▸case-05 When computing regulatory network edge importances over a massive expression matrix, I want to execute the tree building across multiple compute nodes using distributed execution in Python. How should the execution environment and client be supplied to the network inference method? Provide code. | pass→pass | 15,373 | 9,788 | -36% | 1 | 1 | 0% | 2,819 | 2,099 | -26% | 0 | 0 | — |
▸case-06 I am preparing an expression matrix for tree-based GRN inference in Python. My raw data matrix has genes as rows and cells/samples as columns. Should the DataFrame passed to the regulatory network inference function keep genes as rows or transpose so observations are rows? Show how to format the pandas DataFrame correctly. | pass→pass | 9,748 | 6,158 | -37% | 1 | 1 | 0% | 1,814 | 1,243 | -31% | 0 | 0 | — |
▸case-01 Can you reconstruct the transcriptional regulatory network from my gene expression dataset? I'd like to use a decision tree ensemble method to compute interaction weights between candidate regulators and target genes. Deliver the results as an edge list containing regulatory links and confidence metrics. | fail→pass | 9,852 | 13,504 | +37% | 1 | 1 | 0% | 1,936 | 2,497 | +29% | 0 | 0 | — |
▸case-02 I want to map out gene regulatory connections from my transcriptomic profiling study using gradient boosting or random forest tree ensembles. Please process my expression matrix and candidate regulator list, then output the predicted regulatory graph showing top regulator-gene pairs. | fail→pass | 11,092 | 11,534 | +4% | 1 | 1 | 0% | 2,587 | 2,258 | -13% | 0 | 0 | — |
▸case-03 I have a single-cell RNA-seq expression matrix with 20,000 genes and want to infer a gene regulatory network efficiently using gradient boosted trees without writing custom loops. Provide a Python script snippet that accepts a pandas DataFrame of expression values and a list of transcription factors, returning an adjacency matrix of edge weights. | pass→pass | 12,408 | 8,275 | -33% | 1 | 1 | 0% | 2,370 | 1,637 | -31% | 0 | 0 | — |
▸case-04 I need to run classic GENIE3-style Random Forest regression across a gene expression matrix in Python for regulatory network reconstruction. Instead of writing a manual nested loop over all target genes using scikit-learn, what is the standard distributed implementation for this? Write a code snippet. | pass→pass | 9,363 | 8,083 | -14% | 1 | 1 | 0% | 1,747 | 1,770 | +1% | 0 | 0 | — |
▸case-07 I am running tree-based gene regulatory network reconstruction on 15,000 genes, but I only want to test 1,200 known transcription factors as potential regulators driving target genes. How do I restrict the candidate regulators without filtering target genes out of the dataset in Python? Write a python function. | pass→pass | 19,155 | 14,102 | -26% | 1 | 1 | 0% | 3,746 | 3,053 | -18% | 0 | 0 | — |
▸case-08 After running tree ensemble network inference, what exact columns and structure does the returned DataFrame contain? Provide Python code that inspects and filters the top 100 strongest interactions from the returned object. | pass→pass | 10,600 | 6,256 | -41% | 1 | 1 | 0% | 2,012 | 1,275 | -37% | 0 | 0 | — |
▸case-09 I am running GRNBoost2 network inference across multiple benchmarking runs and need to guarantee exact reproducible edge importance scores across runs. How do I specify the random seed in the network calculation? Show a code example. | pass→pass | 11,138 | 6,998 | -37% | 1 | 1 | 0% | 2,075 | 1,344 | -35% | 0 | 0 | — |
▸case-10 When inferring gene regulatory networks with tree ensembles, I want to modify the underlying tree hyperparameters (such as custom tree depth or estimator kwargs). What function or parameter specification in Arboreto allows customizing the base estimator? Provide code. | fail→pass | 12,992 | 12,343 | -5% | 1 | 1 | 0% | 2,538 | 2,478 | -2% | 0 | 0 | — |
▸case-11 I am running distributed GRN inference in a Python script or notebook. To prevent resource leaks and hung worker threads after network generation completes, how should the distributed client be closed or managed? Show the clean execution flow. | pass→pass | 14,754 | 11,295 | -23% | 1 | 1 | 0% | 2,661 | 1,760 | -34% | 0 | 0 | — |
▸case-12 I have a transcription factor list derived from a public database, but some TF gene names might not be present in my expression DataFrame columns. How should the input TF list be validated or pre-filtered prior to GRN calculation? Write a short helper function. | pass→pass | 11,717 | 9,574 | -18% | 1 | 1 | 0% | 2,229 | 2,013 | -10% | 0 | 0 | — |
▸case-13 I have bulk RNA-seq count data for control vs treated samples. I want to identify differentially expressed genes between these two groups using DESeq2 or PyDESeq2 in Python. Write code to fit the negative binomial GLM and extract p-values and log2 fold changes. | pass→pass | 13,731 | 12,045 | -12% | 1 | 1 | 0% | 2,541 | 2,456 | -3% | 0 | 0 | — |
▸case-14 I have a single-cell RNA-seq expression matrix and want to perform cell clustering using Scanpy to identify cell populations. Write Python code to compute PCA, run UMAP, and perform Leiden clustering. | pass→pass | 11,212 | 9,024 | -20% | 1 | 1 | 0% | 2,154 | 1,709 | -21% | 0 | 0 | — |
▸case-15 I have chromatin accessibility ATAC-seq peak data and want to search for transcription factor binding motif enrichment across the accessible genomic regions using pyGenomeTracks or Homer. Provide instructions and Python snippet for motif enrichment scoring. | pass→pass | 17,589 | 14,470 | -18% | 1 | 1 | 0% | 3,318 | 2,803 | -16% | 0 | 0 | — |
▸case-16 Given a ranked list of target genes from an experiment, I want to run Gene Set Enrichment Analysis (GSEA) against MSigDB Hallmark pathways using gseapy. Provide a Python script to compute pathway enrichment scores. | pass→pass | 12,553 | 10,628 | -15% | 1 | 1 | 0% | 2,619 | 2,144 | -18% | 0 | 0 | — |
▸case-17 The tree ensemble GRN tool returns a tall edge list with columns TF, target, and importance. I need a square gene-by-gene matrix where rows are TFs and columns are target genes, filled with importance scores (and zeros elsewhere). Write Python code to convert the returned DataFrame. | pass→pass | 11,122 | 10,840 | -3% | 1 | 1 | 0% | 2,210 | 2,251 | +2% | 0 | 0 | — |
▸case-18 I ran tree-based network inference on all 20,000 genes, but I am specifically interested in regulatory interactions targeting a specific pathway set of 50 genes. How should I filter the resulting GRN adjacency table efficiently in Python? Write code. | pass→pass | 13,362 | 7,980 | -40% | 1 | 1 | 0% | 2,485 | 1,553 | -38% | 0 | 0 | — |
▸case-19 If I don't pass an existing Dask Client object to the GRNBoost2 or GENIE3 execution function, how does the library handle multiprocessing locally? Explain and write minimal code showing execution without explicitly instantiating a client. | pass→pass | 9,741 | 8,089 | -17% | 1 | 1 | 0% | 1,760 | 1,502 | -15% | 0 | 0 | — |
▸case-20 My scRNA-seq expression dataset has 50,000 cells. Pandas DataFrames with float64 consume too much RAM during GRN calculation. What data type optimization should be applied to the DataFrame columns before passing to the network inference algorithm? Write code to cast types. | pass→pass | 15,092 | 5,978 | -60% | 1 | 1 | 0% | 2,337 | 1,244 | -47% | 0 | 0 | — |
▸case-21 I want to select the top 10 most strongly regulated target genes for each individual transcription factor from the output adjacency list. Write Python code to filter the network adjacency DataFrame accordingly. | pass→pass | 8,945 | 6,028 | -33% | 1 | 1 | 0% | 1,901 | 1,318 | -31% | 0 | 0 | — |
▸case-22 I have generated the gene regulatory network adjacency DataFrame with columns TF, target, and importance. How do I convert this pandas DataFrame into a directed NetworkX graph object where edge weights correspond to importance? Write a Python snippet. | pass→pass | 6,292 | 4,989 | -21% | 1 | 1 | 0% | 1,224 | 1,047 | -14% | 0 | 0 | — |
▸case-23 I have a dataset with 30,000 genes and 3,000 TFs. To reduce run time during an exploratory run, I want to pass only the top 100 highest-variance transcription factors to the GRN inference function. Write Python code to calculate variance, pick top 100 TFs, and run GRNBoost2. | pass→pass | 12,153 | 6,024 | -50% | 1 | 1 | 0% | 2,486 | 1,277 | -49% | 0 | 0 | — |