Showing 45 of total 45 results (show query)
bioc
minfi:Analyze Illumina Infinium DNA methylation arrays
Tools to analyze & visualize Illumina Infinium methylation arrays.
Maintained by Kasper Daniel Hansen. Last updated 4 months ago.
immunooncologydnamethylationdifferentialmethylationepigeneticsmicroarraymethylationarraymultichanneltwochanneldataimportnormalizationpreprocessingqualitycontrol
60 stars 12.82 score 996 scripts 27 dependentsbioc
methylumi:Handle Illumina methylation data
This package provides classes for holding and manipulating Illumina methylation data. Based on eSet, it can contain MIAME information, sample information, feature information, and multiple matrices of data. An "intelligent" import function, methylumiR can read the Illumina text files and create a MethyLumiSet. methylumIDAT can directly read raw IDAT files from HumanMethylation27 and HumanMethylation450 microarrays. Normalization, background correction, and quality control features for GoldenGate, Infinium, and Infinium HD arrays are also included.
Maintained by Sean Davis. Last updated 5 months ago.
dnamethylationtwochannelpreprocessingqualitycontrolcpgisland
9 stars 9.90 score 89 scripts 9 dependentsbioc
recount:Explore and download data from the recount project
Explore and download data from the recount project available at https://jhubiostatistics.shinyapps.io/recount/. Using the recount package you can download RangedSummarizedExperiment objects at the gene, exon or exon-exon junctions level, the raw counts, the phenotype metadata used, the urls to the sample coverage bigWig files or the mean coverage bigWig file for a particular study. The RangedSummarizedExperiment objects can be used by different packages for performing differential expression analysis. Using http://bioconductor.org/packages/derfinder you can perform annotation-agnostic differential expression analyses with the data from the recount project as described at http://www.nature.com/nbt/journal/v35/n4/full/nbt.3838.html.
Maintained by Leonardo Collado-Torres. Last updated 4 months ago.
coveragedifferentialexpressiongeneexpressionrnaseqsequencingsoftwaredataimportimmunooncologyannotation-agnosticbioconductorcountderfinderdeseq2exongenehumanilluminajunctionrecount
41 stars 9.57 score 498 scripts 3 dependentsbioc
netZooR:Unified methods for the inference and analysis of gene regulatory networks
netZooR unifies the implementations of several Network Zoo methods (netzoo, netzoo.github.io) into a single package by creating interfaces between network inference and network analysis methods. Currently, the package has 3 methods for network inference including PANDA and its optimized implementation OTTER (network reconstruction using mutliple lines of biological evidence), LIONESS (single-sample network inference), and EGRET (genotype-specific networks). Network analysis methods include CONDOR (community detection), ALPACA (differential community detection), CRANE (significance estimation of differential modules), MONSTER (estimation of network transition states). In addition, YARN allows to process gene expresssion data for tissue-specific analyses and SAMBAR infers missing mutation data based on pathway information.
Maintained by Tara Eicher. Last updated 14 days ago.
networkinferencenetworkgeneregulationgeneexpressiontranscriptionmicroarraygraphandnetworkgene-regulatory-networktranscription-factors
105 stars 7.98 scorebioc
wateRmelon:Illumina DNA methylation array normalization and metrics
15 flavours of betas and three performance metrics, with methods for objects produced by methylumi and minfi packages.
Maintained by Leo C Schalkwyk. Last updated 4 months ago.
dnamethylationmicroarraytwochannelpreprocessingqualitycontrol
7.73 score 247 scripts 2 dependentsbioc
phantasus:Visual and interactive gene expression analysis
Phantasus is a web-application for visual and interactive gene expression analysis. Phantasus is based on Morpheus – a web-based software for heatmap visualisation and analysis, which was integrated with an R environment via OpenCPU API. Aside from basic visualization and filtering methods, R-based methods such as k-means clustering, principal component analysis or differential expression analysis with limma package are supported.
Maintained by Alexey Sergushichev. Last updated 5 months ago.
geneexpressionguivisualizationdatarepresentationtranscriptomicsrnaseqmicroarraynormalizationclusteringdifferentialexpressionprincipalcomponentimmunooncology
43 stars 7.68 score 15 scriptsbioc
shinyMethyl:Interactive visualization for Illumina methylation arrays
Interactive tool for visualizing Illumina methylation array data. Both the 450k and EPIC array are supported.
Maintained by Jean-Philippe Fortin. Last updated 5 months ago.
dnamethylationmicroarraytwochannelpreprocessingqualitycontrolmethylationarray
5 stars 7.34 score 42 scriptsbioc
missMethyl:Analysing Illumina HumanMethylation BeadChip Data
Normalisation, testing for differential variability and differential methylation and gene set testing for data from Illumina's Infinium HumanMethylation arrays. The normalisation procedure is subset-quantile within-array normalisation (SWAN), which allows Infinium I and II type probes on a single array to be normalised together. The test for differential variability is based on an empirical Bayes version of Levene's test. Differential methylation testing is performed using RUV, which can adjust for systematic errors of unknown origin in high-dimensional data by using negative control probes. Gene ontology analysis is performed by taking into account the number of probes per gene on the array, as well as taking into account multi-gene associated probes.
Maintained by Belinda Phipson. Last updated 30 days ago.
normalizationdnamethylationmethylationarraygenomicvariationgeneticvariabilitydifferentialmethylationgenesetenrichment
7.24 score 300 scripts 1 dependentsbioc
psichomics:Graphical Interface for Alternative Splicing Quantification, Analysis and Visualisation
Interactive R package with an intuitive Shiny-based graphical interface for alternative splicing quantification and integrative analyses of alternative splicing and gene expression based on The Cancer Genome Atlas (TCGA), the Genotype-Tissue Expression project (GTEx), Sequence Read Archive (SRA) and user-provided data. The tool interactively performs survival, dimensionality reduction and median- and variance-based differential splicing and gene expression analyses that benefit from the incorporation of clinical and molecular sample-associated features (such as tumour stage or survival). Interactive visual access to genomic mapping and functional annotation of selected alternative splicing events is also included.
Maintained by Nuno Saraiva-Agostinho. Last updated 5 months ago.
sequencingrnaseqalternativesplicingdifferentialsplicingtranscriptionguiprincipalcomponentsurvivalbiomedicalinformaticstranscriptomicsimmunooncologyvisualizationmultiplecomparisongeneexpressiondifferentialexpressionalternative-splicingbioconductordata-analysesdifferential-gene-expressiondifferential-splicing-analysisgene-expressiongtexrecount2rna-seq-datasplicing-quantificationsratcgavast-toolscpp
36 stars 6.95 score 31 scriptsbioc
RnBeads:RnBeads
RnBeads facilitates comprehensive analysis of various types of DNA methylation data at the genome scale.
Maintained by Fabian Mueller. Last updated 2 months ago.
dnamethylationmethylationarraymethylseqepigeneticsqualitycontrolpreprocessingbatcheffectdifferentialmethylationsequencingcpgislandimmunooncologytwochanneldataimport
6.85 score 169 scripts 1 dependentsbioc
MoonlightR:Identify oncogenes and tumor suppressor genes from omics data
Motivation: The understanding of cancer mechanism requires the identification of genes playing a role in the development of the pathology and the characterization of their role (notably oncogenes and tumor suppressors). Results: We present an R/bioconductor package called MoonlightR which returns a list of candidate driver genes for specific cancer types on the basis of TCGA expression data. The method first infers gene regulatory networks and then carries out a functional enrichment analysis (FEA) (implementing an upstream regulator analysis, URA) to score the importance of well-known biological processes with respect to the studied cancer type. Eventually, by means of random forests, MoonlightR predicts two specific roles for the candidate driver genes: i) tumor suppressor genes (TSGs) and ii) oncogenes (OCGs). As a consequence, this methodology does not only identify genes playing a dual role (e.g. TSG in one cancer type and OCG in another) but also helps in elucidating the biological processes underlying their specific roles. In particular, MoonlightR can be used to discover OCGs and TSGs in the same cancer type. This may help in answering the question whether some genes change role between early stages (I, II) and late stages (III, IV) in breast cancer. In the future, this analysis could be useful to determine the causes of different resistances to chemotherapeutic treatments.
Maintained by Matteo Tiberti. Last updated 5 months ago.
dnamethylationdifferentialmethylationgeneregulationgeneexpressionmethylationarraydifferentialexpressionpathwaysnetworksurvivalgenesetenrichmentnetworkenrichment
17 stars 6.57 scorebioc
ChAMP:Chip Analysis Methylation Pipeline for Illumina HumanMethylation450 and EPIC
The package includes quality control metrics, a selection of normalization methods and novel methods to identify differentially methylated regions and to highlight copy number alterations.
Maintained by Yuan Tian. Last updated 5 months ago.
microarraymethylationarraynormalizationtwochannelcopynumberdnamethylation
6.50 score 278 scriptsbioc
Moonlight2R:Identify oncogenes and tumor suppressor genes from omics data
The understanding of cancer mechanism requires the identification of genes playing a role in the development of the pathology and the characterization of their role (notably oncogenes and tumor suppressors). We present an updated version of the R/bioconductor package called MoonlightR, namely Moonlight2R, which returns a list of candidate driver genes for specific cancer types on the basis of omics data integration. The Moonlight framework contains a primary layer where gene expression data and information about biological processes are integrated to predict genes called oncogenic mediators, divided into putative tumor suppressors and putative oncogenes. This is done through functional enrichment analyses, gene regulatory networks and upstream regulator analyses to score the importance of well-known biological processes with respect to the studied cancer type. By evaluating the effect of the oncogenic mediators on biological processes or through random forests, the primary layer predicts two putative roles for the oncogenic mediators: i) tumor suppressor genes (TSGs) and ii) oncogenes (OCGs). As gene expression data alone is not enough to explain the deregulation of the genes, a second layer of evidence is needed. We have automated the integration of a secondary mutational layer through new functionalities in Moonlight2R. These functionalities analyze mutations in the cancer cohort and classifies these into driver and passenger mutations using the driver mutation prediction tool, CScape-somatic. Those oncogenic mediators with at least one driver mutation are retained as the driver genes. As a consequence, this methodology does not only identify genes playing a dual role (e.g. TSG in one cancer type and OCG in another) but also helps in elucidating the biological processes underlying their specific roles. In particular, Moonlight2R can be used to discover OCGs and TSGs in the same cancer type. This may for instance help in answering the question whether some genes change role between early stages (I, II) and late stages (III, IV). In the future, this analysis could be useful to determine the causes of different resistances to chemotherapeutic treatments. An additional mechanistic layer evaluates if there are mutations affecting the protein stability of the transcription factors (TFs) of the TSGs and OCGs, as that may have an effect on the expression of the genes.
Maintained by Matteo Tiberti. Last updated 2 months ago.
dnamethylationdifferentialmethylationgeneregulationgeneexpressionmethylationarraydifferentialexpressionpathwaysnetworksurvivalgenesetenrichmentnetworkenrichment
5 stars 6.41 score 43 scriptsbioc
quantro:A test for when to use quantile normalization
A data-driven test for the assumptions of quantile normalization using raw data such as objects that inherit eSets (e.g. ExpressionSet, MethylSet). Group level information about each sample (such as Tumor / Normal status) must also be provided because the test assesses if there are global differences in the distributions between the user-defined groups.
Maintained by Stephanie Hicks. Last updated 5 months ago.
normalizationpreprocessingmultiplecomparisonmicroarraysequencing
6.40 score 69 scripts 2 dependentsbioc
recountmethylation:Access and analyze public DNA methylation array data compilations
Resources for cross-study analyses of public DNAm array data from NCBI GEO repo, produced using Illumina's Infinium HumanMethylation450K (HM450K) and MethylationEPIC (EPIC) platforms. Provided functions enable download, summary, and filtering of large compilation files. Vignettes detail background about file formats, example analyses, and more. Note the disclaimer on package load and consult the main manuscripts for further info.
Maintained by Sean K Maden. Last updated 5 months ago.
dnamethylationepigeneticsmicroarraymethylationarrayexperimenthub
9 stars 6.28 score 9 scriptsbioc
lumi:BeadArray Specific Methods for Illumina Methylation and Expression Microarrays
The lumi package provides an integrated solution for the Illumina microarray data analysis. It includes functions of Illumina BeadStudio (GenomeStudio) data input, quality control, BeadArray-specific variance stabilization, normalization and gene annotation at the probe level. It also includes the functions of processing Illumina methylation microarrays, especially Illumina Infinium methylation microarrays.
Maintained by Lei Huang. Last updated 5 months ago.
microarrayonechannelpreprocessingdnamethylationqualitycontroltwochannel
6.26 score 294 scripts 5 dependentsbioc
iNETgrate:Integrates DNA methylation data with gene expression in a single gene network
The iNETgrate package provides functions to build a correlation network in which nodes are genes. DNA methylation and gene expression data are integrated to define the connections between genes. This network is used to identify modules (clusters) of genes. The biological information in each of the resulting modules is represented by an eigengene. These biological signatures can be used as features e.g., for classification of patients into risk categories. The resulting biological signatures are very robust and give a holistic view of the underlying molecular changes.
Maintained by Habil Zare. Last updated 5 months ago.
geneexpressionrnaseqdnamethylationnetworkinferencenetworkgraphandnetworkbiomedicalinformaticssystemsbiologytranscriptomicsclassificationclusteringdimensionreductionprincipalcomponentmrnamicroarraynormalizationgenepredictionkeggsurvivalcore-services
74 stars 6.21 score 1 scriptsbioc
ENmix:Quality control and analysis tools for Illumina DNA methylation BeadChip
Tools for quanlity control, analysis and visulization of Illumina DNA methylation array data.
Maintained by Zongli Xu. Last updated 18 days ago.
dnamethylationpreprocessingqualitycontroltwochannelmicroarrayonechannelmethylationarraybatcheffectnormalizationdataimportregressionprincipalcomponentepigeneticsmultichanneldifferentialmethylationimmunooncology
6.01 score 115 scriptsbioc
REMP:Repetitive Element Methylation Prediction
Machine learning-based tools to predict DNA methylation of locus-specific repetitive elements (RE) by learning surrounding genetic and epigenetic information. These tools provide genomewide and single-base resolution of DNA methylation prediction on RE that are difficult to measure using array-based or sequencing-based platforms, which enables epigenome-wide association study (EWAS) and differentially methylated region (DMR) analysis on RE.
Maintained by Yinan Zheng. Last updated 5 months ago.
dnamethylationmicroarraymethylationarraysequencinggenomewideassociationepigeneticspreprocessingmultichanneltwochanneldifferentialmethylationqualitycontroldataimport
2 stars 5.94 score 18 scripts
jmzeng1314
AnnoProbe:annotate the gene symbols for probes in expression array
We curated 147 of expression array, from 3 species(human,mouse,rat), 3 companies(affymetrix,illumina,agilent), by aligning the fasta sequences of all probes of each platform to their corresponding reference genome, and then annotate them to genes.
Maintained by The package maintainer. Last updated 5 years ago.
105 stars 5.88 score 126 scriptsbioc
deconvR:Simulation and Deconvolution of Omic Profiles
This package provides a collection of functions designed for analyzing deconvolution of the bulk sample(s) using an atlas of reference omic signature profiles and a user-selected model. Users are given the option to create or extend a reference atlas and,also simulate the desired size of the bulk signature profile of the reference cell types.The package includes the cell-type-specific methylation atlas and, Illumina Epic B5 probe ids that can be used in deconvolution. Additionally,we included BSmeth2Probe, to make mapping WGBS data to their probe IDs easier.
Maintained by Irem B. Gündüz. Last updated 5 months ago.
dnamethylationregressiongeneexpressionrnaseqsinglecellstatisticalmethodtranscriptomicsbioconductor-packagedeconvolutiondna-methylationomics
10 stars 5.78 score 15 scriptsbioc
methylclock:Methylclock - DNA methylation-based clocks
This package allows to estimate chronological and gestational DNA methylation (DNAm) age as well as biological age using different methylation clocks. Chronological DNAm age (in years) : Horvath's clock, Hannum's clock, BNN, Horvath's skin+blood clock, PedBE clock and Wu's clock. Gestational DNAm age : Knight's clock, Bohlin's clock, Mayne's clock and Lee's clocks. Biological DNAm clocks : Levine's clock and Telomere Length's clock.
Maintained by Dolors Pelegri-Siso. Last updated 5 months ago.
dnamethylationbiologicalquestionpreprocessingstatisticalmethodnormalizationcpp
39 stars 5.52 score 28 scripts
tinnlab
RCPA:Consensus Pathway Analysis
Provides a set of functions to perform pathway analysis and meta-analysis from multiple gene expression datasets, as well as visualization of the results. This package wraps functionality from the following packages: Ritchie et al. (2015) <doi:10.1093/nar/gkv007>, Love et al. (2014) <doi:10.1186/s13059-014-0550-8>, Robinson et al. (2010) <doi:10.1093/bioinformatics/btp616>, Korotkevich et al. (2016) <arxiv:10.1101/060012>, Efron et al. (2015) <https://CRAN.R-project.org/package=GSA>, and Gu et al. (2012) <https://CRAN.R-project.org/package=CePa>.
Maintained by Ha Nguyen. Last updated 5 months ago.
biobasedeseq2geoqueryedgerlimmarcyjsfgseabrowservizsummarizedexperimentannotationdbirontotools
1 stars 5.50 score 70 scriptsbioc
conumee:Enhanced copy-number variation analysis using Illumina DNA methylation arrays
This package contains a set of processing and plotting methods for performing copy-number variation (CNV) analysis using Illumina 450k or EPIC methylation arrays.
Maintained by Volker Hovestadt. Last updated 5 months ago.
copynumbervariationdnamethylationmethylationarraymicroarraynormalizationpreprocessingqualitycontrolsoftware
5.48 score 30 scriptsbioc
bigmelon:Illumina methylation array analysis for large experiments
Methods for working with Illumina arrays using gdsfmt.
Maintained by Leonard C. Schalkwyk. Last updated 5 months ago.
dnamethylationmicroarraytwochannelpreprocessingqualitycontrolmethylationarraydataimportcpgisland
5.47 score 21 scriptsbioc
GEOexplorer:GEOexplorer: a webserver for gene expression analysis and visualisation
GEOexplorer is a webserver and R/Bioconductor package and web application that enables users to perform gene expression analysis. The development of GEOexplorer was made possible because of the excellent code provided by GEO2R (https: //www.ncbi.nlm.nih.gov/geo/geo2r/).
Maintained by Guy Hunt. Last updated 5 months ago.
softwaregeneexpressionmrnamicroarraydifferentialexpressionmicroarraymicrornaarraytranscriptomicsrnaseq
5 stars 5.32 score 14 scriptsbioc
RnaSeqSampleSize:RnaSeqSampleSize
RnaSeqSampleSize package provides a sample size calculation method based on negative binomial model and the exact test for assessing differential expression analysis of RNA-seq data. It controls FDR for multiple testing and utilizes the average read count and dispersion distributions from real data to estimate a more reliable sample size. It is also equipped with several unique features, including estimation for interested genes or pathway, power curve visualization, and parameter optimization.
Maintained by Shilin Zhao Developer. Last updated 5 months ago.
immunooncologyexperimentaldesignsequencingrnaseqgeneexpressiondifferentialexpressioncpp
5.30 score 20 scriptsbioc
RNAAgeCalc:A multi-tissue transcriptional age calculator
It has been shown that both DNA methylation and RNA transcription are linked to chronological age and age related diseases. Several estimators have been developed to predict human aging from DNA level and RNA level. Most of the human transcriptional age predictor are based on microarray data and limited to only a few tissues. To date, transcriptional studies on aging using RNASeq data from different human tissues is limited. The aim of this package is to provide a tool for across-tissue and tissue-specific transcriptional age calculation based on GTEx RNASeq data.
Maintained by Xu Ren. Last updated 5 months ago.
rnaseqgeneexpressionbiological-ageelastic-netgene-expressiongenotype-tissue-expressionpredictionregularized-regressionrna-seq
8 stars 5.20 score 10 scriptsbioc
methylCC:Estimate the cell composition of whole blood in DNA methylation samples
A tool to estimate the cell composition of DNA methylation whole blood sample measured on any platform technology (microarray and sequencing).
Maintained by Stephanie C. Hicks. Last updated 5 months ago.
microarraysequencingdnamethylationmethylationarraymethylseqwholegenome
19 stars 5.18 score 8 scriptsbioc
shinyepico:ShinyÉPICo
ShinyÉPICo is a graphical pipeline to analyze Illumina DNA methylation arrays (450k or EPIC). It allows to calculate differentially methylated positions and differentially methylated regions in a user-friendly interface. Moreover, it includes several options to export the results and obtain files to perform downstream analysis.
Maintained by Octavio Morante-Palacios. Last updated 5 months ago.
differentialmethylationdnamethylationmicroarraypreprocessingqualitycontrol
5 stars 5.00 score 1 scriptsbioc
EGAD:Extending guilt by association by degree
The package implements a series of highly efficient tools to calculate functional properties of networks based on guilt by association methods.
Maintained by Sara Ballouz. Last updated 5 months ago.
softwarefunctionalgenomicssystemsbiologygenepredictionfunctionalpredictionnetworkenrichmentgraphandnetworknetwork
4.92 score 83 scripts
bioc
MethylAid:Visual and interactive quality control of large Illumina DNA Methylation array data sets
A visual and interactive web application using RStudio's shiny package. Bad quality samples are detected using sample-dependent and sample-independent controls present on the array and user adjustable thresholds. In depth exploration of bad quality samples can be performed using several interactive diagnostic plots of the quality control probes present on the array. Furthermore, the impact of any batch effect provided by the user can be explored.
Maintained by L.J.Sinke. Last updated 5 months ago.
dnamethylationmethylationarraymicroarraytwochannelqualitycontrolbatcheffectvisualizationgui
4.51 score 16 scriptsbioc
yarn:YARN: Robust Multi-Condition RNA-Seq Preprocessing and Normalization
Expedite large RNA-Seq analyses using a combination of previously developed tools. YARN is meant to make it easier for the user in performing basic mis-annotation quality control, filtering, and condition-aware normalization. YARN leverages many Bioconductor tools and statistical techniques to account for the large heterogeneity and sparsity found in very large RNA-seq experiments.
Maintained by Joseph N Paulson. Last updated 5 months ago.
softwarequalitycontrolgeneexpressionsequencingpreprocessingnormalizationannotationvisualizationclustering
4.49 score 31 scriptsbioc
MAGAR:MAGAR: R-package to compute methylation Quantitative Trait Loci (methQTL) from DNA methylation and genotyping data
"Methylation-Aware Genotype Association in R" (MAGAR) computes methQTL from DNA methylation and genotyping data from matched samples. MAGAR uses a linear modeling stragety to call CpGs/SNPs that are methQTLs. MAGAR accounts for the local correlation structure of CpGs.
Maintained by Michael Scherer. Last updated 5 months ago.
regressionepigeneticsdnamethylationsnpgeneticvariabilitymethylationarraymicroarraycpgislandmethylseqsequencingmrnamicroarraypreprocessingcopynumbervariationtwochannelimmunooncologydifferentialmethylationbatcheffectqualitycontroldataimportnetworkclusteringgraphandnetwork
4.30 score 3 scriptsbioc
epimutacions:Robust outlier identification for DNA methylation data
The package includes some statistical outlier detection methods for epimutations detection in DNA methylation data. The methods included in the package are MANOVA, Multivariate linear models, isolation forest, robust mahalanobis distance, quantile and beta. The methods compare a case sample with a suspected disease against a reference panel (composed of healthy individuals) to identify epimutations in the given case sample. It also contains functions to annotate and visualize the identified epimutations.
Maintained by Dolors Pelegri-Siso. Last updated 5 months ago.
dnamethylationbiologicalquestionpreprocessingstatisticalmethodnormalizationcpp
4.23 score 28 scriptsbioc
ChIPXpress:ChIPXpress: enhanced transcription factor target gene identification from ChIP-seq and ChIP-chip data using publicly available gene expression profiles
ChIPXpress takes as input predicted TF bound genes from ChIPx data and uses a corresponding database of gene expression profiles downloaded from NCBI GEO to rank the TF bound targets in order of which gene is most likely to be functional TF target.
Maintained by George Wu. Last updated 5 months ago.
3.78 score 2 scriptsbioc
funtooNorm:Normalization Procedure for Infinium HumanMethylation450 BeadChip Kit
Provides a function to normalize Illumina Infinium Human Methylation 450 BeadChip (Illumina 450K), correcting for tissue and/or cell type.
Maintained by Kathleen Klein. Last updated 5 months ago.
dnamethylationpreprocessingnormalization
3.70 scorebioc
SCAN.UPC:Single-channel array normalization (SCAN) and Universal exPression Codes (UPC)
SCAN is a microarray normalization method to facilitate personalized-medicine workflows. Rather than processing microarray samples as groups, which can introduce biases and present logistical challenges, SCAN normalizes each sample individually by modeling and removing probe- and array-specific background noise using only data from within each array. SCAN can be applied to one-channel (e.g., Affymetrix) or two-channel (e.g., Agilent) microarrays. The Universal exPression Codes (UPC) method is an extension of SCAN that estimates whether a given gene/transcript is active above background levels in a given sample. The UPC method can be applied to one-channel or two-channel microarrays as well as to RNA-Seq read counts. Because UPC values are represented on the same scale and have an identical interpretation for each platform, they can be used for cross-platform data integration.
Maintained by Stephen R. Piccolo. Last updated 5 months ago.
immunooncologysoftwaremicroarraypreprocessingrnaseqtwochannelonechannel
3.48 score 15 scriptsbioc
DrugVsDisease:Comparison of disease and drug profiles using Gene set Enrichment Analysis
This package generates ranked lists of differential gene expression for either disease or drug profiles. Input data can be downloaded from Array Express or GEO, or from local CEL files. Ranked lists of differential expression and associated p-values are calculated using Limma. Enrichment scores (Subramanian et al. PNAS 2005) are calculated to a reference set of default drug or disease profiles, or a set of custom data supplied by the user. Network visualisation of significant scores are output in Cytoscape format.
Maintained by j. Saez-Rodriguez. Last updated 5 months ago.
microarraygeneexpressionclustering
3.30 score 8 scriptsbioc
DExMA:Differential Expression Meta-Analysis
performing all the steps of gene expression meta-analysis considering the possible existence of missing genes. It provides the necessary functions to be able to perform the different methods of gene expression meta-analysis. In addition, it contains functions to apply quality controls, download GEO datasets and show graphical representations of the results.
Maintained by Juan Antonio Villatoro-García. Last updated 5 months ago.
differentialexpressiongeneexpressionstatisticalmethodqualitycontrol
3.30 score 7 scriptsbioc
ffpe:Quality assessment and control for FFPE microarray expression data
Identify low-quality data using metrics developed for expression data derived from Formalin-Fixed, Paraffin-Embedded (FFPE) data. Also a function for making Concordance at the Top plots (CAT-plots).
Maintained by Levi Waldron. Last updated 5 months ago.
microarraygeneexpressionqualitycontrol
3.03 score 27 scriptsbioc
iCheck:QC Pipeline and Data Analysis Tools for High-Dimensional Illumina mRNA Expression Data
QC pipeline and data analysis tools for high-dimensional Illumina mRNA expression data.
Maintained by Weiliang Qiu. Last updated 5 months ago.
geneexpressiondifferentialexpressionmicroarraypreprocessingdnamethylationonechanneltwochannelqualitycontrol
3.00 score 1 scripts
nie-xiuquan
EMAS:Epigenome-Wide Mediation Analysis Study
DNA methylation is essential for human, and environment can change the DNA methylation and affect body status. Epigenome-Wide Mediation Analysis Study (EMAS) can find potential mediator CpG sites between exposure (x) and outcome (y) in epigenome-wide. For more information on the methods we used, please see the following references: Tingley, D. (2014) <doi:10.18637/jss.v059.i05>, Turner, S. D. (2018) <doi:10.21105/joss.00731>, Rosseel, D. (2012) <doi:10.18637/jss.v048.i02>.
Maintained by Xiuquan Nie. Last updated 3 years ago.
1.00 score