--- title: "Introduction to HTAN" author: "Sean Davis" date: today format: html: toc: true toc-depth: 3 fig-width: 7 fig-height: 4.5 knitr: opts_chunk: comment: "#>" collapse: true message: false fig.retina: 1 vignette: > %\VignetteIndexEntry{Introduction to HTAN} %\VignetteEngine{quarto::html} %\VignetteEncoding{UTF-8} --- ```{r} #| label: setup #| include: false library(HTAN) library(dplyr) fmt <- function(x) format(x, big.mark = ",", scientific = FALSE) ``` Package: [*HTAN*](https://bioconductor.org/packages/HTAN) `r packageVersion("HTAN")` ([source](https://github.com/seandavi/HTAN)). # Introduction The Human Tumor Atlas Network (HTAN) has released single-cell, spatial, imaging and bulk data from thousands of biospecimens, with detailed clinical and biospecimen annotation. Finding the right file, tracing what it was made from and getting it into an analysis still means working across several systems, each with its own interface. *HTAN* does those steps from R. It queries HTAN's open-access metadata locally, follows the provenance links between files, downloads open files, and reads them into Bioconductor classes. There are other routes to HTAN data. Here is how this package relates to each: - The [HTAN Data Portal](https://humantumoratlas.org/explore) is the place to browse and search interactively. It exports file manifests, but it is not a programming interface to the metadata. - [ISB-CGC](https://isb-cgc.org/) publishes every release's metadata as Google BigQuery tables, which you query in SQL (from R, with bigrquery). That needs a Google Cloud account and a billing project. *HTAN* reads a parquet snapshot of the same tables without an account, and works offline once the snapshot is cached. - Synapse's own clients download a file by its Synapse ID but know nothing of HTAN's data model. *HTAN* finds the file from the metadata, then downloads it from Synapse or the CRDC General Commons and checks its md5. - [GenomicDataCommons](https://bioconductor.org/packages/GenomicDataCommons) is the Bioconductor client for NCI's Genomic Data Commons, with a similar query-then-download design. HTAN's data are not in the GDC, so it does not reach them. This vignette covers what HTAN is, how its data are organised and where they are stored, and what the package can and cannot do, and ends with a short tour of the package. Three companion vignettes go further: - [The HTAN data model, identifiers and lineage](data-model.html) explains the identifiers, the metadata tables, and how a processed file is traced back to a biospecimen and a participant. - [Exploring the HTAN Phase 1 atlas](atlas-overview.html) is a descriptive tour of the whole Phase 1 release: centers, organs, assays, data levels and clinical variables. - [From query to analysis](analysis-workflow.html) works through one dataset, from a metadata query to a spatial transcriptomics analysis in Bioconductor. The code in these vignettes runs against the real HTAN Release 7 metadata each time they are built (see [How these vignettes are built](#how-these-vignettes-are-built)). # Installation Install the package from Bioconductor with BiocManager: ```{r} #| label: install #| eval: false if (!requireNamespace("BiocManager", quietly = TRUE)) { install.packages("BiocManager") } BiocManager::install("HTAN") ``` Or install the development version from GitHub, for example before the package is available from Bioconductor: ```{r} #| label: install-github #| eval: false BiocManager::install("seandavi/HTAN") ``` # Getting started Load the package and dplyr, which is the query language for everything that follows: ```{r} #| label: load library(HTAN) library(dplyr) ``` Open a connection to the Release 7 metadata snapshot. The first call downloads about 20 MB of parquet files from Hugging Face, checks each one against its sha256 checksum and caches it. Later calls read from the cache. You do not need an account for any of this. ```{r} #| label: con con <- htan_con("r7") ``` `htan_files()` returns one row per HTAN data file, as a lazy table that runs in DuckDB until you call `collect()`. Here we count the single-cell RNA-seq Level 4 files by center and file extension: ```{r} #| label: first-query htan_files(assay == "ScRNA-seq", level == 4, con = con) |> count(HTAN_Center, extension) |> arrange(desc(n)) |> collect() ``` `htan_lineage()` traces a processed file back to the raw files and the biospecimen it came from. We take one of participant `HTA12_251`'s files: ```{r} #| label: first-lineage htan_files(assay == "ScRNA-seq", level == 4, participants = "HTA12_251", con = con) |> head(1) |> htan_lineage(con = con) |> select(related_file_id, depth, Component, HTAN_Assayed_Biospecimen_ID) |> arrange(depth) |> collect() ``` The last two steps are downloading a file and reading it. Level 2 images in the CRDC General Commons download anonymously, while processed Level 3 and 4 files are on Synapse and need a free personal access token (see [Downloading files](#downloading-files)). `read_htan()` then reads the file, returning a Bioconductor object where it can. That is the whole workflow: query, trace, download, read. The rest of this vignette explains what you are querying. # What HTAN is The Human Tumor Atlas Network is a National Cancer Institute (NCI) Cancer Moonshot initiative, launched in September 2018, that builds three-dimensional atlases of the cellular, morphological and molecular features of human cancers ([HTAN overview](https://humantumoratlas.org/overview)). The founding paper (Rozenblatt-Rosen et al., *Cell* 2020; [doi:10.1016/j.cell.2020.03.053](https://doi.org/10.1016/j.cell.2020.03.053), PMID 32302568) frames the program around *transitions*: the points at which a tumour's behaviour changes, such as - the progression of a precancerous lesion to invasive cancer, - local expansion and metastasis, and - the response of a tumour to therapy and the emergence of resistance. Bulk sequencing of a single tumour sample averages over every cell in it and records one moment in time. HTAN's premise is that interactions between tumour cells, immune cells, stroma and vasculature drive these transitions, and that seeing them takes measurements at single-cell resolution, in spatial context and over time. So HTAN centers combine single-cell and single-nucleus sequencing, spatial transcriptomics, multiplexed tissue imaging, and conventional bulk sequencing and pathology, on biospecimens with detailed clinical annotation. Many participants contribute several biospecimens: serial sections of one block, several regions of a resection, or samples taken before and after treatment. ## Phase 1 Phase 1 had ten Research Centers, each building an atlas of one or more cancer types, and two NCI pilot projects, the Human Tumor Atlas Pilot Project (HTAPP) and the Pre-Cancer Atlas Pilot Project (PCAPP). A single Data Coordinating Center (DCC) set the data standards, ran ingestion and validation, and built the portal and the other routes to the data ([HTAN overview](https://humantumoratlas.org/overview); de Bruijn et al., *Nature Methods* 2025, [doi:10.1038/s41592-025-02643-0](https://doi.org/10.1038/s41592-025-02643-0)). Four Trans-Network Projects (TNPs) compared technologies across centers. The HTAN manual lists every Phase 1 center with its identifier prefix ([HTAN centers](https://docs.humantumoratlas.org/overview/centers/)): | Prefix | Center | Focus | |---|---|---| | HTA1 | Human Tumor Atlas Pilot Project (HTAPP) | Pilot project | | HTA2 | Pre-Cancer Atlas Pilot Project (PCAPP) | Pilot project | | HTA3 | Boston University | Lung | | HTA4 | Children's Hospital of Philadelphia | Pediatric | | HTA5 | Dana-Farber Cancer Institute | Multiple cancer types | | HTA6 | Duke University | Breast | | HTA7 | Harvard Medical School | Melanoma, colorectal cancer, clonal hematopoiesis | | HTA8 | Memorial Sloan Kettering Cancer Center | Multiple cancer types | | HTA9 | Oregon Health & Science University | Breast | | HTA10 | Stanford University | Familial adenomatous polyposis | | HTA11 | Vanderbilt University | Colorectal | | HTA12 | Washington University in St. Louis | Multiple cancer types | | HTA13 | TNP: SARDANA | Technology comparison | | HTA14 | TNP: Tissue MicroArray (TMA) | Technology comparison | | HTA15 | TNP: SRRS | Technology comparison | | HTA16 | TNP: CASI | Technology comparison | Phase 1 data were released in seven numbered releases between May 2021 and November 2025 ([data release history](https://data.humantumoratlas.org/data-updates)). Release 7, published on 2025-11-26, is the last Phase 1 release and the one this package targets. The Phase 1 data model is archived ([ncihtan/data-models](https://github.com/ncihtan/data-models)), so the release is effectively frozen. The DCC's 2025 paper counts 8,425 biospecimens from 2,042 participants profiled with more than 20 assays. That paper predates Release 7, so the [atlas overview](atlas-overview.html) recounts everything from the Release 7 tables themselves. ## Phase 2 Phase 2 funds ten new atlases, five of pre-cancer and five of cancer, with identifier prefixes HTA200 to HTA209. Its first data release, on 2026-08-03, was 50 files from 11 participants of the prostate atlas ([data release history](https://data.humantumoratlas.org/data-updates)). Phase 2 uses a new data model, aligned with the NCI Cancer Research Data Commons, that changes file formats, valid values and attributes, and marks biospecimen and data file identifiers with `B` and `D` ([HTAN data model overview](https://docs.humantumoratlas.org/data_model/overview/), [identifiers](https://docs.humantumoratlas.org/data_model/identifiers/)). That model is still changing, and the ISB-CGC BigQuery tables for Phase 2 did not exist when this package was written. **This package covers Phase 1 only.** Phase 2 will be added once its tables settle. # How HTAN data are organised ## Participants, biospecimens and files Every HTAN record hangs from a three-level hierarchy: 1. A **participant** is a research participant (a patient or donor), with clinical data: demographics, diagnosis, therapy, follow-up and more. 2. A **biospecimen** is something taken from a participant (a biopsy, a resection, a blood draw) or derived from another biospecimen (a block, a section, a dissociated cell suspension, an extracted analyte). 3. A **data file** is the output of an assay run on a biospecimen, or a file computed from other files. Each level has an HTAN identifier that starts with the center prefix: `HTA11_7862` is a participant at Vanderbilt, `HTA11_7862_2000001011` is one of that participant's biospecimens, and `HTA11_7862_200000101134111` is a data file. The [data model vignette](data-model.html) covers the identifier grammar and its exceptions. ## Data levels Every assay's files are grouped into *levels*, from raw instrument output to analysis-ready summaries. The HTAN manual defines them per assay ([file standards](https://docs.humantumoratlas.org/data_model/file_standards/), [metadata standards](https://docs.humantumoratlas.org/data_model/metadata_standards/)). In outline: | Level | Sequencing (scRNA-seq, bulk, scATAC-seq) | Multiplexed imaging (CyCIF, CODEX, MIBI, H&E, ...) | 10x Visium | |---|---|---|---| | 1 | Raw reads: FASTQ, unaligned BAM | Raw tiles before stitching and registration (rarely submitted) | FASTQ | | 2 | Aligned reads: BAM | One assembled image, preferably a tiled, pyramidal OME-TIFF | Aligned BAM | | 3 | Derived molecular data: gene-by-cell count matrices, VCFs | Segmentation masks, validated channel metadata, QC'd image | Space Ranger output: counts, spot positions, tissue image | | 4 | Sample-level summaries: annotated objects, embeddings, clusters | A cell-by-feature table (marker intensities, centroids, morphology) | Processed objects: clusters, embeddings, `.h5ad` or `.rds` | Other assays follow the same idea. RPPA has no Level 1; mass spectrometry runs from raw spectra (Level 1) to protein abundance (Level 4); and some newer assays, such as 10x Xenium, are submitted as a single experiment bundle instead of separate levels. Auxiliary files (for example Visium alignment images and QC reports) and "Other Assay" files have no level. In practice most users start at Level 3 or 4: a count matrix or an annotated single-cell object, or a cell-by-marker table for imaging. Those are the files this package reads. ## Access tiers HTAN data are released in two tiers ([data access](https://docs.humantumoratlas.org/data_access/introduction/)). Open access covers all metadata (clinical, biospecimen and file-level), multiplexed and H&E images, and processed (Level 3 and 4) genomic data. Controlled access covers unprocessed genomic data (Level 1 and 2 sequencing: FASTQ and BAM files), which could identify a participant; you apply for it through dbGaP study [phs002371](https://www.ncbi.nlm.nih.gov/projects/gap/cgi-bin/study.cgi?study_id=phs002371). The package records the tier of each file in the `access` column of `htan_files()`, with open files split by where they can be fetched from. ## Where the data live, and why HTAN data are spread across several NCI and partner systems, each chosen for one job: | System | Holds | Access | |---|---|---| | [Synapse](https://www.synapse.org) (Sage Bionetworks) | Open Level 3 and 4 files, auxiliary files, submitted metadata | Free account and personal access token | | [CRDC General Commons](https://datacommons.cancer.gov/repository/general-commons) | Controlled Level 1/2 sequencing; open Level 2 images | dbGaP authorisation for controlled data; open images anonymously through GA4GH DRS | | [Imaging Data Commons](https://portal.imaging.datacommons.cancer.gov/) (IDC) | DICOM conversions of some HTAN slide images | Anonymous | | ISB-CGC BigQuery (`isb-cgc-bq.HTAN`, `isb-cgc-bq.HTAN_versioned`) | All open metadata tables, plus some processed expression and cell tables | Google Cloud account and a billing project | | [HTAN Data Portal](https://humantumoratlas.org/explore) | Search, summaries and manifests for every tier | Browser | Synapse is the DCC's ingestion platform: centers upload files and metadata there, and the DCC validates and releases them. The CRDC General Commons is NCI's long-term repository, and the only one of these systems that can hold dbGaP-controlled data. It also serves the open images, because image files are large and the CRDC is built for cloud-scale storage. IDC converts a subset of the slides to DICOM so they can be viewed in a browser alongside other NCI imaging collections. ISB-CGC loads every release's metadata into BigQuery for SQL analysis in Google Cloud ([HTAN BigQuery](https://docs.humantumoratlas.org/data_access/biq_query/), [ISB-CGC HTAN notebooks](https://isb-cancer-genomics-cloud.readthedocs.io/en/latest/sections/HTANNotebooks.html)). For an R user, this means that finding a file and fetching it are separate problems. The metadata tell you which files exist and how they relate; the file itself is on Synapse or the CRDC, behind different authentication. # What this package does, and what it doesn't *HTAN* is a client for the open-access data of HTAN Phase 1. It does five things: 1. Query the metadata locally. `htan_con()` opens a DuckDB connection over a parquet snapshot of the ISB-CGC Release 7 metadata tables; `htan_tables()`, `htan_tbl()` and `htan_columns()` list, open and describe them; `htan_files()` gives one row per file with its assay, level, participants, biospecimens and access tier. Queries are dplyr code that dbplyr translates to SQL, so they run in DuckDB and only the result comes into R. 2. Trace lineage. `htan_lineage()` walks the `id_provenance` table from a file to its ancestors (down to Level 1 and the biospecimen) or its descendants. 3. Download open files. `htan_download()` fetches open Level 2 images anonymously from the CRDC General Commons, and Level 3/4 files from Synapse with a personal access token. It checks each file's md5 and guards against accidentally large downloads. 4. Read files into Bioconductor classes. `read_htan()` turns `.h5ad`, `.rds` (including Seurat objects), 10x `.h5`, Space Ranger output and imaging cell tables into `SingleCellExperiment` or `SpatialExperiment` objects. 5. Assemble a `MultiAssayExperiment`. `htan_experiment()` combines objects from several files, links every cell or spot to its participant and attaches clinical data. It does not: - download controlled-access data (it stops with a pointer to dbGaP and the CRDC tools); - cover HTAN Phase 2; - query the large processed tables in BigQuery (single-cell expression and imaging cell tables, about 2.4 TB), or BigQuery at all; - query IDC, CELLxGENE or the HTAN portal's database; - analyse images (it returns image paths, which RBioFormats or cytomapper can read). Level 4 files are heterogeneous: each center chose its own object classes, assay names and annotations. `read_htan()` is best effort, so check what it returns. # The metadata snapshot ## What it is ISB-CGC publishes HTAN's metadata in two BigQuery datasets: `HTAN`, whose `_current` tables always point at the newest release, and `HTAN_versioned`, which keeps every release's tables under names such as `clinical_tier1_demographics_r7`. The snapshot is an export of the 78 tables that make up Release 7, each pinned to its versioned copy, written as zstd parquet with a manifest of checksums. It is published as the Hugging Face dataset [`seandavis/htan`](https://huggingface.co/datasets/seandavis/htan), one git tag per release, so `htan_con("r7")` always reads the same bytes. `htan_snapshot_info()` reports which snapshot a connection is reading: ```{r} #| label: snapshot-info info <- htan_snapshot_info(con) info[c("release", "phase", "source", "n_tables", "total_rows", "build_date")] ``` The tables fall into two groups: clinical and biospecimen tables, and metadata tables (one or more per assay and level, plus `id_provenance`, `gc_drs_map` and the data-model `schema`): ```{r} #| label: table-groups htan_tables(con) |> count(category) ``` A few tables were not refreshed in Release 7, so the snapshot takes them from the last release that had them: ```{r} #| label: old-tables htan_tables(con) |> filter(source_version != "r7") |> select(name, source_version, rows) ``` ## How it relates to the authoritative sources The snapshot is a copy of a copy. The DCC's records in Synapse are the source of truth. ISB-CGC derives its BigQuery tables from them at each release, and the snapshot is an export of those tables at one release. Three things follow from that: - It can lag or differ. If the HTAN portal and the snapshot disagree, the portal is right. Fixes the DCC made after Release 7 reach the snapshot only if ISB-CGC republishes the r7 tables and the snapshot is rebuilt. - It is unofficial. The DCC does not host or endorse it. It redistributes open-access metadata, so HTAN's terms of use still apply, and work that uses the data should cite HTAN's publications, as the [HTAN manual](https://docs.humantumoratlas.org/data_access/citing_htan/) asks. - It holds metadata only. File contents stay on Synapse and the CRDC. What you get in return is convenience: the snapshot needs no Google account or billing project, works offline once cached, and answers most questions in milliseconds. You can also browse it without R. The Hugging Face page has a data viewer and a SQL console, and DuckDB reads the files directly as `hf://datasets/seandavis/htan@r7/.parquet`. To use a mirror or a local build, set `options(HTAN.base_url = ...)`. The package also ships a tiny fixture of the snapshot (all 78 tables cut down to six participants), which its examples and tests use: ```{r} #| label: fixture fixture <- system.file("extdata", "snapshot", package = "HTAN") con_small <- htan_con("r7", base_url = fixture) htan_snapshot_info(con_small)[c("release", "n_tables", "total_rows")] htan_disconnect(con_small) ``` # A quick tour ## Tables and columns `htan_tbl()` opens any table lazily. Here we count participants by gender in the demographics table, with the counting done in DuckDB: ```{r} #| label: demographics htan_tbl("clinical_tier1_demographics", con) |> count(Gender, sort = TRUE) |> collect() ``` Note the same value spelled several ways. Centers submitted their own metadata, and the DCC's validation did not normalise case, so clean values before you count or join on them: ```{r} #| label: demographics-clean htan_tbl("clinical_tier1_demographics", con) |> mutate(Gender = tolower(Gender)) |> count(Gender, sort = TRUE) |> collect() ``` `htan_columns()` returns the column descriptions recorded in the BigQuery schema. It is the quickest way to learn what a field means: ```{r} #| label: columns htan_columns("id_provenance", con) |> select(column, description) ``` ## Files `htan_files()` combines every file-level table in the snapshot. Its columns include the `Component` (the assay and level, as HTAN names it), `assay` and `level` split out of it, the file `extension`, size and md5, the Synapse `entityId`, the General Commons `drs_uri`, the participants and biospecimens from `id_provenance`, and the `access` tier. Counting files by tier: ```{r} #| label: files files <- htan_files(con = con) files |> count(access) |> arrange(desc(n)) |> collect() ``` There are `r fmt(pull(count(files)))` files in Release 7. The tiers are: - `open-gc`: open Level 2 images in the General Commons, downloadable anonymously; - `open-synapse`: open files on Synapse (Level 3/4, auxiliary and other files, and the images that were never copied to the General Commons), downloadable with a token; - `controlled`: Level 1/2 sequencing under dbGaP, which the package will not download. Filters go straight into `htan_files()`. For example, every single-cell Level 4 file in AnnData format: ```{r} #| label: files-h5ad htan_files(assay == "ScRNA-seq", level == 4, extension == ".h5ad", con = con) |> select(HTAN_Data_File_ID, HTAN_Center, File_Size, n_participants) |> arrange(File_Size) |> collect() ``` Some files, such as merged single-cell objects, come from several participants; their `HTAN_Participant_ID` is a comma-separated list. To find every file that involves a participant, use the `participants` argument instead of `==`; it handles that case: ```{r} #| label: files-participant htan_files(participants = "HTA11_7862", con = con) |> count(Component, access) |> arrange(Component) |> collect() ``` This one participant, from the Vanderbilt colorectal atlas, has Visium spatial transcriptomics, single-cell RNA-seq, whole-exome sequencing and imaging. The [analysis vignette](analysis-workflow.html) analyses a Visium section from another Vanderbilt participant. ## Lineage `htan_lineage()` follows `HTAN_Parent_Data_File_ID` links in `id_provenance`. Depth 0 is the query file; negative depths are its ancestors: ```{r} #| label: lineage htan_lineage("HTA11_7862_200000101134111", con = con) |> select(related_file_id, depth, Filename) |> arrange(depth, related_file_id) |> collect() |> mutate(Filename = basename(Filename)) ``` The Level 4 Visium object was computed from four Level 3 Space Ranger files, which came from a Level 2 alignment, which came from two Level 1 FASTQ files. The [data model vignette](data-model.html) explains how these links are recorded and where they get complicated. ## Downloading files `htan_download()` routes each file by its `access` tier, checks its md5, and returns a table of local paths. Open images in the General Commons download anonymously; this one is a 250 KB imaging mass cytometry image: ```{r} #| label: download-gc tiff <- htan_files( drs_uri == "dg.4DFC/6231a7d4-7e68-11ee-b51d-e7db3b9207b8", con = con) tiff |> select(HTAN_Data_File_ID, Component, File_Size, access) |> collect() dl <- htan_download(tiff, con = con) dl$status read_htan(dl) |> basename() ``` `read_htan()` returns image paths unchanged; RBioFormats or cytomapper can read them. Processed files are on Synapse. To fetch them: 1. create a free account at ; 2. make a personal access token (Account Settings, then Personal Access Tokens) with download permission; 3. put it in the `SYNAPSE_AUTH_TOKEN` environment variable, for example in `~/.Renviron`, and restart R. This chunk is not run when the vignette is built, because it needs a token; the [analysis vignette](analysis-workflow.html) runs the same steps when one is set. ```{r} #| label: download-synapse #| eval: false visium <- htan_files(HTAN_Data_File_ID == "HTA11_99999933430_8422134111", con = con) dl <- htan_download(visium, con = con) spe <- read_htan(dl) ``` Some HTAN files are tens of gigabytes. `htan_download()` refuses to start if the files total more than `max_bytes` (20 GB by default). It keeps every file it fetches in a download cache, so asking for a file again rechecks its md5 and serves it from the cache instead of downloading it. Pass `dest` to copy the files to a directory of your choice as well. Controlled files are refused with a classed error: ```{r} #| label: download-controlled fastq <- htan_files(access == "controlled", con = con) |> arrange(HTAN_Data_File_ID) |> head(1) tryCatch( htan_download(fastq, con = con), htan_error_controlled = function(e) cat(conditionMessage(e)) ) ``` ## Reading and assembling `read_htan()` dispatches on the file extension: | File | Returns | |---|---| | `.h5ad` | `SingleCellExperiment` (via anndataR), or `SpatialExperiment` when there is a spatial embedding | | `.rds`, `.RData` | the object; Seurat objects become `SingleCellExperiment` | | 10x `.h5` | `SingleCellExperiment` (via TENxIO) | | Space Ranger `outs/` directory | `SpatialExperiment` (via VisiumIO) | | Imaging Level 4 `.csv` cell table | `SpatialExperiment`, cells as columns | | `.tif`, `.ome.tiff`, `.svs` | the path | `htan_experiment()` then builds a `MultiAssayExperiment` from a named list of objects, using the file IDs to find each object's participant and attaching that participant's demographics and diagnosis as `colData`. The [analysis vignette](analysis-workflow.html) does this with real data. # How these vignettes are built The vignettes run their code when the package is built. They read the Release 7 snapshot from Hugging Face with `htan_con("r7")`, about 20 MB that BiocFileCache downloads once and then reuses. The tables, the figures and most counts in the text are computed from the real metadata at build time; the few typed in by hand are file sizes and the published totals from the DCC's 2025 paper. With the release pinned to `r7`, the numbers should not change between builds. The [analysis vignette](analysis-workflow.html) also downloads a file from Synapse, which needs a personal access token. Where `SYNAPSE_AUTH_TOKEN` is set, as in the package's own continuous integration, those steps run too. Without it, for example on the Bioconductor build machines, they are shown as code without output. # References - Rozenblatt-Rosen O, Regev A, Oberdoerffer P, et al. The Human Tumor Atlas Network: charting tumor transitions across space and time at single-cell resolution. *Cell* 2020;181(2):236-249. [doi:10.1016/j.cell.2020.03.053](https://doi.org/10.1016/j.cell.2020.03.053). PMID 32302568. - de Bruijn I, Nikolov M, Lau C, et al. Sharing data from the Human Tumor Atlas Network through standards, infrastructure and community engagement. *Nature Methods* 2025;22(4):664-671. [doi:10.1038/s41592-025-02643-0](https://doi.org/10.1038/s41592-025-02643-0). PMID 40164800. - HTAN website: ; data release history: . - HTAN manual: , in particular [centers](https://docs.humantumoratlas.org/overview/centers/), [identifiers](https://docs.humantumoratlas.org/data_model/identifiers/), [file standards](https://docs.humantumoratlas.org/data_model/file_standards/), [data access](https://docs.humantumoratlas.org/data_access/introduction/) and [BigQuery](https://docs.humantumoratlas.org/data_access/biq_query/). - ISB-CGC HTAN notebooks: . - The metadata snapshot: . # Session information ```{r} #| label: session-info sessionInfo() ```