Multimodal training data of paired transcriptomes and text
To establish a large training dataset of transcriptomes and their matched textual annotations, we processed two community-scale repositories: GEO10,11 and CELLxGENE Census (version 2023-12-15)12.
From GEO we obtained RNA-seq count matrices of 722,425 human transcriptomes that were uniformly processed by the ARCHS4 project (version 2.2, 30 May 2023)16. We removed 7,049 samples with fewer than 250 expressed genes and 9,946 samples that overlapped with our Human Diseases dataset (described below), resulting in 705,430 transcriptomes. For each transcriptome, we obtained the associated metadata using the Entrez API with either the sample’s experiment accession, Bio-Sample accession or GEO accession (in this order of priority) and we removed binary data and special characters using the unidecode package. We included metadata fields describing study-level descriptions such as series design, growth protocol and series summary, as well as sample-level fields including sample title, treatment, and other fields that varied across studies.
From CELLxGENE Census we obtained scRNA-seq count matrices of 257 studies that were conducted on human samples using one of four assays with comparable data types (10x Genomics, Seq-Well, Drop-seq and CEL-seq2). We excluded cells with fewer than 100 expressed genes, resulting in a total of 19,663,838 scRNA-seq profiles. Within each sample, we grouped cells on the basis of their cell-level metadata, only considering metadata fields with fewer than 500 distinct values across all cells in the corresponding study, and we calculated pseudo-bulk transcriptomes by taking the mean of the scRNA-seq count values across all cells with identical metadata. Each of these 376,983 pseudo-bulk transcriptomes was linked to its cell-level metadata such as the cell type and to study-level metadata such as a study title and study abstract.
For each sample (from GEO) or pseudo-bulk transcriptome (from CELLxGENE Census), we generated a concise natural-language summary from the metadata using Mixtral 8x7b (Q5_K_M quantized version)58, using the llama.cpp Python bindings with a sampling temperature of 0.2, nucleus sampling (top_p) of 0.9 and top probability sampling (top_k) of 50. This LLM-based generation of concise textual annotations was guided by a prompt that we engineered on the basis of established practices such as pre-action reasoning59, role playing60 and few-shot learning61 with a manually curated set of examples. The prompt and illustrative examples of generated textual annotations are shown in Supplementary Note 2.
Data processing was performed on compute nodes with eight A100 80-GB GPUs. We estimate a total of 5,000 GPU hours for the LLM-assisted generation of textual annotations. The training dataset is available for download through the project website (https://cellwhisperer.bocklab.org).
Multimodal design of the CellWhisperer embedding model
To enable transcriptome data analysis using natural language, we pursued a multimodal contrastive learning approach, with a neural network architecture that integrates matched pairs of transcriptomes and text into the same embedding space. Specifically, we adapted the CLIP method9, originally developed for joint multimodal embedding of images and text, and implemented it using pytorch62 with the lightning63 and the transformers64 libraries.
To account for the different properties of transcriptomes and natural-language text, CellWhisperer embeds the transcriptomes with Geneformer17 and the textual annotations with BioBERT18. The outputs of these two models are transformed into two 2,048-dimensional vectors using separate adapter modules, each consisting of two learnable linear layers connected by a rectified linear unit nonlinearity (ReLU) and followed by batch layer normalization. To enhance computational efficiency, we adopted the LiT approach47, initializing both models with pretrained weights and fine-tuning the text model and the adapter modules, while keeping the transcriptome model frozen.
The Geneformer model for transcriptome embedding uses 12 transformer encoder layers to process transcriptomes as ‘sentences of genes’ ranked by their expression; it was trained on ~30 million scRNA-seq profiles17. The BioBERT model for text embedding was trained on large biomedical text corpora18. We also tested alternative models (scGPT24 and UCE25 for transcriptome embedding; BioGPT65 for text embedding), which led to similar results (Supplementary Note 3).
Training of the multimodal CellWhisperer embedding model
We trained the CellWhisperer embedding model on the 1,082,413 matched pairs of transcriptomes and textual annotations that we curated from GEO and CELLxGENE Census. For each pair, the transcriptome and the textual annotation were tokenized for processing with the two modality-specific transformer models, Geneformer and BioBERT. Specifically, the transcriptomes were sorted by gene expression levels and the top 2,048 most highly expressed genes were tokenized with a dictionary of human gene symbols17. The textual annotations were tokenized using WordPiece18,66 and trimmed to a maximum of 128 tokens for training efficiency (the vast majority of textual annotations were shorter and, thus, remained untrimmed).
We trained the multimodal embedding model with a mini-batch size of 512 and InfoNCE-based loss, which maximizes the cosine similarity between matched pairs of transcriptomes and textual annotations while minimizing the cosine similarity between all other (unmatched) pairs in a given training batch. Training was scheduled for 16 epochs at a maximum learning rate of 0.00001. For the first 3% of all training steps, we froze the Geneformer and BioBERT models to only train the embedding adapters and we linearly increased the learning rate from 0 to its maximum value (warmup). We then unfroze the BioBERT model and continued training with a second learning rate warmup for an additional 3% of the total number of training steps, followed by a learning rate cosine schedule over the remaining 94% of steps of the 16 epochs. The outputs of the consistently frozen Geneformer model were cached to decrease computational complexity during training.
Optimal hyperparameters, such as the maximum learning rate, were determined by stochastic grid search. As the performance metric for this optimization procedure, we tested the model’s ability to retrieve the correct textual annotation for a given transcriptome in our Human Diseases dataset. We used a deduplicated version of this dataset to increase the robustness of retrieval scoring, thereby reducing the impact of data points with very similar or identical textual annotations. We also used this metric to control for overfitting during model training. The corresponding validation scores of our final model are shown in Extended Data Fig. 1b.
A full training run (16 epochs) was completed in less than 24 h on an A100 GPU. The model checkpoints are available for download on the project website (https://cellwhisperer.bocklab.org). An ablation study providing a technical evaluation of the final model is described in Supplementary Note 3.
Collection and curation of evaluation and demonstration data
To assess CellWhisperer’s performance and to demonstrate its functionality, we prepared the following bulk RNA-seq and scRNA-seq datasets and made sure to exclude them from all training data.
Human Diseases
We obtained 14,112 disease-annotated tissue samples from GEO, by querying the MetaSRA database for the terms ‘primary tissue’ and ‘disease state’, followed by manual curation based on metadata obtained from SRA, GEO and PubMed. To emulate a realistic application scenario with differences in the initial data processing, we processed these data with a different bioinformatics pipeline and a different LLM than what was used for the training dataset. Specifically, we used the fetchngs and rnaseq pipelines67,68,69 for preparing the transcriptome data, while the textual annotations used for retrieval analysis were prepared from metadata downloaded through the Entrez API (biopython) using GPT-4 through the OpenAI API with zero-shot prompting. Because this dataset contains many samples with identical or highly similar textual annotations, we also derived a Human Diseases (deduplicated) dataset for use in retrieval scoring. To that end, we processed all 14,112 textual annotations with BioBERT, performed hierarchical clustering on the embedding vectors (metric: cosine, linkage: average), retained the top 100 clusters and selected the transcriptome that was closest to the cluster center for each cluster, resulting in a total of 100 deduplicated transcriptomes with their nonredundant textual annotations.
Tabula Sapiens
From the Tabula Sapiens atlas of scRNA-seq profiles19, we obtained transcriptomes for 483,152 single cells across 15 individuals, 24 organs and 177 annotated cell types, with their cell_ontology_class annotation as cell type annotations (we standardized spelling and capitalization). Because of many infrequent cell types in the Tabula Sapiens dataset, we also derived a Tabula Sapiens (20 common cell types) dataset by retaining only the 20 most common cell types in liver, lung and blood (184,450 single cells).
ImmGen
From the RNA-seq profiles and manually curated cell types provided by the ImmGen consortium20 (GSE227743), we established a dataset comprising 42 bulk transcriptome profiles across five human immune cell types.
Asian Immune Diversity Atlas
From the Asian Immune Diversity Atlas21, we obtained scRNA-seq profiles for peripheral blood mononuclear cells from 619 healthy individuals spanning seven population groups in five countries across Asia. We randomly selected up to 1,000 cells per annotated cell type (including all cells if fewer than 1,000 were available), resulting in a total of 7,842 cells across nine cell types.
Human Development
We obtained scRNA-seq data of human embryonic development, spanning 3–38 days after fertilization (European Nucleotide Archive, ArrayExpress and GEO accessions: PRJEB30442 (ref. 28), E-MTAB-3929 (ref. 26), E-MTAB-8060 (ref. 27), PRJEB40781 (ref. 29), GSE232861 (ref. 30) and GSE155121 (ref. 31)). Raw sequencing data were uniformly processed and aligned to the human genome (assembly GRCh38) using Cell Ranger70 (E-MTAB-8060, GSE232861 and GSE155121) or using the rnaseq pipeline67,69 (all other datasets). The count matrices were corrected for batch effects with scANVI71, resulting in an integrated dataset of 95,092 scRNA-seq profiles.
Pancreas
We obtained a scRNA-seq meta-analysis dataset of human pancreas (https://figshare.com/ndownloader/files/43480497) comprising 16,382 scRNA-seq profiles. This dataset was previously assembled from individual studies that used different transcriptome profiling technologies and was used for benchmarking of methods for single-cell data processing and dataset integration22.
Colonic Epithelium
To showcase CellWhisperer’s analysis of user-provided datasets, we obtained scRNA-seq data for epithelial and other cells from the colon, starting from a normalized read count matrix retrieved from GEO (GSE116222). The dataset consists of 11,175 cells from three healthy individuals and three patients with inflammatory bowel disease. For these patients, samples were taken from inflamed and noninflamed regions.
Evaluation of the CellWhisperer embedding model
We evaluated the trained multimodal embedding model in four complementary ways. First, we assessed CellWhisperer’s capability to predict cell characteristics such as cell types, tissues, organs and diseases in a zero-shot manner, by comparing CellWhisperer’s transcriptome embeddings to the text embeddings of the corresponding metadata-provided cell characteristic from the evaluation datasets. To that end, we embedded the transcriptomes and the corresponding cell characteristics (as natural-language statements, for example in the form ‘A sample of
Second, we evaluated how well the CellWhisperer embeddings capture biological (rather than technical) differences between cells and compared this to scFMs (Geneformer, scGPT and UCE). To that end, we embedded the Tabula Sapiens transcriptomes using either the CellWhisperer embedding model or any of the three scFMs and then compared batch effect correction and cell type clustering for the embeddings following a previously described workflow72. For cell type clustering, we used the average bio score24, which is the arithmetic mean of three metrics: average silhouette width22, normalized mutual information and adjusted rank index. For batch effect correction, we used a variant of average silhouette width as implemented in the silhouette_batch function22.
Third, we leveraged the broad catalog of biological phenomena provided by gene set libraries to assess which aspects of molecular biology were implicitly learnt by the CellWhisperer embedding model. To that end, we obtained 8,812 gene sets from gene set libraries of cell types19,73,74, diseases75 and Gene Ontology (GO) terms76 and we performed Gene Set Variation Analysis (GSVA) using the GSVA package77 with the ssgsea enrichment function78. GSVA supports gene set enrichment analysis based on read count matrices, providing quantitative results for individual samples against a background of unrelated samples. We ran GSVA across all 8,812 gene sets on the Human Diseases dataset, resulting in an 8,812-dimensional vector of gene set enrichments for each transcriptome. For the same transcriptomes, we also obtained CellWhisperer scores by embedding the names of the gene sets (such as ‘colorectal cancer’ from the OMIM_Extended disease library or ‘response to type I interferon’ corresponding to GO:0071357 from GO_Biological_Process_2023). For each gene set, we compared the GSVA and CellWhisperer scores across the transcriptomes in the dataset using the Pearson correlation and Kolmogorov–Smirnoff statistic (the latter was included for its robustness to nonlinear data).
Fourth, we evaluated complex queries and their variations using the Human Development dataset. To that end, we prepared queries for four key embryonic developmental stages (zygote, blastula, gastrula and organogenesis) and compared CellWhisperer scores for variations of these queries. We established the queries on the basis of the 23 Carnegie stages obtained from the Human Developmental Stages ontology (https://bioportal.bioontology.org/ontologies/HSAPDV), which we grouped into zygote (stages 1–2), blastula (stages 3–5), gastrula (stages 6–8) and organogenesis (stages 9–23). We condensed their annotations using GPT-4o with the following prompt: ‘Please, summarize the following sentences in just two sentences (max 500 characters)’. We visualized CellWhisperer scores for the four queries in a time-resolved manner (Fig. 3a). Moreover, to assess CellWhisperer’s robustness to query variations (Extended Data Fig. 2f), we generated five variants per query using GPT-4o with the following prompt: ‘Rewrite the provided text in five different variants. The length of the variants may vary slightly, but make sure that the semantics (i.e. the meaning of the generated variant text) remains close to the initially provided text. Return the variants as a JSON-formatted list of strings (key = “variants”)’. We then compared the CellWhisperer scores in the Human Development dataset between the query variants.
Benchmarking of CellWhisperer’s zero-shot cell type prediction performance against alternative methods
We benchmarked CellWhisperer’s performance in zero-shot cell type prediction against a widely used marker-based method and against scFMs that we fine-tuned for cell type prediction.
Marker-based prediction was performed with CellAssign23 and the CellMarker 2.0 cell type marker database79, enabling reference-free cell type prediction akin to CellWhisperer’s zero-shot predictions. To resolve cell type naming mismatches between the CellMarker 2.0 database and our evaluation datasets, we mapped them using GPT-4o with the following prompt applied to each cell type in each evaluation dataset: ‘Assign the cell type
For scFM-based cell type predictions, we fine-tuned three scFMs (Geneformer, scGPT and UCE) using 376,983 pseudo-bulk transcriptomes from CELLxGENE Census. To optimize performance, we tested three configurations for each scFM: (1) freeze the scFM and train only its classification head; (2) unfreeze the scFM and fine-tune the entire model; and (3) augment the pseudo-bulk data with single-cell data (at a 1:4 ratio) during training. We assessed the performance on our evaluation datasets by mapping the classifier outputs (class probabilities) to the cell types in each evaluation dataset using an LLM with the following prompt: ‘Assign the query cell type
Identification of marker genes of human organ development using CellWhisperer
The CellWhisperer-identified marker genes for each organ were obtained by selecting the cells with the top 3% highest CellWhisperer scores for the corresponding organ-specific query (such as ‘heart’) and then identifying the differentially expressed genes (log2-transformed fold-change threshold: 1.5) for the selected cells against all other cells.
For comparison, we obtained a list of marker genes that was based on an atlas of fetal gene expression32. To facilitate a fair comparison to these marker genes, we selected matching numbers of CellWhisperer-identified organ marker genes (using a flexible log2 fold-change threshold). We then compared the number of papers in PubMed that co-mentioned each gene name and the corresponding organ in titles, keywords or abstracts using the following PubMed query (run with Biopython’s entrez.esearch function): ‘
Gene set enrichment was assessed using gseapy’s enrichr method80 for the gene set libraries GO_Biological_Process_2023, GO_Molecular_Function_2023, GO_Cellular_Component_2023 and KEGG_2021_Human. Spatial expression patterns for specific genes and lineage gene sets were derived from a corresponding Carnegie stage 8 embryo dataset33 that is accessible online (https://cs8.3dembryo.com).
Multimodal training dataset of chat conversations
To enable natural-language chats with CellWhisperer, we fine-tuned the Mistral 7B LLM with conversations about transcriptomes and cell states. We generated 106,610 such conversations for transcriptomes from our training dataset of 1,082,413 GEO and CELLxGENE Census data points. To mitigate abundance biases in these repositories, we took a weighted subsample with sampling probabilities that were inversely proportional to the local point density calculated with densMAP81, such that transcriptome–text pairs in lowly covered regions were preferentially picked. For each of these subsampled transcriptome–text pairs, we generated a chat-like conversation using one of two LLMs (GPT-4 or Mixtral) on the basis of (1) the transcriptome’s top 50 most highly expressed genes (based on normalized expression across all transcriptomes in the dataset to improve the representation of lowly expressed but important genes such as certain transcription factors); (2) the top 50 GSVA-derived gene sets; and (3) the transcriptome’s textual annotation. We prepared conversations in four different ways, resulting in ‘simple’, ‘detailed’, ‘complex’ and ‘conversational’ chats. The LLM prompts and examples of the generated conversations are shown in Supplementary Note 2.
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Simple chats were generated for 10,000 transcriptomes by using a generic question (randomly selected from a list of ten manually prepared candidates: ‘What does the sample represent?’, ‘What do these cells represent?’, etc.) and the transcriptome’s generated textual annotation as the answer.
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Detailed chats were generated for 10,000 transcriptomes using an LLM (GPT-4 through the OpenAI API) with zero-shot prompting and the same questions as in the simple chats. GPT-4 produced much more extensive answers compared to the transcriptome’s textual annotations used in the simple chats.
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Complex chats were generated for 5,000 transcriptomes using an LLM (GPT-4 through the OpenAI API) with a few-shot prompt using pre-action reasoning59 to produce more profound question-answer pairs.
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Conversational chats for 81,610 transcriptomes were generated using an LLM (Mixtral 8x7b) with a few-shot prompt that instructed a natural conversation with multiple questions and answers between a researcher and an AI assistant about the biological state of the corresponding sample or cell cluster.
Multimodal architecture and training of the CellWhisperer chat model
The CellWhisperer chat model follows the LLaVA approach14. We initialized a two-layer adapter module that transforms the 2,048-dimensional CellWhisperer multimodal embedding for a bulk or single-cell transcriptome into eight 4,096-dimensional embeddings, corresponding to eight token embeddings in the Mistral 7B LLM13, which is the basis for the CellWhisperer chat model. To train our model on the basis of the training conversations, we passed transcriptome-derived token embeddings alongside their corresponding questions to the LLM and optimized the LLM and the transcriptome–token adapter to generate the corresponding answers using the original autoregressive learning objective of the Mistral LLM, with a loss mask for the question and the transcriptome-related parts of the conversations. In a first training step, we kept the LLM frozen and trained the adapter layers with supervision on an extended version of the simple chats that included our entire training dataset (1,082,213 question-answer pairs) for one epoch. In a second training step, we unfroze the Mistral LLM and fine-tuned both the LLM and the adapter layers on the 106,610 generated training conversations for one epoch.
LLM fine-tuning was performed on four A100 80-GB GPUs with a total runtime of 3 hours. The generated conversation datasets and model checkpoints are available for download on the project website.
Evaluation of the CellWhisperer chat model
To evaluate the CellWhisperer chat model, we created two question-answer conversation datasets and assessed CellWhisperer’s propensity for the correct answers, quantified on the basis of its predicted output token probabilities using the perplexity metric. This metric is defined as the exponentiated average negative log-likelihood of the sequence of tokens that correspond to the correct (ground truth) answer for a given prompt (that is, the response to the question). In other words, it measures the degree of surprise for the model when confronted with an answer to a given pair of a question and the corresponding transcriptome embedding.
The first dataset (termed Evaluation Conversations) provides a general evaluation of CellWhisperer’s chat functionality by randomly selecting 200 of the 106,610 training conversations, half based on GEO (bulk transcriptomes) and half based on CELLxGENE Census (pseudo-bulk transcriptomes from scRNA-seq data), with proportional representation of simple, complex and conversational chats. These conversations were trimmed to the first question-answer pairs, text that did not refer to biological insights was manually removed, and the data points associated with these conversations were excluded from CellWhisperer embedding and chat model training. To assess how well CellWhisperer took the transcriptome data into account when generating its chat response, we computed the perplexity for each test conversation not only with the matched transcriptome embedding but also with 30 mismatched transcriptome embeddings, randomly sampled from the dataset. Against this background, we reported the quantile of the matched-transcriptome perplexity.
The second dataset (termed Cell Type Conversations) assesses how well the CellWhisperer chat model learned information about the cell types associated with the transcriptome embeddings. For each of the 177 cell types in Tabula Sapiens, we sampled up to 100 pseudo-bulk transcriptomes, resulting in a total of 15,158 individual transcriptomes. Conversations in this dataset all contain the same question (‘Which cell type is this cell?’) and use the annotated cell type label as a natural-language answer by prefixing them with the following text: ‘This cell is a …’. To evaluate the preference of a given transcriptome for its annotated cell type, we calculated the perplexity against two backgrounds: (1) by randomly swapping the transcriptome embeddings (30 times, as above) and (2) by randomly swapping the cell type answer texts (176 times, to all other possible cell types).
For both datasets we compared the perplexity quantile scores of the CellWhisperer chat model with two text-only LLMs: Mistral 7B (without fine-tuning) and the much larger Llama 3.3 with 70 billion parameters, representing state-of-the-art LLMs (we could not compute the perplexity for closed models such as GPT-4 or Gemini as they do not provide output token probabilities). We provided the transcriptome context to these text-only LLMs through the following prepended prompt, containing the list of dataset-normalized top 50 genes: ‘USER: Respond to my request regarding a sample of cells characterized by its top expressed genes being
Integration of CellWhisperer into CELLxGENE Explorer, web hosting and user-provided dataset processing
We integrated CellWhisperer into CELLxGENE Explorer for web-based analysis of scRNA-seq data15 (version 1.2.0; https://docs.cellxgene.cziscience.com). To that end, we added a chat box to the CELLxGENE Explorer user interface and implemented two API endpoints: (1) natural-language chat functionality to retrieve information about a selected group of cells and (2) search interface to obtain CellWhisperer scores for user-provided free-text queries, which are displayed as cell-level color maps. User requests that start with ‘show’, ‘show me’, ‘search’ or ‘search for’ are always routed to the second endpoint. Other requests are routed to the second endpoint only if the user has not selected any cells. The CellWhisperer chats inside CELLxGENE Explorer are primed with a user-hidden prompt (Supplementary Note 2) designed to reduce the prevalence of donor-specific metadata in the responses. This prompt also includes the top 50 expressed genes for the selected cells, as we observed better biological interpretability of responses and a slight performance improvement when providing the CellWhisperer chat model with these gene names in addition to the transcriptome embedding (Extended Data Fig. 4c).
We host the CellWhisperer-augmented version of CELLxGENE Explorer as a web tool using docker/podman/docker-compose. Each dataset receives its dedicated server job and these jobs are jointly exposed through an nginx web server. CellWhisperer’s embedding and chat capabilities are hosted through independent API server jobs, which are accessible online to enable running the software tool locally on computers without a GPU.
User-provided datasets have to be prepared for CellWhisperer analysis using an automated pipeline (instructions are provided in the source code repository; https://github.com/epigen/cellwhisperer). Their interactive analysis requires a local installation of the CellWhisperer web tool, as it is currently not supported to upload user-provided datasets to our publicly accessible CellWhisperer instance (https://cellwhisperer.bocklab.org). The pipeline first processes all transcriptome measurements using the CellWhisperer embedding model, facilitating efficient CellWhisperer scoring. Next, a UMAP is calculated for the embeddings, followed by clustering with the Leiden algorithm and generation of a brief textual annotation for each cluster. Then, the CellWhisperer chat model processes the cluster-averaged transcriptome embeddings to generate detailed textual descriptions, which are subsequently condensed into brief textual annotations with an LLM such as GPT-4 (through the OpenAI API, used in this study) or Mixtral 8x7B (which can be run locally and is also supported by our source code). All prompts are provided in Supplementary Note 2. The user-provided dataset with all preprocessed elements is stored as a single h5ad object for use with the CellWhisperer web tool. This data-processing pipeline is run with a single shell command and was used to process the datasets for Figs. 1, 3–5 and for the demonstration video (Supplementary Video 1). Data processing has a runtime on the order of minutes for typical datasets on a compute node with one A100 GPU or on the order of hours when relying on a standard laptop without a GPU.
Conventional bioinformatics analysis of the Colonic Epithelium dataset
To compare the CellWhisperer analysis of the Colonic Epithelium dataset to a conventional bioinformatics analysis of the same data, we wrote custom Python code following established practices for scRNA-seq analysis. We retrieved the total-read-normalized and log1p-transformed scRNA-seq read count matrix from GEO (GSE116222) and used the inverse transform (exp1m) to obtain gene expression profiles that formed the basis for our analysis. We used scVI44 with sampleID as a batch variable to remove batch effects (parameters: 4,000 highly variable genes, n_layers = 2, n_latent = 30, gene_likelihood = ‘nb’). Next, we computed and plotted a UMAP based on scVI’s latent space using scanpy82. We used CellTypist45 with the ‘Cells_Intestinal_Tract.pkl’ model83 to automatically annotate cell types, first with majority voting (as recommended by the CellTypist instructions) and then without (which allowed us to identify transcriptomes that were annotated as stem cells). On the basis of these annotations, we determined differentially expressed genes for selected cell types in a one-versus-all manner using scanpy and the Wilcoxon rank sum test. Lastly, to obtain a measure of stemness of inflamed and noninflamed cells, we computed a gene module score based on a corresponding gene set46.
Reporting summary
Further information on research design is available in the Nature Portfolio Reporting Summary linked to this article.
