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.	At Hannover Medical School, patients with lung cancer were recruited in the course of their operation (that is, surgical tumor resection was performed according to the ethical vote of the German Centre for Lung Research, ethical vote 7414 and data safety guidelines).
PMC10287567	An integrated cell atlas of the lung in health and disease.	There was no bias in patient recruitment since the samples were collected as fresh native tissue following surgical tumor resection and according to the availability of surplus adjacent nonmalignant lung tissue, which was resected in parallel to the tumor tissue.
PMC10287567	An integrated cell atlas of the lung in health and disease.	Metadata of the donors’ sex were based on self-report or reported by medical professionals during consenting.
PMC10287567	An integrated cell atlas of the lung in health and disease.	Fresh adjacent normal tumor-free lung tissues from patients with non-small cell lung cancer tumors were obtained by the Lung Research group (D. Jonigk, Pathology, Hannover Medical School) and processed for single-cell isolation immediately.
PMC10287567	An integrated cell atlas of the lung in health and disease.	Lung tissue was chopped with a scalpel and scissors and digested using BD Tumor Dissociation Reagent (BD Biosciences) for 30 min in a 37 °C water bath.
PMC10287567	An integrated cell atlas of the lung in health and disease.	The digestion was stopped with 1% FCS and 2 mM EDTA in PBS without Ca/Mg and cells were filtered over a 70 µm cell strainer (BD Falcon).
PMC10287567	An integrated cell atlas of the lung in health and disease.	Erythrocytes were removed using a human MACSxpress Erythrocyte Depletion Kit (Miltenyi Biotec) and cells were filtered using a 40 µm cell strainer (BD Falcon).
PMC10287567	An integrated cell atlas of the lung in health and disease.	The viability of the cells was assessed microscopically and by flow cytometry using a LIVE/DEAD Fixable Yellow Dead Cell Stain Kit (Invitrogen) and was ~84%.
PMC10287567	An integrated cell atlas of the lung in health and disease.	The single-cell suspension was processed for scRNA-seq and library preparation with the Seq-Well protocol.
PMC10287567	An integrated cell atlas of the lung in health and disease.	Library pools with fewer than 100 cells were discarded and merged into one object.
PMC10287567	An integrated cell atlas of the lung in health and disease.	The Seurat v3.2 pipeline was used to further analyze the data.
PMC10287567	An integrated cell atlas of the lung in health and disease.	Genes in fewer than five cells in the dataset, as well as the mitochondrial genes MT-RNR1 and MT-RNR2, were removed.
PMC10287567	An integrated cell atlas of the lung in health and disease.	Cells with fewer than 200 genes were discarded, whereas cells with <5% mitochondrial genes or <30% intronic reads were kept for further analysis.
PMC10287567	An integrated cell atlas of the lung in health and disease.	The data were log normalized and 2,000 variable genes were calculated for each sample for integration with Seurat’s Canonical Correlation Analysis algorithm.
PMC10287567	An integrated cell atlas of the lung in health and disease.	The data were scaled, 50 principle components were selected and the data were clustered with 0.6 resolution.
PMC10287567	An integrated cell atlas of the lung in health and disease.	Cluster annotation revealed a low-quality cluster that was subsequently removed and the process (the calculation of variable genes, calculation of 30 principal components, clustering with 0.4 resolution) was repeated.
PMC10287567	An integrated cell atlas of the lung in health and disease.	Raw counts of the cells that passed all filtering were provided as input for the HLCA.
PMC10287567	An integrated cell atlas of the lung in health and disease.	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.
PMC10287567	An integrated cell atlas of the lung in health and disease.	Count matrices were generated using varying software (Supplementary Table 1).
PMC10287567	An integrated cell atlas of the lung in health and disease.	Previously published scRNA-seq data were partly realigned by the dataset generators: the raw sequencing data of four previously published studies were realigned to GRCh38 using Ensembl84 for the HLCA.
PMC10287567	An integrated cell atlas of the lung in health and disease.	For two of these studies, the Cell Ranger 3.1.0-based HLCA pipeline was used for realignment.
PMC10287567	An integrated cell atlas of the lung in health and disease.	For the remaining two, data were processed as previously described, but with the reference genome and genome annotation adapted to the HLCA (GRCh38; Ensembl84).
PMC10287567	An integrated cell atlas of the lung in health and disease.	All other datasets in the HLCA core were originally already aligned to GRCh38 (Ensembl84) except data from ref. (
PMC10287567	An integrated cell atlas of the lung in health and disease.	GRCh38; Ensembl93) (Supplementary Table 1).
PMC10287567	An integrated cell atlas of the lung in health and disease.	For ref. ,
PMC10287567	An integrated cell atlas of the lung in health and disease.	the count matrices provided had ambient RNA removed, as described previously.
PMC10287567	An integrated cell atlas of the lung in health and disease.	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 (Supplementary Table 2).
PMC10287567	An integrated cell atlas of the lung in health and disease.	Ethnicity metadata were based on self-reported ethnicity for live donors or retrieved from medical records or assigned by the organ procurement team in the case of organ donors, as collected in the individual studies.
PMC10287567	An integrated cell atlas of the lung in health and disease.	For donor ethnicity, the following categories of self-reported ethnicity were used during metadata collection: Black, white, Latino, Asian, Pacific Islander and mixed.
PMC10287567	An integrated cell atlas of the lung in health and disease.	To conform to pre-existing 1,000 Genomes ancestry superpopulations, these self-reported ethnicity categories were then harmonized with the superpopulation categories as follows: Black was categorized as African, white as European and Latino as admixed American, while keeping the category Asian (merging the superpopulations East Asians and South Asians as this granularity was missing from the collected self-reported ethnicity data) and keeping Pacific Islander, as this category did not correspond to any of the superpopulations but does constitute a separate population in HANCESTRO.
PMC10287567	An integrated cell atlas of the lung in health and disease.	We refer to the resulting categories as harmonized ethnicity.
PMC10287567	An integrated cell atlas of the lung in health and disease.	Both self-reported ethnicity (as collected) and harmonized ethnicity per donor are detailed in Supplementary Table 2.
PMC10287567	An integrated cell atlas of the lung in health and disease.	Cell type annotations from dataset providers were included in all datasets.
PMC10287567	An integrated cell atlas of the lung in health and disease.	For tissue dissociation protocols, protocols were grouped based on: (1) enzyme(s) used for tissue dissociation; and (2) the digestion time in cases where large differences were observed between protocols (that is, cold protease protocols were split into two groups: 30–60 min versus overnight).
PMC10287567	An integrated cell atlas of the lung in health and disease.	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.
PMC10287567	An integrated cell atlas of the lung in health and disease.	For the HLCA core, all matrices were gene filtered based on Cell Ranger Ensembl84 gene-type filtering (resulting in 33,694 gene IDs).
PMC10287567	An integrated cell atlas of the lung in health and disease.	Cells with fewer than 200 genes detected were removed (removing 2,335 cells and 21 extra erythrocytes with close to 200 genes expressed as these hampered SCRAN normalization; see below), along with genes expressed in fewer than ten cells (removing 5,167 out of 33,694 genes).
PMC10287567	An integrated cell atlas of the lung in health and disease.	To normalize for differences in total UMI counts per cell, we performed SCRAN normalization.
PMC10287567	An integrated cell atlas of the lung in health and disease.	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.
PMC10287567	An integrated cell atlas of the lung in health and disease.	To this end, we first performed total count normalization, by dividing each count by its cell’s total count and multiplying by 10,000.
PMC10287567	An integrated cell atlas of the lung in health and disease.	We then performed a log transformation using natural log and pseudocount 1.
PMC10287567	An integrated cell atlas of the lung in health and disease.	A PCA was subsequently performed.
PMC10287567	An integrated cell atlas of the lung in health and disease.	Using the first 50 principal components, a neighborhood graph was calculated with the number of neighbors set to k = 15.
PMC10287567	An integrated cell atlas of the lung in health and disease.	Data were subsequently clustered with Louvain clustering at a resolution of r = 0.5.
PMC10287567	An integrated cell atlas of the lung in health and disease.	SCRAN normalization was then performed on the raw counts, using the Louvain clusters as input clusters and with the minimum mean (library size adjusted) average count of genes to be used for normalization set to 0.1.
PMC10287567	An integrated cell atlas of the lung in health and disease.	The resulting size factors were used for normalization.
PMC10287567	An integrated cell atlas of the lung in health and disease.	For the final HLCA (and not the benchmarking subset), cells with abnormally low size factors (<0.01) or abnormally high total counts after normalization (>10 × 10) were removed from the data (267 cells in total).
PMC10287567	An integrated cell atlas of the lung in health and disease.	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).
PMC10287567	An integrated cell atlas of the lung in health and disease.	To accommodate labels at different levels of detail, the cell type reference was made hierarchical, with level 1 containing the coarsest possible labels (immune, epithelial and so on) and level 5 containing the finest possible labels (for example, naive CD4 T cells).
PMC10287567	An integrated cell atlas of the lung in health and disease.	Levels were created in a data-driven fashion, recursively breaking up coarser-level labels into finer ones where finer labels were available.
PMC10287567	An integrated cell atlas of the lung in health and disease.	To map anatomical location to a 1D CCF score that could be used for modeling, a distinction was made between upper and lower airways.
PMC10287567	An integrated cell atlas of the lung in health and disease.	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 locations: inferior turbinate, nasopharynx, oropharnyx, vesibula and larynx.
PMC10287567	An integrated cell atlas of the lung in health and disease.	The trachea received the next anatomical coordinate score using the same linear increment as in the upper airways (a score of 2.5).
PMC10287567	An integrated cell atlas of the lung in health and disease.	In the lower airways, the coordinate score within the bronchial tree was based on the generation airway, with the trachea being the first generation and the total number of generations assumed to be 23 (ref. ).
PMC10287567	An integrated cell atlas of the lung in health and disease.	The alveolar sac was assigned the coordinate score of the 23rd generation airway.
PMC10287567	An integrated cell atlas of the lung in health and disease.	The coordinate score of each generation airway was calculated by taking the log2 value of the generation and adding it to the score of the trachea.
PMC10287567	An integrated cell atlas of the lung in health and disease.	Using this methodology, the alveolus received an anatomical coordinate score of 7.02.
PMC10287567	An integrated cell atlas of the lung in health and disease.	To calculate the final CCF coordinate, the coordinate scores (ranging from 0 to 7.02) were scaled to a value between 0 (inferior turbinate) and 1 (alveolus).
PMC10287567	An integrated cell atlas of the lung in health and disease.	Samples were then mapped to this coordinate system using the best approximation of the sampling location for each of the samples of the core HLCA (Supplementary Table 3).
PMC10287567	An integrated cell atlas of the lung in health and disease.	For computational efficiency, benchmarking was performed on a subset of the total atlas, including data from ten studies split into 13 datasets (ref.
PMC10287567	An integrated cell atlas of the lung in health and disease.	was split into 10xv1 and 10xv2 data; ref.
PMC10287567	An integrated cell atlas of the lung in health and disease.	was split into 10xv2 and 10xv3 data; and the pooled data from ref.
PMC10287567	An integrated cell atlas of the lung in health and disease.	and associated unpublished data were split into two based on the processing site).
PMC10287567	An integrated cell atlas of the lung in health and disease.	The data came from 72 donors, 124 samples and 372,111 cells.
PMC10287567	An integrated cell atlas of the lung in health and disease.	Preprocessing of the benchmarking data included the filtering of cells (minimum number of total UMI counts: 500) and genes (minimum number of cells expressing the gene: 5).
PMC10287567	An integrated cell atlas of the lung in health and disease.	For integration benchmarking, the scIB benchmarking framework was used with default integration parameter settings unless otherwise specified.
PMC10287567	An integrated cell atlas of the lung in health and disease.	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 2,000 most HVGs; and on the 6,000 most HVGs.
PMC10287567	An integrated cell atlas of the lung in health and disease.	Of these methods, all that did not require raw counts as input were run twice on each gene set: once with gene counts scaled to have a mean of 0 and standard deviation of 1; and once with unscaled gene counts.
PMC10287567	An integrated cell atlas of the lung in health and disease.	scVI and scANVI, which require raw counts as input, were not run on scaled gene counts.
PMC10287567	An integrated cell atlas of the lung in health and disease.	scGen was only tested on 2,000 unscaled HVGs.
PMC10287567	An integrated cell atlas of the lung in health and disease.	For HVG selection, first, HVGs were calculated per dataset using Cell Ranger-based HVG selection (default parameter settings: min_disp=0.5, min_mean=0.0125, max_mean=3, span=0.3, n_bins=20).
PMC10287567	An integrated cell atlas of the lung in health and disease.	Then, genes that were highly variable in all datasets were considered overall highly variable, followed by genes highly variable in all datasets but one, in all datasets but two and so on until a predetermined number of genes were selected (2,000 or 6,000 genes).
PMC10287567	An integrated cell atlas of the lung in health and disease.	For scANVI and scVI, genes were subset to the HVG set and the resulting raw count matrix was used as input.
PMC10287567	An integrated cell atlas of the lung in health and disease.	For all other methods, SCRAN-normalized (as described above) data were used.
PMC10287567	An integrated cell atlas of the lung in health and disease.	Genes were then subset to the precalculated HVG sets.
PMC10287567	An integrated cell atlas of the lung in health and disease.	For integration of gene-scaled data, all genes were scaled to have mean of 0 and standard deviation of 1.
PMC10287567	An integrated cell atlas of the lung in health and disease.	Two integration methods allowed for input of cell type labels to guide the integration: scGen and scANVI.
PMC10287567	An integrated cell atlas of the lung in health and disease.	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 2 labels.
PMC10287567	An integrated cell atlas of the lung in health and disease.	Since scGen does not accept unlabeled cells, cells that did not have annotations available at these levels (that is, cells annotated as cycling, epithelial, stromal or lymphoid cells with no further annotations; 4,499 cells in total) were left out of the benchmarking data.
PMC10287567	An integrated cell atlas of the lung in health and disease.	The dataset rather than the donor of the sample was used as the batch parameter.
PMC10287567	An integrated cell atlas of the lung in health and disease.	The maximum memory usage was set to 376 Gb and all methods requiring more memory were excluded from the analysis.
PMC10287567	An integrated cell atlas of the lung in health and disease.	The quality of each of the integrations was scored using 12 metrics, with four metrics measuring the batch correction quality and eight metrics quantifying the conservation of biological signal after integration (Supplementary Fig. 1; metrics previously described).
PMC10287567	An integrated cell atlas of the lung in health and disease.	Overall scores were computed by taking a 0.4:0.6 weighted mean of batch effect removal to biological variation conservation (bioconservation), respectively.
PMC10287567	An integrated cell atlas of the lung in health and disease.	Methods were ranked based on overall score (Supplementary Fig. 1).
PMC10287567	An integrated cell atlas of the lung in health and disease.	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).
PMC10287567	An integrated cell atlas of the lung in health and disease.	Reasons for possible splitting were: (1) different 10x versions used within a study (for example, 10xv2 versus 10xv3); or (2) the processing of samples at different institutes within a study.
PMC10287567	An integrated cell atlas of the lung in health and disease.	To determine whether these covariates caused batch effects within a study, we performed principal component regression.
PMC10287567	An integrated cell atlas of the lung in health and disease.	To this end, we preprocessed single studies separately (total count normalization to median total counts across cells and subsequent PCA with 50 principal components).
PMC10287567	An integrated cell atlas of the lung in health and disease.	For each study, we then calculated the fraction of the variance in the first 50 principal components that could be explained (PCexpl) by the covariate of interest (that is, 10x version or processing institute):[12pt] $$}_}} = _^ }( }} )}}_^ }( }_i} )}}$$=∑i=150varcov∑i=150varPCiwhere var(PCi\|cov) is the variance in scores for the ith principal component across cells that can be explained by the covariate under consideration, based on a linear regression.
PMC10287567	An integrated cell atlas of the lung in health and disease.	Then, 10x version or processing institute assignments were randomly shuffled between samples and PCexpl was calculated for the randomized covariate.
PMC10287567	An integrated cell atlas of the lung in health and disease.	This was repeated over ten random shufflings and the mean and standard deviation of PCexpl were then calculated for the covariate.
PMC10287567	An integrated cell atlas of the lung in health and disease.	If the nonrandomized PCexpl was more than 1.5 standard deviations above the randomized PCexpl, we considered the covariate a source of batch effect and split the study into separate datasets.
PMC10287567	An integrated cell atlas of the lung in health and disease.	Thus, both Jain_Misharin_2021 and ref.
PMC10287567	An integrated cell atlas of the lung in health and disease.	were split into 10xv1 and 10xv2; ref.
PMC10287567	An integrated cell atlas of the lung in health and disease.	was split into 10xv2 and 10xv3; and ref.
PMC10287567	An integrated cell atlas of the lung in health and disease.	and its pooled unpublished data were not split based on 10x version nor on processing location.
PMC10287567	An integrated cell atlas of the lung in health and disease.	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, KRT5KRT17 epithelial, lymphatic endothelial cell, macrophages, mast cells, megakaryocytes, mesothelium, monocytes, neutrophils, natural killer/natural killer T cells, proliferating cells, rare, secretory, smooth muscle, squamous, submucosal secretory, T cell lineage, venous and unlabeled), except cells with lacking annotations were set to unlabeled instead of being removed.
PMC10287567	An integrated cell atlas of the lung in health and disease.	scANVI was run on the raw counts of the 2,000 most HVGs (calculated as described above), using datasets as the batch variable to enable the conservation of interindividual variation.
PMC10287567	An integrated cell atlas of the lung in health and disease.	The following parameter settings were used: number of layers: 2; number of latent dimensions: 30; encode covariates: True; deeply inject covariates: False; use layer norm: both; use batch norm: none; gene likelihood: nb; n epochs unsupervised: 500; n epochs semi-supervised: 200; and frequency: 1.
PMC10287567	An integrated cell atlas of the lung in health and disease.	For the unsupervised training, the following early-stopping parameters were used: early stopping metric: elbo; save best state metric: elbo; patience: 10; threshold: 0; reduce lr on plateau: True; lr patience: 8; and lr_factor: 0.1.

PMC10287567

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

At Hannover Medical School, patients with lung cancer were recruited in the course of their operation (that is, surgical tumor resection was performed according to the ethical vote of the German Centre for Lung Research, ethical vote 7414 and data safety guidelines).

PMC10287567

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

There was no bias in patient recruitment since the samples were collected as fresh native tissue following surgical tumor resection and according to the availability of surplus adjacent nonmalignant lung tissue, which was resected in parallel to the tumor tissue.

PMC10287567

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

Metadata of the donors’ sex were based on self-report or reported by medical professionals during consenting.

PMC10287567

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

Fresh adjacent normal tumor-free lung tissues from patients with non-small cell lung cancer tumors were obtained by the Lung Research group (D. Jonigk, Pathology, Hannover Medical School) and processed for single-cell isolation immediately.

PMC10287567

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

Lung tissue was chopped with a scalpel and scissors and digested using BD Tumor Dissociation Reagent (BD Biosciences) for 30 min in a 37 °C water bath.

PMC10287567

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

The digestion was stopped with 1% FCS and 2 mM EDTA in PBS without Ca/Mg and cells were filtered over a 70 µm cell strainer (BD Falcon).

PMC10287567

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

Erythrocytes were removed using a human MACSxpress Erythrocyte Depletion Kit (Miltenyi Biotec) and cells were filtered using a 40 µm cell strainer (BD Falcon).

PMC10287567

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

The viability of the cells was assessed microscopically and by flow cytometry using a LIVE/DEAD Fixable Yellow Dead Cell Stain Kit (Invitrogen) and was ~84%.

PMC10287567

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

The single-cell suspension was processed for scRNA-seq and library preparation with the Seq-Well protocol.

PMC10287567

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

Library pools with fewer than 100 cells were discarded and merged into one object.

PMC10287567

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

The Seurat v3.2 pipeline was used to further analyze the data.

PMC10287567

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

Genes in fewer than five cells in the dataset, as well as the mitochondrial genes MT-RNR1 and MT-RNR2, were removed.

PMC10287567

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

Cells with fewer than 200 genes were discarded, whereas cells with <5% mitochondrial genes or <30% intronic reads were kept for further analysis.

PMC10287567

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

The data were log normalized and 2,000 variable genes were calculated for each sample for integration with Seurat’s Canonical Correlation Analysis algorithm.

PMC10287567

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

The data were scaled, 50 principle components were selected and the data were clustered with 0.6 resolution.

PMC10287567

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

Cluster annotation revealed a low-quality cluster that was subsequently removed and the process (the calculation of variable genes, calculation of 30 principal components, clustering with 0.4 resolution) was repeated.

PMC10287567

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

Raw counts of the cells that passed all filtering were provided as input for the HLCA.

PMC10287567

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

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.

PMC10287567

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

Count matrices were generated using varying software (Supplementary Table 1).

PMC10287567

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

Previously published scRNA-seq data were partly realigned by the dataset generators: the raw sequencing data of four previously published studies were realigned to GRCh38 using Ensembl84 for the HLCA.

PMC10287567

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

For two of these studies, the Cell Ranger 3.1.0-based HLCA pipeline was used for realignment.

PMC10287567

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

For the remaining two, data were processed as previously described, but with the reference genome and genome annotation adapted to the HLCA (GRCh38; Ensembl84).

PMC10287567

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

All other datasets in the HLCA core were originally already aligned to GRCh38 (Ensembl84) except data from ref. (

PMC10287567

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

GRCh38; Ensembl93) (Supplementary Table 1).

PMC10287567

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

For ref. ,

PMC10287567

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

the count matrices provided had ambient RNA removed, as described previously.

PMC10287567

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

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 (Supplementary Table 2).

PMC10287567

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

Ethnicity metadata were based on self-reported ethnicity for live donors or retrieved from medical records or assigned by the organ procurement team in the case of organ donors, as collected in the individual studies.

PMC10287567

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

For donor ethnicity, the following categories of self-reported ethnicity were used during metadata collection: Black, white, Latino, Asian, Pacific Islander and mixed.

PMC10287567

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

To conform to pre-existing 1,000 Genomes ancestry superpopulations, these self-reported ethnicity categories were then harmonized with the superpopulation categories as follows: Black was categorized as African, white as European and Latino as admixed American, while keeping the category Asian (merging the superpopulations East Asians and South Asians as this granularity was missing from the collected self-reported ethnicity data) and keeping Pacific Islander, as this category did not correspond to any of the superpopulations but does constitute a separate population in HANCESTRO.

PMC10287567

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

We refer to the resulting categories as harmonized ethnicity.

PMC10287567

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

Both self-reported ethnicity (as collected) and harmonized ethnicity per donor are detailed in Supplementary Table 2.

PMC10287567

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

Cell type annotations from dataset providers were included in all datasets.

PMC10287567

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

For tissue dissociation protocols, protocols were grouped based on: (1) enzyme(s) used for tissue dissociation; and (2) the digestion time in cases where large differences were observed between protocols (that is, cold protease protocols were split into two groups: 30–60 min versus overnight).

PMC10287567

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

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.

PMC10287567

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

For the HLCA core, all matrices were gene filtered based on Cell Ranger Ensembl84 gene-type filtering (resulting in 33,694 gene IDs).

PMC10287567

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

Cells with fewer than 200 genes detected were removed (removing 2,335 cells and 21 extra erythrocytes with close to 200 genes expressed as these hampered SCRAN normalization; see below), along with genes expressed in fewer than ten cells (removing 5,167 out of 33,694 genes).

PMC10287567

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

To normalize for differences in total UMI counts per cell, we performed SCRAN normalization.

PMC10287567

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

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.

PMC10287567

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

To this end, we first performed total count normalization, by dividing each count by its cell’s total count and multiplying by 10,000.

PMC10287567

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

We then performed a log transformation using natural log and pseudocount 1.

PMC10287567

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

A PCA was subsequently performed.

PMC10287567

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

Using the first 50 principal components, a neighborhood graph was calculated with the number of neighbors set to k = 15.

PMC10287567

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

Data were subsequently clustered with Louvain clustering at a resolution of r = 0.5.

PMC10287567

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

SCRAN normalization was then performed on the raw counts, using the Louvain clusters as input clusters and with the minimum mean (library size adjusted) average count of genes to be used for normalization set to 0.1.

PMC10287567

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

The resulting size factors were used for normalization.

PMC10287567

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

For the final HLCA (and not the benchmarking subset), cells with abnormally low size factors (<0.01) or abnormally high total counts after normalization (>10 × 10) were removed from the data (267 cells in total).

PMC10287567

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

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).

PMC10287567

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

To accommodate labels at different levels of detail, the cell type reference was made hierarchical, with level 1 containing the coarsest possible labels (immune, epithelial and so on) and level 5 containing the finest possible labels (for example, naive CD4 T cells).

PMC10287567

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

Levels were created in a data-driven fashion, recursively breaking up coarser-level labels into finer ones where finer labels were available.

PMC10287567

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

To map anatomical location to a 1D CCF score that could be used for modeling, a distinction was made between upper and lower airways.

PMC10287567

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

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 locations: inferior turbinate, nasopharynx, oropharnyx, vesibula and larynx.

PMC10287567

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

The trachea received the next anatomical coordinate score using the same linear increment as in the upper airways (a score of 2.5).

PMC10287567

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

In the lower airways, the coordinate score within the bronchial tree was based on the generation airway, with the trachea being the first generation and the total number of generations assumed to be 23 (ref. ).

PMC10287567

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

The alveolar sac was assigned the coordinate score of the 23rd generation airway.

PMC10287567

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

The coordinate score of each generation airway was calculated by taking the log2 value of the generation and adding it to the score of the trachea.

PMC10287567

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

Using this methodology, the alveolus received an anatomical coordinate score of 7.02.

PMC10287567

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

To calculate the final CCF coordinate, the coordinate scores (ranging from 0 to 7.02) were scaled to a value between 0 (inferior turbinate) and 1 (alveolus).

PMC10287567

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

Samples were then mapped to this coordinate system using the best approximation of the sampling location for each of the samples of the core HLCA (Supplementary Table 3).

PMC10287567

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

For computational efficiency, benchmarking was performed on a subset of the total atlas, including data from ten studies split into 13 datasets (ref.

PMC10287567

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

was split into 10xv1 and 10xv2 data; ref.

PMC10287567

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

was split into 10xv2 and 10xv3 data; and the pooled data from ref.

PMC10287567

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

and associated unpublished data were split into two based on the processing site).

PMC10287567

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

The data came from 72 donors, 124 samples and 372,111 cells.

PMC10287567

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

Preprocessing of the benchmarking data included the filtering of cells (minimum number of total UMI counts: 500) and genes (minimum number of cells expressing the gene: 5).

PMC10287567

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

For integration benchmarking, the scIB benchmarking framework was used with default integration parameter settings unless otherwise specified.

PMC10287567

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

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 2,000 most HVGs; and on the 6,000 most HVGs.

PMC10287567

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

Of these methods, all that did not require raw counts as input were run twice on each gene set: once with gene counts scaled to have a mean of 0 and standard deviation of 1; and once with unscaled gene counts.

PMC10287567

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

scVI and scANVI, which require raw counts as input, were not run on scaled gene counts.

PMC10287567

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

scGen was only tested on 2,000 unscaled HVGs.

PMC10287567

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

For HVG selection, first, HVGs were calculated per dataset using Cell Ranger-based HVG selection (default parameter settings: min_disp=0.5, min_mean=0.0125, max_mean=3, span=0.3, n_bins=20).

PMC10287567

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

Then, genes that were highly variable in all datasets were considered overall highly variable, followed by genes highly variable in all datasets but one, in all datasets but two and so on until a predetermined number of genes were selected (2,000 or 6,000 genes).

PMC10287567

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

For scANVI and scVI, genes were subset to the HVG set and the resulting raw count matrix was used as input.

PMC10287567

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

For all other methods, SCRAN-normalized (as described above) data were used.

PMC10287567

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

Genes were then subset to the precalculated HVG sets.

PMC10287567

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

For integration of gene-scaled data, all genes were scaled to have mean of 0 and standard deviation of 1.

PMC10287567

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

Two integration methods allowed for input of cell type labels to guide the integration: scGen and scANVI.

PMC10287567

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

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 2 labels.

PMC10287567

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

Since scGen does not accept unlabeled cells, cells that did not have annotations available at these levels (that is, cells annotated as cycling, epithelial, stromal or lymphoid cells with no further annotations; 4,499 cells in total) were left out of the benchmarking data.

PMC10287567

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

The dataset rather than the donor of the sample was used as the batch parameter.

PMC10287567

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

The maximum memory usage was set to 376 Gb and all methods requiring more memory were excluded from the analysis.

PMC10287567

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

The quality of each of the integrations was scored using 12 metrics, with four metrics measuring the batch correction quality and eight metrics quantifying the conservation of biological signal after integration (Supplementary Fig. 1; metrics previously described).

PMC10287567

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

Overall scores were computed by taking a 0.4:0.6 weighted mean of batch effect removal to biological variation conservation (bioconservation), respectively.

PMC10287567

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

Methods were ranked based on overall score (Supplementary Fig. 1).

PMC10287567

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

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).

PMC10287567

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

Reasons for possible splitting were: (1) different 10x versions used within a study (for example, 10xv2 versus 10xv3); or (2) the processing of samples at different institutes within a study.

PMC10287567

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

To determine whether these covariates caused batch effects within a study, we performed principal component regression.

PMC10287567

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

To this end, we preprocessed single studies separately (total count normalization to median total counts across cells and subsequent PCA with 50 principal components).

PMC10287567

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

For each study, we then calculated the fraction of the variance in the first 50 principal components that could be explained (PCexpl) by the covariate of interest (that is, 10x version or processing institute):[12pt] $$}_}} = _^ }( }} )}}_^ }( }_i} )}}$$=∑i=150varcov∑i=150varPCiwhere var(PCi|cov) is the variance in scores for the ith principal component across cells that can be explained by the covariate under consideration, based on a linear regression.

PMC10287567

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

Then, 10x version or processing institute assignments were randomly shuffled between samples and PCexpl was calculated for the randomized covariate.

PMC10287567

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

This was repeated over ten random shufflings and the mean and standard deviation of PCexpl were then calculated for the covariate.

PMC10287567

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

If the nonrandomized PCexpl was more than 1.5 standard deviations above the randomized PCexpl, we considered the covariate a source of batch effect and split the study into separate datasets.

PMC10287567

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

Thus, both Jain_Misharin_2021 and ref.

PMC10287567

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

were split into 10xv1 and 10xv2; ref.

PMC10287567

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

was split into 10xv2 and 10xv3; and ref.

PMC10287567

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

and its pooled unpublished data were not split based on 10x version nor on processing location.

PMC10287567

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

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, KRT5KRT17 epithelial, lymphatic endothelial cell, macrophages, mast cells, megakaryocytes, mesothelium, monocytes, neutrophils, natural killer/natural killer T cells, proliferating cells, rare, secretory, smooth muscle, squamous, submucosal secretory, T cell lineage, venous and unlabeled), except cells with lacking annotations were set to unlabeled instead of being removed.

PMC10287567

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

scANVI was run on the raw counts of the 2,000 most HVGs (calculated as described above), using datasets as the batch variable to enable the conservation of interindividual variation.

PMC10287567

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

The following parameter settings were used: number of layers: 2; number of latent dimensions: 30; encode covariates: True; deeply inject covariates: False; use layer norm: both; use batch norm: none; gene likelihood: nb; n epochs unsupervised: 500; n epochs semi-supervised: 200; and frequency: 1.

PMC10287567

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

For the unsupervised training, the following early-stopping parameters were used: early stopping metric: elbo; save best state metric: elbo; patience: 10; threshold: 0; reduce lr on plateau: True; lr patience: 8; and lr_factor: 0.1.