R-universe search: topic:timecourse

bioc

limma:Linear Models for Microarray and Omics Data

Data analysis, linear models and differential expression for omics data.

Maintained by Gordon Smyth. Last updated 11 days ago.

exonarray geneexpression transcription alternativesplicing differentialexpression differentialsplicing genesetenrichment dataimport bayesian clustering regression timecourse microarray micrornaarray mrnamicroarray onechannel proprietaryplatforms twochannel sequencing rnaseq batcheffect multiplecomparison normalization preprocessing qualitycontrol biomedicalinformatics cellbiology cheminformatics epigenetics functionalgenomics genetics immunooncology metabolomics proteomics systemsbiology transcriptomics

13.81 score 16k scripts 586 dependents

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edgeR:Empirical Analysis of Digital Gene Expression Data in R

Differential expression analysis of sequence count data. Implements a range of statistical methodology based on the negative binomial distributions, including empirical Bayes estimation, exact tests, generalized linear models, quasi-likelihood, and gene set enrichment. Can perform differential analyses of any type of omics data that produces read counts, including RNA-seq, ChIP-seq, ATAC-seq, Bisulfite-seq, SAGE, CAGE, metabolomics, or proteomics spectral counts. RNA-seq analyses can be conducted at the gene or isoform level, and tests can be conducted for differential exon or transcript usage.

Maintained by Yunshun Chen. Last updated 19 days ago.

alternativesplicing batcheffect bayesian biomedicalinformatics cellbiology chipseq clustering coverage differentialexpression differentialmethylation differentialsplicing dnamethylation epigenetics functionalgenomics geneexpression genesetenrichment genetics immunooncology multiplecomparison normalization pathways proteomics qualitycontrol regression rnaseq sage sequencing singlecell systemsbiology timecourse transcription transcriptomics openblas

13.40 score 17k scripts 255 dependents

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tradeSeq:trajectory-based differential expression analysis for sequencing data

tradeSeq provides a flexible method for fitting regression models that can be used to find genes that are differentially expressed along one or multiple lineages in a trajectory. Based on the fitted models, it uses a variety of tests suited to answer different questions of interest, e.g. the discovery of genes for which expression is associated with pseudotime, or which are differentially expressed (in a specific region) along the trajectory. It fits a negative binomial generalized additive model (GAM) for each gene, and performs inference on the parameters of the GAM.

Maintained by Hector Roux de Bezieux. Last updated 5 months ago.

clustering regression timecourse differentialexpression geneexpression rnaseq sequencing software singlecell transcriptomics multiplecomparison visualization

251 stars 10.06 score 440 scripts

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MSstats:Protein Significance Analysis in DDA, SRM and DIA for Label-free or Label-based Proteomics Experiments

A set of tools for statistical relative protein significance analysis in DDA, SRM and DIA experiments.

Maintained by Meena Choi. Last updated 24 days ago.

immunooncology massspectrometry proteomics software normalization qualitycontrol timecourse openblas cpp

8.49 score 164 scripts 7 dependents

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edge: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.

Maintained by John D. Storey. Last updated 5 months ago.

multiplecomparison differentialexpression timecourse regression geneexpression dataimport

21 stars 7.77 score 62 scripts

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Mfuzz:Soft clustering of omics time series data

The Mfuzz package implements noise-robust soft clustering of omics time-series data, including transcriptomic, proteomic or metabolomic data. It is based on the use of c-means clustering. For convenience, it includes a graphical user interface.

Maintained by Matthias Futschik. Last updated 5 months ago.

microarray clustering timecourse preprocessing visualization

7.64 score 338 scripts 4 dependents

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MOSim:Multi-Omics Simulation (MOSim)

MOSim package simulates multi-omic experiments that mimic regulatory mechanisms within the cell, allowing flexible experimental design including time course and multiple groups.

Maintained by Sonia Tarazona. Last updated 5 months ago.

software timecourse experimentaldesign rnaseq cpp

9 stars 7.42 score 11 scripts

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CoGAPS:Coordinated Gene Activity in Pattern Sets

Coordinated Gene Activity in Pattern Sets (CoGAPS) implements a Bayesian MCMC matrix factorization algorithm, GAPS, and links it to gene set statistic methods to infer biological process activity. It can be used to perform sparse matrix factorization on any data, and when this data represents biomolecules, to do gene set analysis.

Maintained by Elana J. Fertig. Last updated 17 days ago.

geneexpression transcription genesetenrichment differentialexpression bayesian clustering timecourse rnaseq microarray multiplecomparison dimensionreduction immunooncology cpp

6.97 score 104 scripts

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CellNOptR:Training of boolean logic models of signalling networks using prior knowledge networks and perturbation data

This package does optimisation of boolean logic networks of signalling pathways based on a previous knowledge network and a set of data upon perturbation of the nodes in the network.

Maintained by Attila Gabor. Last updated 5 days ago.

cellbasedassays cellbiology proteomics pathways network timecourse immunooncology

6.95 score 98 scripts 6 dependents

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msqrob2:Robust statistical inference for quantitative LC-MS proteomics

msqrob2 provides a robust linear mixed model framework for assessing differential abundance in MS-based Quantitative proteomics experiments. Our workflows can start from raw peptide intensities or summarised protein expression values. The model parameter estimates can be stabilized by ridge regression, empirical Bayes variance estimation and robust M-estimation. msqrob2's hurde workflow can handle missing data without having to rely on hard-to-verify imputation assumptions, and, outcompetes state-of-the-art methods with and without imputation for both high and low missingness. It builds on QFeature infrastructure for quantitative mass spectrometry data to store the model results together with the raw data and preprocessed data.

Maintained by Lieven Clement. Last updated 1 months ago.

proteomics massspectrometry differentialexpression multiplecomparison regression experimentaldesign software immunooncology normalization timecourse preprocessing

10 stars 6.94 score 83 scripts

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GOexpress:Visualise microarray and RNAseq data using gene ontology annotations

The package contains methods to visualise the expression profile of genes from a microarray or RNA-seq experiment, and offers a supervised clustering approach to identify GO terms containing genes with expression levels that best classify two or more predefined groups of samples. Annotations for the genes present in the expression dataset may be obtained from Ensembl through the biomaRt package, if not provided by the user. The default random forest framework is used to evaluate the capacity of each gene to cluster samples according to the factor of interest. Finally, GO terms are scored by averaging the rank (alternatively, score) of their respective gene sets to cluster the samples. P-values may be computed to assess the significance of GO term ranking. Visualisation function include gene expression profile, gene ontology-based heatmaps, and hierarchical clustering of experimental samples using gene expression data.

Maintained by Kevin Rue-Albrecht. Last updated 5 months ago.

software geneexpression transcription differentialexpression genesetenrichment datarepresentation clustering timecourse microarray sequencing rnaseq annotation multiplecomparison pathways go visualization immunooncology bioconductor bioconductor-package bioconductor-stats geneontology geneset-enrichment

9 stars 6.75 score 31 scripts

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Trendy:Breakpoint analysis of time-course expression data

Trendy implements segmented (or breakpoint) regression models to estimate breakpoints which represent changes in expression for each feature/gene in high throughput data with ordered conditions.

Maintained by Rhonda Bacher. Last updated 5 months ago.

timecourse rnaseq regression immunooncology

6 stars 6.53 score 14 scripts

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dearseq:Differential Expression Analysis for RNA-seq data through a robust variance component test

Differential Expression Analysis RNA-seq data with variance component score test accounting for data heteroscedasticity through precision weights. Perform both gene-wise and gene set analyses, and can deal with repeated or longitudinal data. Methods are detailed in: i) Agniel D & Hejblum BP (2017) Variance component score test for time-course gene set analysis of longitudinal RNA-seq data, Biostatistics, 18(4):589-604 ; and ii) Gauthier M, Agniel D, Thiébaut R & Hejblum BP (2020) dearseq: a variance component score test for RNA-Seq differential analysis that effectively controls the false discovery rate, NAR Genomics and Bioinformatics, 2(4):lqaa093.

Maintained by Boris P. Hejblum. Last updated 5 months ago.

biomedicalinformatics cellbiology differentialexpression dnaseq geneexpression genetics genesetenrichment immunooncology kegg regression rnaseq sequencing systemsbiology timecourse transcription transcriptomics

8 stars 6.20 score 11 scripts 1 dependents

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metaseqR2:An R package for the analysis and result reporting of RNA-Seq data by combining multiple statistical algorithms

Provides an interface to several normalization and statistical testing packages for RNA-Seq gene expression data. Additionally, it creates several diagnostic plots, performs meta-analysis by combinining the results of several statistical tests and reports the results in an interactive way.

Maintained by Panagiotis Moulos. Last updated 18 days ago.

software geneexpression differentialexpression workflowstep preprocessing qualitycontrol normalization reportwriting rnaseq transcription sequencing transcriptomics bayesian clustering cellbiology biomedicalinformatics functionalgenomics systemsbiology immunooncology alternativesplicing differentialsplicing multiplecomparison timecourse dataimport atacseq epigenetics regression proprietaryplatforms genesetenrichment batcheffect chipseq

7 stars 6.05 score 3 scripts

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EventPointer:An effective identification of alternative splicing events using junction arrays and RNA-Seq data

EventPointer is an R package to identify alternative splicing events that involve either simple (case-control experiment) or complex experimental designs such as time course experiments and studies including paired-samples. The algorithm can be used to analyze data from either junction arrays (Affymetrix Arrays) or sequencing data (RNA-Seq). The software returns a data.frame with the detected alternative splicing events: gene name, type of event (cassette, alternative 3',...,etc), genomic position, statistical significance and increment of the percent spliced in (Delta PSI) for all the events. The algorithm can generate a series of files to visualize the detected alternative splicing events in IGV. This eases the interpretation of results and the design of primers for standard PCR validation.

Maintained by Juan A. Ferrer-Bonsoms. Last updated 5 months ago.

alternativesplicing differentialsplicing mrnamicroarray rnaseq transcription sequencing timecourse immunooncology

4 stars 6.00 score 6 scripts

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timeOmics:Time-Course Multi-Omics data integration

timeOmics is a generic data-driven framework to integrate multi-Omics longitudinal data measured on the same biological samples and select key temporal features with strong associations within the same sample group. The main steps of timeOmics are: 1. Plaform and time-specific normalization and filtering steps; 2. Modelling each biological into one time expression profile; 3. Clustering features with the same expression profile over time; 4. Post-hoc validation step.

Maintained by Antoine Bodein. Last updated 5 months ago.

clustering featureextraction timecourse dimensionreduction software sequencing microarray metabolomics metagenomics proteomics classification regression immunooncology geneprediction multiplecomparison cluster integration multi-omics time-series

24 stars 5.98 score 10 scripts

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TCseq:Time course sequencing data analysis

Quantitative and differential analysis of epigenomic and transcriptomic time course sequencing data, clustering analysis and visualization of the temporal patterns of time course data.

Maintained by Mengjun Wu. Last updated 5 months ago.

epigenetics timecourse sequencing chipseq rnaseq differentialexpression clustering visualization

5.92 score 28 scripts 1 dependents

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DiscoRhythm:Interactive Workflow for Discovering Rhythmicity in Biological Data

Set of functions for estimation of cyclical characteristics, such as period, phase, amplitude, and statistical significance in large temporal datasets. Supporting functions are available for quality control, dimensionality reduction, spectral analysis, and analysis of experimental replicates. Contains a R Shiny web interface to execute all workflow steps.

Maintained by Matthew Carlucci. Last updated 5 months ago.

software timecourse qualitycontrol visualization gui principalcomponent bioconductor data-visualization oscillations rhythm-detection webapp

13 stars 5.89 score 9 scripts

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CNORode:ODE add-on to CellNOptR

Logic based ordinary differential equation (ODE) add-on to CellNOptR.

Maintained by Attila Gabor. Last updated 5 months ago.

immunooncology cellbasedassays cellbiology proteomics bioinformatics timecourse

5.74 score 37 scripts 1 dependents

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MultiRNAflow:An R package for integrated analysis of temporal RNA-seq data with multiple biological conditions

Our R package MultiRNAflow provides an easy to use unified framework allowing to automatically make both unsupervised and supervised (DE) analysis for datasets with an arbitrary number of biological conditions and time points. In particular, our code makes a deep downstream analysis of DE information, e.g. identifying temporal patterns across biological conditions and DE genes which are specific to a biological condition for each time.

Maintained by Rodolphe Loubaton. Last updated 5 months ago.

sequencing rnaseq geneexpression transcription timecourse preprocessing visualization normalization principalcomponent clustering differentialexpression genesetenrichment pathways

6 stars 5.26 score 4 scripts

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ASpli:Analysis of Alternative Splicing Using RNA-Seq

Integrative pipeline for the analysis of alternative splicing using RNAseq.

Maintained by Ariel Chernomoretz. Last updated 5 months ago.

immunooncology geneexpression transcription alternativesplicing coverage differentialexpression differentialsplicing timecourse rnaseq genomeannotation sequencing alignment

5.21 score 45 scripts 1 dependents

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maSigPro:Significant Gene Expression Profile Differences in Time Course Gene Expression Data

maSigPro is a regression based approach to find genes for which there are significant gene expression profile differences between experimental groups in time course microarray and RNA-Seq experiments.

Maintained by Maria Jose Nueda. Last updated 5 months ago.

microarray rna-seq differential expression timecourse

5.18 score 76 scripts

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RolDE:RolDE: Robust longitudinal Differential Expression

RolDE detects longitudinal differential expression between two conditions in noisy high-troughput data. Suitable even for data with a moderate amount of missing values.RolDE is a composite method, consisting of three independent modules with different approaches to detecting longitudinal differential expression. The combination of these diverse modules allows RolDE to robustly detect varying differences in longitudinal trends and expression levels in diverse data types and experimental settings.

Maintained by Medical Bioinformatics Centre. Last updated 5 months ago.

statisticalmethod software timecourse regression proteomics differentialexpression

5 stars 5.18 score 1 scripts

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AMARETTO:Regulatory Network Inference and Driver Gene Evaluation using Integrative Multi-Omics Analysis and Penalized Regression

Integrating an increasing number of available multi-omics cancer data remains one of the main challenges to improve our understanding of cancer. One of the main challenges is using multi-omics data for identifying novel cancer driver genes. We have developed an algorithm, called AMARETTO, that integrates copy number, DNA methylation and gene expression data to identify a set of driver genes by analyzing cancer samples and connects them to clusters of co-expressed genes, which we define as modules. We applied AMARETTO in a pancancer setting to identify cancer driver genes and their modules on multiple cancer sites. AMARETTO captures modules enriched in angiogenesis, cell cycle and EMT, and modules that accurately predict survival and molecular subtypes. This allows AMARETTO to identify novel cancer driver genes directing canonical cancer pathways.

Maintained by Olivier Gevaert. Last updated 5 months ago.

statisticalmethod differentialmethylation generegulation geneexpression methylationarray transcription preprocessing batcheffect dataimport mrnamicroarray micrornaarray regression clustering rnaseq copynumbervariation sequencing microarray normalization network bayesian exonarray onechannel twochannel proprietaryplatforms alternativesplicing differentialexpression differentialsplicing genesetenrichment multiplecomparison qualitycontrol timecourse

4.88 score 15 scripts

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GRmetrics:Calculate growth-rate inhibition (GR) metrics

Functions for calculating and visualizing growth-rate inhibition (GR) metrics.

Maintained by Nicholas Clark. Last updated 5 months ago.

immunooncology cellbasedassays cellbiology software timecourse visualization

1 stars 4.83 score 17 scripts

xinghuq

DA:Discriminant Analysis for Evolutionary Inference

Discriminant Analysis (DA) for evolutionary inference (Qin, X. et al, 2020, <doi:10.22541/au.159256808.83862168>), especially for population genetic structure and community structure inference. This package incorporates the commonly used linear and non-linear, local and global supervised learning approaches (discriminant analysis), including Linear Discriminant Analysis of Kernel Principal Components (LDAKPC), Local (Fisher) Linear Discriminant Analysis (LFDA), Local (Fisher) Discriminant Analysis of Kernel Principal Components (LFDAKPC) and Kernel Local (Fisher) Discriminant Analysis (KLFDA). These discriminant analyses can be used to do ecological and evolutionary inference, including demography inference, species identification, and population/community structure inference.

Maintained by Xinghu Qin. Last updated 4 years ago.

biomedicalinformatics chipseq clustering coverage dnamethylation differentialexpression differentialmethylation software differentialsplicing epigenetics functionalgenomics geneexpression genesetenrichment genetics immunooncology multiplecomparison normalization pathways qualitycontrol rnaseq regression sage sequencing systemsbiology timecourse transcription transcriptomics dapc discriminant-analysis ecological kernel kernel-local kernel-principle-components population-structure-inference principal-components

1 stars 4.70 score 1 scripts

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RTCA:Open-source toolkit to analyse data from xCELLigence System (RTCA)

Import, analyze and visualize data from Roche(R) xCELLigence RTCA systems. The package imports real-time cell electrical impedance data into R. As an alternative to commercial software shipped along the system, the Bioconductor package RTCA provides several unique transformation (normalization) strategies and various visualization tools.

Maintained by Jitao David Zhang. Last updated 5 months ago.

immunooncology cellbasedassays infrastructure visualization timecourse

4.60 score 4 scripts

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affylmGUI:GUI for limma Package with Affymetrix Microarrays

A Graphical User Interface (GUI) for analysis of Affymetrix microarray gene expression data using the affy and limma packages.

Maintained by Gordon Smyth. Last updated 5 months ago.

gui geneexpression transcription differentialexpression dataimport bayesian regression timecourse microarray mrnamicroarray onechannel proprietaryplatforms batcheffect multiplecomparison normalization preprocessing qualitycontrol

4.60 score 3 scripts

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limmaGUI:GUI for limma Package With Two Color Microarrays

A Graphical User Interface for differential expression analysis of two-color microarray data using the limma package.

Maintained by Gordon Smyth. Last updated 5 months ago.

gui geneexpression differentialexpression dataimport bayesian regression timecourse microarray mrnamicroarray twochannel batcheffect multiplecomparison normalization preprocessing qualitycontrol

4.60 score 1 scripts

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flowTime:Annotation and analysis of biological dynamical systems using flow cytometry

This package facilitates analysis of both timecourse and steady state flow cytometry experiments. This package was originially developed for quantifying the function of gene regulatory networks in yeast (strain W303) expressing fluorescent reporter proteins using BD Accuri C6 and SORP cytometers. However, the functions are for the most part general and may be adapted for analysis of other organisms using other flow cytometers. Functions in this package facilitate the annotation of flow cytometry data with experimental metadata, as often required for publication and general ease-of-reuse. Functions for creating, saving and loading gate sets are also included. In the past, we have typically generated summary statistics for each flowset for each timepoint and then annotated and analyzed these summary statistics. This method loses a great deal of the power that comes from the large amounts of individual cell data generated in flow cytometry, by essentially collapsing this data into a bulk measurement after subsetting. In addition to these summary functions, this package also contains functions to facilitate annotation and analysis of steady-state or time-lapse data utilizing all of the data collected from the thousands of individual cells in each sample.

Maintained by R. Clay Wright. Last updated 5 months ago.

flowcytometry timecourse visualization dataimport cellbasedassays immunooncology

4.48 score 8 scripts

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INSPEcT:Modeling RNA synthesis, processing and degradation with RNA-seq data

INSPEcT (INference of Synthesis, Processing and dEgradation rates from Transcriptomic data) RNA-seq data in time-course experiments or steady-state conditions, with or without the support of nascent RNA data.

Maintained by Stefano de Pretis. Last updated 5 months ago.

sequencing rnaseq generegulation timecourse systemsbiology

4.38 score 9 scripts

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tigre:Transcription factor Inference through Gaussian process Reconstruction of Expression

The tigre package implements our methodology of Gaussian process differential equation models for analysis of gene expression time series from single input motif networks. The package can be used for inferring unobserved transcription factor (TF) protein concentrations from expression measurements of known target genes, or for ranking candidate targets of a TF.

Maintained by Antti Honkela. Last updated 5 months ago.

microarray timecourse geneexpression transcription generegulation networkinference bayesian

4.38 score 6 scripts

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GRENITS:Gene Regulatory Network Inference Using Time Series

The package offers four network inference statistical models using Dynamic Bayesian Networks and Gibbs Variable Selection: a linear interaction model, two linear interaction models with added experimental noise (Gaussian and Student distributed) for the case where replicates are available and a non-linear interaction model.

Maintained by Edward Morrissey. Last updated 5 months ago.

networkinference generegulation timecourse graphandnetwork geneexpression network bayesian openblas cpp

4.20 score 2 scripts

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RNAdecay:Maximum Likelihood Decay Modeling of RNA Degradation Data

RNA degradation is monitored through measurement of RNA abundance after inhibiting RNA synthesis. This package has functions and example scripts to facilitate (1) data normalization, (2) data modeling using constant decay rate or time-dependent decay rate models, (3) the evaluation of treatment or genotype effects, and (4) plotting of the data and models. Data Normalization: functions and scripts make easy the normalization to the initial (T0) RNA abundance, as well as a method to correct for artificial inflation of Reads per Million (RPM) abundance in global assessments as the total size of the RNA pool decreases. Modeling: Normalized data is then modeled using maximum likelihood to fit parameters. For making treatment or genotype comparisons (up to four), the modeling step models all possible treatment effects on each gene by repeating the modeling with constraints on the model parameters (i.e., the decay rate of treatments A and B are modeled once with them being equal and again allowing them to both vary independently). Model Selection: The AICc value is calculated for each model, and the model with the lowest AICc is chosen. Modeling results of selected models are then compiled into a single data frame. Graphical Plotting: functions are provided to easily visualize decay data model, or half-life distributions using ggplot2 package functions.

Maintained by Reed Sorenson. Last updated 5 months ago.

immunooncology software geneexpression generegulation differentialexpression transcription transcriptomics timecourse regression rnaseq normalization workflowstep

4.18 score 2 scripts

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moanin:An R Package for Time Course RNASeq Data Analysis

Simple and efficient workflow for time-course gene expression data, built on publictly available open-source projects hosted on CRAN and bioconductor. moanin provides helper functions for all the steps required for analysing time-course data using functional data analysis: (1) functional modeling of the timecourse data; (2) differential expression analysis; (3) clustering; (4) downstream analysis.

Maintained by Nelle Varoquaux. Last updated 5 months ago.

timecourse geneexpression rnaseq microarray differentialexpression clustering

4.15 score 14 scripts

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splineTimeR:Time-course differential gene expression data analysis using spline regression models followed by gene association network reconstruction

This package provides functions for differential gene expression analysis of gene expression time-course data. Natural cubic spline regression models are used. Identified genes may further be used for pathway enrichment analysis and/or the reconstruction of time dependent gene regulatory association networks.

Maintained by Herbert Braselmann. Last updated 5 months ago.

geneexpression differentialexpression timecourse regression genesetenrichment networkenrichment networkinference graphandnetwork

4.01 score 17 scripts

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mirTarRnaSeq:mirTarRnaSeq

mirTarRnaSeq R package can be used for interactive mRNA miRNA sequencing statistical analysis. This package utilizes expression or differential expression mRNA and miRNA sequencing results and performs interactive correlation and various GLMs (Regular GLM, Multivariate GLM, and Interaction GLMs ) analysis between mRNA and miRNA expriments. These experiments can be time point experiments, and or condition expriments.

Maintained by Mercedeh Movassagh. Last updated 5 months ago.

mirna regression software sequencing smallrna timecourse differentialexpression

4.00 score 9 scripts

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pengls:Fit Penalised Generalised Least Squares models

Combine generalised least squares methodology from the nlme package for dealing with autocorrelation with penalised least squares methods from the glmnet package to deal with high dimensionality. This pengls packages glues them together through an iterative loop. The resulting method is applicable to high dimensional datasets that exhibit autocorrelation, such as spatial or temporal data.

Maintained by Stijn Hawinkel. Last updated 5 months ago.

transcriptomics regression timecourse spatial

4.00 score 4 scripts

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CellTrails:Reconstruction, visualization and analysis of branching trajectories

CellTrails is an unsupervised algorithm for the de novo chronological ordering, visualization and analysis of single-cell expression data. CellTrails makes use of a geometrically motivated concept of lower-dimensional manifold learning, which exhibits a multitude of virtues that counteract intrinsic noise of single cell data caused by drop-outs, technical variance, and redundancy of predictive variables. CellTrails enables the reconstruction of branching trajectories and provides an intuitive graphical representation of expression patterns along all branches simultaneously. It allows the user to define and infer the expression dynamics of individual and multiple pathways towards distinct phenotypes.

Maintained by Daniel Ellwanger. Last updated 5 months ago.

immunooncology clustering datarepresentation differentialexpression dimensionreduction geneexpression sequencing singlecell software timecourse

4.00 score 7 scripts

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Rnits:R Normalization and Inference of Time Series data

R/Bioconductor package for normalization, curve registration and inference in time course gene expression data.

Maintained by Dipen P. Sangurdekar. Last updated 5 months ago.

geneexpression microarray timecourse differentialexpression normalization

4.00 score 1 scripts

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ctsGE:Clustering of Time Series Gene Expression data

Methodology for supervised clustering of potentially many predictor variables, such as genes etc., in time series datasets Provides functions that help the user assigning genes to predefined set of model profiles.

Maintained by Michal Sharabi-Schwager. Last updated 5 months ago.

immunooncology geneexpression transcription differentialexpression genesetenrichment genetics bayesian clustering timecourse sequencing rnaseq

1 stars 4.00 score 3 scripts

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CONFESS:Cell OrderiNg by FluorEScence Signal

Single Cell Fluidigm Spot Detector.

Maintained by Diana LOW. Last updated 5 months ago.

immunooncology geneexpression dataimport cellbiology clustering rnaseq qualitycontrol visualization timecourse regression classification

3.90 score 2 scripts

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timecourse:Statistical Analysis for Developmental Microarray Time Course Data

Functions for data analysis and graphical displays for developmental microarray time course data.

Maintained by Yu Chuan Tai. Last updated 5 months ago.

microarray timecourse differentialexpression

3.90 score 7 scripts

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rain:Rhythmicity Analysis Incorporating Non-parametric Methods

This package uses non-parametric methods to detect rhythms in time series. It deals with outliers, missing values and is optimized for time series comprising 10-100 measurements. As it does not assume expect any distinct waveform it is optimal or detecting oscillating behavior (e.g. circadian or cell cycle) in e.g. genome- or proteome-wide biological measurements such as: micro arrays, proteome mass spectrometry, or metabolome measurements.

Maintained by Paul F. Thaben. Last updated 5 months ago.

timecourse genetics systemsbiology proteomics microarray multiplecomparison

3.88 score 19 scripts

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CNORdt:Add-on to CellNOptR: Discretized time treatments

This add-on to the package CellNOptR handles time-course data, as opposed to steady state data in CellNOptR. It scales the simulation step to allow comparison and model fitting for time-course data. Future versions will optimize delays and strengths for each edge.

Maintained by A. MacNamara. Last updated 5 months ago.

immunooncology cellbasedassays cellbiology proteomics timecourse

3.78 score 15 scripts

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LiquidAssociation:LiquidAssociation

The package contains functions for calculate direct and model-based estimators for liquid association. It also provides functions for testing the existence of liquid association given a gene triplet data.

Maintained by Yen-Yi Ho. Last updated 5 months ago.

pathways geneexpression cellbiology genetics network timecourse

3.78 score 3 scripts 1 dependents

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cycle:Significance of periodic expression pattern in time-series data

Package for assessing the statistical significance of periodic expression based on Fourier analysis and comparison with data generated by different background models

Maintained by Matthias Futschik. Last updated 5 months ago.

microarray timecourse

3.72 score 13 scripts

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TMixClust:Time Series Clustering of Gene Expression with Gaussian Mixed-Effects Models and Smoothing Splines

Implementation of a clustering method for time series gene expression data based on mixed-effects models with Gaussian variables and non-parametric cubic splines estimation. The method can robustly account for the high levels of noise present in typical gene expression time series datasets.

Maintained by Monica Golumbeanu. Last updated 5 months ago.

software statisticalmethod clustering timecourse geneexpression

3.60 score 5 scripts

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acde:Artificial Components Detection of Differentially Expressed Genes

This package provides a multivariate inferential analysis method for detecting differentially expressed genes in gene expression data. It uses artificial components, close to the data's principal components but with an exact interpretation in terms of differential genetic expression, to identify differentially expressed genes while controlling the false discovery rate (FDR). The methods on this package are described in the vignette or in the article 'Multivariate Method for Inferential Identification of Differentially Expressed Genes in Gene Expression Experiments' by J. P. Acosta, L. Lopez-Kleine and S. Restrepo (2015, pending publication).

Maintained by Juan Pablo Acosta. Last updated 5 months ago.

differentialexpression timecourse principalcomponent geneexpression microarray mrnamicroarray

3.30 score 1 scripts

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deltaGseg:deltaGseg

Identifying distinct subpopulations through multiscale time series analysis

Maintained by Diana Low. Last updated 5 months ago.

proteomics timecourse visualization clustering

3.30 score 2 scripts

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roastgsa:Rotation based gene set analysis

This package implements a variety of functions useful for gene set analysis using rotations to approximate the null distribution. It contributes with the implementation of seven test statistic scores that can be used with different goals and interpretations. Several functions are available to complement the statistical results with graphical representations.

Maintained by Adria Caballe. Last updated 5 months ago.

microarray preprocessing normalization geneexpression survival transcription sequencing transcriptomics bayesian clustering regression rnaseq micrornaarray mrnamicroarray functionalgenomics systemsbiology immunooncology differentialexpression genesetenrichment batcheffect multiplecomparison qualitycontrol timecourse metabolomics proteomics epigenetics cheminformatics exonarray onechannel twochannel proprietaryplatforms cellbiology biomedicalinformatics alternativesplicing differentialsplicing dataimport pathways

2.30 score