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Downstream analysis

Whilst MultiQC is typically used as a final reporting step in an analysis, it can also be used as an intermediate in your analysis.

MultiQC saves a directory of machine-readable outputs called multiqc_data/. In here there are files from each module and table, as well as a verbose multiqc.log file and, a BETA-multiqc.parquet file that contains all the intermediate data and metadata needed to regenereate a report.

Most of these files are tab-separated .tsv files by default, but you can choose to have them as JSON, YAML if you prefer with the -k/--data-format flag or the data_format option in a config file.

These files can be useful as MultiQC essentially standardises the outputs from a lot of different tools. Typical usage of MultiQC outputs could be filtering of large datasets (eg. single-cell analysis) or trend-monitoring of repeated runs.

Below are a few tools that are specifically designed to work with MultiQC. They are not created by or endorsed by the MultiQC author but may be helpful for your research.

TidyMultiqc​

Provides the means to convert multiqc_data.json files into tidy data frames for downstream analysis in R.

This analysis might involve cohort analysis, quality control visualisation, change-point detection, statistical process control, clustering, or any other type of quality analysis.

MegaQC​

Started off by MultiQC author @ewels this project has had further development by a team of several contributors. It is functional but still has several parts of its codebase that have never quite been finished.

MegaQC imports data from multiple MultiQC runs and provides an interface to explore this with an interactive web server using a database backend. It can plot data over time, across runs and even has an interactive dashboard builder. It's useful for anyone who wants to monitor MultiQC statistics (eg. clinical labs) or work interactively with large datasets (eg. single cell analysis).

ChronQC​

ChronQC is a quality control (QC) tracking system for clinical implementation of next-generation sequencing (NGS). ChronQC generates time series plots for various QC metrics, which allows comparison of the current run to historical runs. ChronQC has multiple features for tracking QC data including Westgard rules for clinical validity, laboratory-defined thresholds, and historical observations within a specified period. Users can record their notes and corrective actions directly onto the plots for long-term recordkeeping.

MultiQC Parquet Output (BETA)​

Starting from version 1.29, MultiQC writes out all plot and table data in a standardized Apache Parquet file format (BETA-multiqc.parquet) in the multiqc_data directory. This feature provides several significant benefits:

  • Persistence: The parquet file contains all the data necessary to regenerate MultiQC reports without needing access to the original analysis files
  • Reusability: The data is structured in a way that's optimized for cross-run analysis and data warehousing
  • Interoperability: Parquet is a widely supported columnar format that can be used with various data analysis tools and platforms
note

Note that the format is unstable as of 1.29 may change in 1.30, where it will be finally renamed to multiqc.parquet.

Parquet File Structure​

The BETA-multiqc.parquet file contains several different types of rows that can be distinguished by the type column:

  1. run_metadata: Contains metadata about the MultiQC run, including:

    • creation_date: Timestamp when the report was generated
    • modules: JSON-encoded list of modules included in the report
    • data_sources: JSON-encoded information about the data source files
    • config: JSON-encoded MultiQC configuration used for the run
    • multiqc_version: The version of MultiQC used

  2. plot_input: Contains the serialized plot configuration and data:

    • anchor: Unique identifier for the plot
    • plot_type: Type of plot (e.g., "line", "bar", "heatmap", "violin", "scatter", "table")
    • plot_input_data: JSON-encoded representation of the plot data and configuration

  3. table_row: Contains tabular data for samples and metrics:

    • sample_name: Name of the sample
    • metric_name: Name of the metric
    • val_raw: Raw value of the metric (numeric)
    • val_raw_type: Type of the raw value (e.g., "int", "float", "bool")
    • val_str: String representation of the value
    • metric_col_name: Column name in the source table
    • module: Name of the module that generated this data
    • section: Section within the module

Additional columns may be present depending on the specific plot or table type.

Rows and Schema​

The schema is dynamically created based on the data, but here's a representative schema of the core columns:

{
    "anchor": pl.Utf8,
    "type": pl.Utf8,
    "creation_date": pl.Datetime(time_unit="us"),  # no timezone specifier, but assumed UTC (for compatibility with Iceberg)
    "plot_type": pl.Utf8,
    "plot_input_data": pl.Utf8,
    "sample_name": pl.Utf8,
    "metric_name": pl.Utf8,
    "val_raw": pl.Float64,
    "val_raw_type": pl.Utf8,
    "val_str": pl.Utf8,
    "module": pl.Utf8,
    "section": pl.Utf8,
}

Working with Parquet Data​

To explore the structure programmatically:

import polars as pl

# Load the parquet file
df = pl.read_parquet("multiqc_data/BETA-multiqc.parquet")

# Get unique row types
print(df.select("type").unique())

# Access metadata
metadata_rows = df.filter(pl.col("type") == "run_metadata")

# Get all plot configurations
plot_inputs = df.filter(pl.col("type") == "plot_input")

# Extract tabular data from a specific module
module_data = df.filter(
    (pl.col("type") == "table_row") &
    (pl.col("module") == "fastqc")
)

# Get all unique metrics available
metrics = df.filter(pl.col("type") == "table_row").select("metric_name").unique()

Relationships Between Data​

  • The anchor column connects plot_input rows with their corresponding data rows
  • The module and section columns in tabular data connect rows to their source modules
  • creation_date allows tracking when the data was generated

Developers can use these relationships to reconstruct the full structure of the MultiQC report from the parquet file alone.

Rerunning MultiQC from Parquet​

One of the key benefits of the parquet output is the ability to regenerate MultiQC reports without needing the original data files:

multiqc multiqc_data/BETA-multiqc.parquet

This will load all the data from the parquet file and generate a new report.

Combining Multiple MultiQC Runs​

The parquet output enables easy aggregation of data from multiple MultiQC runs:

# Run MultiQC on the first set of data
multiqc /path/to/analysis1/ -o run1_output

# Run MultiQC on both the second set of data and the parquet from the first run
multiqc /path/to/analysis2/ run1_output/multiqc_data/BETA-multiqc.parquet -o combined_output

This will generate a report containing data from both runs. You can combine any number of parquet files with new data in a single command.

Using MultiQC Data in Python Scripts​

For programmatic access to MultiQC data, you can use the Python API to load parquet files directly:

import multiqc

# Load data from a parquet file
multiqc.parse_logs('multiqc_data/BETA-multiqc.parquet')

# List loaded modules and access data
modules = multiqc.list_modules()
plots = multiqc.list_plots()
data = multiqc.get_module_data(module="fastp")

Integrating with OLAP Databases​

The structured format of MultiQC's parquet output makes it ideal for integration with analytical databases and OLAP systems like Apache Iceberg:

import polars as pl
from pyiceberg.catalog import load_catalog

# Load the MultiQC parquet file
multiqc_df = pl.read_parquet("multiqc_data/BETA-multiqc.parquet")

# Configure and load Iceberg catalog
catalog = load_catalog(
    "glue",
    **{
        "type": "glue",
        "warehouse": "s3://your-bucket/iceberg-warehouse/"
    }
)

# Create or load Iceberg table
table = catalog.load_table("your_database.multiqc_data")

# Append data to Iceberg table
table.append(multiqc_df.to_arrow())

This approach enables more sophisticated analysis workflows, better reproducibility, and easier collaboration across teams - all while maintaining the comprehensive and intuitive reporting that MultiQC is known for.

Parquet Format Options​

Currently MultiQC offers two format options for the parquet output, but we might settle with only one format in the future.

  1. Long format (default): Data is stored with columns 'sample_name', 'metric_name', 'val_raw', 'val_raw_type', and 'val_str'. This format is very flexible and ensures all data types can be preserved.

  2. Wide format: Data is stored with each metric as a separate column, prefixed with the table name and optional namespace. While more intuitive for analytics, it may hit limits on the maximum number of columns in certain edge cases, and can have issues with mixed types (since Parquet requires columns to have consistent types).

You can configure the format in your MultiQC configuration file:

parquet_format: "long" # or "wide"