Showing 110 of total 110 results (show query)

hugheylab

seeker:Simplified Fetching and Processing of Microarray and RNA-Seq Data

Wrapper around various existing tools and command-line interfaces, providing a standard interface, simple parallelization, and detailed logging. For microarray data, maps probe sets to standard gene IDs, building on 'GEOquery' Davis and Meltzer (2007) <doi:10.1093/bioinformatics/btm254>, 'ArrayExpress' Kauffmann et al. (2009) <doi:10.1093/bioinformatics/btp354>, Robust multi-array average 'RMA' Irizarry et al. (2003) <doi:10.1093/biostatistics/4.2.249>, and 'BrainArray' Dai et al. (2005) <doi:10.1093/nar/gni179>. For RNA-seq data, fetches metadata and raw reads from National Center for Biotechnology Information (NCBI) Sequence Read Archive (SRA), performs standard adapter and quality trimming using 'TrimGalore' Krueger <https://github.com/FelixKrueger/TrimGalore>, performs quality control checks using 'FastQC' Andrews <https://github.com/s-andrews/FastQC>, quantifies transcript abundances using 'salmon' Patro et al. (2017) <doi:10.1038/nmeth.4197> and potentially 'refgenie' Stolarczyk et al. (2020) <doi:10.1093/gigascience/giz149>, aggregates the results using 'MultiQC' Ewels et al. (2016) <doi:10.1093/bioinformatics/btw354>, maps transcripts to genes using 'biomaRt' Durinkck et al. (2009) <doi:10.1038/nprot.2009.97>, and summarizes transcript-level quantifications for gene-level analyses using 'tximport' Soneson et al. (2015) <doi:10.12688/f1000research.7563.2>.

Maintained by Jake Hughey. Last updated 7 months ago.

3 stars 4.78 score 1 scripts

bioc

MatrixQCvis:Shiny-based interactive data-quality exploration for omics data

Data quality assessment is an integral part of preparatory data analysis to ensure sound biological information retrieval. We present here the MatrixQCvis package, which provides shiny-based interactive visualization of data quality metrics at the per-sample and per-feature level. It is broadly applicable to quantitative omics data types that come in matrix-like format (features x samples). It enables the detection of low-quality samples, drifts, outliers and batch effects in data sets. Visualizations include amongst others bar- and violin plots of the (count/intensity) values, mean vs standard deviation plots, MA plots, empirical cumulative distribution function (ECDF) plots, visualizations of the distances between samples, and multiple types of dimension reduction plots. Furthermore, MatrixQCvis allows for differential expression analysis based on the limma (moderated t-tests) and proDA (Wald tests) packages. MatrixQCvis builds upon the popular Bioconductor SummarizedExperiment S4 class and enables thus the facile integration into existing workflows. The package is especially tailored towards metabolomics and proteomics mass spectrometry data, but also allows to assess the data quality of other data types that can be represented in a SummarizedExperiment object.

Maintained by Thomas Naake. Last updated 5 months ago.

visualizationshinyappsguiqualitycontroldimensionreductionmetabolomicsproteomicstranscriptomics

4.74 score 4 scripts

bioc

frma:Frozen RMA and Barcode

Preprocessing and analysis for single microarrays and microarray batches.

Maintained by Matthew N. McCall. Last updated 5 months ago.

softwaremicroarraypreprocessing

4.72 score 87 scripts 1 dependents

bioc

frmaTools:Frozen RMA Tools

Tools for advanced use of the frma package.

Maintained by Matthew N. McCall. Last updated 5 months ago.

softwaremicroarraypreprocessing

3.90 score 6 scripts

romanhornung

bapred:Batch Effect Removal and Addon Normalization (in Phenotype Prediction using Gene Data)

Various tools dealing with batch effects, in particular enabling the removal of discrepancies between training and test sets in prediction scenarios. Moreover, addon quantile normalization and addon RMA normalization (Kostka & Spang, 2008) is implemented to enable integrating the quantile normalization step into prediction rules. The following batch effect removal methods are implemented: FAbatch, ComBat, (f)SVA, mean-centering, standardization, Ratio-A and Ratio-G. For each of these we provide an additional function which enables a posteriori ('addon') batch effect removal in independent batches ('test data'). Here, the (already batch effect adjusted) training data is not altered. For evaluating the success of batch effect adjustment several metrics are provided. Moreover, the package implements a plot for the visualization of batch effects using principal component analysis. The main functions of the package for batch effect adjustment are ba() and baaddon() which enable batch effect removal and addon batch effect removal, respectively, with one of the seven methods mentioned above. Another important function here is bametric() which is a wrapper function for all implemented methods for evaluating the success of batch effect removal. For (addon) quantile normalization and (addon) RMA normalization the functions qunormtrain(), qunormaddon(), rmatrain() and rmaaddon() can be used.

Maintained by Roman Hornung. Last updated 3 years ago.

1.78 score 20 scripts