Welcome to tascCODA’s documentation!

tascCODA is a statistical model to analyze tree-aggregated changes in compositional data from high-throughput sequencing experiments. The model in detail is described by Ostner et al. (2021). Nevertheless, we want to give a short introduction to the intuition behind tascCODA.

The package is available on github.

Please also check out the tutorials that explain the most important functionalities.

Motivation

When analyzing biological processes via high-throughput sequencing (HTS) experiments, such as single-cell RNA sequencing or 16S rRNA sequencing, it is often of interest to assess how cell populations change under one or more conditions. This task, however, is non-trivial, as there are several limitations that have to be addressed:

• HTS population data is compositional. This must be considered to avoid an inflation of false-positive results.

• Most datasets consist only of few samples, making frequentist tests inaccurate.

• A condition usually only effects a fraction of features. Therefore, sparse effects are preferable.

The scCODA model (Büttner, Ostner et al., 2021) tackled these limitations in a fully Bayesian model, outperforming other compositional and non-compositional methods.

In addition, HTS datasets are often ordered hierarchically by a tree structure, such as a cell lineage or taxonomic tree. In many cases, a biological process can influence entire groups of features that form a subtree in the same manner. For example, multiple types of T- and B-cells were found to be decreased in the intestinal epithelium in subjects with Ulcerative Colitis (Smillie et al., 2019). tascCODA is an extension of scCODA that is able to infer these group-wise changes automatically in a dynamic fashion. It creates a balance between the increased simplicity of aggregated effects with the lost accuracy of a less sparse model, that can be adjusted by the user as desired.

Note: Due to its similarity and dependency on scCODA, this documentation often links to the documentation of scCODA, wherever the two packages share functionality or API.

Contents

• About tascCODA
  • Compositional data analysis in HTS
  • Features of tascCODA
• Data structure
  • Data import methods
• The tascCODA model
  • Model structure
  • Inference
  • Result analysis
• Installation
• API
  • Data acquisition
  • Model setup and inference
  • Result evaluation

Tutorials

• tascCODA - Tree-aggregated compositional analysis of high-throughput sequencing data

Reference

Ostner et al. (2021), tascCODA: Bayesian Tree-Aggregated Analysis of Compositional Amplicon and Single-Cell Data Frontiers in Genetics.

Indices and tables

• Index

• Module Index

• Search Page