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bioc

EnrichmentBrowser:Seamless navigation through combined results of set-based and network-based enrichment analysis

The EnrichmentBrowser package implements essential functionality for the enrichment analysis of gene expression data. The analysis combines the advantages of set-based and network-based enrichment analysis in order to derive high-confidence gene sets and biological pathways that are differentially regulated in the expression data under investigation. Besides, the package facilitates the visualization and exploration of such sets and pathways.

Maintained by Ludwig Geistlinger. Last updated 5 months ago.

immunooncologymicroarrayrnaseqgeneexpressiondifferentialexpressionpathwaysgraphandnetworknetworkgenesetenrichmentnetworkenrichmentvisualizationreportwriting

20 stars 8.37 score 164 scripts 3 dependents

bioc

maser:Mapping Alternative Splicing Events to pRoteins

This package provides functionalities for downstream analysis, annotation and visualizaton of alternative splicing events generated by rMATS.

Maintained by Diogo F.T. Veiga. Last updated 5 months ago.

alternativesplicingtranscriptomicsvisualization

17 stars 6.74 score 18 scripts

stamats

MKinfer:Inferential Statistics

Computation of various confidence intervals (Altman et al. (2000), ISBN:978-0-727-91375-3; Hedderich and Sachs (2018), ISBN:978-3-662-56657-2) including bootstrapped versions (Davison and Hinkley (1997), ISBN:978-0-511-80284-3) as well as Hsu (Hedderich and Sachs (2018), ISBN:978-3-662-56657-2), permutation (Janssen (1997), <doi:10.1016/S0167-7152(97)00043-6>), bootstrap (Davison and Hinkley (1997), ISBN:978-0-511-80284-3), intersection-union (Sozu et al. (2015), ISBN:978-3-319-22005-5) and multiple imputation (Barnard and Rubin (1999), <doi:10.1093/biomet/86.4.948>) t-test; furthermore, computation of intersection-union z-test as well as multiple imputation Wilcoxon tests. Graphical visualization by volcano and Bland-Altman plots (Bland and Altman (1986), <doi:10.1016/S0140-6736(86)90837-8>; Shieh (2018), <doi:10.1186/s12874-018-0505-y>).

Maintained by Matthias Kohl. Last updated 12 months ago.

6 stars 6.56 score 71 scripts 4 dependents

stamats

MKpower:Power Analysis and Sample Size Calculation

Power analysis and sample size calculation for Welch and Hsu (Hedderich and Sachs (2018), ISBN:978-3-662-56657-2) t-tests including Monte-Carlo simulations of empirical power and type-I-error. Power and sample size calculation for Wilcoxon rank sum and signed rank tests via Monte-Carlo simulations. Power and sample size required for the evaluation of a diagnostic test(-system) (Flahault et al. (2005), <doi:10.1016/j.jclinepi.2004.12.009>; Dobbin and Simon (2007), <doi:10.1093/biostatistics/kxj036>) as well as for a single proportion (Fleiss et al. (2003), ISBN:978-0-471-52629-2; Piegorsch (2004), <doi:10.1016/j.csda.2003.10.002>; Thulin (2014), <doi:10.1214/14-ejs909>), comparing two negative binomial rates (Zhu and Lakkis (2014), <doi:10.1002/sim.5947>), ANCOVA (Shieh (2020), <doi:10.1007/s11336-019-09692-3>), reference ranges (Jennen-Steinmetz and Wellek (2005), <doi:10.1002/sim.2177>), multiple primary endpoints (Sozu et al. (2015), ISBN:978-3-319-22005-5), and AUC (Hanley and McNeil (1982), <doi:10.1148/radiology.143.1.7063747>).

Maintained by Matthias Kohl. Last updated 6 months ago.

7 stars 5.65 score 32 scripts

mingshi1

LipidomicsR:Elegant Tools for Processing and Visualization of Lipidomics Data

An elegant tool for processing and visualizing lipidomics data generated by mass spectrometry. 'LipidomicsR' simplifies channel and replicate handling while providing thorough lipid species annotation. Its visualization capabilities encompass principal components analysis plots, heatmaps, volcano plots, and radar plots, enabling concise data summarization and quality assessment. Additionally, it can generate bar plots and line plots to visualize the abundance of each lipid species.

Maintained by Hengyu Zhu. Last updated 11 months ago.

3.30 score 1 scripts

cran

IntegratedJM:Joint Modeling of the Gene-Expression and Bioassay Data, Taking Care of the Effect Due to a Fingerprint Feature

Offers modeling the association between gene-expression and bioassay data, taking care of the effect due to a fingerprint feature and helps with several plots to better understand the analysis.

Maintained by Rudradev Sengupta. Last updated 8 years ago.

1.00 score

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