ArticleSoftwareX2022
TumorDecon: A digital cytometry software.
Article in SoftwareX, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
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Who cites it
5 citing papers in PubMed.
- Oxygen, angiogenesis, cancer and immune interplay in breast tumour microenvironment: a computational investigation.Royal Society open science · 2024Article
- Mathematical and Machine Learning Models of Renal Cell Carcinoma: A Review.Bioengineering (Basel, Switzerland) · 2023Review
- Deconvolution of bulk gene expression profiles reveals the association between immune cell polarization and the prognosis of hepatocellular carcinoma patients.Cancer medicine · 2023Article
- Article
- Patient-Specific Mathematical Model of the Clear Cell Renal Cell Carcinoma Microenvironment.Journal of personalized medicine · 2022Article
Corrections and comments
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Authors and funding
5 authors.
Funding
Abstract
There are many experimental methods for characterizing immune profiles of tumors, such as flow and mass cytometry. However, these approaches are time and resource intensive. Thus, several "digital cytometry" methods have been developed to extract cell frequencies from RNA-seq data. Here, we introduce TumorDecon, named for its potential to deconvolve the distribution of cells from the gene expression levels of a bulk of cells, such as a tumor. The Python package provides an accessible way of applying these methods. It includes four deconvolution methods as well as several gene sets, signature matrices, and functions for generating custom signature matrices.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.