Showing 8 of total 8 results (show query)
ropensci
UCSCXenaTools:Download and Explore Datasets from UCSC Xena Data Hubs
Download and explore datasets from UCSC Xena data hubs, which are a collection of UCSC-hosted public databases such as TCGA, ICGC, TARGET, GTEx, CCLE, and others. Databases are normalized so they can be combined, linked, filtered, explored and downloaded.
Maintained by Shixiang Wang. Last updated 5 months ago.
api-clientbioinformaticsccledownloadericgctcgatoiltreehouseucscucsc-xena
11.5 match 106 stars 8.55 score 163 scripts 1 dependents
bioc
PDATK:Pancreatic Ductal Adenocarcinoma Tool-Kit
Pancreatic ductal adenocarcinoma (PDA) has a relatively poor prognosis and is one of the most lethal cancers. Molecular classification of gene expression profiles holds the potential to identify meaningful subtypes which can inform therapeutic strategy in the clinical setting. The Pancreatic Cancer Adenocarcinoma Tool-Kit (PDATK) provides an S4 class-based interface for performing unsupervised subtype discovery, cross-cohort meta-clustering, gene-expression-based classification, and subsequent survival analysis to identify prognostically useful subtypes in pancreatic cancer and beyond. Two novel methods, Consensus Subtypes in Pancreatic Cancer (CSPC) and Pancreatic Cancer Overall Survival Predictor (PCOSP) are included for consensus-based meta-clustering and overall-survival prediction, respectively. Additionally, four published subtype classifiers and three published prognostic gene signatures are included to allow users to easily recreate published results, apply existing classifiers to new data, and benchmark the relative performance of new methods. The use of existing Bioconductor classes as input to all PDATK classes and methods enables integration with existing Bioconductor datasets, including the 21 pancreatic cancer patient cohorts available in the MetaGxPancreas data package. PDATK has been used to replicate results from Sandhu et al (2019) [https://doi.org/10.1200/cci.18.00102] and an additional paper is in the works using CSPC to validate subtypes from the included published classifiers, both of which use the data available in MetaGxPancreas. The inclusion of subtype centroids and prognostic gene signatures from these and other publications will enable researchers and clinicians to classify novel patient gene expression data, allowing the direct clinical application of the classifiers included in PDATK. Overall, PDATK provides a rich set of tools to identify and validate useful prognostic and molecular subtypes based on gene-expression data, benchmark new classifiers against existing ones, and apply discovered classifiers on novel patient data to inform clinical decision making.
Maintained by Benjamin Haibe-Kains. Last updated 5 months ago.
geneexpressionpharmacogeneticspharmacogenomicssoftwareclassificationsurvivalclusteringgeneprediction
19.7 match 1 stars 4.31 score 17 scriptsbioc
maftools:Summarize, Analyze and Visualize MAF Files
Analyze and visualize Mutation Annotation Format (MAF) files from large scale sequencing studies. This package provides various functions to perform most commonly used analyses in cancer genomics and to create feature rich customizable visualzations with minimal effort.
Maintained by Anand Mayakonda. Last updated 5 months ago.
datarepresentationdnaseqvisualizationdrivermutationvariantannotationfeatureextractionclassificationsomaticmutationsequencingfunctionalgenomicssurvivalbioinformaticscancer-genome-atlascancer-genomicsgenomicsmaf-filestcgacurlbzip2xz-utilszlib
2.8 match 459 stars 14.63 score 948 scripts 18 dependents
lightbluetitan
OncoDataSets:A Comprehensive Collection of Cancer Types and Cancer-related DataSets
Offers a rich collection of data focused on cancer research, covering survival rates, genetic studies, biomarkers, and epidemiological insights. Designed for researchers, analysts, and bioinformatics practitioners, the package includes datasets on various cancer types such as melanoma, leukemia, breast, ovarian, and lung cancer, among others. It aims to facilitate advanced research, analysis, and understanding of cancer epidemiology, genetics, and treatment outcomes.
Maintained by Renzo Caceres Rossi. Last updated 3 months ago.
3.6 match 3 stars 4.18 score 6 scripts
cran
heterocop:Semi-Parametric Estimation with Gaussian Copula
A method for generating random vectors which are linked by a Gaussian copula. It also enables to estimate the correlation matrix of the Gaussian copula in order to identify independencies within the data.
Maintained by Ekaterina Tomilina. Last updated 4 months ago.
4.5 match 2.70 score
shixiangwang
IDConverter:Convert Identifiers in Biological Databases
Identifiers in biological databases connect different levels of metadata, phenotype data or genotype data. This tool is designed to easily convert identifiers within or between different biological databases (Wang, Shixiang, et al. (2021) <DOI:10.1371/journal.pgen.1009557>).
Maintained by Shixiang Wang. Last updated 2 years ago.
4.0 match 9 stars 3.00 score 22 scriptscran
ggrisk:Risk Score Plot for Cox Regression
The risk plot may be one of the most commonly used figures in tumor genetic data analysis. We can conclude the following two points: Comparing the prediction results of the model with the real survival situation to see whether the survival rate of the high-risk group is lower than that of the low-level group, and whether the survival time of the high-risk group is shorter than that of the low-risk group. The other is to compare the heat map and scatter plot to see the correlation between the predictors and the outcome.
Maintained by Jing Zhang. Last updated 4 years ago.
3.8 match 2 stars 2.08 score
openbiox
UCSCXenaShiny:Interactive Analysis of UCSC Xena Data
Provides functions and a Shiny application for downloading, analyzing and visualizing datasets from UCSC Xena (<http://xena.ucsc.edu/>), which is a collection of UCSC-hosted public databases such as TCGA, ICGC, TARGET, GTEx, CCLE, and others.
Maintained by Shixiang Wang. Last updated 4 months ago.
cancer-datasetshiny-appsucsc-xena
0.5 match 96 stars 8.54 score 35 scripts