SIMD - Statistical Inferences with MeDIP-seq Data (SIMD) to infer the methylation level for each CpG site
This package provides a inferential analysis method for detecting differentially expressed CpG sites in MeDIP-seq data. It uses statistical framework and EM algorithm, to identify differentially expressed CpG sites. The methods on this package are described in the article 'Methylation-level Inferences and Detection of Differential Methylation with Medip-seq Data' by Yan Zhou, Jiadi Zhu, Mingtao Zhao, Baoxue Zhang, Chunfu Jiang and Xiyan Yang (2018, pending publication).
Last updated 4 months ago
immunooncologydifferentialmethylationsinglecelldifferentialexpression
5.81 score 7 dependenciesMEB - A normalization-invariant minimum enclosing ball method to detect differentially expressed genes for RNA-seq and scRNA-seq data
This package provides a method to identify differential expression genes in the same or different species. Given that non-DE genes have some similarities in features, a scaling-free minimum enclosing ball (SFMEB) model is built to cover those non-DE genes in feature space, then those DE genes, which are enormously different from non-DE genes, being regarded as outliers and rejected outside the ball. The method on this package is described in the article 'A minimum enclosing ball method to detect differential expression genes for RNA-seq data'. The SFMEB method is extended to the scMEB method that considering two or more potential types of cells or unknown labels scRNA-seq dataset DEGs identification.
Last updated 4 months ago
differentialexpressiongeneexpressionnormalizationclassificationsequencing
1.90 score 105 dependenciesCAEN - Category encoding method for selecting feature genes for the classification of single-cell RNA-seq
With the development of high-throughput techniques, more and more gene expression analysis tend to replace hybridization-based microarrays with the revolutionary technology.The novel method encodes the category again by employing the rank of samples for each gene in each class. We then consider the correlation coefficient of gene and class with rank of sample and new rank of category. The highest correlation coefficient genes are considered as the feature genes which are most effective to classify the samples.
Last updated 4 months ago
differentialexpressionsequencingclassificationrnaseqatacseqsinglecellgeneexpressionripseq
1.31 score 29 dependenciesSCBN - A statistical normalization method and differential expression analysis for RNA-seq data between different species
This package provides a scale based normalization (SCBN) method to identify genes with differential expression between different species. It takes into account the available knowledge of conserved orthologous genes and the hypothesis testing framework to detect differentially expressed orthologous genes. The method on this package are described in the article 'A statistical normalization method and differential expression analysis for RNA-seq data between different species' by Yan Zhou, Jiadi Zhu, Tiejun Tong, Junhui Wang, Bingqing Lin, Jun Zhang (2018, pending publication).
Last updated 4 months ago
differentialexpressiongeneexpressionnormalization
1.08 score 0 dependencies 1 dependents