Metadata Prediction

Overview

Metadata prediction models infer biological metadata from observed expression data. Given a gene expression profile, the model predicts the likely biological characteristics such as cell type, tissue, disease state, and more.

This is useful when you want to:

  • Annotate samples of unknown origin

  • Validate sample labels against expression patterns

  • Discover potential mislabeled or contaminated samples

  • Understand the biological characteristics captured in expression data

Available Models

  • gem-1-bulk_predict-metadata: Bulk RNA-seq metadata prediction model

  • gem-1-sc_predict-metadata: Single-cell RNA-seq metadata prediction model

Note

These endpoints may require 1-2 minutes of startup time if they have been scaled down. Plan accordingly for interactive use.

import pysynthbio

How It Works

Metadata prediction encodes your expression data into the model’s latent space and then uses classifiers to predict the most likely metadata values for each sample. The model returns:

  1. Classifier probabilities: For each categorical metadata field, the probability distribution over possible values

  2. Predicted labels: The most likely value for each metadata field

  3. Latent representations: The biological, technical, and perturbation latent vectors

Creating a Query

Metadata prediction queries are simpler than other model types—you only need to provide expression counts:

# Get the example query structure
example_query = pysynthbio.get_example_query(model_id="gem-1-bulk_predict-metadata")["example_query"]

# Inspect the query structure
print(example_query)

The query structure includes:

  1. inputs: A list of count vectors, where each element is a dictionary with a counts field containing expression values

  2. seed (optional): Random seed for reproducibility

Example: Predicting Sample Metadata

Here’s a complete example predicting metadata for expression samples:

# Start with example query structure
query = pysynthbio.get_example_query(model_id="gem-1-bulk_predict-metadata")["example_query"]

# Replace with your actual expression counts
# Each input should be a dictionary with a counts list
query["inputs"] = [
    {"counts": sample1_counts},
    {"counts": sample2_counts},
    {"counts": sample3_counts}
]

# Optional: set seed for reproducibility
query["seed"] = 42

# Submit the query
result = pysynthbio.predict_query(query, model_id="gem-1-bulk_predict-metadata")

Example: Single Sample Prediction

For predicting metadata of a single sample:

query = pysynthbio.get_example_query(model_id="gem-1-bulk_predict-metadata")["example_query"]

# Single sample
query["inputs"] = [
    {"counts": my_sample_counts}
]

result = pysynthbio.predict_query(query, model_id="gem-1-bulk_predict-metadata")

# Access the predictions for the first (and only) sample
print(result[0]["metadata"])

Query Parameters

inputs (list, required)

A list of expression count vectors. Each element should be a dictionary containing:

  • counts: A list of non-negative integers representing gene expression counts

query["inputs"] = [
    {"counts": [0, 12, 5, 0, 33, 7, ...]},  # Sample 1
    {"counts": [3, 0, 0, 7, 1, 0, ...]}     # Sample 2
]

seed (int, optional)

Random seed for reproducibility.

query["seed"] = 123

Understanding the Results

The results from metadata prediction are returned as a list of output dictionaries, one per input sample. Each output dictionary contains:

  • metadata: Predicted metadata values for the sample

  • classifier_probs: Probability distributions over possible values for each metadata field

  • latents: Latent representations (biological, technical, perturbation)

# result is a list of outputs, one per input sample
print(f"Number of outputs: {len(result)}")

# Access the first sample's output
first_output = result[0]
print(first_output.keys())  # dict_keys(['metadata', 'classifier_probs', 'latents'])

Predicted Metadata

Each output’s metadata field contains the predicted values for that sample:

# Access predictions for each sample
for i, output in enumerate(result):
    print(f"Sample {i}: {output['metadata']}")

# Access specific predictions for first sample
first_sample = result[0]["metadata"]
print(first_sample.get("cell_type_ontology_id"))
print(first_sample.get("tissue_ontology_id"))
print(first_sample.get("disease_ontology_id"))

Classifier Probabilities

For categorical metadata fields, the model returns probability distributions over all possible values. These are useful for understanding prediction confidence:

# Access cell type probabilities for first sample
first_output = result[0]
cell_type_probs = first_output["classifier_probs"]["cell_type"]
sorted_probs = sorted(cell_type_probs.items(), key=lambda x: x[1], reverse=True)
print("Top predicted cell types:", sorted_probs[:5])

Latent Representations

The model also returns latent vectors that capture biological, technical, and perturbation characteristics:

# Access latent representations for first sample
first_output = result[0]
biological_latents = first_output["latents"]["biological"]
technical_latents = first_output["latents"]["technical"]

Use Cases

Sample Annotation

Annotate unlabeled samples with predicted metadata:

import pandas as pd

# Load your unlabeled samples
unlabeled_counts = pd.read_csv("unlabeled_samples.csv", index_col=0)

# Create query
query = pysynthbio.get_example_query(model_id="gem-1-bulk_predict-metadata")["example_query"]
query["inputs"] = [
    {"counts": unlabeled_counts.iloc[:, i].tolist()}
    for i in range(unlabeled_counts.shape[1])
]

# Predict metadata
result = pysynthbio.predict_query(query, model_id="gem-1-bulk_predict-metadata")

# Combine with sample IDs - result is a list of outputs
annotations = pd.DataFrame([output["metadata"] for output in result])
annotations["sample_id"] = unlabeled_counts.columns.tolist()

Quality Control

Validate existing sample labels against predicted metadata:

# Compare predicted vs. provided labels
provided_labels = ["UBERON:0002107", "UBERON:0002107", "UBERON:0000955", "UBERON:0000955"]
predicted_labels = [output["metadata"].get("tissue_ontology_id") for output in result]

# Identify potential mismatches
mismatches = [
    i for i, (p, pred) in enumerate(zip(provided_labels, predicted_labels))
    if p != pred
]
if mismatches:
    print(f"Potential mislabeled samples: {mismatches}")

Batch Characterization

Understand batch-specific technical characteristics:

import numpy as np

# Group samples by batch
batch_labels = ["batch1", "batch1", "batch2", "batch2"]

# Check if technical predictions cluster by batch
# This can help identify batch effects
# Extract technical latents from each sample's output
technical_latents = [output["latents"]["technical"] for output in result]
for batch in set(batch_labels):
    batch_indices = [i for i, b in enumerate(batch_labels) if b == batch]
    batch_mean = np.mean([technical_latents[i][0] for i in batch_indices])
    print(f"{batch} technical latent mean: {batch_mean}")

Important Notes

Counts Vector Length

The counts vector for each sample must match the model’s expected number of genes. If the length doesn’t match, the API will return a validation error.

Use get_example_query() to see the expected structure.

Gene Order

Ensure your counts are in the same gene order expected by the model. The gene order should match what the baseline model expects—you can retrieve this from any prediction result’s gene_order field.

Non-Negative Counts

All count values must be non-negative integers. Floats that are whole numbers (like 10.0) are accepted, but negative values will cause validation errors.