DSGym/DSGym-Tasks huggingface dataset:
Context: Domain: single-nucleus multiome (RNA+ATAC); Study: Breast tissue dataset with epithelial subtypes BM_BAα, BM_BAβ, LASP_AP, LASP_BL, LHS_HSα, LHS_HSβ annotated in epi_sub field. Dataset includes both RNA and ATAC quality metrics.

Question: Filter for cells with defined epithelial subtypes (BM_BAα, BM_BAβ, LASP_AP, LASP_BL, LHS_HSα, LHS_HSβ). Apply multiome quality filters: nucleosome_signal < 2 AND TSS.enrichment > 2. How many epithelial cells pass both ATAC quality criteria?
Answer guideline: Answer must be a single numeric value (e.g., 42) with no units or text.
Answer: 32708
Source paper: Single-nucleus chromatin accessibility and transcriptomic map of breast tissues of women of diverse genetic ancestry
Context: Domain: single-nucleus multiome; Study: Breast epithelial identity genes including ESR1, FOXA1, and GATA3. Dataset includes LHS and LASP cell populations.

Question: Among the three genes ESR1, FOXA1, and GATA3, which gene shows the highest mean RNA expression in LHS cells compared to LASP cells?
Answer guideline: Answer must be one of 'ESR1', 'FOXA1', or 'GATA3' exactly as shown, case-sensitive.
Answer: ESR1
Source paper: Single-nucleus chromatin accessibility and transcriptomic map of breast tissues of women of diverse genetic ancestry
Context: Domain: single-nucleus multiome; Study: Breast tissue from women of diverse genetic ancestry including European American, Native American, Japanese, and Asian groups. Dataset enables ancestry-specific LASP subtype analysis.

Question: Within LASP cells, calculate the proportion of LASP_AP cells for each ancestry group. Which ancestry group shows the highest proportion of LASP_AP cells among their LASP population?
Answer guideline: Answer must be one of the ancestry groups exactly as shown in the metadata, case-sensitive. For example: 'European American'.
Answer: Native American
Source paper: Single-nucleus chromatin accessibility and transcriptomic map of breast tissues of women of diverse genetic ancestry
Context: Domain: single-nucleus multiome; Study: Breast epithelial markers ELF5 and KRT14 with differential expression between LASP and LHS cells.

Question: Which gene is more specifically upregulated in LASP cells: ELF5 or KRT14?
Answer guideline: Answer must be either 'ELF5' or 'KRT14' exactly as shown, case-sensitive.
Answer: ELF5
Source paper: Single-nucleus chromatin accessibility and transcriptomic map of breast tissues of women of diverse genetic ancestry
Context: Domain: single-nucleus multiome (RNA+ATAC); Study: Breast tissue multiome dataset with 51,367 nuclei and 35,477 genes. Dataset contains 8 major cell types including fibroblasts, endothelial cells, T cells, macrophages, and multiple epithelial subtypes with comprehensive metadata.

Question: Among the top 1000 most variable genes, calculate the regulatory potential score for genes in fibroblasts by combining: (1) cell-type specificity, (2) expression coefficient of variation, (3) correlation network degree centrality (using r>0.3 threshold with top 500 variable genes). The regulatory potential score is the product of these three normalized metrics. Which gene has the highest regulatory potential score in fibroblasts?
Answer guideline: Answer must be a single gene ID (e.g., ENSG00000123456), case-sensitive.
Answer: ENSG00000254762
Source paper: Single-nucleus chromatin accessibility and transcriptomic map of breast tissues of women of diverse genetic ancestry
Context: Domain: single-nucleus multiome; Study: Breast tissue with immune cells including T cells and macrophages. Dataset enables ligand-receptor interaction analysis using expression patterns.

Question: Analyze potential ligand-receptor interactions between T cells and macrophages. For ligand-receptor pairs from CellPhoneDB database where the ligand is expressed in T cells (mean expression > 0.05) and receptor is expressed in macrophages (mean expression > 0.1), calculate an interaction score as: (ligand_expr_Tcell * receptor_expr_macro) * pearson_correlation(ligand_in_Tcells, receptor_in_macros across all cells). Which ligand gene shows the highest interaction score? Use these pairs: (CD3D->CD3E, IL2->IL2RA, IFNG->IFNGR1, TNF->TNFRSF1A, CCL5->CCR5, GZMA->IGF2R, PRF1->LRP1, IL7R->IL7).
Answer guideline: Answer must be a single ligand name (e.g., TNF), case-sensitive.
Answer: IL7R
Source paper: Single-nucleus chromatin accessibility and transcriptomic map of breast tissues of women of diverse genetic ancestry
Context: Domain: single-nucleus multiome; Study: Breast tissue dataset from 16 different donors enabling batch effect analysis. Dataset includes donor metadata and cell type annotations.

Question: Among the top 2000 most variable genes, identify the gene with the strongest donor-specific expression pattern while controlling for cell type composition. The donor-specificity score is variance_between_donors / (variance_within_donors + cell_type_effect + 0.01). Which gene has the highest donor-specificity score?
Answer guideline: Answer must be a single gene ID (e.g., ENSG00000123456), case-sensitive.
Answer: ENSG00000135222
Source paper: Single-nucleus chromatin accessibility and transcriptomic map of breast tissues of women of diverse genetic ancestry
Context: Domain: single-nucleus multiome; Study: Breast tissue with basal-myoepithelial cells and luminal cells. Dataset contains basal cell subclusters that may represent transitional states.

Question: Identify which basal subcluster shows the highest transcriptional similarity to luminal cell types. Which basal subcluster has the highest average correlation?
Answer guideline: Answer must be a basal subcluster name exactly as it appears in the dataset (e.g., Basal_1), case-sensitive.
Answer: LP
Source paper: Single-nucleus chromatin accessibility and transcriptomic map of breast tissues of women of diverse genetic ancestry
Context: Domain: single-nucleus multiome; Study: Breast fibroblasts enabling co-expression network analysis. Dataset contains 6,526 fibroblast cells with comprehensive gene expression profiles.

Question: In fibroblasts, construct a gene co-expression network using the top 1000 variable genes. Apply correlation threshold of |r| > 0.4 to define edges. Identify co-expression modules using the Louvain community detection algorithm. Within the largest module, which gene has the highest eigenvector centrality?
Answer guideline: Answer must be a single gene ID (e.g., ENSG00000123456), case-sensitive.
Answer: ENSG00000151388
Source paper: Single-nucleus chromatin accessibility and transcriptomic map of breast tissues of women of diverse genetic ancestry
Context: Domain: single-nucleus multiome (RNA+ATAC); Study: Breast tissue multiome dataset enabling analysis of RNA-chromatin concordance. Dataset includes TSS enrichment scores as proxy for chromatin accessibility.

Question: Analyze the relationship between RNA expression and chromatin accessibility across all cells. For each of the top 100 variable genes, calculate the Spearman correlation between RNA expression and the gene's TSS enrichment score (use 'TSS.enrichment' as proxy for chromatin accessibility). Identify the gene with the LOWEST correlation, indicating discordant regulation where high chromatin accessibility corresponds to low RNA expression.
Answer guideline: Answer must be a single gene ID (e.g., ENSG00000123456), case-sensitive.
Answer: ENSG00000135222
Source paper: Single-nucleus chromatin accessibility and transcriptomic map of breast tissues of women of diverse genetic ancestry
Context: Domain: single-nucleus multiome; Study: Breast tissue with 8 major cell types enabling transcriptional complexity analysis. Dataset allows comparison of transcriptional diversity across cell types.

Question: Calculate the transcriptional entropy for each cell type using the top 2000 variable genes. For each cell type, compute: (1) mean expression of each gene across all cells of that type, (2) normalize to create a probability distribution (sum to 1), (3) calculate entropy. Which cell type indicates the most complex/diverse transcriptional profile?
Answer guideline: Answer must be a cell type name exactly as it appears in the dataset (e.g., fibroblast), case-sensitive.
Answer: luminal adaptive secretory precursor cell of mammary gland
Source paper: Single-nucleus chromatin accessibility and transcriptomic map of breast tissues of women of diverse genetic ancestry
Context: Domain: single-nucleus multiome; Study: Breast luminal hormone-sensing cells with comprehensive marker gene analysis. Dataset enables multi-metric marker identification.

Question: For 'luminal hormone-sensing cell of mammary gland' cell type, identify marker genes using a composite scoring approach. For each of the top 100 variable genes, calculate: (1) log2 fold-change, (2) detection rate difference, (3) AUROC score for classification. Composite score = log2FC * detection_rate_diff * AUROC. Which gene has the SECOND highest composite score?
Answer guideline: Answer must be a single gene ID (e.g., ENSG00000123456), case-sensitive.
Answer: ENSG00000144218
Source paper: Single-nucleus chromatin accessibility and transcriptomic map of breast tissues of women of diverse genetic ancestry
Context: Domain: single-nucleus multiome; Study: Breast tissue from donors of varying ages with pooled age information. Dataset enables age-associated gene expression analysis.

Question: Data has already been normalized. Identify genes whose expression is associated with donor age. Since age information is pooled, extract median age from each donor's age string. For each of the top 150 variable genes, calculate: (1) mean expression per donor, (2) Spearman correlation between donor mean expression and donor median age. Apply Benjamini-Hochberg FDR correction (threshold 0.6). Among significant genes, which has the strongest absolute correlation?
Answer guideline: Answer must be a single gene ID (e.g., ENSG00000123456), case-sensitive.
Answer: ENSG00000100154
Source paper: Single-nucleus chromatin accessibility and transcriptomic map of breast tissues of women of diverse genetic ancestry
Context: Domain: single-nucleus multiome; Study: Breast tissue enabling cell state heterogeneity analysis within cell types. Dataset contains sufficient cells per type for robust distance calculations.

Question: Quantify transcriptional heterogeneity within each cell type using the top 1000 variable genes. Which cell type has the most diverse cell states within that population?
Answer guideline: Answer must be a cell type name exactly as it appears in the dataset (e.g., fibroblast), case-sensitive.
Answer: T cell
Source paper: Single-nucleus chromatin accessibility and transcriptomic map of breast tissues of women of diverse genetic ancestry
Context: Domain: molecular QTL and genetics; Dataset: Sensory neuron QTL dataset with eQTL, sQTL, and ATAC-QTL data from HipSci project. Dataset includes expression data for genes, splicing QTL results for splicing clusters, chromatin accessibility QTL results for ATAC peaks, sample metadata with quality metrics, and comprehensive nominal and permutation-based association results.

Question: Identify the lead SNP for gene ENSG00000151240 . Then, determine if any other SNPs in the dataset provide independent signals for this gene. To do this, perform a partial correlation analysis or fit a linear model. Since you don't have the raw expression values, simulate a synthetic expression vector based on the provided slope and dist, then test if the residuals are still significantly associated with the secondary SNPs. Report the SNP ID with the highest independent significance.
Answer guideline: Answer must be a single SNP ID (e.g., rs12345678), case-sensitive.
Answer: rs12763400
Source paper: Molecular and functional variation in iPSC-derived sensory neurons
Context: Domain: molecular QTL and multi-omics; Dataset: Sensory neuron QTL dataset with matched eQTL and ATAC-QTL data. Dataset enables colocalization analysis to identify genes where genetic variants affect both gene expression and chromatin accessibility.

Question: Identify genes with eQTL-ATAC QTL colocalization. For SNPs that are significant in both eQTL (FDR < 0.05) and ATAC-QTL (q < 0.05) analyses, calculate a colocalization score combining effect sizes and significance levels. Which gene shows the strongest colocalization with its associated ATAC peak? Return the gene ID.
Answer guideline: Answer must be a single gene ID (e.g., ENSG00000123456), case-sensitive.
Answer: ENSG00000166573
Source paper: Molecular and functional variation in iPSC-derived sensory neurons
Context: Domain: molecular QTL; Dataset: Sensory neuron expression data with biological replicates. Dataset includes metadata identifying samples from the same donor with multiple technical/biological replicates, enabling analysis of gene expression concordance.

Question: Analyze replicate concordance to identify potential batch effects. For donors with multiple biological replicates, calculate the coefficient of variation (CV) of gene expression across replicates for each gene. Which gene suggests the most variable expression despite being from the same donor? Return the gene ID.
Answer guideline: Answer must be a single gene ID (e.g., ENSG00000123456), case-sensitive.
Answer: ENSG00000208017
Source paper: Molecular and functional variation in iPSC-derived sensory neurons
Context: Domain: molecular QTL; Dataset: Splicing QTL and expression QTL data for sensory neurons. Dataset enables analysis of coordinated regulation between splicing changes and overall gene expression changes.

Question: Identify splicing-expression coordination by finding SNPs that are both affecting splicing and affecting expression for genes in the same region. Among significant sQTL-eQTL pairs (FDR < 0.05 for both), find the splicing cluster with strongest anti-coordination. Return the cluster ID.
Answer guideline: Answer must be a cluster ID in format 'chr:start:end:clu_XXXXX' (e.g., '17:50378895:50379107:clu_51475'), case-sensitive.
Answer: 20:38422241:38426419:clu_796
Source paper: Molecular and functional variation in iPSC-derived sensory neurons
Context: Domain: molecular QTL; Dataset: RASQUAL eQTL results providing allele-specific expression information. Dataset includes effect size estimates, chi-square statistics, overdispersion parameters, and mapping quality metrics that enable calculation of genetic variance explained.

Question: Calculate the proportion of gene expression variance explained by genetic effects using RASQUAL results. For each gene with significant eQTL (q < 0.05), calculate the variance explained as chi_sq / (chi_sq + overdispersion). What is the maximum proportion of variance explained across all genes? Round to 4 decimal places.
Answer guideline: Answer must be a numeric value rounded to 4 decimal places (e.g., 0.1234).
Answer: 0.9989
Source paper: Molecular and functional variation in iPSC-derived sensory neurons
Context: Domain: molecular QTL; Dataset: Sensory neuron expression and metadata with technical quality metrics. Dataset includes RIN scores, sequencing depth, fibroblast contamination fractions, and other quality indicators that affect QTL detection power.

Question: Analyze the impact of sample quality on QTL discovery. Calculate the correlation between technical quality metrics (RIN, sequencing depth, fibroblast contamination) and gene detection rate (number of genes with counts > 10). Which technical factor shows the strongest absolute correlation with gene detection? Return the factor name: 'RIN', 'Depth', or 'Fibroblast'.
Answer guideline: Answer must be one of: 'RIN', 'Depth', or 'Fibroblast', case-sensitive.
Answer: Depth
Source paper: Molecular and functional variation in iPSC-derived sensory neurons
Context: Domain: molecular QTL; Dataset: eQTL results with SNP-gene distance information. Dataset enables modeling of the relationship between regulatory element distance and effect size, identifying genes that deviate from expected distance-decay patterns.

Question: Model the distance-effect size relationship for cis-eQTLs (<1Mb). Fit a linear model predicting log10(effect size) from log10(distance). Among genes with positive residuals > 2 standard deviations, which gene shows the largest deviation? Return the gene ID.
Answer guideline: Answer must be a single gene ID (e.g., ENSG00000123456), case-sensitive.
Answer: ENSG00000160221
Source paper: Molecular and functional variation in iPSC-derived sensory neurons
Context: Domain: molecular QTL and multi-omics; Dataset: Integrated eQTL, sQTL, and ATAC-QTL data. Dataset enables identification of genes with regulatory variation across all three molecular layers (expression, splicing, chromatin accessibility).

Question: Find SNPs that are significant QTLs in all three modalities: eQTL (FDR < 0.05), sQTL (FDR < 0.05), and ATAC-QTL (q < 0.05). How many unique genes have significant eQTLs where the lead SNP is also a significant sQTL and ATAC-QTL?
Answer guideline: Answer must be a single numeric value (e.g., 42) with no units or text.
Answer: 3
Source paper: Molecular and functional variation in iPSC-derived sensory neurons
Context: Domain: molecular QTL; Dataset: Comprehensive nominal eQTL results with all SNP-gene associations. Dataset enables analysis of effect size heterogeneity across multiple SNPs per gene, indicating complex or context-dependent regulation.

Question: For each significant eQTL gene (FDR < 0.05), calculate the coefficient of variation of effect sizes across all nominally significant SNPs (p < 0.01). Create a heterogeneity index combining CV, number of SNPs, and bidirectional effect proportion. Which gene has the highest heterogeneity index? Return the gene ID.
Answer guideline: Answer must be a single gene ID (e.g., ENSG00000123456), case-sensitive.
Answer: ENSG00000213462
Source paper: Molecular and functional variation in iPSC-derived sensory neurons
Context: Domain: molecular QTL; Dataset: Comprehensive eQTL dataset with nominal associations. Dataset enables calculation of regulatory complexity scores based on number of independent signals, effect heterogeneity, cis vs trans regulation, and association strength.

Question: Calculate a regulatory complexity score for each gene combining: (1) number of independent eQTL signals (SNPs >100kb apart with p < 1e-5), (2) effect size heterogeneity, (3) trans regulation (distance > 1Mb), (4) number of suggestive associations (p < 0.001), (5)log-normalized number of tested SNPs, (6) log-normalized eQTL strength (7)log-normalized number of suggestive associations (p<0.001). Which gene has the highest overall regulatory complexity score? Return the gene ID. For significant eQTLs, use FDR < 0.0000001
Answer guideline: Answer must be a single gene ID (e.g., ENSG00000123456), case-sensitive.
Answer: ENSG00000213402
Source paper: Molecular and functional variation in iPSC-derived sensory neurons
Context: Domain: single-cell RNA-seq; Dataset: scRNA-seq data from three human GI tract regions (Ileum: 5,980 cells, Colon: 4,329 cells, Rectum: 3,797 cells) with 7 cell types (Enterocyte, Progenitor, Goblet, TA, Stem Cell, Paneth-like, Enteroendocrine). Samples from adenocarcinoma and neuroendocrine carcinoma patients with donor and sex metadata.

Question: Identify genes with highest cross-tissue variance in Enterocytes. Filter to genes expressed (mean > 0.5) in all three tissues and perform Kruskal-Wallis test with FDR correction (< 0.05). What are the top 5 gene symbols with highest variance with respect to tissues?
Answer guideline: Answer must be a list of 5 gene symbols, for example: ['GENE1', 'GENE2', 'GENE3', 'GENE4', 'GENE5'], case-sensitive.
Answer: PRAP1, ADIRF, PIGR, FABP2, ANPEP
Source paper: Single-cell transcriptome analysis reveals differential nutrient absorption functions in human intestine
Context: Domain: single-cell RNA-seq; Dataset: scRNA-seq from Ileum, Colon, and Rectum with 7 cell types across tissues. Dataset enables cell type proportion analysis and tissue-specific enrichment testing.

Question: Perform chi-square test for cell type proportions across the three tissues. Create contingency tables for each cell type. Which cell type shows the most significant tissue-specific enrichment?
Answer guideline: Answer must be a single cell type name, case-sensitive.
Answer: Enterocyte
Source paper: Single-cell transcriptome analysis reveals differential nutrient absorption functions in human intestine
Context: Domain: single-cell RNA-seq; Dataset: GI tract scRNA-seq with multiple cell types. Dataset enables identification of tissue-specific marker genes using combined specificity and fold-change metrics.

Question: For Goblet cells, calculate specificity score: (mean_ileum / sum_of_all_means) × log2_fold_change_vs_others. Filter: mean expression > 1.0 in Ileum, Wilcoxon p-value < 0.01. What are the top 3 gene symbols with highest specificity score?
Answer guideline: Answer must be a list of 3 gene symbols, for example: ['GENE1', 'GENE2', 'GENE3'], case-sensitive.
Answer: RBP2, ALDOB, APOA1
Source paper: Single-cell transcriptome analysis reveals differential nutrient absorption functions in human intestine
Context: Domain: single-cell RNA-seq; Dataset: Multi-tissue GI tract scRNA-seq with sex metadata. Dataset enables sex-biased gene expression analysis using effect size calculations across tissues.

Question: Identify sex-biased genes in Stem Cells across all three tissues combined. Calculate Cohen's d effect size for male vs female expression. Filter: mean expression > 0.8 in at least one sex, Mann-Whitney U p-value < 0.001, present in at least 2 tissues (mean > 0.1). What gene symbol has the highest absolute Cohen's d?
Answer guideline: Answer must be a single gene symbol or gene ID, case-sensitive.
Answer: ENSG00000229807
Source paper: Single-cell transcriptome analysis reveals differential nutrient absorption functions in human intestine
Context: Domain: single-cell RNA-seq; Dataset: Ileum scRNA-seq with disease annotations (adenocarcinoma and neuroendocrine carcinoma). Dataset enables disease-specific differential expression analysis with multiple testing correction.

Question: In Ileum Progenitor cells, compare adenocarcinoma vs neuroendocrine carcinoma. Filter genes with: |log2FC| > 1.5, FDR < 0.01 (Mann-Whitney U test with Benjamini-Hochberg correction), mean expression > 0.5 in both disease types. What is the median absolute log2 fold change of significant genes (rounded to 2 decimals)?
Answer guideline: Answer must be a number rounded to 2 decimal places.
Answer: 1.57
Source paper: Single-cell transcriptome analysis reveals differential nutrient absorption functions in human intestine
Context: Domain: single-cell RNA-seq; Dataset: Multi-tissue GI tract scRNA-seq with highly variable gene annotations. Dataset enables analysis of gene expression variability patterns across tissues using Jaccard similarity.

Question: For each tissue, identify the top 500 highly variable genes based on dispersion_norm. Calculate Jaccard similarity coefficient for each tissue pair. Which tissue pair has the highest Jaccard similarity (rounded to 3 decimals)? Format answer as 'tissue1_tissue2' in alphabetical order.
Answer guideline: Answer must be formatted as 'Tissue1_Tissue2' with tissue names in alphabetical order, case-sensitive.
Answer: Colon_Rectum
Source paper: Single-cell transcriptome analysis reveals differential nutrient absorption functions in human intestine
Context: Domain: single-cell RNA-seq; Dataset: GI tract scRNA-seq with QC metrics including percent_mito for each cell. Dataset enables cell type-specific quality control analysis across tissues.

Question: For cell types present in all three tissues, calculate the coefficient of variation of percent_mito across tissues. Which cell type shows the highest CV for percent_mito?
Answer guideline: Answer must be a single cell type name, case-sensitive.
Answer: Enterocyte
Source paper: Single-cell transcriptome analysis reveals differential nutrient absorption functions in human intestine