the lung in health and disease.
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To identify more systematic patterns across genes and changes happening at the gene set level, a gene set enrichment analysis was performed using correlation-adjusted mean-rank gene set tests.
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PMC10287567
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An integrated cell atlas of the lung in health and disease.
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The analysis was performed in R using the cameraPR function in the limma package, with the differential expression test statistic.
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PMC10287567
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An integrated cell atlas of the lung in health and disease.
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Gene Ontology biological process terms were tested separately for each comparison.
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PMC10287567
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An integrated cell atlas of the lung in health and disease.
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These sets were obtained from MSigDB (version 7.1), as provided by the Walter and Eliza Hall Institute (https://bioinf.wehi.edu.au/MSigDB/index.html).
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PMC10287567
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An integrated cell atlas of the lung in health and disease.
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To stratify GWAS results from several lung diseases by lung cell type, we applied stratified linkage disequilibrium score regression in single cells (sc-LDSC).
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PMC10287567
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An integrated cell atlas of the lung in health and disease.
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sc-LDSC can link GWAS results to cell types by calculating, for each cell type, whether disease-associated variants are enriched in genomic regions of cell-type specific genes (i.e. the region of each gene and its surrounding base pairs), while taking into account the genetic signal of proximal linkage disequilibrium-associated regions.
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PMC10287567
|
An integrated cell atlas of the lung in health and disease.
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Here cell-type specific genes are defined as genes differentially expressed in the cell type of interest.
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PMC10287567
|
An integrated cell atlas of the lung in health and disease.
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In contrast with simple enrichment testing of only significantly disease-associated genes from a GWAS among genes differentially expressed in a cell type, this method takes into account all SNPs included in the GWAS.
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PMC10287567
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An integrated cell atlas of the lung in health and disease.
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Thus, consistent enrichment of weakly disease-associated genes (that would not individually pass significance tests) in a cell type could still lead to a significant association between the disease and the cell type.
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PMC10287567
|
An integrated cell atlas of the lung in health and disease.
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In this way, sc-LDSC provides more statistical power to detect associations between cell types and heritable phenotypes such as lung diseases.
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PMC10287567
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An integrated cell atlas of the lung in health and disease.
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To perform sc-LDSC on the HLCA, first a differential gene expression test was performed for every grouped cell type (Supplementary Table 5) in the HLCA using a Wilcoxon rank-sum test, testing against the rest of the atlas.
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PMC10287567
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An integrated cell atlas of the lung in health and disease.
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The top 1,000 most significant genes with positive fold changes were stored as genes characterizing that cell type (cell type genes) and used as input for LDSC.
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PMC10287567
|
An integrated cell atlas of the lung in health and disease.
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Gene coordinates of cell type genes were obtained based on the GRCh37.13 genome annotation.
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PMC10287567
|
An integrated cell atlas of the lung in health and disease.
|
For SNP data (names, locations and linkage-related information), the 1000 Genomes European reference (GRCh37) was used, as previously described.
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PMC10287567
|
An integrated cell atlas of the lung in health and disease.
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Only SNPs from the HapMap 3 project were included in the analysis.
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PMC10287567
|
An integrated cell atlas of the lung in health and disease.
|
For identification of SNPs in the vicinity of cell type genes, we used a window size of 100,000 base pairs.
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PMC10287567
|
An integrated cell atlas of the lung in health and disease.
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Genes from X and Y chromosomes, as well as human leukocyte antigen genes, were excluded because of their unusual genetic architecture and linkage patterns.
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