Daniel J. Arenas's repositories
Inter-Rater
Inter-rater quantifies the reliability between multiple raters who evaluate a group of subjects. It calculates the group quantity, Fleiss kappa, and it improves on existing software by keeping information about each user and quantifying how each user agreed with the rest of the group. This is accomplished through permutations of user pairs. The software was written in Python, can be run in Linux, and the code is deposited in Zenodo and GitHub. This software can be used for evaluation of inter-rater reliability in systematic reviews, medical diagnosis algorithms, education applications, and others.
Full_Compositional_Analysis
This upload acts as the repository for the compositional analysis software used in the manuscript "Increased mTOR activation in idiopathic multicentric Castleman disease" by Arenas et al. (https://ashpublications.org/blood/article/135/19/1673/452765/Increased-mTOR-activation-in-idiopathic)
accuracy_asymptote
Functions to analyze accuracy/effectiveness versus time/size as an asymptote
FindConceptWords
Search for concepts within a text file. As a concept can have different words and conjugations, each one of the permutations are searched.
Pathochip_analysis_algorithms
Various analysis algorithms for PathoChIP data. A new method, top-percentile-probe-ratio, is explained in the manual.
Pedagogical-Functions-in-CPlusPlus
My versions of useful functions and algorithms with C++
Proportions-RepeatedMeasures-LLM
Analysis of longitudinal proportion data. Uses compositional analysis and the linear mixed model.
Raman_BoseEinstein_Scaling
Raman scattering spectra --> Out-of-phase ("Imaginary") component of the Raman susceptibility. Updated Oct 2022.
Simulate-Optical_Signals_from_MicroArrays
This code consists of a new simple theoretical model to simulate optical signals from microarrays. Searching for multiple organisms with multiple probes per organism presents important challenges. Multiple steps in the analysis algorithms, the use of multiple probes searching for the same organism, and the non-normality of experimental data, make nontrivial the calculation of type I (T1E), type II errors (T2E), and statistical power (1 – T2E). The purpose of this program is to generate simulated optical data from microchip arrays (such as PathoChip1) by two methods: an analytical theoretical method and from bootstrapping of experimental data. This type of simulated signal could be utilized in Monte Carlo simulations to test the T1E and statistical power of various data analysis algorithms.