@inbook{e394201a98ba4d2f9ac2638c28e4b6be,
title = "Immunedeconv: An R package for unified access to computational methods for estimating immune cell fractions from bulk RNA-sequencing data",
abstract = "Since the performance of in silico approaches for estimating immune-cell fractions from bulk RNA-seq data can vary, it is often advisable to compare results of several methods. Given numerous dependencies and differences in input and output format of the various computational methods, comparative analyses can become quite complex. This motivated us to develop immunedeconv, an R package providing uniform and user-friendly access to seven state-of-the-art computational methods for deconvolution of cell-type fractions from bulk RNA-seq data. Here, we show how immunedeconv can be installed and applied to a typical dataset. First, we give an example for obtaining cell-type fractions using quanTIseq. Second, we show how dimensionless scores produced by MCP-counter can be used for cross-sample comparisons. For each of these examples, we provide R code illustrating how immunedeconv results can be summarized graphically.",
keywords = "Benchmark, Cell-type deconvolution, Comparative analyses, Immuno-oncology, R package",
author = "Gregor Sturm and Francesca Finotello and Markus List",
note = "Publisher Copyright: {\textcopyright} Springer Science+Business Media, LLC, part of Springer Nature 2020.",
year = "2020",
doi = "10.1007/978-1-0716-0327-7_16",
language = "English",
series = "Methods in Molecular Biology",
publisher = "Humana Press Inc.",
pages = "223--232",
booktitle = "Methods in Molecular Biology",
}