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bioc

DESeq2:Differential gene expression analysis based on the negative binomial distribution

Estimate variance-mean dependence in count data from high-throughput sequencing assays and test for differential expression based on a model using the negative binomial distribution.

Maintained by Michael Love. Last updated 25 days ago.

sequencingrnaseqchipseqgeneexpressiontranscriptionnormalizationdifferentialexpressionbayesianregressionprincipalcomponentclusteringimmunooncologyopenblascpp

375 stars 16.11 score 17k scripts 115 dependents

cajoterbraak

Gendis2unmix:Calculates a generalized discriminant function to unmix two classes, typically sexes of birds

The goal of Gendis2unmix is to sex birds from a population on the basis of several measurements. The key feature is that the birds from different populations may differ in size but that within populations females are smaller than males (or reversely). The predict function for a set of unsexed birds from a new population therefore estimates a new cutoff value which thus depends on the sizes of the birds in the new population. In the training phase, a generalized discriminant function (GDF) is calculated from a birds of known sex of different populations, in which the algorithm uses a common within-covariance matrix across populations and sexes. In the prediction phase Gendis2unmix then applies the GDF to measurements of individuals of unknow sex or class. The cutoff value is determined by unmixing the distribution in terms of two normal distributions with unequal means and variances using an EM algorithm. The parametric approach taken in Gendis2unmix make it suitable for small number of samples in both the training and prediction phase (say 20-100 per sex/population).

Maintained by Cajo J.F. ter Braak. Last updated 2 years ago.

1.70 score

ivanlizaga

fingerPro:Sediment Source Fingerprinting

Quantifies the provenance of the sediments in a catchment or study area. Based on a comprehensive characterization of the sediment sources and the end sediment mixtures a mixing model algorithm is applied to the sediment mixtures in order to estimate the relative contribution of each potential source. The package includes several statistical methods such as Kruskal-Wallis test, discriminant function analysis ('DFA'), principal component plot ('PCA') to select the optimal subset of tracer properties. The variability within each sediment source is also considered to estimate the statistical distribution of the sources contribution.

Maintained by Ivan Lizaga. Last updated 7 years ago.

gslcpp

1.11 score 13 scripts

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