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insightsengineering

tern:Create Common TLGs Used in Clinical Trials

Table, Listings, and Graphs (TLG) library for common outputs used in clinical trials.

Maintained by Joe Zhu. Last updated 2 months ago.

clinical-trialsgraphslistingsnestoutputstables

20.2 match 79 stars 12.62 score 186 scripts 9 dependents

bioc

BiocGenerics:S4 generic functions used in Bioconductor

The package defines many S4 generic functions used in Bioconductor.

Maintained by Hervé Pagès. Last updated 1 months ago.

infrastructurebioconductor-packagecore-package

4.1 match 12 stars 14.22 score 612 scripts 2.2k dependents

wraff

wrProteo:Proteomics Data Analysis Functions

Data analysis of proteomics experiments by mass spectrometry is supported by this collection of functions mostly dedicated to the analysis of (bottom-up) quantitative (XIC) data. Fasta-formatted proteomes (eg from UniProt Consortium <doi:10.1093/nar/gky1049>) can be read with automatic parsing and multiple annotation types (like species origin, abbreviated gene names, etc) extracted. Initial results from multiple software for protein (and peptide) quantitation can be imported (to a common format): MaxQuant (Tyanova et al 2016 <doi:10.1038/nprot.2016.136>), Dia-NN (Demichev et al 2020 <doi:10.1038/s41592-019-0638-x>), Fragpipe (da Veiga et al 2020 <doi:10.1038/s41592-020-0912-y>), ionbot (Degroeve et al 2021 <doi:10.1101/2021.07.02.450686>), MassChroq (Valot et al 2011 <doi:10.1002/pmic.201100120>), OpenMS (Strauss et al 2021 <doi:10.1038/nmeth.3959>), ProteomeDiscoverer (Orsburn 2021 <doi:10.3390/proteomes9010015>), Proline (Bouyssie et al 2020 <doi:10.1093/bioinformatics/btaa118>), AlphaPept (preprint Strauss et al <doi:10.1101/2021.07.23.453379>) and Wombat-P (Bouyssie et al 2023 <doi:10.1021/acs.jproteome.3c00636>. Meta-data provided by initial analysis software and/or in sdrf format can be integrated to the analysis. Quantitative proteomics measurements frequently contain multiple NA values, due to physical absence of given peptides in some samples, limitations in sensitivity or other reasons. Help is provided to inspect the data graphically to investigate the nature of NA-values via their respective replicate measurements and to help/confirm the choice of NA-replacement algorithms. Meta-data in sdrf-format (Perez-Riverol et al 2020 <doi:10.1021/acs.jproteome.0c00376>) or similar tabular formats can be imported and included. Missing values can be inspected and imputed based on the concept of NA-neighbours or other methods. Dedicated filtering and statistical testing using the framework of package 'limma' <doi:10.18129/B9.bioc.limma> can be run, enhanced by multiple rounds of NA-replacements to provide robustness towards rare stochastic events. Multi-species samples, as frequently used in benchmark-tests (eg Navarro et al 2016 <doi:10.1038/nbt.3685>, Ramus et al 2016 <doi:10.1016/j.jprot.2015.11.011>), can be run with special options considering such sub-groups during normalization and testing. Subsequently, ROC curves (Hand and Till 2001 <doi:10.1023/A:1010920819831>) can be constructed to compare multiple analysis approaches. As detailed example the data-set from Ramus et al 2016 <doi:10.1016/j.jprot.2015.11.011>) quantified by MaxQuant, ProteomeDiscoverer, and Proline is provided with a detailed analysis of heterologous spike-in proteins.

Maintained by Wolfgang Raffelsberger. Last updated 4 months ago.

15.4 match 3.67 score 17 scripts 1 dependents

bioc

SIM:Integrated Analysis on two human genomic datasets

Finds associations between two human genomic datasets.

Maintained by Renee X. de Menezes. Last updated 5 months ago.

microarrayvisualization

6.0 match 4.30 score 3 scripts

kurthornik

clue:Cluster Ensembles

CLUster Ensembles.

Maintained by Kurt Hornik. Last updated 4 months ago.

2.0 match 2 stars 9.85 score 496 scripts 401 dependents

wraff

wrMisc:Analyze Experimental High-Throughput (Omics) Data

The efficient treatment and convenient analysis of experimental high-throughput (omics) data gets facilitated through this collection of diverse functions. Several functions address advanced object-conversions, like manipulating lists of lists or lists of arrays, reorganizing lists to arrays or into separate vectors, merging of multiple entries, etc. Another set of functions provides speed-optimized calculation of standard deviation (sd), coefficient of variance (CV) or standard error of the mean (SEM) for data in matrixes or means per line with respect to additional grouping (eg n groups of replicates). A group of functions facilitate dealing with non-redundant information, by indexing unique, adding counters to redundant or eliminating lines with respect redundancy in a given reference-column, etc. Help is provided to identify very closely matching numeric values to generate (partial) distance matrixes for very big data in a memory efficient manner or to reduce the complexity of large data-sets by combining very close values. Other functions help aligning a matrix or data.frame to a reference using partial matching or to mine an experimental setup to extract patterns of replicate samples. Many times large experimental datasets need some additional filtering, adequate functions are provided. Convenient data normalization is supported in various different modes, parameter estimation via permutations or boot-strap as well as flexible testing of multiple pair-wise combinations using the framework of 'limma' is provided, too. Batch reading (or writing) of sets of files and combining data to arrays is supported, too.

Maintained by Wolfgang Raffelsberger. Last updated 7 months ago.

4.2 match 4.44 score 33 scripts 4 dependents

r-gregmisc

gmodels:Various R Programming Tools for Model Fitting

Various R programming tools for model fitting.

Maintained by Gregory R. Warnes. Last updated 3 months ago.

1.8 match 1 stars 10.01 score 3.5k scripts 30 dependents

trinker

textshape:Tools for Reshaping Text

Tools that can be used to reshape and restructure text data.

Maintained by Tyler Rinker. Last updated 12 months ago.

data-reshapingmanipulationsentence-boundary-detectiontext-datatext-formatingtidy

1.8 match 50 stars 9.18 score 266 scripts 34 dependents

projectmosaic

mosaicCore:Common Utilities for Other MOSAIC-Family Packages

Common utilities used in other MOSAIC-family packages are collected here.

Maintained by Randall Pruim. Last updated 1 years ago.

2.0 match 1 stars 7.07 score 113 scripts 26 dependents

brockk

escalation:A Modular Approach to Dose-Finding Clinical Trials

Methods for working with dose-finding clinical trials. We provide implementations of many dose-finding clinical trial designs, including the continual reassessment method (CRM) by O'Quigley et al. (1990) <doi:10.2307/2531628>, the toxicity probability interval (TPI) design by Ji et al. (2007) <doi:10.1177/1740774507079442>, the modified TPI (mTPI) design by Ji et al. (2010) <doi:10.1177/1740774510382799>, the Bayesian optimal interval design (BOIN) by Liu & Yuan (2015) <doi:10.1111/rssc.12089>, EffTox by Thall & Cook (2004) <doi:10.1111/j.0006-341X.2004.00218.x>; the design of Wages & Tait (2015) <doi:10.1080/10543406.2014.920873>, and the 3+3 described by Korn et al. (1994) <doi:10.1002/sim.4780131802>. All designs are implemented with a common interface. We also offer optional additional classes to tailor the behaviour of all designs, including avoiding skipping doses, stopping after n patients have been treated at the recommended dose, stopping when a toxicity condition is met, or demanding that n patients are treated before stopping is allowed. By daisy-chaining together these classes using the pipe operator from 'magrittr', it is simple to tailor the behaviour of a dose-finding design so it behaves how the trialist wants. Having provided a flexible interface for specifying designs, we then provide functions to run simulations and calculate dose-paths for future cohorts of patients.

Maintained by Kristian Brock. Last updated 2 months ago.

1.7 match 15 stars 7.91 score 67 scripts

bioc

ropls:PCA, PLS(-DA) and OPLS(-DA) for multivariate analysis and feature selection of omics data

Latent variable modeling with Principal Component Analysis (PCA) and Partial Least Squares (PLS) are powerful methods for visualization, regression, classification, and feature selection of omics data where the number of variables exceeds the number of samples and with multicollinearity among variables. Orthogonal Partial Least Squares (OPLS) enables to separately model the variation correlated (predictive) to the factor of interest and the uncorrelated (orthogonal) variation. While performing similarly to PLS, OPLS facilitates interpretation. Successful applications of these chemometrics techniques include spectroscopic data such as Raman spectroscopy, nuclear magnetic resonance (NMR), mass spectrometry (MS) in metabolomics and proteomics, but also transcriptomics data. In addition to scores, loadings and weights plots, the package provides metrics and graphics to determine the optimal number of components (e.g. with the R2 and Q2 coefficients), check the validity of the model by permutation testing, detect outliers, and perform feature selection (e.g. with Variable Importance in Projection or regression coefficients). The package can be accessed via a user interface on the Workflow4Metabolomics.org online resource for computational metabolomics (built upon the Galaxy environment).

Maintained by Etienne A. Thevenot. Last updated 5 months ago.

regressionclassificationprincipalcomponenttranscriptomicsproteomicsmetabolomicslipidomicsmassspectrometryimmunooncology

1.7 match 7.55 score 210 scripts 8 dependents

trinker

qdapTools:Tools for the 'qdap' Package

A collection of tools associated with the 'qdap' package that may be useful outside of the context of text analysis.

Maintained by Tyler Rinker. Last updated 2 years ago.

1.8 match 16 stars 7.04 score 408 scripts 5 dependents

murrayefford

openCR:Open Population Capture-Recapture

Non-spatial and spatial open-population capture-recapture analysis.

Maintained by Murray Efford. Last updated 5 months ago.

cpp

1.9 match 4 stars 5.98 score 53 scripts

richardhooijmaijers

R3port:Report Functions to Create HTML and PDF Files

Create and combine HTML and PDF reports from within R. Possibility to design tables and listings for reporting and also include R plots.

Maintained by Richard Hooijmaijers. Last updated 1 years ago.

1.9 match 10 stars 5.71 score 34 scripts 1 dependents

simonmoulds

lulcc:Land Use Change Modelling in R

Classes and methods for spatially explicit land use change modelling in R.

Maintained by Simon Moulds. Last updated 5 years ago.

1.8 match 41 stars 5.37 score 38 scripts

cran

epitools:Epidemiology Tools

Tools for training and practicing epidemiologists including methods for two-way and multi-way contingency tables.

Maintained by Adam Omidpanah. Last updated 5 years ago.

1.8 match 2 stars 4.89 score 12 dependents

mjuraska

seqDesign:Simulation and Group-Sequential Monitoring of Randomized Treatment Efficacy Trials with Time-to-Event Endpoints

A broad spectrum of both event-driven and fixed follow-up preventive vaccine efficacy trial designs, including designs of Gilbert, Grove et al. (2011, Statistical Communications in Infectious Diseases), are implemented, with application generally to individual-randomized clinical trials with multiple active treatment groups and a shared control group, and a study endpoint that is a time-to-event endpoint subject to right-censoring. The design accommodates the following features: (1) the possibility that the efficacy of the treatment/vaccine groups may take time to accrue while the multiple treatment administrations/vaccinations are given, (2) hazard ratio and cumulative incidence-based treatment/vaccine efficacy parameters and multiple estimation/hypothesis testing procedures are available, (3) interim/group-sequential monitoring of each treatment group for potential harm, non-efficacy (lack of benefit), efficacy (benefit), and high efficacy, (3) arbitrary alpha spending functions for different monitoring outcomes, (4) arbitrary timing of interim looks, separate for each monitoring outcome, in terms of either event accrual or calendar time, (5) flexible analysis cohort characterization (intention-to-treat vs. per-protocol/as-treated; counting only events for analysis that occur after a specific point in study time), and (6) division of the trial into two stages of time periods where each treatment is first evaluated for efficacy in the first stage of follow-up, and, if and only if it shows significant treatment efficacy in stage one, it is evaluated for longer-term durability of efficacy in stage two. The package produces plots and tables describing operating characteristics of a specified design including a description of monitoring boundaries on multiple scales for the different outcomes; event accrual since trial initiation; probabilities of stopping early for potential harm, non-efficacy, etc.; an unconditional power for intention-to-treat and per-protocol analyses; calendar time to crossing a monitoring boundary or reaching the target number of endpoints if no boundary is crossed; trial duration; unconditional power for comparing treatment efficacies; and the distribution of the number of endpoints within an arbitrary study time interval (e.g., events occurring after the treatments/vaccinations are given), useful as input parameters for the design of studies of the association of biomarkers with a clinical outcome (surrogate endpoint problem). The code can be used for a single active treatment versus control design and for a single-stage design.

Maintained by Michal Juraska. Last updated 2 years ago.

1.7 match 2 stars 4.60 score 7 scripts

rwoldford

eikosograms:The Picture of Probability

An eikosogram (ancient Greek for probability picture) divides the unit square into rectangular regions whose areas, sides, and widths, represent various probabilities associated with the values of one or more categorical variates. Rectangle areas are joint probabilities, widths are always marginal (though possibly joint margins, i.e. marginal joint distributions of two or more variates), and heights of rectangles are always conditional probabilities. Eikosograms embed the rules of probability and are useful for introducing elementary probability theory, including axioms, marginal, conditional, and joint probabilities, and their relationships (including Bayes theorem as a completely trivial consequence). They are markedly superior to Venn diagrams for this purpose, especially in distinguishing probabilistic independence, mutually exclusive events, coincident events, and associations. They also are useful for identifying and understanding conditional independence structure. As data analysis tools, eikosograms display categorical data in a manner similar to Mosaic plots, especially when only two variates are involved (the only case in which they are essentially identical, though eikosograms purposely disallow spacing between rectangles). Unlike Mosaic plots, eikosograms do not alternate axes as each new categorical variate (beyond two) is introduced. Instead, only one categorical variate, designated the "response", presents on the vertical axis and all others, designated the "conditioning" variates, appear on the horizontal. In this way, conditional probability appears only as height and marginal probabilities as widths. The eikosogram is therefore much better suited to a response model analysis (e.g. logistic model) than is a Mosaic plot. Mosaic plots are better suited to log-linear style modelling as in discrete multivariate analysis. Of course, eikosograms are also suited to discrete multivariate analysis with each variate in turn appearing as the response. This makes it better suited than Mosaic plots to discrete graphical models based on conditional independence graphs (i.e. "Bayesian Networks" or "BayesNets"). The eikosogram and its superiority to Venn diagrams in teaching probability is described in W.H. Cherry and R.W. Oldford (2003) <https://math.uwaterloo.ca/~rwoldfor/papers/eikosograms/paper.pdf>, its value in exploring conditional independence structure and relation to graphical and log-linear models is described in R.W. Oldford (2003) <https://math.uwaterloo.ca/~rwoldfor/papers/eikosograms/independence/paper.pdf>, and a number of problems, puzzles, and paradoxes that are easily explained with eikosograms are given in R.W. Oldford (2003) <https://math.uwaterloo.ca/~rwoldfor/papers/eikosograms/examples/paper.pdf>.

Maintained by Wayne Oldford. Last updated 6 years ago.

1.5 match 4 stars 4.92 score 14 scripts

fmicompbio

swissknife:Handy code shared in the FMI CompBio group

A collection of useful R functions performing various tasks that might be re-usable and worth sharing.

Maintained by Michael Stadler. Last updated 2 months ago.

cpp

1.7 match 8 stars 3.76 score 12 scripts

numbersman77

reporttools:Generate "LaTeX"" Tables of Descriptive Statistics

These functions are especially helpful when writing reports of data analysis using "Sweave".

Maintained by Kaspar Rufibach. Last updated 3 years ago.

1.8 match 2 stars 3.35 score 113 scripts