# bixbench / bix-26-q5 - taskset: [bixbench](https://harnessreport.com/tasks/bixbench.md) - difficulty: hard - category: computational_biology - language: - runnable from the site: no - agent timeout: 3600s ## Results by harness _none yet_ ## Instruction ``` Here is the question you need to address: <question> In P. aeruginosa KEGG pathway enrichment analysis, how many pathways are significantly enriched under iron-depleted conditions but show no significant enrichment under innate media conditions? Significantly enriched pathways are defined as have an absolute log fold change greater than 1.5 and an adjusted p-values less than 0.05. </question> Follow these steps to create your notebook, using chain-of-thought reasoning at each stage: 1. Load Data and Perform Descriptive Statistics: <analysis_planning> - Identify which data files are most relevant to resolving the task. - Plan how to load these files efficiently in python. - List the specific descriptive statistics you plan to use (e.g., summary(), str(), head()). - Consider potential issues like missing data or unexpected formats. How will you handle each? - Plan how to present this information clearly in the notebook. - Write down key statistics you expect to see and how you'll interpret them. - Consider potential data quality issues and how you'll address them. </analysis_planning> Execute your plan to load data and perform descriptive statistics. 2. Develop Analysis Plan: <analysis_planning> - Break down each task into testable components. List these components. - For each component, list appropriate statistical tests or visualizations. - Consider alternative approaches for each component and justify your choices. - Identify potential confounding factors and how to address them. - Plan the sequence of your analysis steps, explaining the rationale for each. - Consider how this analysis plan will be documented in the notebook. - List potential statistical assumptions for your chosen methods and how you'll test them. - Think about how your analysis plan addresses your original task. </analysis_planning> Write out your analysis plan as comments in the notebook. 3. Execute Analysis Plan: <analysis_planning> - For each step in your analysis plan, list the python or bash functions and libraries you'll use. - Think about how to structure your code for readability and efficiency. - Plan how to document your code with clear comments. - Consider how to present results clearly, using tables or visualizations where appropriate. - Ensure that all outputs are clearly labeled and explained in the context of the task. - Plan how you'll interpret each result in relation to the original task. - Consider potential unexpected results and how you'll handle them. </analysis_planning> Execute your analysis plan, creating new cells as needed. 4. Conclude and Submit Answer: <thought_process> - Reflect on how your results relate to the original task. - Consider any limitations or uncertainties in your analysis. - Plan a concise summary of your findings. - Think about how to phrase your conclusion as clear statements. - Ensure that the notebook contains all necessary information for another model to derive these answers. - Consider any additional insights or patterns you've noticed during the analysis. - Think about potential follow-up questions or areas for further investigation. </thought_process> [Use the submit_answer tool to submit your final answer as a single string with your short answer] IMPORTANT: Wrap your answer in XML tags <answer> </answer> Example output: ``` submit_answer("<answer>Your concise answer to the question</answer>") ``` Remember, the final notebook should contain all necessary artifacts (plots, tables, print outputs) to solve the task provided. General Guidelines: - Write small to medium-sized cells for easier debugging. - Edit existing cells by their index number when fixing bugs, rather than creating new ones. - Check dataframe shapes before printing. Use head() for large dataframes. - Ensure each cell executes successfully before moving to the next. - Assume you already have the packages you need installed and only install new ones if you receive errors. - If you need to install packages, use pip or mamba. - All cells are by default python cells. Use python or bash tools for all analysis. - You can use bash cells by adding %%bash to the first line of the cell or running a subprocess. - You can only create code cells, no markdown cells. Guidelines for using the R programming language: 1. Load packages using this format to minimize verbose output: ```r if (!requireNamespace("package_name", quietly = TRUE)) { install.packages("package_name") } suppressPackageStartupMessages(library(package_name)) ``` 2. You must use the tidyverse wherever possible: dplyr, tidyr, ggplot2, readr, stringr, forcats, purrr, tibble, and lubridate. 3. All plots must be made using ggplot2. Here is an example of how to make a plot: # Create a density scatter plot of FSC-A vs SSC-A plot_data <- as.data.frame(dmso_data[, c("FSC-A", "SSC-A")]) scatter_plot <- ggplot2::ggplot(plot_data, ggplot2::aes(x = `FSC-A`, y = `SSC-A`)) + ggplot2::geom_hex(bins = 100) + ggplot2::scale_fill_viridis_c(trans = "log10") + ggplot2::labs( title = "FSC-A vs SSC-A Density Plot (DMSO Control)", x = "FSC-A", y = "SSC-A" ) + ggplot2::theme_minimal() 3. Use explicit namespace qualification for functions. For example, use dplyr::select() instead of select(). 4. For data operations, suppress messages about column name repairs: ```r variable_name <- read_excel("<fpath>.csv", col_names = FALSE, .name_repair = "minimal") ``` AVOID USING PLOTS/IMAGES. USE TABLES AND PRINT OUTPUTS INSTEAD AS MUCH AS POSSIBLE. ``` --- Harness Report runs agent harnesses from their GitHub repos on Harbor tasks and records every model call. Every page is also `.md` and `.json`; index: https://harnessreport.com/llms.txt · MCP: https://harnessreport.com/mcp