Y_CT

A dataset containing clonal tracking cell counts from three different clinical trials.

datasets

This is a stochastic framework that combines biochemical reaction networks with extended Kalman filter and Rauch-Tung-Striebel smoothing.
This framework allows to investigate the dynamics of cell differentiation from high-dimensional clonal tracking data subject to measurement noise, false negative errors, and systematically unobserved cell types.
Our tool can provide statistical support to biologists in gene therapy clonal tracking studies for a deeper understanding of clonal reconstitution dynamics. Further details on the methods can be found in L. Del Core et al., (2022) <doi:10.1101/2022.07.08.499353>.

Luca Del Core

Karen

Kalman Reaction Networks

Danilo Pellin

Marco Grzegorczyk

Ernst Wit

Y_CT function

A list containing the clonal tracking data for each clinical trial (WAS, \(\beta 0 \beta E\), \(\beta S \beta S\)).
Each clonal tracking dataset is a 3-dimensional array whose dimensions identify<dl>
<dt>1</dt>
<dd>time, in months</dd><dt>2</dt>
<dd>cell types: T, B, NK, Macrophages(M) and Granulocytes(G)</dd><dt>3</dt>
<dd>unique barcodes (clones)</dd>
</dl>

Format

Clonal tracking data from clinical trials — Y_CT

A list containing the clonal tracking data for each clinical trial (WAS, \(\beta 0 \beta E\), \(\beta S \beta S\)).
Each clonal tracking dataset is a 3-dimensional array whose dimensions identify<dl>
<dt>1</dt>
<dd>time, in months</dd>

<dt>2</dt>
<dd>cell types: T, B, NK, Macrophages(M) and Granulocytes(G)</dd>

<dt>3</dt>
<dd>unique barcodes (clones)</dd>


</dl>

Y_CT: Clonal tracking data from clinical trials

Description

Usage

Arguments

Format