Mardiyyah/TAPT_data_V2_split · Datasets at Hugging Face

PMCID stringclasses 24 values	Title stringclasses 24 values	Sentences stringlengths 2 40.7k
PMC10287567	An integrated cell atlas of the lung in health and disease.	For linkage disequilibrium score calculation, a 1 cM window was used.
PMC10287567	An integrated cell atlas of the lung in health and disease.	Significance of the link between a phenotype and a cell type was calculated using LDSC.
PMC10287567	An integrated cell atlas of the lung in health and disease.	P values yielded by LDSC were corrected for multiple testing for every disease tested using the Benjamini–Hochberg correction procedure.
PMC10287567	An integrated cell atlas of the lung in health and disease.	As a negative control, the analysis was performed with a GWAS of depression and no cell types were found to be significant (Supplementary Fig. 7).
PMC10287567	An integrated cell atlas of the lung in health and disease.	The numbers of cases and controls per GWAS study were as follows: n = 2,668 cases and 8,591 controls for IPF; n = 35,735 cases and 222,076 controls for COPD; n = 11,273 cases and 55,483 controls for lung adenocarcinoma; n = 321,047 individuals for lung function; n = 88,486 cases and 447,859 controls for asthma; and n = 113,769 cases and 208,811 controls for depression (used as negative control).
PMC10287567	An integrated cell atlas of the lung in health and disease.	To enable deconvolution of bulk expression data on the basis of the HLCA, HLCA cell type signature matrices were generated.
PMC10287567	An integrated cell atlas of the lung in health and disease.	One generic-purpose signature matrix was created per sublocation of the respiratory system (that is, one parenchyma, one airway and one nose tissue matrix; Supplementary Table 10).
PMC10287567	An integrated cell atlas of the lung in health and disease.	Additionally, a script to generate custom reference sets from the HLCA data is provided together with the HLCA code on GitHub (https://github.com/LungCellAtlas/HLCA) to tailor the deconvolution signature matrix to any specific research question.
PMC10287567	An integrated cell atlas of the lung in health and disease.	Cell types were included in the bulk deconvolution signature matrix on the basis of cell proportions (constituting >2% of cells within samples of the corresponding tissue in the HLCA core).
PMC10287567	An integrated cell atlas of the lung in health and disease.	In addition, cell types were merged when they were deemed too transcriptionally similar.
PMC10287567	An integrated cell atlas of the lung in health and disease.	For each included cell type, 200 cells were randomly sampled from the HLCA core, while all cells were included for cell types with fewer than 200 cells present in the HLCA core.
PMC10287567	An integrated cell atlas of the lung in health and disease.	Cells were sampled from the matching anatomical location (for example, nose T cells rather than parenchymal T cells were used for the nose signature matrix).
PMC10287567	An integrated cell atlas of the lung in health and disease.	Signature matrices were constructed using CIBERSORTx (version 1.0) according to default settings, and no cross-platform batch correction was applied.
PMC10287567	An integrated cell atlas of the lung in health and disease.	The reference data were optimized by deconvolution of pseudo-bulk samples constructed from the HLCA core data, assessing deconvolution performance per included cell type based on the correlation of predicted proportions with ground truth composition (Supplementary Fig. 8a).
PMC10287567	An integrated cell atlas of the lung in health and disease.	The following cell types were included in the deconvolution: endothelial cell arterial, endothelial cell capillary, lymphatic endothelial cell, basal and secretory (merged), multiciliated lineage, AT2, B cell lineage, innate lymphoid cell (ILC) natural killer and T cell lineage (merged), dendritic cells, alveolar macrophages, interstitial macrophages, mast cells, fibroblast lineage, smooth muscle, endothelial cell venous and monocytes (for the parenchyma); basal resting and suprabasal (merged), multiciliated lineage, club, goblet, dendritic cells, hillock like and T cell lineage (for the nose); and endothelial cell venous, CD4 T cells, fibroblasts, smooth muscle, basal and secretory (merged), multiciliated lineage, endothelial cell capillary, interstitial macrophages, B cell lineage, natural killer cells, CD8 T cells, dendritic cells, alveolar macrophages, mast cells and monocytes (for the airway).
PMC10287567	An integrated cell atlas of the lung in health and disease.	Capillary endothelial cells and interstitial macrophages (airway) were excluded from statistical testing due to poor performance in the benchmark.
PMC10287567	An integrated cell atlas of the lung in health and disease.	Venous endothelial cells and monocytes (parenchyma), hillock-like cells and T cell lineage cells (nose) and B cell lineage cells, natural killer cells, CD8 T cells, dendritic cells, alveolar macrophages, mast cells and monocytes (airways) were excluded from statistical testing due to >60% zero proportions.
PMC10287567	An integrated cell atlas of the lung in health and disease.	The parenchymal signature matrix was used to deconvolve RNA expression data of samples from the Lung Tissue Database (GEO accession number GSE23546) using only lung tissue samples from patients with COPD GOLD stages 3 and 4 (n = 27 and 56, respectively) and matched controls (n = 281).
PMC10287567	An integrated cell atlas of the lung in health and disease.	The Lung Tissue Database dataset was run on the Rosetta/Merck Human RSTA Custom Affymetrix 2.0 microarray platform (HuRSTA-2a520709; GPL10379).
PMC10287567	An integrated cell atlas of the lung in health and disease.	As this platform has multiple probe sets for each gene, we focused on the probe sets that were derived from curated RefSeq records (with NM_ accession prefixes) when present to maximize the accuracy of the deconvolution.
PMC10287567	An integrated cell atlas of the lung in health and disease.	Where genes did not have probe sets based on curated RefSeq records or had multiple probe sets mapping to curated RefSeq records, the probe set with the highest average microarray intensity across samples was selected.
PMC10287567	An integrated cell atlas of the lung in health and disease.	Quantile normalization of the data and subsequent deconvolution were performed using CIBERSORTx.
PMC10287567	An integrated cell atlas of the lung in health and disease.	A Wilcoxon rank-sum test between control and GOLD stage 3/4 samples was performed to identify statistically significant compositional changes in advanced-stage COPD compared with control tissue.
PMC10287567	An integrated cell atlas of the lung in health and disease.	GOLD 3/4 and control samples were matched for age and smoking history.
PMC10287567	An integrated cell atlas of the lung in health and disease.	Cell types with >60% of samples predicted to have a proportion of zero were excluded from the Wilcoxon test, as the high number of tied ranks (zeros in both groups) would result in inflated P values.
PMC10287567	An integrated cell atlas of the lung in health and disease.	P values were multiple testing corrected using the Benjamini–Hochberg procedure.
PMC10287567	An integrated cell atlas of the lung in health and disease.	The same procedure was followed for a dataset of nasal brush bulk RNA-seq samples from asthmatic donors pre- and postinhalation of corticosteroids (n = 54 and 26, respectively) and a dataset of airway biopsy bulk RNA-seq samples from asthmatic donors and controls (n = 95 and 38, respectively).
PMC10287567	An integrated cell atlas of the lung in health and disease.	As these consisted of RNA-seq data, no quantile normalization was applied.
PMC10287567	An integrated cell atlas of the lung in health and disease.	To map unseen scRNA-seq and single-nucleus RNA-seq data to the HLCA, we used scArches, our transfer learning-based method that enables mapping of new data to an existing reference atlas.
PMC10287567	An integrated cell atlas of the lung in health and disease.	scArches trains an adaptor added to a reference embedding model, thereby enabling it to generate a common embedding of the new data and the reference, allowing reanalysis and de novo clustering of the joint data.
PMC10287567	An integrated cell atlas of the lung in health and disease.	The data to map were subsetted to the same 2,000 HVGs that were used for HLCA integration and embedding, and HVGs that were absent in the new data were set to 0 counts for all cells.
PMC10287567	An integrated cell atlas of the lung in health and disease.	Raw counts were used as input for scArches, except for the ref.
PMC10287567	An integrated cell atlas of the lung in health and disease.	dataset, for which ambient RNA removal was run previously on the raw counts.
PMC10287567	An integrated cell atlas of the lung in health and disease.	Healthy lung data were split into two datasets: 3′ and 5′ based.
PMC10287567	An integrated cell atlas of the lung in health and disease.	Lung cancer data were also split into two datasets: 10xv1 and 10xv2.
PMC10287567	An integrated cell atlas of the lung in health and disease.	The model that was learned previously for HLCA integration using scANVI was used as the basis for the scArches mapping.
PMC10287567	An integrated cell atlas of the lung in health and disease.	scArches was then run to train adaptor weights that allowed for mapping of new data into the existing HLCA embedding, using the following parameter settings: freeze-dropout: true; surgery_epochs: 500; train base model: false; metrics to monitor: accuracy and elbo; weight-decay: 0; and frequency: 1.
PMC10287567	An integrated cell atlas of the lung in health and disease.	The following early-stopping criteria were used: early stopping metric: elbo; save best state metric: elbo; on: full dataset; patience: 10; threshold: 0.001; reduce lr on plateau: True; lr patience: 8l and lr_factor: 0.1.
PMC10287567	An integrated cell atlas of the lung in health and disease.	To enable cross-dataset gene-level analysis, harmonization of gene names from different datasets (using different reference genome builds and genome annotations; Supplementary Table 1) was necessary.
PMC10287567	An integrated cell atlas of the lung in health and disease.	Both annotation sources (for example, Ensembl or RefSeq) and annotation versions (for example, Ensembl release 84 or Ensembl release 91) contribute to the variation between different gene naming schemes.
PMC10287567	An integrated cell atlas of the lung in health and disease.	Therefore, both annotation sources and versions, including outdated ones, need to be taken into account to enable the mapping of all gene names to a single naming scheme.
PMC10287567	An integrated cell atlas of the lung in health and disease.	For the harmonization of gene names, we aimed to map all original gene names to the target scheme HUGO Gene Nomenclature Committee gene name, corresponding to the Ensembl release 107 publication.
PMC10287567	An integrated cell atlas of the lung in health and disease.	To find the most likely match between each original gene name and a target gene name, we first downloaded Ensembl releases 79 to 107, which included for each release: (1) all Ensembl gene IDs from the downloaded release (for example, ENSG00000081237.21); (2) corresponding Ensembl transcript and protein IDs (for example, ENST00000442510.8 and ENSP00000411355.3); (3) matching Ensembl IDs from the previous release; (4) matching gene IDs from other genome annotation sources (for example, RefSeq); and (5) matching gene, transcript and protein identifiers from various external resources, such as UniProt, the HUGO Gene Nomenclature Committee and the Consensus Coding Sequence Project.
PMC10287567	An integrated cell atlas of the lung in health and disease.	We then constructed a graph, with each Ensembl ID, other genome annotation ID and external resource identifier represented by a single node per release.
PMC10287567	An integrated cell atlas of the lung in health and disease.	Nodes were then connected based on the matching (points 2–5) provided by Ensembl, weighing edges based on Ensembl similarity scores where available.
PMC10287567	An integrated cell atlas of the lung in health and disease.	For each original gene name from the HLCA datasets, the path with the lowest mean edge weight from that gene name to a gene name from the target names (Ensembl release 107) was selected to find the most likely matching gene name from the target (Supplementary Table 17).
PMC10287567	An integrated cell atlas of the lung in health and disease.	Genes for which no target could be found were excluded from downstream analysis.
PMC10287567	An integrated cell atlas of the lung in health and disease.	When multiple genes were matched with the same target gene name, counts from the original genes were summed.
PMC10287567	An integrated cell atlas of the lung in health and disease.	To identify the genes most commonly exhibiting batch-specific expression, the HLCA was split by cell type and a differential expression analysis was performed (based on a Wilcoxon rank-sum test) in each cell type, comparing cells from one dataset (batch) with those from all other datasets and repeating this for all datasets.
PMC10287567	An integrated cell atlas of the lung in health and disease.	Datasets that had fewer than ten cells of the cell type or fewer than three samples with cells of the cell type were excluded from the test.
PMC10287567	An integrated cell atlas of the lung in health and disease.	For each test, genes were filtered such that only genes that were significantly upregulated were retained.
PMC10287567	An integrated cell atlas of the lung in health and disease.	Next, the fraction of included datasets in which a gene was significantly upregulated in the cell type (affected dataset fraction) was calculated for all genes.
PMC10287567	An integrated cell atlas of the lung in health and disease.	To find genes that were often batch effect associated across many cell types, the mean of the affected dataset fractions was calculated across cell types for each gene.
PMC10287567	An integrated cell atlas of the lung in health and disease.	To perform label transfer from the HLCA core to the mapped datasets from the extended HLCA, we used the scArches k nearest neighbor-based label transfer algorithm.
PMC10287567	An integrated cell atlas of the lung in health and disease.	Briefly, a k nearest neighbor graph was generated from the joint embedding of the HLCA core and the new, mapped dataset, setting the number of neighbors to k = 50.
PMC10287567	An integrated cell atlas of the lung in health and disease.	Based on the abundance and proximity in a cell’s neighborhood of reference cells of different types, the most likely cell type label for that cell was selected.
PMC10287567	An integrated cell atlas of the lung in health and disease.	Furthermore, a matching uncertainty score was calculated based on the consistency of reference annotations among the k nearest neighbors of the cell of interest[12pt] $$u_}_c} = 1 - p( }_}\!
PMC10287567	An integrated cell atlas of the lung in health and disease.	.$$,y,Nc=1−pY=y∣X=c,Ncwhere uc,y,Nc is the uncertainty score for a query cell c with transferred label y; Nc is its set of k nearest neighbors; and p(Y = y\|X = c, Nc) is the weighted (by edge weights) proportion of Nc with label y, as previously described.
PMC10287567	An integrated cell atlas of the lung in health and disease.	Thus, high consistency in HLCA core annotations leads to low uncertainty scores and low consistency (that is, mixing of distinct reference annotations) leads to high uncertainty scores.
PMC10287567	An integrated cell atlas of the lung in health and disease.	For label transfer to lung cancer and healthy, spatially annotated projected data (Fig. 5b and Extended Data Fig. 7g), cells with an uncertainty score above 0.3 were set to unknown.
PMC10287567	An integrated cell atlas of the lung in health and disease.	Disagreement between original labels and transferred annotations (that is, transferred annotations with high certainty but not matching the original label) in the data from ref.
PMC10287567	An integrated cell atlas of the lung in health and disease.	highlighted three different cases: annotations not included in the mapped data (for example, preterminal bronchiole secretory cells, which were labeled as club and goblet in the mapped data; these may not be incorrect label transfers but cannot be verified by label comparison alone); cell types that are part of a continuum, with cutoffs between cell types chosen differently in the reference than in the projected data (for example, macrophage subtypes); and cell types missing in the HLCA core that have high transcriptional similarity to other cell types that are present in the HLCA, which was observed for several finely annotated immune cell identities.
PMC10287567	An integrated cell atlas of the lung in health and disease.	For example, γδ T cells, ILCs, megakaryocytes, natural killer T cells and regulatory T cells were not annotated in the HLCA core, as these cell types could not be distinguished with confidence in the integrated object and were often lacking in the constituent datasets.
PMC10287567	An integrated cell atlas of the lung in health and disease.	Indeed, cell types from the T cell/ILC/natural killer lineages are known to be particularly difficult to annotate using transcriptomic data only.
PMC10287567	An integrated cell atlas of the lung in health and disease.	Therefore, cells annotated with these labels in the projected dataset were largely incorrectly annotated as CD4 T cells, CD8 T cells and natural killer cells through label transfer (Fig. 5b and Extended Data Fig. 6e) For the extended atlas, we calibrated the uncertainty score cutoff by determining which uncertainty levels indicate possible failure of label transfer.
PMC10287567	An integrated cell atlas of the lung in health and disease.	To determine the uncertainty score at which technical variability from residual batch effects impairs correct label transfer, we evaluated how label transfer performed at the level of datasets, as these predominantly differ in experimental design.
PMC10287567	An integrated cell atlas of the lung in health and disease.	To determine an uncertainty threshold indicative of possible failure of label transfer, we harmonized original labels for 12 projected datasets (one unpublished: Duong_lungMAP_unpubl) and assessed the correspondence between original labels with the transferred annotations.
PMC10287567	An integrated cell atlas of the lung in health and disease.	Only cells with level 3 or 4 original annotations were considered, as these levels represent informative annotations while not representing the finest detail.
PMC10287567	An integrated cell atlas of the lung in health and disease.	Level 5 annotations will often display high uncertainty levels due to high annotation granularity rather than remaining batch effects.
PMC10287567	An integrated cell atlas of the lung in health and disease.	To assess the optimal uncertainty cutoff for labeling a new cell as unknown, we used these results to generate a receiver operating characteristic curve.
PMC10287567	An integrated cell atlas of the lung in health and disease.	We chose a cutoff around the elbow point, keeping the false positive rate below 0.5 (uncertainty cutoff = 0.2; true positive rate = 0.879; false positive rate = 0.495) to best distinguish correct from incorrect label transfers (Supplementary Fig. 10a).
PMC10287567	An integrated cell atlas of the lung in health and disease.	False positives were either due to incorrect label transfer or incorrect annotations in the original datasets.
PMC10287567	An integrated cell atlas of the lung in health and disease.	Cells with an uncertainty higher than 0.2 were set to unknown.
PMC10287567	An integrated cell atlas of the lung in health and disease.	The ref.
PMC10287567	An integrated cell atlas of the lung in health and disease.	study of healthy lung included cell type annotations based on matched spatial transcriptomic data.
PMC10287567	An integrated cell atlas of the lung in health and disease.	Many of these annotations were not present in the HLCA core.
PMC10287567	An integrated cell atlas of the lung in health and disease.	To determine whether these spatial cell types could still be recovered after mapping to the HLCA core, we looked for clusters specifically grouping these cells.
PMC10287567	An integrated cell atlas of the lung in health and disease.	We focused on six spatial cell types: perineurial nerve-associated fibroblasts; endoneurial nerve-associated fibroblasts; immune-recruiting fibroblasts; chondrocytes; myelinating Schwann cells; and nonmyelinating Schwann cells.
PMC10287567	An integrated cell atlas of the lung in health and disease.	As these cell types were often present at very small frequencies, we performed clustering at different resolutions to determine whether these cells were clustered separately at any of these resolutions.
PMC10287567	An integrated cell atlas of the lung in health and disease.	We clustered at resolutions of 0.1, 0.2, 0.5, 1, 2, 3, 5, 10, 15, 20, 25, 30, 50, 80 and 100, with the number of neighbors set to k = 30 for resolutions under 25 and k = 15 for resolutions of 25 or higher, to enable the detection of smaller clusters.
PMC10287567	An integrated cell atlas of the lung in health and disease.	Minimum recall (the percentage of cells with the spatial cell type annotation captured in the cluster) and minimum precision (the percentage of cells from ref.
PMC10287567	An integrated cell atlas of the lung in health and disease.	in the cluster that had the spatial cell type annotation) were both set to 25%.
PMC10287567	An integrated cell atlas of the lung in health and disease.	The cluster with the highest recall was selected for every spatial cell type (unless this cluster decreased precision by >33% compared with the cluster with the second highest recall).
PMC10287567	An integrated cell atlas of the lung in health and disease.	If the precision of the next best cluster was doubled compared with the cluster with the highest recall and recall did not decrease by >20%, this cluster was selected.
PMC10287567	An integrated cell atlas of the lung in health and disease.	To learn disease-specific signatures based on label transfer uncertainty scores, cells from the mapped data with the same transferred label (either alveolar fibroblasts or alveolar macrophages) were split into low-uncertainty cells (<0.2) and high-uncertainty cells (>0.4), excluding cells between these extremes (for alveolar fibroblasts, n = 11,119 (<0.2) and n = 2,863 (>0.4); for alveolar macrophages, n = 1,770 (<0.2) and n = 577 (>0.4)).
PMC10287567	An integrated cell atlas of the lung in health and disease.	We then performed a differential expression analysis on SCRAN-normalized counts using a Wilcoxon rank-sum test with default parameters, comparing high- and low-uncertainty cells.
PMC10287567	An integrated cell atlas of the lung in health and disease.	The 20 most upregulated genes based on log-fold changes were selected after filtering out genes with a false discovery rate-corrected P value (using the Benjamini–Hochberg procedure) above 0.05 and genes with a mean expression below 0.1 in the high-uncertainty group.
PMC10287567	An integrated cell atlas of the lung in health and disease.	To calculate the score of a cell for the given set of genes, the average expression of the set of genes was calculated, after which the average expression of a reference set of genes was subtracted from the original average, as described previously.
PMC10287567	An integrated cell atlas of the lung in health and disease.	The reference set consists of a randomly sampled set of genes for each binned expression value.
PMC10287567	An integrated cell atlas of the lung in health and disease.	The resulting score was considered the cell’s disease signature score.
PMC10287567	An integrated cell atlas of the lung in health and disease.	To uncover the cell identities affected in IPF, label transfer uncertainty was analyzed for three mapped datasets from the extended HLCA that included both IPF and healthy samples.
PMC10287567	An integrated cell atlas of the lung in health and disease.	Cell types of interest were determined based on the largest increase in mean label transfer uncertainty in IPF compared with healthy samples, while checking for consistency in increments across the three datasets.
PMC10287567	An integrated cell atlas of the lung in health and disease.	This highlighted alveolar fibroblasts as the main cell type of interest.
PMC10287567	An integrated cell atlas of the lung in health and disease.	To find IPF-specific alveolar fibroblast states, all alveolar fibroblasts from the abovementioned datasets and two more IPF datasets (for which no healthy data were mapped, as these were already in the core) were clustered together with the alveolar fibroblasts from the HLCA core.
PMC10287567	An integrated cell atlas of the lung in health and disease.	For clustering, a k nearest neighbor graph was calculated on the joint scArches-derived 30-dimensional embedding space setting k = 30, after which the cells were clustered using the Leiden algorithm with a resolution of 0.3.
PMC10287567	An integrated cell atlas of the lung in health and disease.	The resolution was chosen such that datasets were not isolated in single clusters, thus avoiding clustering driven solely by dataset-specific batch effects.
PMC10287567	An integrated cell atlas of the lung in health and disease.	One cluster (cluster 5) was small (n = 460 cells) and displayed low donor entropy (0.17), indicating that it almost exclusively came from a single donor (96% of cells from HLCA core donor 390C).
PMC10287567	An integrated cell atlas of the lung in health and disease.	It was therefore excluded from further analysis.
PMC10287567	An integrated cell atlas of the lung in health and disease.	To perform differential gene expression analysis, gene counts were normalized to a total of 7,666 counts (the median number of counts across the HLCA) and then log transformed with a pseudocount of 1.
PMC10287567	An integrated cell atlas of the lung in health and disease.	Finally, a Wilcoxon rank-sum test was used on the normalized data to detect differentially expressed genes for cluster 0 (n = 6,765 cells versus a total of n = 14,731).

PMC10287567

An integrated cell atlas of the lung in health and disease.

For linkage disequilibrium score calculation, a 1 cM window was used.

PMC10287567

An integrated cell atlas of the lung in health and disease.

Significance of the link between a phenotype and a cell type was calculated using LDSC.

PMC10287567

An integrated cell atlas of the lung in health and disease.

P values yielded by LDSC were corrected for multiple testing for every disease tested using the Benjamini–Hochberg correction procedure.

PMC10287567

An integrated cell atlas of the lung in health and disease.

As a negative control, the analysis was performed with a GWAS of depression and no cell types were found to be significant (Supplementary Fig. 7).

PMC10287567

An integrated cell atlas of the lung in health and disease.

The numbers of cases and controls per GWAS study were as follows: n = 2,668 cases and 8,591 controls for IPF; n = 35,735 cases and 222,076 controls for COPD; n = 11,273 cases and 55,483 controls for lung adenocarcinoma; n = 321,047 individuals for lung function; n = 88,486 cases and 447,859 controls for asthma; and n = 113,769 cases and 208,811 controls for depression (used as negative control).

PMC10287567

An integrated cell atlas of the lung in health and disease.

To enable deconvolution of bulk expression data on the basis of the HLCA, HLCA cell type signature matrices were generated.

PMC10287567

An integrated cell atlas of the lung in health and disease.

One generic-purpose signature matrix was created per sublocation of the respiratory system (that is, one parenchyma, one airway and one nose tissue matrix; Supplementary Table 10).

PMC10287567

An integrated cell atlas of the lung in health and disease.

Additionally, a script to generate custom reference sets from the HLCA data is provided together with the HLCA code on GitHub (https://github.com/LungCellAtlas/HLCA) to tailor the deconvolution signature matrix to any specific research question.

PMC10287567

An integrated cell atlas of the lung in health and disease.

Cell types were included in the bulk deconvolution signature matrix on the basis of cell proportions (constituting >2% of cells within samples of the corresponding tissue in the HLCA core).

PMC10287567

An integrated cell atlas of the lung in health and disease.

In addition, cell types were merged when they were deemed too transcriptionally similar.

PMC10287567

An integrated cell atlas of the lung in health and disease.

For each included cell type, 200 cells were randomly sampled from the HLCA core, while all cells were included for cell types with fewer than 200 cells present in the HLCA core.

PMC10287567

An integrated cell atlas of the lung in health and disease.

Cells were sampled from the matching anatomical location (for example, nose T cells rather than parenchymal T cells were used for the nose signature matrix).

PMC10287567

An integrated cell atlas of the lung in health and disease.

Signature matrices were constructed using CIBERSORTx (version 1.0) according to default settings, and no cross-platform batch correction was applied.

PMC10287567

An integrated cell atlas of the lung in health and disease.

The reference data were optimized by deconvolution of pseudo-bulk samples constructed from the HLCA core data, assessing deconvolution performance per included cell type based on the correlation of predicted proportions with ground truth composition (Supplementary Fig. 8a).

PMC10287567

An integrated cell atlas of the lung in health and disease.

The following cell types were included in the deconvolution: endothelial cell arterial, endothelial cell capillary, lymphatic endothelial cell, basal and secretory (merged), multiciliated lineage, AT2, B cell lineage, innate lymphoid cell (ILC) natural killer and T cell lineage (merged), dendritic cells, alveolar macrophages, interstitial macrophages, mast cells, fibroblast lineage, smooth muscle, endothelial cell venous and monocytes (for the parenchyma); basal resting and suprabasal (merged), multiciliated lineage, club, goblet, dendritic cells, hillock like and T cell lineage (for the nose); and endothelial cell venous, CD4 T cells, fibroblasts, smooth muscle, basal and secretory (merged), multiciliated lineage, endothelial cell capillary, interstitial macrophages, B cell lineage, natural killer cells, CD8 T cells, dendritic cells, alveolar macrophages, mast cells and monocytes (for the airway).

PMC10287567

An integrated cell atlas of the lung in health and disease.

Capillary endothelial cells and interstitial macrophages (airway) were excluded from statistical testing due to poor performance in the benchmark.

PMC10287567

An integrated cell atlas of the lung in health and disease.

Venous endothelial cells and monocytes (parenchyma), hillock-like cells and T cell lineage cells (nose) and B cell lineage cells, natural killer cells, CD8 T cells, dendritic cells, alveolar macrophages, mast cells and monocytes (airways) were excluded from statistical testing due to >60% zero proportions.

PMC10287567

An integrated cell atlas of the lung in health and disease.

The parenchymal signature matrix was used to deconvolve RNA expression data of samples from the Lung Tissue Database (GEO accession number GSE23546) using only lung tissue samples from patients with COPD GOLD stages 3 and 4 (n = 27 and 56, respectively) and matched controls (n = 281).

PMC10287567

An integrated cell atlas of the lung in health and disease.

The Lung Tissue Database dataset was run on the Rosetta/Merck Human RSTA Custom Affymetrix 2.0 microarray platform (HuRSTA-2a520709; GPL10379).

PMC10287567

An integrated cell atlas of the lung in health and disease.

As this platform has multiple probe sets for each gene, we focused on the probe sets that were derived from curated RefSeq records (with NM_ accession prefixes) when present to maximize the accuracy of the deconvolution.

PMC10287567

An integrated cell atlas of the lung in health and disease.

Where genes did not have probe sets based on curated RefSeq records or had multiple probe sets mapping to curated RefSeq records, the probe set with the highest average microarray intensity across samples was selected.

PMC10287567

An integrated cell atlas of the lung in health and disease.

Quantile normalization of the data and subsequent deconvolution were performed using CIBERSORTx.

PMC10287567

An integrated cell atlas of the lung in health and disease.

A Wilcoxon rank-sum test between control and GOLD stage 3/4 samples was performed to identify statistically significant compositional changes in advanced-stage COPD compared with control tissue.

PMC10287567

An integrated cell atlas of the lung in health and disease.

GOLD 3/4 and control samples were matched for age and smoking history.

PMC10287567

An integrated cell atlas of the lung in health and disease.

Cell types with >60% of samples predicted to have a proportion of zero were excluded from the Wilcoxon test, as the high number of tied ranks (zeros in both groups) would result in inflated P values.

PMC10287567

An integrated cell atlas of the lung in health and disease.

P values were multiple testing corrected using the Benjamini–Hochberg procedure.

PMC10287567

An integrated cell atlas of the lung in health and disease.

The same procedure was followed for a dataset of nasal brush bulk RNA-seq samples from asthmatic donors pre- and postinhalation of corticosteroids (n = 54 and 26, respectively) and a dataset of airway biopsy bulk RNA-seq samples from asthmatic donors and controls (n = 95 and 38, respectively).

PMC10287567

An integrated cell atlas of the lung in health and disease.

As these consisted of RNA-seq data, no quantile normalization was applied.

PMC10287567

An integrated cell atlas of the lung in health and disease.

To map unseen scRNA-seq and single-nucleus RNA-seq data to the HLCA, we used scArches, our transfer learning-based method that enables mapping of new data to an existing reference atlas.

PMC10287567

An integrated cell atlas of the lung in health and disease.

scArches trains an adaptor added to a reference embedding model, thereby enabling it to generate a common embedding of the new data and the reference, allowing reanalysis and de novo clustering of the joint data.

PMC10287567

An integrated cell atlas of the lung in health and disease.

The data to map were subsetted to the same 2,000 HVGs that were used for HLCA integration and embedding, and HVGs that were absent in the new data were set to 0 counts for all cells.

PMC10287567

An integrated cell atlas of the lung in health and disease.

Raw counts were used as input for scArches, except for the ref.

PMC10287567

An integrated cell atlas of the lung in health and disease.

dataset, for which ambient RNA removal was run previously on the raw counts.

PMC10287567

An integrated cell atlas of the lung in health and disease.

Healthy lung data were split into two datasets: 3′ and 5′ based.

PMC10287567

An integrated cell atlas of the lung in health and disease.

Lung cancer data were also split into two datasets: 10xv1 and 10xv2.

PMC10287567

An integrated cell atlas of the lung in health and disease.

The model that was learned previously for HLCA integration using scANVI was used as the basis for the scArches mapping.

PMC10287567

An integrated cell atlas of the lung in health and disease.

scArches was then run to train adaptor weights that allowed for mapping of new data into the existing HLCA embedding, using the following parameter settings: freeze-dropout: true; surgery_epochs: 500; train base model: false; metrics to monitor: accuracy and elbo; weight-decay: 0; and frequency: 1.

PMC10287567

An integrated cell atlas of the lung in health and disease.

The following early-stopping criteria were used: early stopping metric: elbo; save best state metric: elbo; on: full dataset; patience: 10; threshold: 0.001; reduce lr on plateau: True; lr patience: 8l and lr_factor: 0.1.

PMC10287567

An integrated cell atlas of the lung in health and disease.

To enable cross-dataset gene-level analysis, harmonization of gene names from different datasets (using different reference genome builds and genome annotations; Supplementary Table 1) was necessary.

PMC10287567

An integrated cell atlas of the lung in health and disease.

Both annotation sources (for example, Ensembl or RefSeq) and annotation versions (for example, Ensembl release 84 or Ensembl release 91) contribute to the variation between different gene naming schemes.

PMC10287567

An integrated cell atlas of the lung in health and disease.

Therefore, both annotation sources and versions, including outdated ones, need to be taken into account to enable the mapping of all gene names to a single naming scheme.

PMC10287567

An integrated cell atlas of the lung in health and disease.

For the harmonization of gene names, we aimed to map all original gene names to the target scheme HUGO Gene Nomenclature Committee gene name, corresponding to the Ensembl release 107 publication.

PMC10287567

An integrated cell atlas of the lung in health and disease.

To find the most likely match between each original gene name and a target gene name, we first downloaded Ensembl releases 79 to 107, which included for each release: (1) all Ensembl gene IDs from the downloaded release (for example, ENSG00000081237.21); (2) corresponding Ensembl transcript and protein IDs (for example, ENST00000442510.8 and ENSP00000411355.3); (3) matching Ensembl IDs from the previous release; (4) matching gene IDs from other genome annotation sources (for example, RefSeq); and (5) matching gene, transcript and protein identifiers from various external resources, such as UniProt, the HUGO Gene Nomenclature Committee and the Consensus Coding Sequence Project.

PMC10287567

An integrated cell atlas of the lung in health and disease.

We then constructed a graph, with each Ensembl ID, other genome annotation ID and external resource identifier represented by a single node per release.

PMC10287567

An integrated cell atlas of the lung in health and disease.

Nodes were then connected based on the matching (points 2–5) provided by Ensembl, weighing edges based on Ensembl similarity scores where available.

PMC10287567

An integrated cell atlas of the lung in health and disease.

For each original gene name from the HLCA datasets, the path with the lowest mean edge weight from that gene name to a gene name from the target names (Ensembl release 107) was selected to find the most likely matching gene name from the target (Supplementary Table 17).

PMC10287567

An integrated cell atlas of the lung in health and disease.

Genes for which no target could be found were excluded from downstream analysis.

PMC10287567

An integrated cell atlas of the lung in health and disease.

When multiple genes were matched with the same target gene name, counts from the original genes were summed.

PMC10287567

An integrated cell atlas of the lung in health and disease.

To identify the genes most commonly exhibiting batch-specific expression, the HLCA was split by cell type and a differential expression analysis was performed (based on a Wilcoxon rank-sum test) in each cell type, comparing cells from one dataset (batch) with those from all other datasets and repeating this for all datasets.

PMC10287567

An integrated cell atlas of the lung in health and disease.

Datasets that had fewer than ten cells of the cell type or fewer than three samples with cells of the cell type were excluded from the test.

PMC10287567

An integrated cell atlas of the lung in health and disease.

For each test, genes were filtered such that only genes that were significantly upregulated were retained.

PMC10287567

An integrated cell atlas of the lung in health and disease.

Next, the fraction of included datasets in which a gene was significantly upregulated in the cell type (affected dataset fraction) was calculated for all genes.

PMC10287567

An integrated cell atlas of the lung in health and disease.

To find genes that were often batch effect associated across many cell types, the mean of the affected dataset fractions was calculated across cell types for each gene.

PMC10287567

An integrated cell atlas of the lung in health and disease.

To perform label transfer from the HLCA core to the mapped datasets from the extended HLCA, we used the scArches k nearest neighbor-based label transfer algorithm.

PMC10287567

An integrated cell atlas of the lung in health and disease.

Briefly, a k nearest neighbor graph was generated from the joint embedding of the HLCA core and the new, mapped dataset, setting the number of neighbors to k = 50.

PMC10287567

An integrated cell atlas of the lung in health and disease.

Based on the abundance and proximity in a cell’s neighborhood of reference cells of different types, the most likely cell type label for that cell was selected.

PMC10287567

An integrated cell atlas of the lung in health and disease.

Furthermore, a matching uncertainty score was calculated based on the consistency of reference annotations among the k nearest neighbors of the cell of interest[12pt] $$u_}_c} = 1 - p( }_}\!

PMC10287567

An integrated cell atlas of the lung in health and disease.

.$$,y,Nc=1−pY=y∣X=c,Ncwhere uc,y,Nc is the uncertainty score for a query cell c with transferred label y; Nc is its set of k nearest neighbors; and p(Y = y|X = c, Nc) is the weighted (by edge weights) proportion of Nc with label y, as previously described.

PMC10287567

An integrated cell atlas of the lung in health and disease.

Thus, high consistency in HLCA core annotations leads to low uncertainty scores and low consistency (that is, mixing of distinct reference annotations) leads to high uncertainty scores.

PMC10287567

An integrated cell atlas of the lung in health and disease.

For label transfer to lung cancer and healthy, spatially annotated projected data (Fig. 5b and Extended Data Fig. 7g), cells with an uncertainty score above 0.3 were set to unknown.

PMC10287567

An integrated cell atlas of the lung in health and disease.

Disagreement between original labels and transferred annotations (that is, transferred annotations with high certainty but not matching the original label) in the data from ref.

PMC10287567

An integrated cell atlas of the lung in health and disease.

highlighted three different cases: annotations not included in the mapped data (for example, preterminal bronchiole secretory cells, which were labeled as club and goblet in the mapped data; these may not be incorrect label transfers but cannot be verified by label comparison alone); cell types that are part of a continuum, with cutoffs between cell types chosen differently in the reference than in the projected data (for example, macrophage subtypes); and cell types missing in the HLCA core that have high transcriptional similarity to other cell types that are present in the HLCA, which was observed for several finely annotated immune cell identities.

PMC10287567

An integrated cell atlas of the lung in health and disease.

For example, γδ T cells, ILCs, megakaryocytes, natural killer T cells and regulatory T cells were not annotated in the HLCA core, as these cell types could not be distinguished with confidence in the integrated object and were often lacking in the constituent datasets.

PMC10287567

An integrated cell atlas of the lung in health and disease.

Indeed, cell types from the T cell/ILC/natural killer lineages are known to be particularly difficult to annotate using transcriptomic data only.

PMC10287567

An integrated cell atlas of the lung in health and disease.

Therefore, cells annotated with these labels in the projected dataset were largely incorrectly annotated as CD4 T cells, CD8 T cells and natural killer cells through label transfer (Fig. 5b and Extended Data Fig. 6e) For the extended atlas, we calibrated the uncertainty score cutoff by determining which uncertainty levels indicate possible failure of label transfer.

PMC10287567

An integrated cell atlas of the lung in health and disease.

To determine the uncertainty score at which technical variability from residual batch effects impairs correct label transfer, we evaluated how label transfer performed at the level of datasets, as these predominantly differ in experimental design.

PMC10287567

An integrated cell atlas of the lung in health and disease.

To determine an uncertainty threshold indicative of possible failure of label transfer, we harmonized original labels for 12 projected datasets (one unpublished: Duong_lungMAP_unpubl) and assessed the correspondence between original labels with the transferred annotations.

PMC10287567

An integrated cell atlas of the lung in health and disease.

Only cells with level 3 or 4 original annotations were considered, as these levels represent informative annotations while not representing the finest detail.

PMC10287567

An integrated cell atlas of the lung in health and disease.

Level 5 annotations will often display high uncertainty levels due to high annotation granularity rather than remaining batch effects.

PMC10287567

An integrated cell atlas of the lung in health and disease.

To assess the optimal uncertainty cutoff for labeling a new cell as unknown, we used these results to generate a receiver operating characteristic curve.

PMC10287567

An integrated cell atlas of the lung in health and disease.

We chose a cutoff around the elbow point, keeping the false positive rate below 0.5 (uncertainty cutoff = 0.2; true positive rate = 0.879; false positive rate = 0.495) to best distinguish correct from incorrect label transfers (Supplementary Fig. 10a).

PMC10287567

An integrated cell atlas of the lung in health and disease.

False positives were either due to incorrect label transfer or incorrect annotations in the original datasets.

PMC10287567

An integrated cell atlas of the lung in health and disease.

Cells with an uncertainty higher than 0.2 were set to unknown.

PMC10287567

An integrated cell atlas of the lung in health and disease.

The ref.

PMC10287567

An integrated cell atlas of the lung in health and disease.

study of healthy lung included cell type annotations based on matched spatial transcriptomic data.

PMC10287567

An integrated cell atlas of the lung in health and disease.

Many of these annotations were not present in the HLCA core.

PMC10287567

An integrated cell atlas of the lung in health and disease.

To determine whether these spatial cell types could still be recovered after mapping to the HLCA core, we looked for clusters specifically grouping these cells.

PMC10287567

An integrated cell atlas of the lung in health and disease.

We focused on six spatial cell types: perineurial nerve-associated fibroblasts; endoneurial nerve-associated fibroblasts; immune-recruiting fibroblasts; chondrocytes; myelinating Schwann cells; and nonmyelinating Schwann cells.

PMC10287567

An integrated cell atlas of the lung in health and disease.

As these cell types were often present at very small frequencies, we performed clustering at different resolutions to determine whether these cells were clustered separately at any of these resolutions.

PMC10287567

An integrated cell atlas of the lung in health and disease.

We clustered at resolutions of 0.1, 0.2, 0.5, 1, 2, 3, 5, 10, 15, 20, 25, 30, 50, 80 and 100, with the number of neighbors set to k = 30 for resolutions under 25 and k = 15 for resolutions of 25 or higher, to enable the detection of smaller clusters.

PMC10287567

An integrated cell atlas of the lung in health and disease.

Minimum recall (the percentage of cells with the spatial cell type annotation captured in the cluster) and minimum precision (the percentage of cells from ref.

PMC10287567

An integrated cell atlas of the lung in health and disease.

in the cluster that had the spatial cell type annotation) were both set to 25%.

PMC10287567

An integrated cell atlas of the lung in health and disease.

The cluster with the highest recall was selected for every spatial cell type (unless this cluster decreased precision by >33% compared with the cluster with the second highest recall).

PMC10287567

An integrated cell atlas of the lung in health and disease.

If the precision of the next best cluster was doubled compared with the cluster with the highest recall and recall did not decrease by >20%, this cluster was selected.

PMC10287567

An integrated cell atlas of the lung in health and disease.

To learn disease-specific signatures based on label transfer uncertainty scores, cells from the mapped data with the same transferred label (either alveolar fibroblasts or alveolar macrophages) were split into low-uncertainty cells (<0.2) and high-uncertainty cells (>0.4), excluding cells between these extremes (for alveolar fibroblasts, n = 11,119 (<0.2) and n = 2,863 (>0.4); for alveolar macrophages, n = 1,770 (<0.2) and n = 577 (>0.4)).

PMC10287567

An integrated cell atlas of the lung in health and disease.

We then performed a differential expression analysis on SCRAN-normalized counts using a Wilcoxon rank-sum test with default parameters, comparing high- and low-uncertainty cells.

PMC10287567

An integrated cell atlas of the lung in health and disease.

The 20 most upregulated genes based on log-fold changes were selected after filtering out genes with a false discovery rate-corrected P value (using the Benjamini–Hochberg procedure) above 0.05 and genes with a mean expression below 0.1 in the high-uncertainty group.

PMC10287567

An integrated cell atlas of the lung in health and disease.

To calculate the score of a cell for the given set of genes, the average expression of the set of genes was calculated, after which the average expression of a reference set of genes was subtracted from the original average, as described previously.

PMC10287567

An integrated cell atlas of the lung in health and disease.

The reference set consists of a randomly sampled set of genes for each binned expression value.

PMC10287567

An integrated cell atlas of the lung in health and disease.

The resulting score was considered the cell’s disease signature score.

PMC10287567

An integrated cell atlas of the lung in health and disease.

To uncover the cell identities affected in IPF, label transfer uncertainty was analyzed for three mapped datasets from the extended HLCA that included both IPF and healthy samples.

PMC10287567

An integrated cell atlas of the lung in health and disease.

Cell types of interest were determined based on the largest increase in mean label transfer uncertainty in IPF compared with healthy samples, while checking for consistency in increments across the three datasets.

PMC10287567

An integrated cell atlas of the lung in health and disease.

This highlighted alveolar fibroblasts as the main cell type of interest.

PMC10287567

An integrated cell atlas of the lung in health and disease.

To find IPF-specific alveolar fibroblast states, all alveolar fibroblasts from the abovementioned datasets and two more IPF datasets (for which no healthy data were mapped, as these were already in the core) were clustered together with the alveolar fibroblasts from the HLCA core.

PMC10287567

An integrated cell atlas of the lung in health and disease.

For clustering, a k nearest neighbor graph was calculated on the joint scArches-derived 30-dimensional embedding space setting k = 30, after which the cells were clustered using the Leiden algorithm with a resolution of 0.3.

PMC10287567

An integrated cell atlas of the lung in health and disease.

The resolution was chosen such that datasets were not isolated in single clusters, thus avoiding clustering driven solely by dataset-specific batch effects.

PMC10287567

An integrated cell atlas of the lung in health and disease.

One cluster (cluster 5) was small (n = 460 cells) and displayed low donor entropy (0.17), indicating that it almost exclusively came from a single donor (96% of cells from HLCA core donor 390C).

PMC10287567

An integrated cell atlas of the lung in health and disease.

It was therefore excluded from further analysis.

PMC10287567

An integrated cell atlas of the lung in health and disease.

To perform differential gene expression analysis, gene counts were normalized to a total of 7,666 counts (the median number of counts across the HLCA) and then log transformed with a pseudocount of 1.

PMC10287567

An integrated cell atlas of the lung in health and disease.

Finally, a Wilcoxon rank-sum test was used on the normalized data to detect differentially expressed genes for cluster 0 (n = 6,765 cells versus a total of n = 14,731).