DSGym/DSGym-Tasks huggingface dataset:
Context: Domain: single-cell transcriptomics; Study: Single-Cell Transcriptomic Profiling Identifies Molecular Phenotypes of Newborn Human Lung Cells (Genes 2024). Dataset: Newborn human lung cells scRNA-seq (CELLxGENE collection).

Question: Which 4 immune cell markers distinguish macrophages (Cluster 7) from T cells (Cluster 9) in the newborn lung?
Answer guideline: Answer must be a list of gene symbols, for example: ['GENE1', 'GENE2'], case-sensitive.
Answer: CST3, FTL, HLA-DRA, AIF1
Source paper: Single-Cell Transcriptomic Profiling Identifies Molecular Phenotypes of Newborn Human Lung Cells
Reference: Page 6-7: 'Immune cells (n = 618) were defined by expression of leukocyte and lymphocyte cell markers PTPRC, CD8A, CD19, and CD3E... macrophages (Cluster 7, n = 349), T cells (Cluster 9, n = 190)'

Question: Which 2 mesenchymal cell marker genes show co-expression with HES1 in myofibroblast?
Answer guideline: Answer must be a list of gene symbols, for example: ['GENE1', 'GENE2'], case-sensitive.
Answer: TAGLN, ACTA2
Source paper: Single-Cell Transcriptomic Profiling Identifies Molecular Phenotypes of Newborn Human Lung Cells
Reference: Page 11: 'We identified human cells expressing HES1 alone, and cells co-expressing HES1 with matrix fibroblast markers (COL6A3 or TCF21)'
Context: Regulatory motif enrichment analysis of fibroblast differentially expressed genes between ascending and descending aorta.

Question: Which developmental transcription factors along with ISL1 and MEF2C shows significant motif enrichment in genes differentially expressed between ascending and descending fibroblasts?
Answer guideline: Answer must be a single gene symbol (e.g., 'GENE1'), case-sensitive.
Answer: HAND2
Source paper: A cell and transcriptome atlas of human arterial vasculature
Context: Cross-modal integration of scRNA-seq and Slide-seqV2 to identify dominant cell-type signatures per spatial spot.

Question: Which cell type contributes most strongly to the spatial transcriptome variance across arterial segments after integration with scRNA-seq data? If cell type annotations are needed, refer to 'author_cell_type'.
Answer guideline: Answer must be the exact single cell type name as shown in the metadata (e.g., 'Endothelial'), case-sensitive.
Answer: Smooth Muscle
Source paper: A cell and transcriptome atlas of human arterial vasculature
Reference: Lines 393-396: SMCs and fibroblasts account for most inter-segmental transcriptomic differences.
Context: Identification of genes differentially expressed between two spatially defined domains inferred from transcriptomic clustering.

Question: In Slide-seqV2 of the aorta, which gene shows the greatest log-fold change between seurac_clusters 0 (smooth muscle-rich) and seurac_clusters 1 (fibroblast-rich) regions? If cell type annotations are needed, refer to 'author_cell_type'.
Answer guideline: Answer must be a single gene symbol (e.g., 'ENSG00000123456') case-sensitive.
Answer: ENSG00000133392
Source paper: A cell and transcriptome atlas of human arterial vasculature
Reference: Lines 178-185: MYH11 is a canonical smooth muscle marker distinguishing SMC-rich regions.
Context: Quantitative correlation of average gene expression between spatial clusters and scRNA-seq cell types.

Question: Which scRNA-seq cell type shows the highest expression correlation with the largest spatial cluster in the aortic Slide-seqV2 dataset? Hint: look at 'author_cell_type' annotation in metadata.
Answer guideline: Answer must be the exact single cell type name as shown in the metadata (e.g., 'Endothelial'), case-sensitive.
Answer: Smooth Muscle
Source paper: A cell and transcriptome atlas of human arterial vasculature
Reference: Lines 239-242: SMCs form the largest cluster in both datasets.
Context: Domain: single-nucleus multiome; Focus: basal-myoepithelial (BM) identity.

Question: Which gene shows the strongest RNA enrichment in BM cells compared with LHS and LASP?
Answer guideline: Answer must be a concrete gene, case-sensitive. e.g., 'KRT14'
Answer: TP63
Source paper: Single-nucleus chromatin accessibility and transcriptomic map of breast tissues of women of diverse genetic ancestry
Reference: TP63 predominantly expressed in BM (also KRT14), see Main text (BM markers).
Context: Domain: chromatin QTL analysis; Study: quantifying enhancer priming across stimuli.

Question: Among response eQTL-caQTL pairs, what fraction shows chromatin QTL activity in naive macrophages before stimulation (fold change > 1.5)? Choose among these options: 20%, 40%, 60%, or 80%.
Answer guideline: Answer must be one of the provided options exactly as shown, case-sensitive. For example: '20%'.
Answer: 60%
Source paper: Shared genetic effects on chromatin and gene expression indicate a role for enhancer priming in immune response
Reference: Fig. 2b-c, enhancer priming quantification
Context: Domain: regulatory genomics (chromatin QTL). Dataset: RASQUAL caQTL summary stats in ATAC-seq peaks; BH q-values provided.

Question: How many ATAC-seq peaks are significant caQTLs at 10% FDR using the Benjamini–Hochberg q-value reported by RASQUAL?
Answer guideline: Answer must be a single numeric value (e.g., 42) with no units or text.
Answer: 6318
Source paper: Molecular and functional variation in iPSC-derived sensory neurons
Reference: “we … used them to map 6,318 chromatin accessibility QTLs (caQTLs) at an FDR of 10%”
Context: Domain: chromatin QTL geometry. Dataset: RASQUAL caQTLs and consensus ATAC peak coordinates.

Question: What proportion of significant caQTL lead SNPs lie within 1,000 bp of their peak center? Choose from these approximate options: 20%, 50%, 75%, or 100%.
Answer guideline: Answer must be one of the provided options exactly as shown, case-sensitive. For example: '50%'.
Answer: 100%
Source paper: Molecular and functional variation in iPSC-derived sensory neurons
Reference: Methods state peaks were tested “within 1 kb of the center of the peak.” Expect ~all lead SNPs to be ≤1 kb from peak center.
Context: Domain: single-cell transcriptomics; Study: Human γδ T cells in diverse tissues exhibit site-specific maturation dynamics across the life span (Sci. Immunol. 2024). Dataset: Adult γδ T cells processed scRNA-seq (CELLxGENE collection) with metadata annotations.

Question: Identify doublets with scanpy scrublet with random seed set to 42. How many cells are flagged as doublets and removed if you run scrublet per library only on libraries with >30 cells, using the top 30 principal components at expected rate 0.06? Remember to silence any output or warning messages from scrublet as they could be really long.
Answer guideline: Answer must be a single numeric value (e.g., 42) with no units or text.
Answer: 1557
Source paper: Human γδ T cells in diverse tissues exhibit site-specific maturation dynamics across the life span

Question: Perform Leiden clustering with resolution=1 after PCA with 30 principal components and using 10 neighbors.
Answer guideline: Answer must be a single numeric value (e.g., 42) with no units or text.
Answer: 22
Source paper: Human γδ T cells in diverse tissues exhibit site-specific maturation dynamics across the life span

Question: How many clusters are identified using Leiden clustering with resolution=1 after PCA if you use harmony batch correction with 10 iterations across all libraries first? Use 50 principle components and calculate 10 neighbors.
Answer guideline: Answer must be a single numeric value (e.g., 42) with no units or text.
Answer: 20
Source paper: Human γδ T cells in diverse tissues exhibit site-specific maturation dynamics across the life span
Context: Domain: single-cell transcriptomics; Study: Newborn human lung cells with 5499 cells and 29371 genes from 2 donors with different gestational ages (31 weeks and 39 weeks). Dataset contains 11 cell types including mesenchymal, epithelial, endothelial, and immune cells.

Question: Using Hallmark gene sets, perform gene set enrichment analysis comparing cells from the two different gestational ages (39 weeks vs 31 weeks). Among mesenchymal cell types (Myofibroblast, Matrix_Fibroblast, Smooth_Muscle_Cell, Pericyte), how many unique Hallmark pathways are significantly enriched (FDR < 0.05) in the preterm donor (31 weeks) compared to term donor (39 weeks)?
Answer guideline: Answer must be a single numeric value (e.g., 42) with no units or text.
Answer: 1
Source paper: Single-Cell Transcriptomic Profiling Identifies Molecular Phenotypes of Newborn Human Lung Cells

Question: Calculate the specificity score for each gene across all 11 cell types using the tau index. Among the top 50 most specific genes for Matrix_Fibroblast cells, how many of them have mean expression > 0.5 in Matrix_Fibroblast cells?
Answer guideline: Answer must be a single numeric value (e.g., 42) with no units or text.
Answer: 0
Source paper: Single-Cell Transcriptomic Profiling Identifies Molecular Phenotypes of Newborn Human Lung Cells
Context: Domain: single-cell transcriptomics; Study: Newborn human lung cells with 5499 cells and 29371 genes from 2 donors. Dataset contains immune cells (Macrophage, T_cell, B_Cell) and non-immune cells.

Question: Calculate the Shannon entropy of gene expression for each cell to measure transcriptional diversity. Compare the mean entropy between immune cells and non-immune cells. What is the difference in mean entropy rounded to 4 decimal places?
Answer guideline: Answer must be a numeric value rounded to 4 decimal places (e.g., 0.1234), can be negative.
Answer: -0.1969
Source paper: Single-Cell Transcriptomic Profiling Identifies Molecular Phenotypes of Newborn Human Lung Cells
Context: Domain: single-cell transcriptomics; Study: Newborn human lung cells with 5499 cells and 29371 genes. Dataset contains 1119 endothelial cells with gene expression profiles.

Question: Identify co-expression modules in endothelial cells using hierarchical clustering on gene-gene correlation matrix. Using Pearson correlation on the top 500 most variable genes, cut the dendrogram at height 0.7 to define modules. How many genes belong to the largest co-expression module?
Answer guideline: Answer must be a single numeric value (e.g., 42) with no units or text.
Answer: 19
Source paper: Single-Cell Transcriptomic Profiling Identifies Molecular Phenotypes of Newborn Human Lung Cells
Context: Domain: single-cell transcriptomics; Study: Newborn human lung cells with 5499 cells from 2 donors (D038 and D051). Dataset allows batch effect quantification between donors.

Question: Calculate the k-NN batch mixing metric to quantify how well the two donors are mixed in PCA space. Using k=30 neighbors and the first 50 principal components, what is the mean proportion of nearest neighbors from the same donor across all cells, rounded to 4 decimal places?
Answer guideline: Answer must be a numeric value rounded to 4 decimal places (e.g., 0.1234).
Answer: 0.9268
Source paper: Single-Cell Transcriptomic Profiling Identifies Molecular Phenotypes of Newborn Human Lung Cells
Context: Domain: single-cell transcriptomics; Study: Newborn human lung cells with 349 Macrophage and 190 T_cell cells from 2 donors. Dataset enables pseudobulk differential expression analysis.

Question: Create pseudobulk profiles. Using statistical testing for differential expression analysis, compare Macrophage vs T_cell gene expression. How many genes show significant differential expression (FDR < 0.05 and absolute log2FC > 1.5)?
Answer guideline: Answer must be a single numeric value (e.g., 42) with no units or text.
Answer: 0
Source paper: Single-Cell Transcriptomic Profiling Identifies Molecular Phenotypes of Newborn Human Lung Cells
Context: Domain: single-cell transcriptomics; Study: Newborn human lung cells with 5499 cells and 11 cell types. Dataset allows analysis of cell type proportion variance between donors.

Question: Calculate the coefficient of variation of cell type proportions across the two donors. Which cell type shows the highest coefficient of variation? Report its CV value rounded to 4 decimal places.
Answer guideline: Answer must be a numeric value rounded to 4 decimal places (e.g., 0.1234).
Answer: 1.1743
Source paper: Single-Cell Transcriptomic Profiling Identifies Molecular Phenotypes of Newborn Human Lung Cells
Context: Domain: single-cell transcriptomics; Study: Newborn human lung cells with 820 Myofibroblast cells. Dataset allows analysis of gene expression heterogeneity within a cell type.

Question: Calculate the Gini coefficient for each gene within Myofibroblast cells to measure expression heterogeneity. How many genes indicating high heterogeneity in their expression with threshold 0.3?
Answer guideline: Answer must be a single numeric value (e.g., 42) with no units or text.
Answer: 491
Source paper: Single-Cell Transcriptomic Profiling Identifies Molecular Phenotypes of Newborn Human Lung Cells
Context: Domain: single-cell transcriptomics; Study: Newborn human lung cells with 5499 cells. Dataset enables cluster stability analysis through subsampling.

Question: Perform stability analysis of Leiden clustering with sample ratio 0.8. Using resolution=0.5, perform 20 iterations of subsampling. Calculate the adjusted Rand index (ARI) between the full dataset clustering and each subsample clustering, then report the mean ARI rounded to 4 decimal places.
Answer guideline: Answer must be a numeric value rounded to 4 decimal places (e.g., 0.1234).
Answer: 0.9172
Source paper: Single-Cell Transcriptomic Profiling Identifies Molecular Phenotypes of Newborn Human Lung Cells
Context: Domain: single-cell transcriptomics; Study: Newborn human lung cells with Matrix_Fibroblast and Myofibroblast cells. Dataset enables transcription factor activity inference based on target gene expression.

Question: Infer transcription factor (TF) activity in different cell types with enrichment scores. For each TF, calculate the mean expression of its target genes as the TF activity score. Among the top 10 most active TFs in Matrix_Fibroblast cells, what are the TFs that are also in the top 10 most active TFs in Myofibroblast cells?
Answer guideline: Answer must be a list of TF names, for example: ['TF1', 'TF2'], case-sensitive.
Answer: SNAI2, SMAD3, TEAD1, FOXF1, EGR1, SRF
Source paper: Single-Cell Transcriptomic Profiling Identifies Molecular Phenotypes of Newborn Human Lung Cells
Context: Domain: spatial transcriptomics; Dataset: Slide-seqV2 analysis of aorta with 6,142 spots and 20,402 genes. Dataset contains spatial coordinates enabling calculation of spatial autocorrelation using Moran's I statistic.

Question: Calculate Moran's I statistic for the top 500 most variable genes using k=30 nearest neighbors. Which gene shows the highest spatial autocorrelation?
Answer guideline: Answer must be a single gene symbol (e.g., 'MYH11'), case-sensitive.
Answer: APOC1
Source paper: A cell and transcriptome atlas of human arterial vasculature
Context: Domain: spatial transcriptomics; Dataset: Slide-seqV2 analysis of aorta with 6,142 spots and 20,402 genes. Dataset contains spatial coordinates and cell type annotations including smooth muscle cells, fibroblasts, endothelial cells, macrophages, and lymphocytes.

Question: Calculate the nearest neighbor enrichment for cell type pairs using k=10 neighbors. Which cell type pair shows the strongest spatial co-localization based on the enrichment score? Format answer as 'celltype1_celltype2'.
Answer guideline: Answer must be formatted as 'celltype1_celltype2', case-sensitive, using exact cell type names from the dataset.
Answer: fibroblast_lymphocyte
Source paper: A cell and transcriptome atlas of human arterial vasculature
Context: Domain: spatial transcriptomics; Dataset: Slide-seqV2 analysis of aorta with 6,142 spots. Dataset contains blood vessel smooth muscle cells with spatial coordinates that form distinct spatial domains.

Question: Use DBSCAN clustering to identify spatial domains of blood vessel smooth muscle cells. What is the median domain size? Use eps=200.
Answer guideline: Answer must be a single integer value (e.g., 42) with no units or text.
Answer: 11
Source paper: A cell and transcriptome atlas of human arterial vasculature
Context: Domain: spatial transcriptomics; Dataset: Slide-seqV2 analysis of aorta with 6,142 spots and 20,402 genes. Dataset contains spatial coordinates enabling calculation of spatial expression gradients.

Question: For each of the top 500 highly variable genes, calculate the spatial gradient magnitude with k=10 nearest neighbors. Which gene exhibits the steepest spatial expression gradient? Use flavor 'seurat_v3'.
Answer guideline: Answer must be a single gene symbol (e.g., 'MYH11'), case-sensitive.
Answer: MALAT1
Source paper: A cell and transcriptome atlas of human arterial vasculature
Context: Domain: spatial transcriptomics; Dataset: Slide-seqV2 analysis of aorta with 6,142 spots. Dataset enables decomposition of gene expression variance into spatial and non-spatial components.

Question: Using k-NN regression (k=30 neighbors, distance-weighted), predict ACTA2 expression from spatial coordinates alone. What percentage of ACTA2 expression variance is explained by spatial location (R² score × 100)? Round to 1 decimal place.
Answer guideline: Answer must be a numeric percentage rounded to 1 decimal place (e.g., 12.3).
Answer: 100.0
Source paper: A cell and transcriptome atlas of human arterial vasculature
Context: Domain: spatial transcriptomics; Dataset: Slide-seqV2 analysis of aorta with 6,142 spots. Dataset enables analysis of how spatial correlation decays with distance.

Question: For ENSG00000108846 gene expression, calculate correlation between cells binned by spatial distance. At what distance (in spatial units) does the correlation decay to 50% of the initial correlation at close distances? Round to nearest integer. Use random sampling for computational efficiency.
Answer guideline: Answer must be a single integer value (e.g., 250) representing distance in spatial units.
Answer: 317
Source paper: A cell and transcriptome atlas of human arterial vasculature
Context: Domain: spatial transcriptomics; Dataset: Slide-seqV2 analysis of right coronary artery with 12,744 spots and 6 cell types. Dataset enables local microenvironment diversity analysis using spatial neighborhoods.

Question: For each spot, calculate the Shannon entropy of cell type composition in its k=30 nearest neighbors. What percentage of spots have high local cell type diversity? Round to 1 decimal place.
Answer guideline: Answer must be a numeric percentage rounded to 1 decimal place (e.g., 12.3).
Answer: 47.8
Source paper: A cell and transcriptome atlas of human arterial vasculature
Context: Domain: spatial transcriptomics; Dataset: Slide-seqV2 analysis of right coronary artery with 12,744 spots. Dataset enables detection of spatial expression hotspots using DBSCAN clustering.

Question: For TAGLN gene, identify cells in top 20% of expression. Use DBSCAN (eps=300, min_samples=10) to cluster high-expressing cells into spatial hotspots. How many distinct hotspots are detected?
Answer guideline: Answer must be a single integer value (e.g., 5) with no units or text.
Answer: 4
Source paper: A cell and transcriptome atlas of human arterial vasculature
Context: Domain: spatial transcriptomics; Dataset: Slide-seqV2 analysis of aorta with 6,142 spots including 658 macrophages and 257 lymphocytes. Dataset enables spatial interaction enrichment analysis.

Question: For macrophages, count lymphocyte neighbors in k=10 nearest neighbors. Calculate enrichment score. Additionally, perform 1000 permutations to assess significance. What is the enrichment score rounded to 2 decimal places?
Answer guideline: Answer must be a numeric value rounded to 2 decimal places (e.g., 1.01).
Answer: 1.23
Source paper: A cell and transcriptome atlas of human arterial vasculature
Context: Domain: spatial transcriptomics; Dataset: Slide-seqV2 analysis of right coronary artery with 12,744 spots including 3,564 fibroblasts. Dataset enables spatial intermixing analysis between cell types.

Question: For each fibroblast, find its k=20 nearest neighbors and calculate the fraction that are NOT fibroblasts. What is the mean fraction of dissimilar neighbors across all fibroblasts? Round to 2 decimal places.
Answer guideline: Answer must be a numeric value between 0 and 1, rounded to 2 decimal places (e.g., 0.65).
Answer: 0.48
Source paper: A cell and transcriptome atlas of human arterial vasculature
Context: Domain: single-nucleus multiome; Study: Breast tissue multiome dataset with epithelial cell subtypes. Dataset enables clustering analysis of epithelial cells.

Question: Filter for epithelial cells only, then perform standard preprocessing with 2000 hvgs. After PCA with 30 components, run Leiden clustering with resolution=0.8, n_neighbors=10, and random_state=42. How many clusters are identified?
Answer guideline: Answer must be a single numeric value (e.g., 42) with no units or text.
Answer: 20
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 cell subtypes with LASP_AP and LASP_BL populations annotated in epi_sub metadata.

Question: Perform differential expression analysis between LASP_AP and LASP_BL cells using t-test. How many genes are significantly differentially expressed with adjusted p-value < 0.05 and absolute log fold change > 1?
Answer guideline: Answer must be a single numeric value (e.g., 42) with no units or text.
Answer: 2019
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 with batch effects from multiple sample groups. Dataset enables batch correction analysis using Harmony.

Question: Perform standard preprocessing with 2000 highly variable genes and 50 principal components. Apply Harmony batch correction using 'Group' as the batch key with max_iter_harmony=20 and random_state=42. Using harmony-corrected PCA, compute neighbors (n_neighbors=10, n_pcs=50) and run Leiden clustering with resolution=1.0 and random_state=42. How many clusters are identified?
Answer guideline: Answer must be a single numeric value (e.g., 42) with no units or text.
Answer: 20
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 dataset with both RNA and ATAC quality metrics including TSS enrichment and feature counts.

Question: Identify high-quality cells with excellent transcription start site accessibility and RNA complexity: TSS.enrichment > 2 AND nFeature_RNA > 1000. How many cells meet both criteria?
Answer guideline: Answer must be a single numeric value (e.g., 42) with no units or text.
Answer: 33837
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 with 10 author-annotated cell types including LHS (luminal hormone-sensing) cells.

Question: Identify genes that are specifically expressed in LHS cells. How many genes are expressed in >50% of LHS cells but <10% of cells in ALL other cell types?
Answer guideline: Answer must be a single numeric value (e.g., 42) with no units or text.
Answer: 5
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 dataset enabling concordance analysis between RNA expression and chromatin accessibility.

Question: For ESR1 gene, identify cells with both positive RNA expression AND high chromatin accessibility (nCount_ATAC > median). How many cells show this RNA-chromatin concordance for ESR1?
Answer guideline: Answer must be a single numeric value (e.g., 42) with no units or text.
Answer: 7366
Source paper: Single-nucleus chromatin accessibility and transcriptomic map of breast tissues of women of diverse genetic ancestry