为了组装一个综合的泛组织细胞地图集，我们通过scanpy48工具包收集了SCRNA-SEQ数据集并进行了质量控制程序，如随后的部分所述（扩展数据图1和补充表1）。除非另有说明，否则使用默认参数。

　　我们包括了符合以下标准的成年样本的SCRNA-SEQ数据集：（1）利用新鲜而不是冷冻样品；（2）基于细胞类型富集的样品包含：（a）无细胞类型富集；（b）免疫，上皮，内皮和基质室的混合物；（c）富集免疫或非免疫细胞群体；（3）使用10倍基因组平台的单细胞而不是单核的生成。实现这些标准以最大程度地减少数据集49的批处理效应。最终，包括来自26个队列的总共33个数据集，共同代表了35个人体组织中的一个细胞地图集。

　　为了标准用不同版本的人类基因组组件注释的数据集，我们将转录组限制在三种最广泛使用的10倍基因组基因学注释中发现的21,812个基因，特别是GRCH38（ENSEMBL 84），GRCH38，GRCH38（ENSEMBL 93）（ENSEMBL 93）和GRCH38（GENCODE V32/ENSEMEMBL 98）。在原始研究中被鉴定为低质量或细菌系的细胞被排除在外，只有符合以下标准的细胞被保留：500-8,000个基因，1,000–100,000基因计数和小于20％的线粒体基因计数。我们将scrublet50应用于scanpy，并在每个队列中删除了每个队列的细胞，并在所有队列中均超过第90个百分位数。然后，我们排除了少于50个高质量细胞的样品。最后，该分析包括通过严格的质量控制措施的700多个样本。

　　从所有数据集的组合基因计数矩阵开始，我们使用10,000的比例系数随后是对数转换来得出归一化基因表达矩阵（库大小）。然后使用具有以下参数的函数scanpy.pp.highly_variable_genes选择高度可变的基因（HVG）：（ n_top_genes = 2000，float =“ cell_ranger”，batch_key =“ dataSetId”）。值得注意的是，在去除特定基因（包括免疫球蛋白基因，T细胞受体基因，核糖体蛋白质编码基因，热休克蛋白相关基因和线粒体基因）后进行了HVG选择。使用函数scanpy.pp.regress_out解决了几种混杂效应，包括每个细胞的总基因计数，线粒体基因计数的百分比和细胞周期。最后，将HVG集中并缩放在所有细胞中。

　　为了集成这些广泛的数据集，除非另有说明，否则我们将其使用带有默认参数的Scanpy工具包。

　　为了确定数据集的最佳集成方法，我们利用SCIB17基准了几种广泛使用的基于Python的工具：BBKNN18，Harmony51，Scanorama52和基于深度学习的SCVI53，SCANVI54和Scalex55。在SCIB的14个指标中，HVG和轨迹的生物保护不适用，由于内存要求超过2 TB，因此将KBET度量标准排除在外。总分计算为批次校正和生物方差保护的加权平均值（40/60）。重要的是，我们分别对整个地图集和一个子集地图集进行了两个独立的基准分析。最后，BBKNN成为最佳性能者，用于集成泛组织数据集（扩展数据图2）。

　　主成分分析是在中心和缩放的HVG表达矩阵上进行的，以提取50个主要成分。然后集成到Scanpy中的BBKNN随后将数据集作为批处理变量执行。然后，使用批处理图执行UMAP56，以在二维布局上可视化细胞。

　　我们至少进行了两个级别的无监督细胞聚类和注释。使用函数scanpy.tl.leiden进行了群集的第一级，分辨率= 0.1，然后鉴定差异表达的基因（deg; log2 thransformed折叠变化> 1，fdr< 0.05, Student’s t-test). The eight broad cell types were identified on the basis of canonical markers and DEGs. We also received assistance with cell annotation from CellTypist7, an automated cell-type annotation tool, using the Immune_All_High and Immune_All_Low models. Subsequently, further clustering (second or more levels) was performed using context-specific resolutions to obtain several distinct cell subsets for each cell type. Epithelial cells were excluded from further clustering owing to their highly tissue-specific nature. In total, 2,293,951 high-quality cells from 706 samples across 317 donors were annotated into 76 non-epithelial subsets and 26 epithelia cell types.

　　We generated pseudo-bulk profiles for 76 non-epithelial cell subsets by averaging the gene expression of all cells within the same subsets. Next, unsupervised hierarchical clustering was performed using correlation distance and the hclust function (method = “ward.D”). The results were visualized using the dendextend R package.

　　We introduced CoVarNet, a computational framework designed to systematically unravel coordination among multiple cell types. CoVarNet identifies co-occurring CM networks by analysing the covariance in cell subset frequencies across various samples.

　　CoVarNet uses input data on cell subset frequencies within each cell type and sample. It utilizes two parallel modules to jointly determine CM networks by connecting co-occurring subsets (nodes) through edges. The first module applies NMF to the cell subset frequency matrix, identifying factors that prioritize subsets based on their weights. The top subsets of each factor act as co-occurring nodes in a single CM network. The second module identifies specifically correlated subset pairs, which act as potential edges. Multiple CM networks are then constructed to interconnect co-occurring nodes via these potential edges, followed by topological and statistical evaluations.

　　To ensure comparability of cell-subset frequencies across tissues and clinical specimens, we included only samples without cell-type enrichment or those from mixtures of the four cell compartments. Samples with fewer than 50 high-quality cells were excluded. For each eligible sample, we computed the frequencies of cell subsets within their corresponding cell types. Min-max normalization was applied to correct the frequency matrix, mitigating the impact of varying numbers of cell subsets across different cell types. Thus, a corrected frequency matrix, ranging from 0 to 1, was utilized in the CoVarNet procedure. Specifically, we generated a frequency matrix consisting of 76 subsets (rows) and 510 samples (columns) for the pan-tissue atlas.

　　NMF has been used in the analysis of single-cell57,58,59,60 and spatial61,62 expression data to extract gene expression programmes. In this study, CoVarNet applies NMF to the frequency matrix to decipher cellular co-occurring programmes, specifically using the nsNMF method with ranks from 2 to 20, as implemented in the NMF R package63. To ensure robustness, we conducted 30 runs to derive a consensus output, consisting of k factors and their activities in each sample. Specifically, the top ten subsets of each factor were used as co-occurring node candidates in a single CM network for the pan-tissue analysis.

　　To determine the optimal rank for NMF analysis, we used the cophenetic correlation coefficient (CCC) as the evaluation index, in accordance with practices from previous reports1. CCC is used to quantify classification stability, with values ranging from 0 to 1 and 1 indicating maximum stability64. We denoted the CCC at rank k as ρk and established a procedure tailored to this context for consistent stability based on the following criteria: (1) ρk − 2 < ρk − 1 < ρk; (2) ρk > ρk + 1. Among a set of ranks meeting these criteria, the optimal rank was then identified as the one at which CCC is maximized. The optimal rank selected for the pan-tissue atlas was 12 (Extended Data Fig. 3a,b).

　　CoVarNet utilizes Pearson correlation coefficients to assess whether any two cell subsets co-occur. For a given set of s cell subsets, pairwise correlation tests are performed based on the frequency matrix, resulting in an s × s correlation coefficient matrix (denoted R). To quantify the specificity of correlations, an indicator is defined. For each element rij (i < j) in R, its background set Sij and specificity index Spec (rij) are defined as:

　　In other words, the specificity index is defined as the fraction of elements in the background set that do not exceed ri j. The specificity cutoff is determined by an automatic method. If n and N represent the assumed number of subsets in each CM and the total number of subsets, then the specificity cutoff Cutoff (n, N) will be determined as:

　　This approach enables a balanced assessment of the number of subsets within CMs and their co-occurrence. Specifically correlated subset pairs are determined jointly by the correlation (coefficient and FDR) and specificity. We generated 147 pairs for the pan-tissue atlas. These pairs were visualized as a global network (Supplementary Fig. 4).

　　For each NMF factor, the top subsets are designated as potential nodes, and edges connect specifically correlated subset pairs, removing isolated nodes. In each constructed CM network, the connectivity score is calculated as the ratio of observed edges to the total possible edges among all nodes within that network. The statistical significance of this score is assessed using a permutation test (n = 10,000) on the node labels. We used the igraph R package to visualize the CM networks, with nodes colour-coded by cell type and edge colour gradients scaled to reflect specificity.

　　The CM activities in individual samples are measured by the coefficient matrix from the NMF procedure, with the sum of activities for all CMs equalling 1 for each sample. Each sample was assigned a CMT label based on its most abundant CM. For instance, if a sample exhibited the highest activity of CM01 among all CMs, it was labelled as CMT01. All healthy single-cell samples used across tissues were stratified into 12 CMT groups (Extended Data Fig. 3e).

　　We utilized GTEx24 RNA-seq datasets to validate the CMs defined by single-cell data (Extended Data Fig. 4).

　　We retrieved gene transcripts per million (TPM) and metadata for 17,382 bulk RNA-seq samples from the GTEx Portal (V8 release)65. Samples derived from cell lines were excluded, and the ‘Cervix uteri’ category was merged into the ‘Uterus’ category for consistency. To ensure consistency, only tissues represented in the single-cell cohort were retained, narrowing down to a total of 12,240 samples spanning 23 tissues for further analysis. Gene expression data were re-normalized to a uniform library size of 10,000 and log-transformed for comparability with single-cell data.

　　We began by identifying DEGs among pseudo-bulk CMT samples. The top ten DEGs, ranked by fold change, were designated as CMT signature genes. Utilizing the Seurat R package66, we applied the AddModuleScore function to calculate scores for individual RNA-seq samples based on these CMT signature sets. All negative scores were adjusted to zero, and 2.3% (278 out of 12,240) of samples with the highest score less than 0.2 were excluded to ensure robust classification. Ultimately, the remaining 11,962 samples were categorized into 12 distinct CMTs, facilitating a detailed examination of CM representation across the analysed tissues.

　　To assess the prevalence of cell subsets across tissues, we compared observed (o) to expected (e) cell numbers for each subset-tissue combination, expressed as Ro/e = observed/expected, following established methods35,38,39. Expected cell numbers for each subset–tissue combination were derived from the Chi-square test, with enrichment defined as Ro/e > 1 (Fig. 1e and Supplementary Fig. 2). For the assessment of each CM, we computed tissue-level CM activities by averaging its activity across all samples within each tissue. The Ro/e ratio indicated the tissue distribution of CM profiles (Fig. 2c). To compare CM enrichment across 23 overlapping tissues between bulk and single-cell analyses, we independently calculated Ro/e ratios for each data type and combined them for comparison (Extended Data Fig. 4f). Results were visualized using the ComplexHeatmap R package67.

　　We gathered published spatially resolved transcriptomics datasets (Visium and Xenium) of various human tissues and cancer types. Detailed accession numbers and references for these datasets are provided (Supplementary Table 3).

　　For deconvoluting spatial transcriptomics data, we utilized cell2location25, a Bayesian model capable of accurately resolving fine-grained cell types within spatial data. Utilizing both healthy and cancer datasets, we used the corresponding integrated scRNA-seq data as a reference to obtain cell-type signatures. Prior to this process, the scRNA-seq data was subsampled to 1,000 cells per cell subset. In cases where cell types comprised fewer than 1,000 cells, all available cells were included. Following the recommended guidelines of cell2location, we set N = 5 as the expected cell abundance per spot and αy = 20 to regularize within-experiment variation in RNA detection sensitivity. The output yielded the expected cell abundance per cell subset in each spot.

　　To quantify and visualize CMs in spatial transcriptomics, we aggregated the abundance of the component cell subsets within each CM, applying weights derived from NMF results. The resulting CM activities were then scaled to a uniform standard, with the 99th percentile value across all CMs being set to 1, allowing for a direct comparison of their relative magnitudes.

　　To assess the colocalization of cell-subset components within CMs across Visium spots, we calculated the colocalization score for individual spatial sections. For each CM, we calculated Spearman correlation coefficients between subset pairs where at least one subset is within this CM, resulting in a set of correlation coefficients denoted as S. The median correlation coefficient within the CM is termed r. The colocalization score for each CM is defined as the proportion of correlations in S that are less than or equal to r, providing a measure of colocalization relative to global contexts.

　　To assess the regional aggregation of cell-subset components within CMs in spatial transcriptomics, we utilized the global bivariate Moran’s I68 using the spdep R package. Similar to the colocalization score, the aggregation scores were calculated using global Moran’s I instead of the correlation coefficient.

　　To provide orthogonal validation for the identified CMs, we used CellCharter26 to identify cellular niches, clustering Visium spots based on both gene expression and spatial information to enable spatially informed niche categorization. This analysis was performed for each sample independently to mitigate batch effects, allowing cross-validation of results across samples from the same tissues.

　　The Xenium platform enables in situ characterization of hundreds to thousands of genes in cells and tissues with ultra-precise single-cell spatial imaging. Using published intestinal Xenium data, we characterized the spatial locations of CM02 and CM03. We first designed a gene panel to distinguish multiple cell subsets within these CMs, with gene transcript density serving as a proxy for CM intensity (Extended Data Fig. 5i). Spatial regions of the tissue (epithelium or lamina propria) were identified on the basis of k-means clustering (k = 2) in the original dataset. To assess CM distribution across tissue sub-regions, we selected six different areas within the intestinal mucosa and measured CM intensities, providing a spatially resolved, single cell-resolution distribution of CMs.

　　To disentangle complex cellular crosstalk within and across CMs, we performed ligand–receptor-mediated cell–cell communication analysis using single-cell data with the CellPhoneDB Python package11,27.

　　Considering the extensive number of cells, we conducted subsampling to equalize the contribution of each cell subset. Specifically, we subsampled the number of cells to 1,000 cells for each cell subset. However, if the total cell count for certain subsets did not exceed 1,000, all cells were included in the analysis. This approach aimed to ensure that the null distribution accurately represented all cell subsets, avoiding bias towards cell subsets with larger cell numbers. Subsequently, the CellPhoneDB procedure was used for statistical inference of cell–cell interaction specificity, allowing the derivation of cell–cell interaction counts among different cell types (Extended Data Fig. 6c) or CMs (Extended Data Fig. 6d) by averaging the results of corresponding cell subsets. We also validated these results using an alternative tool, CellChat69 (Extended Data Fig. 6e).

　　To explore the effect of tissue microenvironments (CMs) on cellular crosstalk within CMs, we conducted CellPhoneDB analysis using two groups of samples with high or low CM activities, respectively. For each CM, the ‘High’ group included all samples in which that CM showed the highest activity among all CMs, while other samples were used as the ‘Low’ group. Following the previously defined CMTs in ‘CMT classifications of samples’, taking CM01 as an example, all samples labelled as CMT01 were used as the High group, while other samples were used as the Low group. Subsequently, comparative analysis between the two groups focused on cell subsets that were components of each CM (Fig. 3g).

　　The recently reported Immune Dictionary provides a comprehensive overview of cell-type-specific responses to 86 cytokines28. Leveraging this foundational knowledge, we explored intercellular crosstalk within CMs through cytokine responses. CM07 and CM10 were excluded as they lack immune cell subsets, while CM12 had no significant cytokine outputs.

　　The tissue microenvironments exerted a broad influence on cell-subset phenotypes in a CM-dependent manner. Specifically, for each immune subset, we identified its DEGs (log2-transformed fold changes >log2（1.2），fdr< 0.05, Student’s t-test) in samples from corresponding CMTs when compared with those from other CMTs, utilizing the Scanpy toolkit. These identified DEGs were interpreted as responses to cytokines within the tissue microenvironments associated with CMs.

　　First, we built a database of cytokine signatures for each cell type, using supplementary table 3 from the Immune Dictionary publication28. Of note, we performed one-to-one conversion of mouse and human ortholog genes based on the Mouse Genome Informatics (MGI) database70 (http://www.informatics.jax.org/downloads/reports/HMD_HumanPhenotype.rpt). Subsequently, we conducted cell-type-aware immune response enrichment analysis using the hypergeometric test (FDR < 0.05) through the enricher function in the clusterProfiler R package71.

　　To visualize cytokine-mediated multicellular regulation, we constructed cytokine networks by considering both cytokine production and response in a CM-specific manner. For cytokines to be considered in a CM, the normalized expression value of the cytokine gene needed to exceed 0.1 in at least one of non-responsive cell subsets. In the case of heteromeric cytokines or cytokine complexes with two subunits, each subunit was separately represented. The igraph R package was used to generate visual representations of the cytokine networks.

　　We first examined the association between CM activities and tissues, excluding tissues with fewer than five samples. For each CM, we fitted a linear model, with adjusted R2 indicating the proportion of variance explained. The FDR of the F test was reported to ensure the robustness of the results. Given the strong tissue preferences of CMs, subsequent analyses focused on tissue-specific associations.

　　In non-reproductive tissues, CM activities were compared between samples from male and female participants, with FDR-adjusted significance. Samples lacking age information were excluded from the analysis. Age data were categorized into groups: <35, 35–39, 40–49, 50–59, 60–69 and 70–85 years. For the breast dataset (D03) with over 100 samples (Supplementary Table 1), age was categorized as <50 or ≥50 years. Associations between CM activities and age groups were assessed, with adjustments for statistical significance. Specifically, we also examined associations between immune cell-subset frequencies and age groups in the spleen.

　　We performed further association analyses between CMs and specific phenotypic factors. We analysed CM09 in relation to alcohol consumption in the lymph node, CM06 and CM11 with childhood tuberculosis in the lung, CM12 with menopause in the breast, and CM07 with menstrual cycle phases in the uterus.

　　We used the pySCENIC30,72 pipeline to infer regulons for the four subsets (B03, B05, CD4T03, and I06) within CM05, performing the analysis separately for the three cellular lineages (B cells, CD4+ T cells, and innate lymphoid cells). For each subset, regulons were ranked on the basis of their regulon specificity scores (RSS), and the top 50 regulons with the highest RSS were selected for each cell subset. Seventeen regulons were shared among the four subsets.

　　Sample-level activities for shared regulons were determined by averaging cellular activities across all cells in each sample. The mean activity of each regulon was then compared across age-stratified sample groups. Target genes of shared regulons were compared across lineages, and a regulatory network was constructed to illustrate their interrelationships.

　　As different cell types within the same CMs tend to be exposed to similar tissue microenvironments, we hypothesized that they might exhibit coordinated responses. To investigate this, we utilized a method called DIALOGUE3 to map MCPs for CMs. This procedure involved setting the parameter (k = 3) and assessing the association between the MCPs identified and other phenotypes. This analysis was applied to CM12 in the breast and CM08, and resulted MCPs were termed as CM12 programme and CM08 programme.

　　To compare CM08 programme and inflammatory and cytotoxic signatures, we calculated their overall expression in external RNA-seq data, as described previously73,74. Specifically, RNA-seq datasets of samples from individuals with advanced melanoma following anti-PD-1 therapies75 were downloaded from https://github.com/ParkerICI/MORRISON-1-public. The inflammatory signature genes of immune cells are defined as CD3D, IDO1, CIITA, CD3E, CCL5, GZMK, CD2, HLA-DRA, CXCL13, IL2RG, NKG7, HLA-E, CXCR6, LAG3, TAGAP, CXCL10, STAT1 and GZMB76. The cytotoxic signature genes are defined as IFNG, GZMA, GZMB, PRF1, GZMK, ZAP70, GNLY, FASLG, TBX21, EOMES, CD8A, CD8B, CXCL9, CXCL10, CXCL11, CX3CL1, CCL3, CCL4, CX3CR1, CCL5, CXCR3, NKG7, CD160, CD244, NCR1, KLRC2, KLRK1, CD226, GZMH, ITK, CD3D, CD3E, CD3G, TRAC, TRBC1, TRBC2, CD28, CD5, KIRDL4, FGFBP2, KLRF1, SH2D1B and NCR3 (ref. 77).

　　We calculated the sample-level inflammatory scores for immune and fibroblast subsets within CM12. Fibroblasts and immune subsets were scored using the corresponding inflammatory gene sets. The inflammatory signature genes of fibroblasts are defined as PLAU, CHI3L1, MMP3, IL1R1, IL13RA2, TNFSF11, MMP10, OSMR, IL11, STRA6, FAP, WNT2, TWIST1 and IL24 (ref. 78). The inflammatory signature genes of immune cells are defined as above. Specifically, we first calculated the average gene expression among all cells of individual subsets for each sample. Subsequently, we used the R package AUCell30 to calculate the sample-level inflammatory scores.

　　We constructed a menopausal trajectory in the breast (dataset D03; Supplementary Table 1) based on the frequencies of cell subsets within CM12, following the methodology described in a recent study5. To mitigate the impact of frequency differences between cell subsets, we applied z-score normalization to correct the frequency matrix. Subsequently, we computed the k-neighbourhood and performed clustering for breast samples using the function scanpy.pp.neighbours and scanpy.tl.leiden with default parameters. To model trajectories along menopause, we performed PHATE79 with a = 40, followed by pseudotime analysis using the Palantir80 standard pipeline. The starting point was defined as the cluster with a high proportion of pre-menopausal samples.

　　The Reed dataset81 was used to validate the menopausal trajectory. For the epithelial-enriched (‘organoid’) samples in the dataset, cell subset annotation was performed with CellTypist7, using previously annotated gene expression profiles of breast tissue as a reference. The cell subset frequency matrix was subsequently input into the trajectory analysis as described above.

　　To disentangle the rewiring of CMs along malignant progression, we constructed a pan-cancer transcriptomic atlas at single-cell resolution (Fig. 5a).

　　Following the criteria set for healthy datasets, we selectively included scRNA-seq datasets from fresh (not frozen) samples without cell-type enrichment, generated using 10x Genomics single-cell (not single-nucleus) platforms. One exception is the ESCC_GSE160269 cohort, where samples represent a mixture of immune, epithelial, endothelial, and stromal compartments. Quality control and other preprocessing procedures were also applied consistently with healthy datasets. In total, more than 1,000 samples from 48 datasets were incorporated, collectively forming a cell atlas across 29 human major cancer types (Extended Data Fig. 10 and Supplementary Table 7).

　　Following the methodology applied to healthy datasets, we performed dataset integration using BBKNN. Unsupervised clustering for all cells was carried out using the scanpy.tl.leiden function with a resolution of 0.1. Subsequently, we identified the eight broad cell types based on canonical markers.

　　To accurately determine cell identities in cancerous samples, we utilized a transfer-learning-based strategy for cell subset annotation. Initially, we curated a single-cell reference dataset that encompassed 76 non-epithelial cell subsets identified in healthy data and 15 cancer-associated subsets identified in various cancer types35,36,37,38,39 (Supplementary Table 9). The number of cells of each subset was subsampled to 1,000, unless the total cell count did not exceed 1,000. Subsequently, a transformer-based reference model was trained on the reference dataset. Following this, non-epithelial cells from the pan-cancer atlas were annotated using the reference model. These procedures were executed using TOSICA, a multi-head self-attention model, enabling interpretable cell-type annotation82. The epoch number for prediction was selected as 15 (Supplementary Fig. 8c). Cells with predicted probabilities <0.5 were removed (Supplementary Fig. 8d). In the end, a total of 3,038,535 high-quality cells from 1,062 samples of 717 donors were well annotated to be 91 non-epithelial subsets.

　　To compare multicellular dynamics during tumour progression, we included eight cancer types that had at least three samples from each condition (healthy, adjacent non-tumour and tumour).

　　To quantify healthy CMs across different conditions, we aggregated the abundance of the component cell subsets within each CM, applying weights derived from NMF results. The resulting CM activities were then rescaled to range from 0 to 1. For each cancer type, only the most dominant CM was considered.

　　For each cancer type, we derived all specifically correlated cell-subset pairs using the correlation analysis module of CoVarNet. Compared with pan-tissue or pan-cancer analysis, the following more stringent cutoffs are used: coefficients >0.5, FDR <0.05 and specificity >0.95. only identified specifically correlated subset pairs are used to perform comparison across eight cancer types.

　　Specifically, we generated a frequency matrix consisting of 91 subsets and 955 samples for the pan-cancer atlas. Specifically, the top 15 subsets of each factor were used as co-occurring node candidates in a single CM network for the pan-cancer analysis.

　　To quantify cCMs across different conditions, we measured cCM02 activities using the same procedure as in healthy CMs.

　　To explore the dynamics of cCM02 during tumour progression, we constructed two co-occurring networks of which nodes are the same as original nodes in cCM02, while edges were recalculated in two scenarios. One used healthy and adjacent non-tumour samples, while the other used tumour and adjacent non-tumour samples. Thus, CM networks were constructed using unaltered nodes and new edges.

　　For each cell-subset component of cCM02, we identified its DEGs (log2-transformed fold changes >与相邻的非肿瘤样品相比，肿瘤样品中的Log2（1.2），FDR <0.05，Student t-Test）。如上所述，这些确定的DEG用于执行细胞因子分析。

　　我们仅使用肿瘤和邻近的非肿瘤样品进行了MCP识别。根据TCGA门户的RNA-Seq数据集计算了MCP的总体表达，并计算出侵入性肺部病变的微阵列数据45。使用TCGABIOlinkS83 R软件包下载TCGA数据集。仅包括具有十多个肿瘤或邻近非肿瘤样品的项目。从https://github.com/ucl-respiratory/preinvasive下载了侵入前肺部病变的数据集。

　　有关研究设计的更多信息可在与本文有关的自然投资组合报告摘要中获得。