Computes Signaling and Population Stats for Cytometry Data on
Cytobank using 'CytobankAPI'
Tools to process cytometry data from Cytobank into easily usable form for analysis of populations, markers, and signaling using the 'CytobankAPI' package. Learn more about Cytobank at <https://www.cytobank.org>. For more information about types of cytometry data that can be analyzed, please see: Bendall, S. C., Simonds, E. F., Qiu, P., Amir, E. D., Krutzik, P. O., Finck, R.,... Nolan, G. P. (2011) <doi:10.1126/science.1198704> and Adan, A., Alizada, G., Kiraz, Y., Baran, Y., Nalbant, A. (2017). <doi:10.3109/07388551.2015.1128876>.

CytobankAPI, shiny, xlsx, shinyFiles, pheatmap

Bridging and Extending the CytobankAPI Package in R to the
Cytobank Web Application
c(
person("Preston", "Ng", email="preston@cytobank.org", role=c("aut", "cre")),
person("Katherine", "Drake", email="katherine@cytobank.org ", role="aut"),
person("Chris", "Ciccolella", email="cciccolella@cytobank.org", role="aut"))
A collection of tools that leverage the CytobankAPI R package <https://cran.r-project.org/web/packages/CytobankAPI/vignettes/cytobank-quickstart.html> to complete more complex workflows, and add/extend various Cytobank features.
Preston Ng [aut, cre],
Katherine Drake [aut],
Chris Ciccolella [aut]

Predicting Causal Direction from Dependency Features
Gianluca Bontempi, Catharina Olsen, Maxime Flauder
The relationship between statistical dependency and causality lies
at the heart of all statistical approaches to causal inference. The D2C
package implements a supervised machine learning approach to infer the
existence of a directed causal link between two variables in multivariate
settings with n>2 variables. The approach relies on the asymmetry of some
conditional (in)dependence relations between the members of the Markov
blankets of two variables causally connected. The D2C algorithm predicts
the existence of a direct causal link between two variables in a
multivariate setting by (i) creating a set of of features of the
relationship based on asymmetric descriptors of the multivariate dependency
and (ii) using a classifier to learn a mapping between the features and the
presence of a causal link
gRbase, lazy, RBGL, MASS, corpcor, infotheo, methods,
Rgraphviz