Alterlab Eda

Exploratory Data Analysis

Overview

Perform comprehensive exploratory data analysis (EDA) on scientific data files across multiple domains. This skill provides automated file type detection, format-specific analysis, data quality assessment, and generates detailed markdown reports suitable for documentation and downstream analysis planning.

Key Capabilities:

• Automatic detection and analysis of 88 scientific file formats
• Comprehensive format-specific metadata extraction
• Data quality and integrity assessment
• Statistical summaries and distributions
• Visualization recommendations
• Downstream analysis suggestions
• Markdown report generation

When to Use This Skill

Use this skill when:

• User provides a path to a scientific data file for analysis
• User asks to "explore", "analyze", or "summarize" a data file
• User wants to understand the structure and content of scientific data
• User needs a comprehensive report of a dataset before analysis
• User wants to assess data quality or completeness
• User asks what type of analysis is appropriate for a file

Supported File Categories

The skill has comprehensive coverage of scientific file formats organized into six major categories:

1. Chemistry and Molecular Formats (60+ extensions)

Structure files, computational chemistry outputs, molecular dynamics trajectories, and chemical databases.

File types include: .pdb, .cif, .mol, .mol2, .sdf, .xyz, .smi, .gro, .log, .fchk, .cube, .dcd, .xtc, .trr, .prmtop, .psf, and more.

Reference file: references/chemistry_molecular_formats.md

2. Bioinformatics and Genomics Formats (50+ extensions)

Sequence data, alignments, annotations, variants, and expression data.

File types include: .fasta, .fastq, .sam, .bam, .vcf, .bed, .gff, .gtf, .bigwig, .h5ad, .loom, .counts, .mtx, and more.

Reference file: references/bioinformatics_genomics_formats.md

3. Microscopy and Imaging Formats (45+ extensions)

Microscopy images, medical imaging, whole slide imaging, and electron microscopy.

File types include: .tif, .nd2, .lif, .czi, .ims, .dcm, .nii, .mrc, .dm3, .vsi, .svs, .ome.tiff, and more.

Reference file: references/microscopy_imaging_formats.md

4. Spectroscopy and Analytical Chemistry Formats (35+ extensions)

NMR, mass spectrometry, IR/Raman, UV-Vis, X-ray, chromatography, and other analytical techniques.

File types include: .fid, .mzML, .mzXML, .raw, .mgf, .spc, .jdx, .xy, .cif (crystallography), .wdf, and more.

Reference file: references/spectroscopy_analytical_formats.md

5. Proteomics and Metabolomics Formats (30+ extensions)

Mass spec proteomics, metabolomics, lipidomics, and multi-omics data.

File types include: .mzML, .pepXML, .protXML, .mzid, .mzTab, .sky, .mgf, .msp, .h5ad, and more.

Reference file: references/proteomics_metabolomics_formats.md

6. General Scientific Data Formats (30+ extensions)

Arrays, tables, hierarchical data, compressed archives, and common scientific formats.

File types include: .npy, .npz, .csv, .xlsx, .json, .hdf5, .zarr, .parquet, .mat, .fits, .nc, .xml, and more.

Reference file: references/general_scientific_formats.md

Workflow

Step 1: File Type Detection

When a user provides a file path, first identify the file type:

1. Extract the file extension
2. Look up the extension in the appropriate reference file
3. Identify the file category and format description
4. Load format-specific information

Example:

Copy code
User: "Analyze data.fastq"
→ Extension: .fastq
→ Category: bioinformatics_genomics
→ Format: FASTQ Format (sequence data with quality scores)
→ Reference: references/bioinformatics_genomics_formats.md

Step 2: Load Format-Specific Information

Based on the file type, read the corresponding reference file to understand:

• Typical Data: What kind of data this format contains
• Use Cases: Common applications for this format
• Python Libraries: How to read the file in Python
• EDA Approach: What analyses are appropriate for this data type

Search the reference file for the specific extension (e.g., search for "### .fastq" in bioinformatics_genomics_formats.md).

Step 3: Perform Data Analysis

Use the scripts/eda_analyzer.py script OR implement custom analysis:

Option A: Use the analyzer script (auto-detects type, loads the reference, runs format-specific analysis, writes the report)

bashCopy code
uv run python scripts/eda_analyzer.py <filepath> [output.md]

The script has built-in analyzers for tabular (.csv/.tsv), arrays (.npy/.npz/.hdf5), JSON, sequence (.fasta/.fastq), and basic imaging (.tif). For every other format it still detects the type and embeds the reference info, but you perform the data analysis yourself (Option B).

Option B: Custom analysis in the conversation Based on the format information from the reference file, perform appropriate analysis:

For tabular data (CSV, TSV, Excel):

• Load with pandas
• Check dimensions, data types
• Analyze missing values
• Calculate summary statistics
• Identify outliers
• Check for duplicates

For sequence data (FASTA, FASTQ):

• Count sequences
• Analyze length distributions
• Calculate GC content
• Assess quality scores (FASTQ)

For images (TIFF, ND2, CZI):

• Check dimensions (X, Y, Z, C, T)
• Analyze bit depth and value range
• Extract metadata (channels, timestamps, spatial calibration)
• Calculate intensity statistics

For arrays (NPY, HDF5):

• Check shape and dimensions
• Analyze data type
• Calculate statistical summaries
• Check for missing/invalid values

Step 4: Generate Comprehensive Report

Create a markdown report with the following sections:

Required Sections:

1. Title and Metadata

   • Filename and timestamp
   • File size and location

2. Basic Information

   • File properties
   • Format identification

3. File Type Details

   • Format description from reference
   • Typical data content
   • Common use cases
   • Python libraries for reading

4. Data Analysis

   • Structure and dimensions
   • Statistical summaries
   • Quality assessment
   • Data characteristics

5. Key Findings

   • Notable patterns
   • Potential issues
   • Quality metrics

6. Recommendations

   • Preprocessing steps
   • Appropriate analyses
   • Tools and methods
   • Visualization approaches

Template Location

Use assets/report_template.md as a guide for report structure.

Step 5: Save Report

Save the markdown report with a descriptive filename:

• Pattern: {original_filename}_eda_report.md
• Example: experiment_data.fastq → experiment_data_eda_report.md

Detailed Format References

Each reference file contains comprehensive information for dozens of file types. To find information about a specific format:

1. Identify the category from the extension
2. Read the appropriate reference file
3. Search for the section heading matching the extension (e.g., "### .pdb")
4. Extract the format information

Reference File Structure

Each format entry includes:

• Description: What the format is
• Typical Data: What it contains
• Use Cases: Common applications
• Python Libraries: How to read it (with code examples)
• EDA Approach: Specific analyses to perform

Example lookup:

markdownCopy code
### .pdb - Protein Data Bank
**Description:** Standard format for 3D structures of biological macromolecules
**Typical Data:** Atomic coordinates, residue information, secondary structure
**Use Cases:** Protein structure analysis, molecular visualization, docking
**Python Libraries:**
- `Biopython`: `Bio.PDB`
- `MDAnalysis`: `MDAnalysis.Universe('file.pdb')`
**EDA Approach:**
- Structure validation (bond lengths, angles)
- B-factor distribution
- Missing residues detection
- Ramachandran plots

Best Practices

Reading Reference Files

Reference files are large (10,000+ words each). To efficiently use them:

1. Search by extension: Use grep to find the specific format

   pythonCopy code
   import re
   with open('references/chemistry_molecular_formats.md', 'r') as f:
       content = f.read()
       pattern = r'### \.pdb[^#]*?(?=###|\Z)'
       match = re.search(pattern, content, re.IGNORECASE | re.DOTALL)
   

2. Extract relevant sections: Don't load entire reference files into context unnecessarily

3. Cache format info: If analyzing multiple files of the same type, reuse the format information

Data Analysis

1. Sample large files: For files with millions of records, analyze a representative sample
2. Handle errors gracefully: Many scientific formats require specific libraries; provide clear installation instructions
3. Validate metadata: Cross-check metadata consistency (e.g., stated dimensions vs actual data)
4. Consider data provenance: Note instrument, software versions, processing steps

Report Generation

1. Be comprehensive: Include all relevant information for downstream analysis
2. Be specific: Provide concrete recommendations based on the file type
3. Be actionable: Suggest specific next steps and tools
4. Include code examples: Show how to load and work with the data

Examples

The pattern is always: detect extension -> read the matching reference section -> run format-appropriate analysis -> write <stem>_eda_report.md.

• reads.fastq -> bioinformatics. from Bio import SeqIO; SeqIO.parse(path, 'fastq'). Report read count, length distribution, per-read quality, GC content, then QC recommendations.
• experiment_results.csv -> general scientific. pd.read_csv. Report shape, dtypes, missing-value patterns, summary stats, correlations, duplicates, outliers.
• cells.nd2 -> microscopy (Nikon). from nd2reader import ND2Reader. Report XYZCT dimensions, channels/timepoints, pixel size/calibration, intensity stats, then image-analysis recommendations.

Troubleshooting

Missing Libraries

Many scientific formats require specialized libraries:

Problem: Import error when trying to read a file

Solution: Install the parser with uv pip install <pkg> (do NOT use bare pip on this machine), then retry. Common requirements by category:

• Bioinformatics: biopython, pysam, pyBigWig
• Chemistry: rdkit, mdanalysis, cclib
• Microscopy: tifffile, nd2reader, aicsimageio, pydicom
• Spectroscopy: nmrglue, pymzml, pyteomics
• General: pandas, numpy, h5py, scipy

Unknown File Types

If a file extension is not in the references:

1. Ask the user about the file format
2. Check if it's a vendor-specific variant
3. Attempt generic analysis based on file structure (text vs binary)
4. Provide general recommendations

Large Files

For very large files:

1. Use sampling strategies (first N records)
2. Use memory-mapped access (for HDF5, NPY)
3. Process in chunks (for CSV, FASTQ)
4. Provide estimates based on samples

Script Usage

bashCopy code
uv run python scripts/eda_analyzer.py data.csv                 # report -> data_eda_report.md
uv run python scripts/eda_analyzer.py data.csv output_report.md

The script auto-detects the file type, loads the matching reference section, runs built-in analysis where available, and writes the markdown report. For formats without a built-in analyzer, prefer custom analysis in the conversation (Option B) for domain-specific insight. Note: for .csv/.tsv the script samples the first 10,000 rows, so report dimensions/missing counts as sampled unless you re-run on the full file.

Advanced Usage

• Multi-file: EDA each file individually, then write a comparison report noting relationships, dependencies, and integration strategy.
• Quality control: check format compliance, validate metadata consistency (stated vs actual dimensions), assess completeness, flag outliers/anomalies against expected ranges.
• Preprocessing recommendations: tailor to the data — normalization, missing-value imputation, outlier handling, batch correction, format conversion.

Resources

scripts/

• eda_analyzer.py: Comprehensive analysis script that can be run directly or imported

references/

• chemistry_molecular_formats.md: 60+ chemistry/molecular file formats
• bioinformatics_genomics_formats.md: 50+ bioinformatics formats
• microscopy_imaging_formats.md: 45+ imaging formats
• spectroscopy_analytical_formats.md: 35+ spectroscopy formats
• proteomics_metabolomics_formats.md: 30+ omics formats
• general_scientific_formats.md: 30+ general formats

assets/

• report_template.md: Comprehensive markdown template for EDA reports