OTAR3088/single-cell_raw · Datasets at Hugging Face

data dict	annotations list
{ "text": "PMC10287567\nSource paper: PMC10287567" }	[]
{ "text": "An integrated cell atlas of the lung in health and disease\nSource paper: PMC10287567" }	[ { "result": [ { "from_name": "label", "to_name": "text", "type": "labels", "value": { "start": 32, "end": 36, "text": "lung", "labels": [ "Tissue" ] } } ] } ]
{ "text": "Abstract\nSource paper: PMC10287567" }	[]
{ "text": "Single-cell technologies have transformed our understanding of human tissues. Yet, studies typically capture only a limited number of donors and disagree on cell type definitions. Integrating many single-cell datasets can address these limitations of individual studies and capture the variability present i...	[ { "result": [ { "from_name": "label", "to_name": "text", "type": "labels", "value": { "start": 1182, "end": 1193, "text": "macrophages", "labels": [ "Cell" ] } }, { "from_name": "label...
{ "text": "Main\nSource paper: PMC10287567" }	[]
{ "text": "Rapid technological improvements over the past decade have allowed single-cell datasets to grow both in size and number . This has led consortia, such as the Human Cell Atlas, to pursue the generation of large-scale reference atlases of human organs . To advance our understanding of health and disease, suc...	[ { "result": [ { "from_name": "label", "to_name": "text", "type": "labels", "value": { "start": 243, "end": 249, "text": "organs", "labels": [ "Tissue" ] } }, { "from_name": "label", ...
{ "text": "Several foundational studies have started to map the cellular landscape of the healthy human lung . These studies each have a specific bias due to their choice of experimental protocol and technologies, and are therefore not tailored to serve as a universal reference. The studies moreover include only a li...	[ { "result": [ { "from_name": "label", "to_name": "text", "type": "labels", "value": { "start": 93, "end": 97, "text": "lung", "labels": [ "Tissue" ] } }, { "from_name": "label", ...
{ "text": "Integrated single-cell atlases provide novel insights not obtained in individual studies. Recent reference atlases have led to the discovery of unknown cell types , the identification of marker genes that are reproducible across studies , the comparison of animal and in vitro models with human healthy and ...	[ { "result": [ { "from_name": "label", "to_name": "text", "type": "labels", "value": { "start": 499, "end": 504, "text": "organ", "labels": [ "Tissue" ] } }, { "from_name": "label", ...
{ "text": "In this resource, we present an integrated single-cell transcriptomic atlas of the human respiratory system, including the upper and lower airways, from published and newly generated datasets (Fig. 1 ). The Human Lung Cell Atlas (HLCA) comprises data from 486 donors and 49 datasets, including 2.4 million c...	[ { "result": [ { "from_name": "label", "to_name": "text", "type": "labels", "value": { "start": 89, "end": 107, "text": "respiratory system", "labels": [ "Tissue" ] } }, { "from_name": ...
{ "text": "Results\nSource paper: PMC10287567" }	[]
{ "text": "To build the HLCA, we collected single-cell RNA sequencing (scRNA-seq) data and detailed, harmonized technical, biological and demographic metadata from 14 datasets (11 published and three unpublished) . These datasets include samples from 107 individuals, with diversity in age, sex, ethnicity (harmonized ...	[ { "result": [ { "from_name": "label", "to_name": "text", "type": "labels", "value": { "start": 715, "end": 733, "text": "respiratory system", "labels": [ "Tissue" ] } } ] } ]
{ "text": "Consensus definitions of cell types based on single-cell transcriptomic data across studies—particularly of transitional cell states—are lacking. To enable supervised data integration and downstream integrated analysis, we harmonized cell type nomenclature by building a five-level hierarchical cell identit...	[]
{ "text": "To optimally remove dataset-specific batch effects, we evaluated 12 different data integration methods on 12 datasets (Fig. 2d and Supplementary Fig. 1 ) using our previously established benchmarking pipeline . We used the top-performing integration method, scANVI, to create an integrated embedding of all ...	[]
{ "text": "A large-scale integrated atlas provides the unique opportunity to systematically investigate the consensus in cell type labeling across datasets. To identify areas of consensus and disagreement, we iteratively clustered the HLCA core and investigated donor diversity and cell type label agreement in these c...	[ { "result": [ { "from_name": "label", "to_name": "text", "type": "labels", "value": { "start": 1171, "end": 1182, "text": "macrophages", "labels": [ "Cell" ] } }, { "from_name": "label...
{ "text": "As a first step to achieve such a consensus on the diversity of cell types present in the HLCA core, we performed a full re-annotation of the integrated data on the basis of the original annotations and six expert opinions (consensus annotation; Methods and Fig. 3d ). Each of the 61 annotated cell types (S...	[ { "result": [ { "from_name": "label", "to_name": "text", "type": "labels", "value": { "start": 411, "end": 429, "text": "respiratory system", "labels": [ "Tissue" ] } }, { "from_name":...
{ "text": "Rare cell types, such as ionocytes, tuft cells, neuroendocrine cells and specific immune cell subsets, are often difficult to identify in individual datasets. Yet, combining datasets in the HLCA core provides better power for identifying these rare cell types. Ionocytes, tuft and neuroendocrine cells make ...	[ { "result": [ { "from_name": "label", "to_name": "text", "type": "labels", "value": { "start": 742, "end": 751, "text": "monocytes", "labels": [ "Cell" ] } }, { "from_name": "label", ...
{ "text": "We were further able to detect six cell identities that were not previously found in the human lung or were only recently described in individual studies. These cell types include migratory dendritic cells ( n = 312 cells, expressing CCR7 , LAD1 and COL19 ), hematopoietic stem cells ( n = 60, expressing SP...	[ { "result": [ { "from_name": "label", "to_name": "text", "type": "labels", "value": { "start": 190, "end": 205, "text": "dendritic cells", "labels": [ "Cell" ] } }, { "from_name": "lab...
{ "text": "Demographic and other metadata covariates affect cellular transcriptional phenotypes . Better insight into the impact of these covariates (for example, sex, BMI and smoking) on cell type gene expression can shed light on the contribution of these factors to progression from healthy to diseased states. In a...	[ { "result": [ { "from_name": "label", "to_name": "text", "type": "labels", "value": { "start": 1064, "end": 1071, "text": "T cells", "labels": [ "Cell" ] } }, { "from_name": "label", ...
{ "text": "To better characterize how biological variables affect cellular phenotypes, we modeled their cell type-specific effects on the transcriptome at the gene level ( Methods ). Sex-related differences in lymphatic endothelial cells are dominated by differential expression of genes located on the X and Y chromos...	[ { "result": [ { "from_name": "label", "to_name": "text", "type": "labels", "value": { "start": 1857, "end": 1868, "text": "macrophages", "labels": [ "Cell" ] } }, { "from_name": "label...
{ "text": "Biological and technical factors can also affect cell type proportions. Indeed, all cell types show changes in abundance as a function of anatomical location ( Fig. 4c and Extended Data Fig. 5 ). For example, ionocytes are present at comparable proportions in the airway epithelium, from the larger lower ai...	[ { "result": [ { "from_name": "label", "to_name": "text", "type": "labels", "value": { "start": 426, "end": 436, "text": "parenchyma", "labels": [ "Tissue" ] } }, { "from_name": "label"...
{ "text": "The HLCA core contains an unprecedented diversity of donors, sampling protocols and cell identities, and can serve as a transcriptomic reference for lung research. New datasets can be mapped to this reference to substantially speed up data analysis by transferring consensus cell identity annotations to the...	[ { "result": [ { "from_name": "label", "to_name": "text", "type": "labels", "value": { "start": 991, "end": 1002, "text": "fibroblasts", "labels": [ "Cell" ] } }, { "from_name": "label"...
{ "text": "Single-cell studies of disease rely on adequate, matching control samples to allow correct identification of disease-specific changes. To demonstrate the ability of the HLCA core to serve as a comprehensive healthy control and contextualize disease data, we mapped scRNA-seq data from lung cancer samples to...	[ { "result": [ { "from_name": "label", "to_name": "text", "type": "labels", "value": { "start": 1252, "end": 1261, "text": "capillary", "labels": [ "Tissue" ] } }, { "from_name": "label...
{ "text": "In addition, the HLCA can provide context to the results of large-scale genetic studies of disease. Genome-wide association studies (GWASs) link disease with specific genomic variants that may confer an increased risk of disease. Previous studies have linked such variants to cell type-specific mechanistic ...	[ { "result": [ { "from_name": "label", "to_name": "text", "type": "labels", "value": { "start": 995, "end": 1006, "text": "fibroblasts", "labels": [ "Cell" ] } }, { "from_name": "label"...
{ "text": "Finally, the HLCA can be used as a reference for cell type deconvolution of bulk RNA expression samples, which have been shown to reflect cell type proportions more accurately than scRNA-seq datasets . Inferring cell type proportions from bulk RNA samples from nasal brushings and bronchial biopsies using t...	[ { "result": [ { "from_name": "label", "to_name": "text", "type": "labels", "value": { "start": 889, "end": 900, "text": "macrophages", "labels": [ "Cell" ] } }, { "from_name": "label",...
{ "text": "As knowledge of cell types in the lung expands, and the sizes of newly generated datasets increase, annotations in the HLCA core will need to be further refined. The HLCA and its annotations can be updated by learning from new data projected onto the reference. We simulated such an HLCA update using the pr...	[ { "result": [ { "from_name": "label", "to_name": "text", "type": "labels", "value": { "start": 727, "end": 738, "text": "fibroblasts", "labels": [ "Cell" ] } }, { "from_name": "label",...
{ "text": "To extend the atlas and include samples from lung disease, we mapped 1,797,714 cells from 380 healthy and diseased individuals from 37 datasets (four unpublished and 33 published ) to the HLCA core using scArches , bringing the HLCA to a total of 2.4 million cells from 486 individuals (Fig. 6a and Suppleme...	[ { "result": [ { "from_name": "label", "to_name": "text", "type": "labels", "value": { "start": 488, "end": 503, "text": "dendritic cells", "labels": [ "Cell" ] } }, { "from_name": "lab...
{ "text": "Out of 37 new datasets, 27 were observed to map well to the HLCA, as evaluated by the mean label transfer uncertainty score (Fig. 6b , Supplementary Fig. 10a and Methods ). The remaining ten datasets were often from coronavirus disease 2019 (COVID-19) studies or, unlike the HLCA core, contained pediatric s...	[ { "result": [ { "from_name": "label", "to_name": "text", "type": "labels", "value": { "start": 508, "end": 512, "text": "lung", "labels": [ "Tissue" ] } } ] } ]
{ "text": "Pulmonary diseases are characterized by the emergence of unique disease-associated transcriptional phenotypes . We observed higher levels of label transfer uncertainty in datasets from diseased lungs (Fig. 6b , condition), possibly flagging cell types changed in response to disease. Specifically, labels of...	[ { "result": [ { "from_name": "label", "to_name": "text", "type": "labels", "value": { "start": 342, "end": 353, "text": "macrophages", "labels": [ "Cell" ] } }, { "from_name": "label",...
{ "text": "Similar to healthy cellular states, the HLCA can provide insight into disease-specific states that are consistent across demographics and experimental protocols. To demonstrate this, we determined which cell types are consistently affected by IPF across datasets, extending the previous IPF analysis to five...	[ { "result": [ { "from_name": "label", "to_name": "text", "type": "labels", "value": { "start": 370, "end": 381, "text": "fibroblasts", "labels": [ "Cell" ] } }, { "from_name": "label",...
{ "text": "The HLCA contains data across more than ten lung diseases, providing the unique opportunity to discover cellular states shared across diseases. Discovering such common diseased cellular states could improve our understanding of lung diseases and accelerate the identification of effective treatments. For ex...	[ { "result": [ { "from_name": "label", "to_name": "text", "type": "labels", "value": { "start": 348, "end": 359, "text": "macrophages", "labels": [ "Cell" ] } }, { "from_name": "label",...
{ "text": "Discussion\nSource paper: PMC10287567" }	[]
{ "text": "In this study, we built the HLCA: an integrated reference atlas of the human respiratory system. While previous studies have described the cellular heterogeneity within the human lung , study-specific biases due to experimental design and a limited number of sampled individuals constrain their capacity to ...	[ { "result": [ { "from_name": "label", "to_name": "text", "type": "labels", "value": { "start": 77, "end": 95, "text": "respiratory system", "labels": [ "Tissue" ] } }, { "from_name": "...
{ "text": "The ultimate goal of a human lung cell atlas reference is to provide a comprehensive overview of all cells in the healthy human lung, as well as their variation from individual to individual. Despite its overall diversity, the HLCA is limited by the biological, demographic and experimental diversity in the...	[ { "result": [ { "from_name": "label", "to_name": "text", "type": "labels", "value": { "start": 1167, "end": 1177, "text": "cell lines", "labels": [ "Cell" ] } }, { "from_name": "label"...
{ "text": "The constituent datasets of the HLCA vary widely in experimental design, such as the sample handling protocol or single-cell platform used, causing dataset-specific batch effects. The quality of the HLCA hinges on the choice of data integration method, with methods such as Seurat’s RPCA and Harmony failing...	[ { "result": [ { "from_name": "label", "to_name": "text", "type": "labels", "value": { "start": 423, "end": 430, "text": "T cells", "labels": [ "Cell" ] } }, { "from_name": "label", ...
{ "text": "Taken together, the HLCA provides a central single-cell reference of unprecedented size. It offers a model framework for building integrated, consensus-based, population-scale atlases for other organs within the Human Cell Atlas. The HLCA is publicly available, and combined with an open-access platform to ...	[ { "result": [ { "from_name": "label", "to_name": "text", "type": "labels", "value": { "start": 194, "end": 200, "text": "organs", "labels": [ "Tissue" ] } }, { "from_name": "label", ...
{ "text": "Methods\nSource paper: PMC10287567" }	[]
{ "text": "Ethics approval information per study was as follows. For the pooled data from refs. , approval was given by the Vanderbilt Institutional Review Board (IRB) (numbers 060165 and 171657) and Western IRB (number 20181836). All samples were collected from declined organ donors who were also consented for resea...	[ { "result": [ { "from_name": "label", "to_name": "text", "type": "labels", "value": { "start": 79, "end": 83, "text": "refs", "labels": [ "Cell" ] } }, { "from_name": "label", ...
{ "text": "Several previously unpublished datasets were used for the HLCA and generated as follows.\nSource paper: PMC10287567" }	[]
{ "text": "Participants recruited by the Pneumology Unit of Nice University Hospital were sampled between 1 and 15 December 2020. The full procedure, including patient inclusion criteria, is detailed at https://www.clinicaltrials.gov/ct2/show/NCT04529993 . Nasal and tracheobronchial samples were collected from patien...	[ { "result": [ { "from_name": "label", "to_name": "text", "type": "labels", "value": { "start": 1367, "end": 1370, "text": "Raw", "labels": [ "Cell" ] } }, { "from_name": "label", ...
{ "text": "Tumor-free, uninvolved lung samples (peritumor tissues) were obtained during tumor resections at the lung specialist clinic Asklepios Fachkliniken München-Gauting and accessed through the bioArchive of the Comprehensive Pneumology Center in Munich. The study was approved by the local ethics committee of th...	[ { "result": [ { "from_name": "label", "to_name": "text", "type": "labels", "value": { "start": 23, "end": 27, "text": "lung", "labels": [ "Tissue" ] } }, { "from_name": "label", ...
{ "text": "Single-cell suspensions for scRNA-seq were generated as previously described . In brief, lung tissue samples were cut into smaller pieces, washed with phosphate-buffered saline (PBS) and enzymatically digested using an enzyme mix composed of dispase, collagenase, elastase and DNAse for 45 min at 37 °C whil...	[ { "result": [ { "from_name": "label", "to_name": "text", "type": "labels", "value": { "start": 522, "end": 527, "text": "blood", "labels": [ "Tissue" ] } }, { "from_name": "label", ...
{ "text": "The generation of count matrices was performed using the Cell Ranger computational pipeline (v3.1.0; STAR v2.5.3a). The reads were aligned to the GRCh38 human reference genome (GRCh38; Ensembl99). Downstream analysis was performed using the Scanpy package (version 1.8.0). We assessed the quality of our lib...	[ { "result": [ { "from_name": "label", "to_name": "text", "type": "labels", "value": { "start": 641, "end": 644, "text": "Raw", "labels": [ "Cell" ] } } ] } ]
{ "text": "All postmortem human donor lung samples were obtained from BRINDL, supported by the NHLBI LungMAP Human Tissue Core housed at the University of Rochester. Consent, tissue acquisition and storage protocols can be found on the repository’s website ( brindl.urmc.rochester.edu/ ). Data were collected as part o...	[ { "result": [ { "from_name": "label", "to_name": "text", "type": "labels", "value": { "start": 728, "end": 731, "text": "Raw", "labels": [ "Cell" ] } }, { "from_name": "label", ...
{ "text": "These data were a combination of published and unpublished data. In both cases, healthy volunteers were recruited for bronchoscopy at the University Medical Center in Groningen after giving informed consent and according to the protocol approved by the IRB (ABR number NL69765.042.19). Inclusion criteria an...	[ { "result": [ { "from_name": "label", "to_name": "text", "type": "labels", "value": { "start": 2514, "end": 2517, "text": "Raw", "labels": [ "Cell" ] } }, { "from_name": "label", ...
{ "text": "Nasal epithelial samples were collected from healthy volunteers who provided informed consent at Northwestern Medicine in Chicago. The protocol was approved by the Northwestern University IRB (STU00214826). Healthy volunteers were recruited to match a cohort of patients with cystic fibrosis for the ongoing...	[ { "result": [ { "from_name": "label", "to_name": "text", "type": "labels", "value": { "start": 2067, "end": 2070, "text": "Raw", "labels": [ "Cell" ] } }, { "from_name": "label", ...
{ "text": "Human lung tissue wabus available for research purposes following ethical approval from Hannover Medical School (Nr. 7414, 2017). All patients in this study provided written informed consent for sample collection and data analyses. At Hannover Medical School, patients with lung cancer were recruited in the...	[ { "result": [ { "from_name": "label", "to_name": "text", "type": "labels", "value": { "start": 2727, "end": 2730, "text": "Raw", "labels": [ "Cell" ] } }, { "from_name": "label", ...
{ "text": "To accommodate data protection legislation, scRNA-seq datasets of healthy lung tissue were shared by dataset generators as raw count matrices, thereby obviating the need to share genetic information. Count matrices were generated using varying software (Supplementary Table 1 ). Previously published scRNA-s...	[ { "result": [ { "from_name": "label", "to_name": "text", "type": "labels", "value": { "start": 123, "end": 126, "text": "raw", "labels": [ "Cell" ] } }, { "from_name": "label", ...
{ "text": "For all of the datasets from the HLCA core, a preformatted sample metadata form was filled out by the dataset providers for every sample, containing metadata such as the ID of the donor from whom the sample came, the donor’s self-reported ethnicity, the type of sample, the sequencing platform and so on (Su...	[ { "result": [ { "from_name": "label", "to_name": "text", "type": "labels", "value": { "start": 456, "end": 461, "text": "organ", "labels": [ "Tissue" ] } }, { "from_name": "label", ...
{ "text": "Patients with lung conditions affecting larger parts of the lung, such as asthma or pulmonary fibrosis, were excluded from the HLCA core and later added to the extended atlas. For the HLCA core, all matrices were gene filtered based on Cell Ranger Ensembl84 gene-type filtering (resulting in 33,694 gene IDs...	[ { "result": [ { "from_name": "label", "to_name": "text", "type": "labels", "value": { "start": 399, "end": 411, "text": "erythrocytes", "labels": [ "Cell" ] } }, { "from_name": "label"...
{ "text": "To normalize for differences in total UMI counts per cell, we performed SCRAN normalization . Since SCRAN assumes that at least half of the genes in the data being normalized are not differentially expressed between subgroups of cells, we performed SCRAN normalization within clusters. To this end, we first...	[ { "result": [ { "from_name": "label", "to_name": "text", "type": "labels", "value": { "start": 783, "end": 786, "text": "raw", "labels": [ "Cell" ] } } ] } ]
{ "text": "To harmonize cell type labels from different datasets in the HLCA core, a common reference was created to which original cell type labels were mapped (Supplementary Table 4 ). To accommodate labels at different levels of detail, the cell type reference was made hierarchical, with level 1 containing the coa...	[ { "result": [ { "from_name": "label", "to_name": "text", "type": "labels", "value": { "start": 434, "end": 441, "text": "T cells", "labels": [ "Cell" ] } } ] } ]
{ "text": "To map anatomical location to a 1D CCF score that could be used for modeling, a distinction was made between upper and lower airways. First, an anatomical coordinate score was applied to the upper airways, starting at 0 and increasing linearly (with a value of 0.5) between each of the following anatomical ...	[ { "result": [ { "from_name": "label", "to_name": "text", "type": "labels", "value": { "start": 719, "end": 722, "text": "ref", "labels": [ "Cell" ] } }, { "from_name": "label", ...
{ "text": "For computational efficiency, benchmarking was performed on a subset of the total atlas, including data from ten studies split into 13 datasets (ref. was split into 10xv1 and 10xv2 data; ref. was split into 10xv2 and 10xv3 data; and the pooled data from ref. and associated unpublished data were split into ...	[ { "result": [ { "from_name": "label", "to_name": "text", "type": "labels", "value": { "start": 145, "end": 148, "text": "ref", "labels": [ "Cell" ] } }, { "from_name": "label", ...
{ "text": "For integration benchmarking, the scIB benchmarking framework was used with default integration parameter settings unless otherwise specified. All benchmarked methods except scGen (that is, BBKNN, ComBat, Conos, fas tMNN, Harmony, Scanorama, scANVI, scVI and Seurat RPCA) were run at least twice: on the 2,0...	[ { "result": [ { "from_name": "label", "to_name": "text", "type": "labels", "value": { "start": 392, "end": 395, "text": "raw", "labels": [ "Cell" ] } }, { "from_name": "label", ...
{ "text": "For scANVI and scVI, genes were subset to the HVG set and the resulting raw count matrix was used as input. For all other methods, SCRAN-normalized (as described above) data were used. Genes were then subset to the precalculated HVG sets. For integration of gene-scaled data, all genes were scaled to have m...	[ { "result": [ { "from_name": "label", "to_name": "text", "type": "labels", "value": { "start": 72, "end": 75, "text": "raw", "labels": [ "Cell" ] } }, { "from_name": "label", "...
{ "text": "Two integration methods allowed for input of cell type labels to guide the integration: scGen and scANVI. As labels, level 3 harmonized cell type labels were used (Supplementary Table 4 ), except for blood vessel endothelial, fibroblast lineage, mesothelial and smooth muscle cells, for which we used level ...	[ { "result": [ { "from_name": "label", "to_name": "text", "type": "labels", "value": { "start": 226, "end": 236, "text": "fibroblast", "labels": [ "Cell" ] } }, { "from_name": "label", ...
{ "text": "The dataset rather than the donor of the sample was used as the batch parameter. The maximum memory usage was set to 376 Gb and all methods requiring more memory were excluded from the analysis. The quality of each of the integrations was scored using 12 metrics, with four metrics measuring the batch corre...	[]
{ "text": "For integration of the data into the HLCA core, we first determined for which cases studies had to be split into separate datasets (which were treated as batches during integration). Reasons for possible splitting were: (1) different 10x versions used within a study (for example, 10xv2 versus 10xv3); or (2...	[]
{ "text": "Then, 10x version or processing institute assignments were randomly shuffled between samples and PC expl was calculated for the randomized covariate. This was repeated over ten random shufflings and the mean and standard deviation of PC expl were then calculated for the covariate. If the nonrandomized PC e...	[ { "result": [ { "from_name": "label", "to_name": "text", "type": "labels", "value": { "start": 511, "end": 514, "text": "ref", "labels": [ "Cell" ] } }, { "from_name": "label", ...
{ "text": "For integration of the datasets into the HLCA core, coarse cell type labels were used as described for integration benchmarking (AT1, AT2, arterial endothelial cell, B cell lineage, basal, bronchial vessel 1, bronchial vessel 2, capillary, multiciliated, dendritic, fibroblast lineage, KRT5 KRT17 epithelial...	[ { "result": [ { "from_name": "label", "to_name": "text", "type": "labels", "value": { "start": 686, "end": 689, "text": "raw", "labels": [ "Cell" ] } }, { "from_name": "label", ...
{ "text": "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. This choice of k , while improving clustering robustness, could impair the detection of very rare cell type...	[]
{ "text": "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 = 1 k p x i log p x i , where x 1 … x k are the set of labels at that annotation level and p (...	[]
{ "text": "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. Clusters with a ...	[ { "result": [ { "from_name": "label", "to_name": "text", "type": "labels", "value": { "start": 648, "end": 654, "text": "muscle", "labels": [ "Tissue" ] } }, { "from_name": "label", ...
{ "text": "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 (th...	[ { "result": [ { "from_name": "label", "to_name": "text", "type": "labels", "value": { "start": 50, "end": 70, "text": "neuroendocrine cells", "labels": [ "Cell" ] } } ] } ]
{ "text": "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. Annotation was done per cluster, using finer clusters where the...	[ { "result": [ { "from_name": "label", "to_name": "text", "type": "labels", "value": { "start": 645, "end": 651, "text": "T cell", "labels": [ "Cell" ] } }, { "from_name": "label", ...
{ "text": "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). Out of 61 final cell types, 18 included mostly mislabeled cells, 12 of which were previously known cel...	[]
{ "text": "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. Pseudo-bulks were only calculated for a sample if it had at least ten cells of the cell type under consideration. An exception was m...	[ { "result": [ { "from_name": "label", "to_name": "text", "type": "labels", "value": { "start": 2100, "end": 2117, "text": "endothelial cells", "labels": [ "Cell" ] } }, { "from_name": ...
{ "text": "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. We first split the data in the HLCA core by cell type annotation, merging substates of a single ce...	[]
{ "text": "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 expla...	[]
{ "text": "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.\nSource paper: PMC10287567" }	[]
{ "text": "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. If at least one covariate was continuous, we calculated the fraction of variance...	[ { "result": [ { "from_name": "label", "to_name": "text", "type": "labels", "value": { "start": 437, "end": 441, "text": "root", "labels": [ "Tissue" ] } } ] } ]
{ "text": "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. We found that for cell types sampled in fewer than 40 samples ...	[]
{ "text": "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. We furthermore filtered o...	[]
{ "text": "We encoded smoking status as a continuous covariate, setting never to 0, former to 0.5 and current to 1. Anatomical region was encoded into anatomical region CCF scores as described earlier. As we noted that changes from the nose to the rest of the airways and lungs were often independent from continuous c...	[ { "result": [ { "from_name": "label", "to_name": "text", "type": "labels", "value": { "start": 225, "end": 229, "text": "nose", "labels": [ "Tissue" ] } }, { "from_name": "label", ...
{ "text": "To determine whether covariance between covariates was low enough for modeling, we calculated the variance inflation factor (VIF) between covariates at the donor level. The VIF quantifies multicollinearity among covariates of an ordinary least squares regression and a high VIF indicates strong linear depen...	[]
{ "text": "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. We used sample-level pseudo-bulks (split by cell type), since the covariates modeled al...	[ { "result": [ { "from_name": "label", "to_name": "text", "type": "labels", "value": { "start": 571, "end": 575, "text": "lung", "labels": [ "Tissue" ] } }, { "from_name": "label", ...
{ "text": "Gene counts were summed across cells for every sample, within cell type. Sample-wise sums (that is, pseudo-bulks) were normalized using edgeR’s calcNormFactors function, using default parameter settings. We then used voom , a method designed for bulk RNA-seq that estimates observation-specific gene varianc...	[ { "result": [ { "from_name": "label", "to_name": "text", "type": "labels", "value": { "start": 629, "end": 633, "text": "nose", "labels": [ "Tissue" ] } }, { "from_name": "label", ...
{ "text": "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 . The analysis was performed in R using the cameraPR function in the limma package , with the differential expressi...	[]
{ "text": "To stratify GWAS results from several lung diseases by lung cell type, we applied stratified linkage disequilibrium score regression in single cells (sc-LDSC) . sc-LDSC can link GWAS results to cell types by calculating, for each cell type, whether disease-associated variants are enriched in genomic region...	[ { "result": [ { "from_name": "label", "to_name": "text", "type": "labels", "value": { "start": 38, "end": 42, "text": "lung", "labels": [ "Tissue" ] } }, { "from_name": "label", ...
{ "text": "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. The top 1,000 most significant genes with positive fold changes were stored as gene...	[ { "result": [ { "from_name": "label", "to_name": "text", "type": "labels", "value": { "start": 1598, "end": 1602, "text": "lung", "labels": [ "Tissue" ] } }, { "from_name": "label", ...
{ "text": "To enable deconvolution of bulk expression data on the basis of the HLCA, HLCA cell type signature matrices were generated. 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 ). A...	[ { "result": [ { "from_name": "label", "to_name": "text", "type": "labels", "value": { "start": 260, "end": 264, "text": "nose", "labels": [ "Tissue" ] } }, { "from_name": "label", ...
{ "text": "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). In addition, cell types were merged when they were deemed too transcriptionally similar. For each included cell type,...	[ { "result": [ { "from_name": "label", "to_name": "text", "type": "labels", "value": { "start": 533, "end": 540, "text": "T cells", "labels": [ "Cell" ] } }, { "from_name": "label", ...
{ "text": "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, a...	[ { "result": [ { "from_name": "label", "to_name": "text", "type": "labels", "value": { "start": 354, "end": 364, "text": "mast cells", "labels": [ "Cell" ] } }, { "from_name": "label", ...
{ "text": "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). The Lung Tissue Da...	[ { "result": [ { "from_name": "label", "to_name": "text", "type": "labels", "value": { "start": 96, "end": 100, "text": "Lung", "labels": [ "Tissue" ] } }, { "from_name": "label", ...
{ "text": "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) . As these ...	[]
{ "text": "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 . scArches trains an adaptor added to a reference embedding model, thereby enabling it to generate a common embedding of ...	[ { "result": [ { "from_name": "label", "to_name": "text", "type": "labels", "value": { "start": 584, "end": 587, "text": "Raw", "labels": [ "Cell" ] } }, { "from_name": "label", ...
{ "text": "The model that was learned previously for HLCA integration using scANVI was used as the basis for the scArches mapping. 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...	[]
{ "text": "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. Both annotation sources (for example, Ensembl or RefSeq) and annotation versions (for example, Ensembl rel...	[]
{ "text": "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. To find the most likely match between each original gene name and a target gene name, we first downloaded Ensem...	[ { "result": [ { "from_name": "label", "to_name": "text", "type": "labels", "value": { "start": 1017, "end": 1021, "text": "node", "labels": [ "Tissue" ] } }, { "from_name": "label", ...
{ "text": "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 thi...	[]
{ "text": "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 . 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...	[ { "result": [ { "from_name": "label", "to_name": "text", "type": "labels", "value": { "start": 1183, "end": 1187, "text": "lung", "labels": [ "Tissue" ] } }, { "from_name": "label", ...
{ "text": "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. highlighted three different cases: annotations not included in the mapped data (for example, preterminal bronchiole secretory cel...	[ { "result": [ { "from_name": "label", "to_name": "text", "type": "labels", "value": { "start": 613, "end": 623, "text": "macrophage", "labels": [ "Cell" ] } }, { "from_name": "label", ...
{ "text": "For the extended atlas, we calibrated the uncertainty score cutoff by determining which uncertainty levels indicate possible failure of label transfer. To determine the uncertainty score at which technical variability from residual batch effects impairs correct label transfer, we evaluated how label transf...	[]
{ "text": "The ref. study of healthy lung included cell type annotations based on matched spatial transcriptomic data. Many of these annotations were not present in the HLCA core. To determine whether these spatial cell types could still be recovered after mapping to the HLCA core, we looked for clusters specifically...	[ { "result": [ { "from_name": "label", "to_name": "text", "type": "labels", "value": { "start": 397, "end": 408, "text": "fibroblasts", "labels": [ "Cell" ] } }, { "from_name": "label",...
{ "text": "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 ext...	[ { "result": [ { "from_name": "label", "to_name": "text", "type": "labels", "value": { "start": 181, "end": 192, "text": "macrophages", "labels": [ "Cell" ] } }, { "from_name": "label",...
{ "text": "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. Cell types of interest were determined based on the largest increase in mean label transfer uncertainty in IPF compared with h...	[ { "result": [ { "from_name": "label", "to_name": "text", "type": "labels", "value": { "start": 421, "end": 432, "text": "fibroblasts", "labels": [ "Cell" ] } }, { "from_name": "label",...
{ "text": "To investigate whether the HLCA can be used to identify disease-associated cell states shared across multiple diseases, MDMs from the HLCA core, together with all cells from the mapped datasets labeled as MDMs based on label transfer, were jointly analyzed. Datasets and diseases with fewer than 50 MDMs wer...	[]
{ "text": "The following tools and versions were used: R (version 4.1.1 for covariate modeling and version 4.0.3 for GSEA); edgeR (version 3.28.1); lme4 (version 1.1-27.1); LDSC (version 1.0.1); Limma (version 3.46.0); Scanpy (version 1.9.1); scArches (version 0.3.5); scIB (version 0.1.1); scikit-learn (version 0.24....	[]
{ "text": "Further information on research design is available in the Nature Portfolio Reporting Summary linked to this article.\nSource paper: PMC10287567" }	[]
{ "text": "Online content\nSource paper: PMC10287567" }	[]

{ "text": "PMC10287567\nSource paper: PMC10287567" }

[]

{ "text": "An integrated cell atlas of the lung in health and disease\nSource paper: PMC10287567" }

[ { "result": [ { "from_name": "label", "to_name": "text", "type": "labels", "value": { "start": 32, "end": 36, "text": "lung", "labels": [ "Tissue" ] } } ] } ]

{ "text": "Abstract\nSource paper: PMC10287567" }

[]

{ "text": "Single-cell technologies have transformed our understanding of human tissues. Yet, studies typically capture only a limited number of donors and disagree on cell type definitions. Integrating many single-cell datasets can address these limitations of individual studies and capture the variability present i...

[ { "result": [ { "from_name": "label", "to_name": "text", "type": "labels", "value": { "start": 1182, "end": 1193, "text": "macrophages", "labels": [ "Cell" ] } }, { "from_name": "label...

{ "text": "Main\nSource paper: PMC10287567" }

[]

{ "text": "Rapid technological improvements over the past decade have allowed single-cell datasets to grow both in size and number . This has led consortia, such as the Human Cell Atlas, to pursue the generation of large-scale reference atlases of human organs . To advance our understanding of health and disease, suc...

[ { "result": [ { "from_name": "label", "to_name": "text", "type": "labels", "value": { "start": 243, "end": 249, "text": "organs", "labels": [ "Tissue" ] } }, { "from_name": "label", ...

{ "text": "Several foundational studies have started to map the cellular landscape of the healthy human lung . These studies each have a specific bias due to their choice of experimental protocol and technologies, and are therefore not tailored to serve as a universal reference. The studies moreover include only a li...

[ { "result": [ { "from_name": "label", "to_name": "text", "type": "labels", "value": { "start": 93, "end": 97, "text": "lung", "labels": [ "Tissue" ] } }, { "from_name": "label", ...

{ "text": "Integrated single-cell atlases provide novel insights not obtained in individual studies. Recent reference atlases have led to the discovery of unknown cell types , the identification of marker genes that are reproducible across studies , the comparison of animal and in vitro models with human healthy and ...

[ { "result": [ { "from_name": "label", "to_name": "text", "type": "labels", "value": { "start": 499, "end": 504, "text": "organ", "labels": [ "Tissue" ] } }, { "from_name": "label", ...

{ "text": "In this resource, we present an integrated single-cell transcriptomic atlas of the human respiratory system, including the upper and lower airways, from published and newly generated datasets (Fig. 1 ). The Human Lung Cell Atlas (HLCA) comprises data from 486 donors and 49 datasets, including 2.4 million c...

[ { "result": [ { "from_name": "label", "to_name": "text", "type": "labels", "value": { "start": 89, "end": 107, "text": "respiratory system", "labels": [ "Tissue" ] } }, { "from_name": ...

{ "text": "Results\nSource paper: PMC10287567" }

[]

{ "text": "To build the HLCA, we collected single-cell RNA sequencing (scRNA-seq) data and detailed, harmonized technical, biological and demographic metadata from 14 datasets (11 published and three unpublished) . These datasets include samples from 107 individuals, with diversity in age, sex, ethnicity (harmonized ...

[ { "result": [ { "from_name": "label", "to_name": "text", "type": "labels", "value": { "start": 715, "end": 733, "text": "respiratory system", "labels": [ "Tissue" ] } } ] } ]

{ "text": "Consensus definitions of cell types based on single-cell transcriptomic data across studies—particularly of transitional cell states—are lacking. To enable supervised data integration and downstream integrated analysis, we harmonized cell type nomenclature by building a five-level hierarchical cell identit...

[]

{ "text": "To optimally remove dataset-specific batch effects, we evaluated 12 different data integration methods on 12 datasets (Fig. 2d and Supplementary Fig. 1 ) using our previously established benchmarking pipeline . We used the top-performing integration method, scANVI, to create an integrated embedding of all ...

[]

{ "text": "A large-scale integrated atlas provides the unique opportunity to systematically investigate the consensus in cell type labeling across datasets. To identify areas of consensus and disagreement, we iteratively clustered the HLCA core and investigated donor diversity and cell type label agreement in these c...

[ { "result": [ { "from_name": "label", "to_name": "text", "type": "labels", "value": { "start": 1171, "end": 1182, "text": "macrophages", "labels": [ "Cell" ] } }, { "from_name": "label...

{ "text": "As a first step to achieve such a consensus on the diversity of cell types present in the HLCA core, we performed a full re-annotation of the integrated data on the basis of the original annotations and six expert opinions (consensus annotation; Methods and Fig. 3d ). Each of the 61 annotated cell types (S...

[ { "result": [ { "from_name": "label", "to_name": "text", "type": "labels", "value": { "start": 411, "end": 429, "text": "respiratory system", "labels": [ "Tissue" ] } }, { "from_name":...

{ "text": "Rare cell types, such as ionocytes, tuft cells, neuroendocrine cells and specific immune cell subsets, are often difficult to identify in individual datasets. Yet, combining datasets in the HLCA core provides better power for identifying these rare cell types. Ionocytes, tuft and neuroendocrine cells make ...

[ { "result": [ { "from_name": "label", "to_name": "text", "type": "labels", "value": { "start": 742, "end": 751, "text": "monocytes", "labels": [ "Cell" ] } }, { "from_name": "label", ...

{ "text": "We were further able to detect six cell identities that were not previously found in the human lung or were only recently described in individual studies. These cell types include migratory dendritic cells ( n = 312 cells, expressing CCR7 , LAD1 and COL19 ), hematopoietic stem cells ( n = 60, expressing SP...

[ { "result": [ { "from_name": "label", "to_name": "text", "type": "labels", "value": { "start": 190, "end": 205, "text": "dendritic cells", "labels": [ "Cell" ] } }, { "from_name": "lab...

{ "text": "Demographic and other metadata covariates affect cellular transcriptional phenotypes . Better insight into the impact of these covariates (for example, sex, BMI and smoking) on cell type gene expression can shed light on the contribution of these factors to progression from healthy to diseased states. In a...

[ { "result": [ { "from_name": "label", "to_name": "text", "type": "labels", "value": { "start": 1064, "end": 1071, "text": "T cells", "labels": [ "Cell" ] } }, { "from_name": "label", ...

{ "text": "To better characterize how biological variables affect cellular phenotypes, we modeled their cell type-specific effects on the transcriptome at the gene level ( Methods ). Sex-related differences in lymphatic endothelial cells are dominated by differential expression of genes located on the X and Y chromos...

[ { "result": [ { "from_name": "label", "to_name": "text", "type": "labels", "value": { "start": 1857, "end": 1868, "text": "macrophages", "labels": [ "Cell" ] } }, { "from_name": "label...

{ "text": "Biological and technical factors can also affect cell type proportions. Indeed, all cell types show changes in abundance as a function of anatomical location ( Fig. 4c and Extended Data Fig. 5 ). For example, ionocytes are present at comparable proportions in the airway epithelium, from the larger lower ai...

[ { "result": [ { "from_name": "label", "to_name": "text", "type": "labels", "value": { "start": 426, "end": 436, "text": "parenchyma", "labels": [ "Tissue" ] } }, { "from_name": "label"...

{ "text": "The HLCA core contains an unprecedented diversity of donors, sampling protocols and cell identities, and can serve as a transcriptomic reference for lung research. New datasets can be mapped to this reference to substantially speed up data analysis by transferring consensus cell identity annotations to the...

[ { "result": [ { "from_name": "label", "to_name": "text", "type": "labels", "value": { "start": 991, "end": 1002, "text": "fibroblasts", "labels": [ "Cell" ] } }, { "from_name": "label"...

{ "text": "Single-cell studies of disease rely on adequate, matching control samples to allow correct identification of disease-specific changes. To demonstrate the ability of the HLCA core to serve as a comprehensive healthy control and contextualize disease data, we mapped scRNA-seq data from lung cancer samples to...

[ { "result": [ { "from_name": "label", "to_name": "text", "type": "labels", "value": { "start": 1252, "end": 1261, "text": "capillary", "labels": [ "Tissue" ] } }, { "from_name": "label...

{ "text": "In addition, the HLCA can provide context to the results of large-scale genetic studies of disease. Genome-wide association studies (GWASs) link disease with specific genomic variants that may confer an increased risk of disease. Previous studies have linked such variants to cell type-specific mechanistic ...

[ { "result": [ { "from_name": "label", "to_name": "text", "type": "labels", "value": { "start": 995, "end": 1006, "text": "fibroblasts", "labels": [ "Cell" ] } }, { "from_name": "label"...

{ "text": "Finally, the HLCA can be used as a reference for cell type deconvolution of bulk RNA expression samples, which have been shown to reflect cell type proportions more accurately than scRNA-seq datasets . Inferring cell type proportions from bulk RNA samples from nasal brushings and bronchial biopsies using t...

[ { "result": [ { "from_name": "label", "to_name": "text", "type": "labels", "value": { "start": 889, "end": 900, "text": "macrophages", "labels": [ "Cell" ] } }, { "from_name": "label",...

{ "text": "As knowledge of cell types in the lung expands, and the sizes of newly generated datasets increase, annotations in the HLCA core will need to be further refined. The HLCA and its annotations can be updated by learning from new data projected onto the reference. We simulated such an HLCA update using the pr...

[ { "result": [ { "from_name": "label", "to_name": "text", "type": "labels", "value": { "start": 727, "end": 738, "text": "fibroblasts", "labels": [ "Cell" ] } }, { "from_name": "label",...

{ "text": "To extend the atlas and include samples from lung disease, we mapped 1,797,714 cells from 380 healthy and diseased individuals from 37 datasets (four unpublished and 33 published ) to the HLCA core using scArches , bringing the HLCA to a total of 2.4 million cells from 486 individuals (Fig. 6a and Suppleme...

[ { "result": [ { "from_name": "label", "to_name": "text", "type": "labels", "value": { "start": 488, "end": 503, "text": "dendritic cells", "labels": [ "Cell" ] } }, { "from_name": "lab...

{ "text": "Out of 37 new datasets, 27 were observed to map well to the HLCA, as evaluated by the mean label transfer uncertainty score (Fig. 6b , Supplementary Fig. 10a and Methods ). The remaining ten datasets were often from coronavirus disease 2019 (COVID-19) studies or, unlike the HLCA core, contained pediatric s...

[ { "result": [ { "from_name": "label", "to_name": "text", "type": "labels", "value": { "start": 508, "end": 512, "text": "lung", "labels": [ "Tissue" ] } } ] } ]

{ "text": "Pulmonary diseases are characterized by the emergence of unique disease-associated transcriptional phenotypes . We observed higher levels of label transfer uncertainty in datasets from diseased lungs (Fig. 6b , condition), possibly flagging cell types changed in response to disease. Specifically, labels of...

[ { "result": [ { "from_name": "label", "to_name": "text", "type": "labels", "value": { "start": 342, "end": 353, "text": "macrophages", "labels": [ "Cell" ] } }, { "from_name": "label",...

{ "text": "Similar to healthy cellular states, the HLCA can provide insight into disease-specific states that are consistent across demographics and experimental protocols. To demonstrate this, we determined which cell types are consistently affected by IPF across datasets, extending the previous IPF analysis to five...

[ { "result": [ { "from_name": "label", "to_name": "text", "type": "labels", "value": { "start": 370, "end": 381, "text": "fibroblasts", "labels": [ "Cell" ] } }, { "from_name": "label",...

{ "text": "The HLCA contains data across more than ten lung diseases, providing the unique opportunity to discover cellular states shared across diseases. Discovering such common diseased cellular states could improve our understanding of lung diseases and accelerate the identification of effective treatments. For ex...

[ { "result": [ { "from_name": "label", "to_name": "text", "type": "labels", "value": { "start": 348, "end": 359, "text": "macrophages", "labels": [ "Cell" ] } }, { "from_name": "label",...

{ "text": "Discussion\nSource paper: PMC10287567" }

[]

{ "text": "In this study, we built the HLCA: an integrated reference atlas of the human respiratory system. While previous studies have described the cellular heterogeneity within the human lung , study-specific biases due to experimental design and a limited number of sampled individuals constrain their capacity to ...

[ { "result": [ { "from_name": "label", "to_name": "text", "type": "labels", "value": { "start": 77, "end": 95, "text": "respiratory system", "labels": [ "Tissue" ] } }, { "from_name": "...

{ "text": "The ultimate goal of a human lung cell atlas reference is to provide a comprehensive overview of all cells in the healthy human lung, as well as their variation from individual to individual. Despite its overall diversity, the HLCA is limited by the biological, demographic and experimental diversity in the...

[ { "result": [ { "from_name": "label", "to_name": "text", "type": "labels", "value": { "start": 1167, "end": 1177, "text": "cell lines", "labels": [ "Cell" ] } }, { "from_name": "label"...

{ "text": "The constituent datasets of the HLCA vary widely in experimental design, such as the sample handling protocol or single-cell platform used, causing dataset-specific batch effects. The quality of the HLCA hinges on the choice of data integration method, with methods such as Seurat’s RPCA and Harmony failing...

[ { "result": [ { "from_name": "label", "to_name": "text", "type": "labels", "value": { "start": 423, "end": 430, "text": "T cells", "labels": [ "Cell" ] } }, { "from_name": "label", ...

{ "text": "Taken together, the HLCA provides a central single-cell reference of unprecedented size. It offers a model framework for building integrated, consensus-based, population-scale atlases for other organs within the Human Cell Atlas. The HLCA is publicly available, and combined with an open-access platform to ...

[ { "result": [ { "from_name": "label", "to_name": "text", "type": "labels", "value": { "start": 194, "end": 200, "text": "organs", "labels": [ "Tissue" ] } }, { "from_name": "label", ...

{ "text": "Methods\nSource paper: PMC10287567" }

[]

{ "text": "Ethics approval information per study was as follows. For the pooled data from refs. , approval was given by the Vanderbilt Institutional Review Board (IRB) (numbers 060165 and 171657) and Western IRB (number 20181836). All samples were collected from declined organ donors who were also consented for resea...

[ { "result": [ { "from_name": "label", "to_name": "text", "type": "labels", "value": { "start": 79, "end": 83, "text": "refs", "labels": [ "Cell" ] } }, { "from_name": "label", ...

{ "text": "Several previously unpublished datasets were used for the HLCA and generated as follows.\nSource paper: PMC10287567" }

[]

{ "text": "Participants recruited by the Pneumology Unit of Nice University Hospital were sampled between 1 and 15 December 2020. The full procedure, including patient inclusion criteria, is detailed at https://www.clinicaltrials.gov/ct2/show/NCT04529993 . Nasal and tracheobronchial samples were collected from patien...

[ { "result": [ { "from_name": "label", "to_name": "text", "type": "labels", "value": { "start": 1367, "end": 1370, "text": "Raw", "labels": [ "Cell" ] } }, { "from_name": "label", ...

{ "text": "Tumor-free, uninvolved lung samples (peritumor tissues) were obtained during tumor resections at the lung specialist clinic Asklepios Fachkliniken München-Gauting and accessed through the bioArchive of the Comprehensive Pneumology Center in Munich. The study was approved by the local ethics committee of th...

[ { "result": [ { "from_name": "label", "to_name": "text", "type": "labels", "value": { "start": 23, "end": 27, "text": "lung", "labels": [ "Tissue" ] } }, { "from_name": "label", ...

{ "text": "Single-cell suspensions for scRNA-seq were generated as previously described . In brief, lung tissue samples were cut into smaller pieces, washed with phosphate-buffered saline (PBS) and enzymatically digested using an enzyme mix composed of dispase, collagenase, elastase and DNAse for 45 min at 37 °C whil...

[ { "result": [ { "from_name": "label", "to_name": "text", "type": "labels", "value": { "start": 522, "end": 527, "text": "blood", "labels": [ "Tissue" ] } }, { "from_name": "label", ...

{ "text": "The generation of count matrices was performed using the Cell Ranger computational pipeline (v3.1.0; STAR v2.5.3a). The reads were aligned to the GRCh38 human reference genome (GRCh38; Ensembl99). Downstream analysis was performed using the Scanpy package (version 1.8.0). We assessed the quality of our lib...

[ { "result": [ { "from_name": "label", "to_name": "text", "type": "labels", "value": { "start": 641, "end": 644, "text": "Raw", "labels": [ "Cell" ] } } ] } ]

{ "text": "All postmortem human donor lung samples were obtained from BRINDL, supported by the NHLBI LungMAP Human Tissue Core housed at the University of Rochester. Consent, tissue acquisition and storage protocols can be found on the repository’s website ( brindl.urmc.rochester.edu/ ). Data were collected as part o...

[ { "result": [ { "from_name": "label", "to_name": "text", "type": "labels", "value": { "start": 728, "end": 731, "text": "Raw", "labels": [ "Cell" ] } }, { "from_name": "label", ...

{ "text": "These data were a combination of published and unpublished data. In both cases, healthy volunteers were recruited for bronchoscopy at the University Medical Center in Groningen after giving informed consent and according to the protocol approved by the IRB (ABR number NL69765.042.19). Inclusion criteria an...

[ { "result": [ { "from_name": "label", "to_name": "text", "type": "labels", "value": { "start": 2514, "end": 2517, "text": "Raw", "labels": [ "Cell" ] } }, { "from_name": "label", ...

{ "text": "Nasal epithelial samples were collected from healthy volunteers who provided informed consent at Northwestern Medicine in Chicago. The protocol was approved by the Northwestern University IRB (STU00214826). Healthy volunteers were recruited to match a cohort of patients with cystic fibrosis for the ongoing...

[ { "result": [ { "from_name": "label", "to_name": "text", "type": "labels", "value": { "start": 2067, "end": 2070, "text": "Raw", "labels": [ "Cell" ] } }, { "from_name": "label", ...

{ "text": "Human lung tissue wabus available for research purposes following ethical approval from Hannover Medical School (Nr. 7414, 2017). All patients in this study provided written informed consent for sample collection and data analyses. At Hannover Medical School, patients with lung cancer were recruited in the...

[ { "result": [ { "from_name": "label", "to_name": "text", "type": "labels", "value": { "start": 2727, "end": 2730, "text": "Raw", "labels": [ "Cell" ] } }, { "from_name": "label", ...

{ "text": "To accommodate data protection legislation, scRNA-seq datasets of healthy lung tissue were shared by dataset generators as raw count matrices, thereby obviating the need to share genetic information. Count matrices were generated using varying software (Supplementary Table 1 ). Previously published scRNA-s...

[ { "result": [ { "from_name": "label", "to_name": "text", "type": "labels", "value": { "start": 123, "end": 126, "text": "raw", "labels": [ "Cell" ] } }, { "from_name": "label", ...

{ "text": "For all of the datasets from the HLCA core, a preformatted sample metadata form was filled out by the dataset providers for every sample, containing metadata such as the ID of the donor from whom the sample came, the donor’s self-reported ethnicity, the type of sample, the sequencing platform and so on (Su...

[ { "result": [ { "from_name": "label", "to_name": "text", "type": "labels", "value": { "start": 456, "end": 461, "text": "organ", "labels": [ "Tissue" ] } }, { "from_name": "label", ...

{ "text": "Patients with lung conditions affecting larger parts of the lung, such as asthma or pulmonary fibrosis, were excluded from the HLCA core and later added to the extended atlas. For the HLCA core, all matrices were gene filtered based on Cell Ranger Ensembl84 gene-type filtering (resulting in 33,694 gene IDs...

[ { "result": [ { "from_name": "label", "to_name": "text", "type": "labels", "value": { "start": 399, "end": 411, "text": "erythrocytes", "labels": [ "Cell" ] } }, { "from_name": "label"...

{ "text": "To normalize for differences in total UMI counts per cell, we performed SCRAN normalization . Since SCRAN assumes that at least half of the genes in the data being normalized are not differentially expressed between subgroups of cells, we performed SCRAN normalization within clusters. To this end, we first...

[ { "result": [ { "from_name": "label", "to_name": "text", "type": "labels", "value": { "start": 783, "end": 786, "text": "raw", "labels": [ "Cell" ] } } ] } ]

{ "text": "To harmonize cell type labels from different datasets in the HLCA core, a common reference was created to which original cell type labels were mapped (Supplementary Table 4 ). To accommodate labels at different levels of detail, the cell type reference was made hierarchical, with level 1 containing the coa...

[ { "result": [ { "from_name": "label", "to_name": "text", "type": "labels", "value": { "start": 434, "end": 441, "text": "T cells", "labels": [ "Cell" ] } } ] } ]

{ "text": "To map anatomical location to a 1D CCF score that could be used for modeling, a distinction was made between upper and lower airways. First, an anatomical coordinate score was applied to the upper airways, starting at 0 and increasing linearly (with a value of 0.5) between each of the following anatomical ...

[ { "result": [ { "from_name": "label", "to_name": "text", "type": "labels", "value": { "start": 719, "end": 722, "text": "ref", "labels": [ "Cell" ] } }, { "from_name": "label", ...

{ "text": "For computational efficiency, benchmarking was performed on a subset of the total atlas, including data from ten studies split into 13 datasets (ref. was split into 10xv1 and 10xv2 data; ref. was split into 10xv2 and 10xv3 data; and the pooled data from ref. and associated unpublished data were split into ...

[ { "result": [ { "from_name": "label", "to_name": "text", "type": "labels", "value": { "start": 145, "end": 148, "text": "ref", "labels": [ "Cell" ] } }, { "from_name": "label", ...

{ "text": "For integration benchmarking, the scIB benchmarking framework was used with default integration parameter settings unless otherwise specified. All benchmarked methods except scGen (that is, BBKNN, ComBat, Conos, fas tMNN, Harmony, Scanorama, scANVI, scVI and Seurat RPCA) were run at least twice: on the 2,0...

[ { "result": [ { "from_name": "label", "to_name": "text", "type": "labels", "value": { "start": 392, "end": 395, "text": "raw", "labels": [ "Cell" ] } }, { "from_name": "label", ...

{ "text": "For scANVI and scVI, genes were subset to the HVG set and the resulting raw count matrix was used as input. For all other methods, SCRAN-normalized (as described above) data were used. Genes were then subset to the precalculated HVG sets. For integration of gene-scaled data, all genes were scaled to have m...

[ { "result": [ { "from_name": "label", "to_name": "text", "type": "labels", "value": { "start": 72, "end": 75, "text": "raw", "labels": [ "Cell" ] } }, { "from_name": "label", "...

{ "text": "Two integration methods allowed for input of cell type labels to guide the integration: scGen and scANVI. As labels, level 3 harmonized cell type labels were used (Supplementary Table 4 ), except for blood vessel endothelial, fibroblast lineage, mesothelial and smooth muscle cells, for which we used level ...

[ { "result": [ { "from_name": "label", "to_name": "text", "type": "labels", "value": { "start": 226, "end": 236, "text": "fibroblast", "labels": [ "Cell" ] } }, { "from_name": "label", ...

{ "text": "The dataset rather than the donor of the sample was used as the batch parameter. The maximum memory usage was set to 376 Gb and all methods requiring more memory were excluded from the analysis. The quality of each of the integrations was scored using 12 metrics, with four metrics measuring the batch corre...

[]

{ "text": "For integration of the data into the HLCA core, we first determined for which cases studies had to be split into separate datasets (which were treated as batches during integration). Reasons for possible splitting were: (1) different 10x versions used within a study (for example, 10xv2 versus 10xv3); or (2...

[]

{ "text": "Then, 10x version or processing institute assignments were randomly shuffled between samples and PC expl was calculated for the randomized covariate. This was repeated over ten random shufflings and the mean and standard deviation of PC expl were then calculated for the covariate. If the nonrandomized PC e...

[ { "result": [ { "from_name": "label", "to_name": "text", "type": "labels", "value": { "start": 511, "end": 514, "text": "ref", "labels": [ "Cell" ] } }, { "from_name": "label", ...

{ "text": "For integration of the datasets into the HLCA core, coarse cell type labels were used as described for integration benchmarking (AT1, AT2, arterial endothelial cell, B cell lineage, basal, bronchial vessel 1, bronchial vessel 2, capillary, multiciliated, dendritic, fibroblast lineage, KRT5 KRT17 epithelial...

[ { "result": [ { "from_name": "label", "to_name": "text", "type": "labels", "value": { "start": 686, "end": 689, "text": "raw", "labels": [ "Cell" ] } }, { "from_name": "label", ...

{ "text": "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. This choice of k , while improving clustering robustness, could impair the detection of very rare cell type...

[]

{ "text": "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 = 1 k p x i log p x i , where x 1 … x k are the set of labels at that annotation level and p (...

[]

{ "text": "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. Clusters with a ...

[ { "result": [ { "from_name": "label", "to_name": "text", "type": "labels", "value": { "start": 648, "end": 654, "text": "muscle", "labels": [ "Tissue" ] } }, { "from_name": "label", ...

{ "text": "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 (th...

[ { "result": [ { "from_name": "label", "to_name": "text", "type": "labels", "value": { "start": 50, "end": 70, "text": "neuroendocrine cells", "labels": [ "Cell" ] } } ] } ]

{ "text": "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. Annotation was done per cluster, using finer clusters where the...

[ { "result": [ { "from_name": "label", "to_name": "text", "type": "labels", "value": { "start": 645, "end": 651, "text": "T cell", "labels": [ "Cell" ] } }, { "from_name": "label", ...

{ "text": "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). Out of 61 final cell types, 18 included mostly mislabeled cells, 12 of which were previously known cel...

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{ "text": "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. Pseudo-bulks were only calculated for a sample if it had at least ten cells of the cell type under consideration. An exception was m...

[ { "result": [ { "from_name": "label", "to_name": "text", "type": "labels", "value": { "start": 2100, "end": 2117, "text": "endothelial cells", "labels": [ "Cell" ] } }, { "from_name": ...

{ "text": "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. We first split the data in the HLCA core by cell type annotation, merging substates of a single ce...

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{ "text": "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 expla...

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{ "text": "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.\nSource paper: PMC10287567" }

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{ "text": "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. If at least one covariate was continuous, we calculated the fraction of variance...

[ { "result": [ { "from_name": "label", "to_name": "text", "type": "labels", "value": { "start": 437, "end": 441, "text": "root", "labels": [ "Tissue" ] } } ] } ]

{ "text": "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. We found that for cell types sampled in fewer than 40 samples ...

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{ "text": "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. We furthermore filtered o...

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{ "text": "We encoded smoking status as a continuous covariate, setting never to 0, former to 0.5 and current to 1. Anatomical region was encoded into anatomical region CCF scores as described earlier. As we noted that changes from the nose to the rest of the airways and lungs were often independent from continuous c...

[ { "result": [ { "from_name": "label", "to_name": "text", "type": "labels", "value": { "start": 225, "end": 229, "text": "nose", "labels": [ "Tissue" ] } }, { "from_name": "label", ...

{ "text": "To determine whether covariance between covariates was low enough for modeling, we calculated the variance inflation factor (VIF) between covariates at the donor level. The VIF quantifies multicollinearity among covariates of an ordinary least squares regression and a high VIF indicates strong linear depen...

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{ "text": "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. We used sample-level pseudo-bulks (split by cell type), since the covariates modeled al...

[ { "result": [ { "from_name": "label", "to_name": "text", "type": "labels", "value": { "start": 571, "end": 575, "text": "lung", "labels": [ "Tissue" ] } }, { "from_name": "label", ...

{ "text": "Gene counts were summed across cells for every sample, within cell type. Sample-wise sums (that is, pseudo-bulks) were normalized using edgeR’s calcNormFactors function, using default parameter settings. We then used voom , a method designed for bulk RNA-seq that estimates observation-specific gene varianc...

[ { "result": [ { "from_name": "label", "to_name": "text", "type": "labels", "value": { "start": 629, "end": 633, "text": "nose", "labels": [ "Tissue" ] } }, { "from_name": "label", ...

{ "text": "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 . The analysis was performed in R using the cameraPR function in the limma package , with the differential expressi...

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{ "text": "To stratify GWAS results from several lung diseases by lung cell type, we applied stratified linkage disequilibrium score regression in single cells (sc-LDSC) . sc-LDSC can link GWAS results to cell types by calculating, for each cell type, whether disease-associated variants are enriched in genomic region...

[ { "result": [ { "from_name": "label", "to_name": "text", "type": "labels", "value": { "start": 38, "end": 42, "text": "lung", "labels": [ "Tissue" ] } }, { "from_name": "label", ...

{ "text": "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. The top 1,000 most significant genes with positive fold changes were stored as gene...

[ { "result": [ { "from_name": "label", "to_name": "text", "type": "labels", "value": { "start": 1598, "end": 1602, "text": "lung", "labels": [ "Tissue" ] } }, { "from_name": "label", ...

{ "text": "To enable deconvolution of bulk expression data on the basis of the HLCA, HLCA cell type signature matrices were generated. 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 ). A...

[ { "result": [ { "from_name": "label", "to_name": "text", "type": "labels", "value": { "start": 260, "end": 264, "text": "nose", "labels": [ "Tissue" ] } }, { "from_name": "label", ...

{ "text": "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). In addition, cell types were merged when they were deemed too transcriptionally similar. For each included cell type,...

[ { "result": [ { "from_name": "label", "to_name": "text", "type": "labels", "value": { "start": 533, "end": 540, "text": "T cells", "labels": [ "Cell" ] } }, { "from_name": "label", ...

{ "text": "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, a...

[ { "result": [ { "from_name": "label", "to_name": "text", "type": "labels", "value": { "start": 354, "end": 364, "text": "mast cells", "labels": [ "Cell" ] } }, { "from_name": "label", ...

{ "text": "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). The Lung Tissue Da...

[ { "result": [ { "from_name": "label", "to_name": "text", "type": "labels", "value": { "start": 96, "end": 100, "text": "Lung", "labels": [ "Tissue" ] } }, { "from_name": "label", ...

{ "text": "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) . As these ...

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{ "text": "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 . scArches trains an adaptor added to a reference embedding model, thereby enabling it to generate a common embedding of ...

[ { "result": [ { "from_name": "label", "to_name": "text", "type": "labels", "value": { "start": 584, "end": 587, "text": "Raw", "labels": [ "Cell" ] } }, { "from_name": "label", ...

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

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{ "text": "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. Both annotation sources (for example, Ensembl or RefSeq) and annotation versions (for example, Ensembl rel...

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{ "text": "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. To find the most likely match between each original gene name and a target gene name, we first downloaded Ensem...

[ { "result": [ { "from_name": "label", "to_name": "text", "type": "labels", "value": { "start": 1017, "end": 1021, "text": "node", "labels": [ "Tissue" ] } }, { "from_name": "label", ...

{ "text": "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 thi...

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{ "text": "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 . 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...

[ { "result": [ { "from_name": "label", "to_name": "text", "type": "labels", "value": { "start": 1183, "end": 1187, "text": "lung", "labels": [ "Tissue" ] } }, { "from_name": "label", ...

{ "text": "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. highlighted three different cases: annotations not included in the mapped data (for example, preterminal bronchiole secretory cel...

[ { "result": [ { "from_name": "label", "to_name": "text", "type": "labels", "value": { "start": 613, "end": 623, "text": "macrophage", "labels": [ "Cell" ] } }, { "from_name": "label", ...

{ "text": "For the extended atlas, we calibrated the uncertainty score cutoff by determining which uncertainty levels indicate possible failure of label transfer. To determine the uncertainty score at which technical variability from residual batch effects impairs correct label transfer, we evaluated how label transf...

[]

{ "text": "The ref. study of healthy lung included cell type annotations based on matched spatial transcriptomic data. Many of these annotations were not present in the HLCA core. To determine whether these spatial cell types could still be recovered after mapping to the HLCA core, we looked for clusters specifically...

[ { "result": [ { "from_name": "label", "to_name": "text", "type": "labels", "value": { "start": 397, "end": 408, "text": "fibroblasts", "labels": [ "Cell" ] } }, { "from_name": "label",...

{ "text": "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 ext...

[ { "result": [ { "from_name": "label", "to_name": "text", "type": "labels", "value": { "start": 181, "end": 192, "text": "macrophages", "labels": [ "Cell" ] } }, { "from_name": "label",...

{ "text": "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. Cell types of interest were determined based on the largest increase in mean label transfer uncertainty in IPF compared with h...

[ { "result": [ { "from_name": "label", "to_name": "text", "type": "labels", "value": { "start": 421, "end": 432, "text": "fibroblasts", "labels": [ "Cell" ] } }, { "from_name": "label",...

{ "text": "To investigate whether the HLCA can be used to identify disease-associated cell states shared across multiple diseases, MDMs from the HLCA core, together with all cells from the mapped datasets labeled as MDMs based on label transfer, were jointly analyzed. Datasets and diseases with fewer than 50 MDMs wer...

[]

{ "text": "The following tools and versions were used: R (version 4.1.1 for covariate modeling and version 4.0.3 for GSEA); edgeR (version 3.28.1); lme4 (version 1.1-27.1); LDSC (version 1.0.1); Limma (version 3.46.0); Scanpy (version 1.9.1); scArches (version 0.3.5); scIB (version 0.1.1); scikit-learn (version 0.24....

[]

{ "text": "Further information on research design is available in the Nature Portfolio Reporting Summary linked to this article.\nSource paper: PMC10287567" }

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{ "text": "Online content\nSource paper: PMC10287567" }

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Datasets:

OTAR3088
/

single-cell_raw

OTAR3088 SingleCell raw data