Welcome to SCALT’s documentation!

SCALT (Single Cell Annotation Likelihood Tool) is an innovative tool which classifies cells from single cell RNA seq experiments at single cell resolution level taking the advantage of a maximum likelihood-based approach and a collection of pre-compiled cell type specific lists of genes constructed by extensive re-analysis of comprehensive and expert curated catalogues.

The application does not require any clustering, dimensionality reduction or manual annotation.

For further details, we advise to read the paper available at DOI

As mentioned in the paper, SCALT is composed of other parallel utilities that allow to perform more complex analysis such as:

1. build cell type specific lists of genes in a deterministic fashion starting from a counts matrix and the correspoding annotation for each cell;

2. build cell type specific lists of genes starting from a count matrix and a gathering of user-defined cell type specific lists of genes making the use of an hypergeometric test.

If you want to learn more about, please see the manual for point to point for instructions and tips.

SCALT: Basic usage

• For impatient people

Prerequisites

• Conda Installation
• SCALT download

Databases

• Key point
• Human Protein Atlas (HPA)
• DISCO

SCALT: Classification

• Inputs & Outputs
• SCALT parameters
• Run SCALT.py
• Report
• Workflow

SCALT: Advanced

• Advanced utilities
• Workflows
  • Build the cell-type specific lists of genes starting from annotated data
  • Build the cell-type specific lists of genes starting from user-defined lists

SCALT: From Annotation

• Inputs formats
• Parameters
• Run SCALT_AnnotaionListsBuilder.py
• Outputs

SCALT: From User-defiend Lists

• Inputs formats
• Parameters
• Run SCALT_NaiveListsBuilder.py
• Outputs

SCALT: For Brave People

• Advanced use cases
  • Use custom lists in SCALT.py
  • Use naive lists in SCALT.py

Auxiliary programs

• Formats converter