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Input Arguments

This page outlines the required inputs and optional parameters for running Statescope.

Required Datasetsā€‹

Signature Matricesā€‹

The signature matrix defines the gene expression profiles of different cell types.

Available Options for Signature Matricesā€‹

There are two ways to specify the signature matrix:

šŸ“Œ Option 1: Using Pre-processed Signaturesā€‹

  • Statescope provides pre-processed signatures for various tumor types.
  • To use these signatures, specify the TumorType and the number of cell types (Ncelltypes).
  • Available options for TumorType and Ncelltypes can be found in the Processed Signatures page.

Example using pre-processed signatures:

Statescope_model = Initialize_Statescope(Bulk, TumorType='PBMC', Ncelltypes=7, Ncores=40)
  • PBMC is the pre-processed signature used.
  • Ncelltypes=7 specifies the number of cell types to use.
  • Ncores=40 defines the number of CPU cores allocated.

šŸ“Œ Option 2: Using Your Own Single-Cell RNA Dataā€‹

  • Users can also provide their own custom single-cell data in .h5ad format.
  • The cell type annotations should be present in the key specified in celltype_key.

Example using a custom signature matrix:

Statescope_model = Initialize_Statescope(
    Bulk, 
    Signature=Signature, 
    celltype_key='leiden', 
    Ncores=40
)
  • Bulk is the bulk RNA-seq dataset (transposed).
  • Signature is the custom signature matrix derived from single-cell data.
  • celltype_key='leiden' specifies the annotation key in the .h5ad file.

āš ļø Note:

  • Single-cell data should be preprocessed (filtering, QC, normalization).
  • Statescope handles internal normalization and preprocessing automatically.
  • Ensure the cell type annotations exist under the key celltype_key in .obs.

Bulk Gene Expression Dataā€‹

  • Y: An Ngene by Nsample matrix containing bulk gene expression data.
  • Should be provided in linear scale (without log-transformation).
  • Format: Ideally, a pandas DataFrame.

Example format in Python:

import pandas as pd 
Bulk = pd.read_csv("bulk_expression.csv", index_col=0)

Expected Cell Fractions (Optional)ā€‹

  • Expectation: An Nsample by Ncelltype matrix.
  • Specifies prior expectations of cell type proportions (used by OncoBLADE).
  • Format: Ideally, a pandas DataFrame.

Example format in Python:

expected_fractions = pd.read_csv("expected_cell_fractions.csv", index_col=0)

āš ļø Important Notesā€‹

  • Bulk RNA-seq data should be in linear scale (not log-transformed).
  • Signature matrices should be in log-scale.
  • Single-cell .h5ad files should contain filtered, QCā€™d, and annotated cell types.
  • pandas DataFrames** are recommended for structured inputs.

šŸ”— Further Resourcesā€‹