qvalue - Q-value estimation for false discovery rate control
This package takes a list of p-values resulting from the simultaneous testing of many hypotheses and estimates their q-values and local FDR values. The q-value of a test measures the proportion of false positives incurred (called the false discovery rate) when that particular test is called significant. The local FDR measures the posterior probability the null hypothesis is true given the test's p-value. Various plots are automatically generated, allowing one to make sensible significance cut-offs. Several mathematical results have recently been shown on the conservative accuracy of the estimated q-values from this software. The software can be applied to problems in genomics, brain imaging, astrophysics, and data mining.
Last updated 23 days ago
multiplecomparisons
14.09 score 111 stars 140 packages 3.2k scripts 27k downloadsbiobroom - 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.
Last updated 23 days ago
multiplecomparisondifferentialexpressionregressiongeneexpressionproteomicsdataimport
8.23 score 48 stars 1 packages 292 scripts 470 downloadsedge - 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.
Last updated 23 days ago
multiplecomparisondifferentialexpressiontimecourseregressiongeneexpressiondataimport
7.77 score 21 stars 62 scripts 270 downloadssnm - Supervised Normalization of Microarrays
SNM is a modeling strategy especially designed for normalizing high-throughput genomic data. The underlying premise of our approach is that your data is a function of what we refer to as study-specific variables. These variables are either biological variables that represent the target of the statistical analysis, or adjustment variables that represent factors arising from the experimental or biological setting the data is drawn from. The SNM approach aims to simultaneously model all study-specific variables in order to more accurately characterize the biological or clinical variables of interest.
Last updated 23 days ago
microarrayonechanneltwochannelmultichanneldifferentialexpressionexonarraygeneexpressiontranscriptionmultiplecomparisonpreprocessingqualitycontrol
4.38 score 60 scripts 316 downloadstrigger - Transcriptional Regulatory Inference from Genetics of Gene ExpRession
This R package provides tools for the statistical analysis of integrative genomic data that involve some combination of: genotypes, high-dimensional intermediate traits (e.g., gene expression, protein abundance), and higher-order traits (phenotypes). The package includes functions to: (1) construct global linkage maps between genetic markers and gene expression; (2) analyze multiple-locus linkage (epistasis) for gene expression; (3) quantify the proportion of genome-wide variation explained by each locus and identify eQTL hotspots; (4) estimate pair-wise causal gene regulatory probabilities and construct gene regulatory networks; and (5) identify causal genes for a quantitative trait of interest.
Last updated 23 days ago
geneexpressionsnpgeneticvariabilitymicroarraygenetics
3.30 score 3 scripts 241 downloads