Showing 15 of total 15 results (show query)
bioc
sva:Surrogate Variable Analysis
The sva package contains functions for removing batch effects and other unwanted variation in high-throughput experiment. Specifically, the sva package contains functions for the identifying and building surrogate variables for high-dimensional data sets. Surrogate variables are covariates constructed directly from high-dimensional data (like gene expression/RNA sequencing/methylation/brain imaging data) that can be used in subsequent analyses to adjust for unknown, unmodeled, or latent sources of noise. The sva package can be used to remove artifacts in three ways: (1) identifying and estimating surrogate variables for unknown sources of variation in high-throughput experiments (Leek and Storey 2007 PLoS Genetics,2008 PNAS), (2) directly removing known batch effects using ComBat (Johnson et al. 2007 Biostatistics) and (3) removing batch effects with known control probes (Leek 2014 biorXiv). Removing batch effects and using surrogate variables in differential expression analysis have been shown to reduce dependence, stabilize error rate estimates, and improve reproducibility, see (Leek and Storey 2007 PLoS Genetics, 2008 PNAS or Leek et al. 2011 Nat. Reviews Genetics).
Maintained by Jeffrey T. Leek. Last updated 5 months ago.
immunooncologymicroarraystatisticalmethodpreprocessingmultiplecomparisonsequencingrnaseqbatcheffectnormalization
72.0 match 10.05 score 3.2k scripts 50 dependentsbioc
RnBeads:RnBeads
RnBeads facilitates comprehensive analysis of various types of DNA methylation data at the genome scale.
Maintained by Fabian Mueller. Last updated 1 months ago.
dnamethylationmethylationarraymethylseqepigeneticsqualitycontrolpreprocessingbatcheffectdifferentialmethylationsequencingcpgislandimmunooncologytwochanneldataimport
16.5 match 6.85 score 169 scripts 1 dependents
huanglabumn
oncoPredict:Drug Response Modeling and Biomarker Discovery
Allows for building drug response models using screening data between bulk RNA-Seq and a drug response metric and two additional tools for biomarker discovery that have been developed by the Huang Laboratory at University of Minnesota. There are 3 main functions within this package. (1) calcPhenotype is used to build drug response models on RNA-Seq data and impute them on any other RNA-Seq dataset given to the model. (2) GLDS is used to calculate the general level of drug sensitivity, which can improve biomarker discovery. (3) IDWAS can take the results from calcPhenotype and link the imputed response back to available genomic (mutation and CNV alterations) to identify biomarkers. Each of these functions comes from a paper from the Huang research laboratory. Below gives the relevant paper for each function. calcPhenotype - Geeleher et al, Clinical drug response can be predicted using baseline gene expression levels and in vitro drug sensitivity in cell lines. GLDS - Geeleher et al, Cancer biomarker discovery is improved by accounting for variability in general levels of drug sensitivity in pre-clinical models. IDWAS - Geeleher et al, Discovering novel pharmacogenomic biomarkers by imputing drug response in cancer patients from large genomics studies.
Maintained by Robert Gruener. Last updated 12 months ago.
svapreprocesscorestringrbiomartgenefilterorg.hs.eg.dbgenomicfeaturestxdb.hsapiens.ucsc.hg19.knowngenetcgabiolinksbiocgenericsgenomicrangesirangess4vectors
11.0 match 18 stars 6.47 score 41 scripts
sva-se
freedom:Demonstration of Disease Freedom (DDF)
Implements the formulae required to calculate freedom from disease according to Cameron and Baldock (1998) <doi:10.1016/S0167-5877(97)00081-0>. These are the methods used at the Swedish national veterinary institute (SVA) to evaluate the performance of our nation animal disease surveillance programmes.
Maintained by Thomas Rosendal. Last updated 2 years ago.
15.5 match 1 stars 3.85 score 14 scriptsbioc
iasva:Iteratively Adjusted Surrogate Variable Analysis
Iteratively Adjusted Surrogate Variable Analysis (IA-SVA) is a statistical framework to uncover hidden sources of variation even when these sources are correlated. IA-SVA provides a flexible methodology to i) identify a hidden factor for unwanted heterogeneity while adjusting for all known factors; ii) test the significance of the putative hidden factor for explaining the unmodeled variation in the data; and iii), if significant, use the estimated factor as an additional known factor in the next iteration to uncover further hidden factors.
Maintained by Donghyung Lee. Last updated 5 months ago.
preprocessingqualitycontrolbatcheffectrnaseqsoftwarestatisticalmethodfeatureextractionimmunooncology
9.8 match 4.65 score 45 scripts
yufree
enviGCMS:GC/LC-MS Data Analysis for Environmental Science
Gas/Liquid Chromatography-Mass Spectrometer(GC/LC-MS) Data Analysis for Environmental Science. This package covered topics such molecular isotope ratio, matrix effects and Short-Chain Chlorinated Paraffins analysis etc. in environmental analysis.
Maintained by Miao YU. Last updated 2 months ago.
environmentmass-spectrometrymetabolomics
5.2 match 17 stars 6.49 score 30 scripts 1 dependentsbioc
compcodeR:RNAseq data simulation, differential expression analysis and performance comparison of differential expression methods
This package provides extensive functionality for comparing results obtained by different methods for differential expression analysis of RNAseq data. It also contains functions for simulating count data. Finally, it provides convenient interfaces to several packages for performing the differential expression analysis. These can also be used as templates for setting up and running a user-defined differential analysis workflow within the framework of the package.
Maintained by Charlotte Soneson. Last updated 3 months ago.
immunooncologyrnaseqdifferentialexpression
3.5 match 11 stars 8.06 score 26 scriptsbioc
biobroom:Turn Bioconductor objects into tidy data frames
This package contains methods for converting standard objects constructed by bioinformatics packages, especially those in Bioconductor, and converting them to tidy data. It thus serves as a complement to the broom package, and follows the same the tidy, augment, glance division of tidying methods. Tidying data makes it easy to recombine, reshape and visualize bioinformatics analyses.
Maintained by John D. Storey. Last updated 5 months ago.
multiplecomparisondifferentialexpressionregressiongeneexpressionproteomicsdataimport
1.9 match 49 stars 8.22 score 280 scripts 1 dependentsbioc
debrowser:Interactive Differential Expresion Analysis Browser
Bioinformatics platform containing interactive plots and tables for differential gene and region expression studies. Allows visualizing expression data much more deeply in an interactive and faster way. By changing the parameters, users can easily discover different parts of the data that like never have been done before. Manually creating and looking these plots takes time. With DEBrowser users can prepare plots without writing any code. Differential expression, PCA and clustering analysis are made on site and the results are shown in various plots such as scatter, bar, box, volcano, ma plots and Heatmaps.
Maintained by Alper Kucukural. Last updated 5 months ago.
sequencingchipseqrnaseqdifferentialexpressiongeneexpressionclusteringimmunooncology
1.7 match 61 stars 7.80 score 65 scriptsbioc
MBECS:Evaluation and correction of batch effects in microbiome data-sets
The Microbiome Batch Effect Correction Suite (MBECS) provides a set of functions to evaluate and mitigate unwated noise due to processing in batches. To that end it incorporates a host of batch correcting algorithms (BECA) from various packages. In addition it offers a correction and reporting pipeline that provides a preliminary look at the characteristics of a data-set before and after correcting for batch effects.
Maintained by Michael Olbrich. Last updated 5 months ago.
batcheffectmicrobiomereportwritingvisualizationnormalizationqualitycontrol
1.9 match 4 stars 4.60 score 4 scripts
bioc
BatchQC:Batch Effects Quality Control Software
Sequencing and microarray samples often are collected or processed in multiple batches or at different times. This often produces technical biases that can lead to incorrect results in the downstream analysis. BatchQC is a software tool that streamlines batch preprocessing and evaluation by providing interactive diagnostics, visualizations, and statistical analyses to explore the extent to which batch variation impacts the data. BatchQC diagnostics help determine whether batch adjustment needs to be done, and how correction should be applied before proceeding with a downstream analysis. Moreover, BatchQC interactively applies multiple common batch effect approaches to the data and the user can quickly see the benefits of each method. BatchQC is developed as a Shiny App. The output is organized into multiple tabs and each tab features an important part of the batch effect analysis and visualization of the data. The BatchQC interface has the following analysis groups: Summary, Differential Expression, Median Correlations, Heatmaps, Circular Dendrogram, PCA Analysis, Shape, ComBat and SVA.
Maintained by Jessica McClintock. Last updated 5 months ago.
batcheffectgraphandnetworkmicroarraynormalizationprincipalcomponentsequencingsoftwarevisualizationqualitycontrolrnaseqpreprocessingdifferentialexpressionimmunooncology
0.5 match 7 stars 8.99 score 54 scriptsbioc
edge:Extraction of Differential Gene Expression
The edge package implements methods for carrying out differential expression analyses of genome-wide gene expression studies. Significance testing using the optimal discovery procedure and generalized likelihood ratio tests (equivalent to F-tests and t-tests) are implemented for general study designs. Special functions are available to facilitate the analysis of common study designs, including time course experiments. Other packages such as sva and qvalue are integrated in edge to provide a wide range of tools for gene expression analysis.
Maintained by John D. Storey. Last updated 5 months ago.
multiplecomparisondifferentialexpressiontimecourseregressiongeneexpressiondataimport
0.5 match 21 stars 7.77 score 62 scripts
cran
ttScreening:Genome-Wide DNA Methylation Sites Screening by Use of Training and Testing Samples
A screening process utilizing training and testing samples to filter out uninformative DNA methylation sites. Surrogate variables (SVs) of DNA methylation are included in the filtering process to explain unknown factor effects.
Maintained by Meredith Ray. Last updated 6 years ago.
1.8 match 1.85 scorecran
SmartSVA:Fast and Robust Surrogate Variable Analysis
Introduces a fast and efficient Surrogate Variable Analysis algorithm that captures variation of unknown sources (batch effects) for high-dimensional data sets. The algorithm is built on the 'irwsva.build' function of the 'sva' package and proposes a revision on it that achieves an order of magnitude faster running time while trading no accuracy loss in return.
Maintained by Jun Chen. Last updated 8 years ago.
0.5 match 3.03 score 36 scripts 1 dependents