Conor Lawless CnrLwlss
CnrLwlss
/ random_walk_noise.R
Created
July 29, 2021 14:03
Visual representation of difference between stochastic process and samples from random distribution
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| N=10000 | |
| noise = rnorm(N,mean=0,sd=0.5) | |
| random_walk=cumsum(noise) | |
| ylim = range(random_walk) | |
| op = par(mfrow=c(1,2)) | |
| plot(noise,type="l",ylim=ylim,xlab="time",cex.lab=1.75,cex.axis=1.75) | |
| plot(random_walk,type="l",ylim=ylim,xlab="time",cex.lab=1.75,cex.axis=1.75) | |
| par(op) |
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| # Make some synthetic data, like patient with v-shaped 2Dmito scatterplot | |
| N = 500 | |
| mu_x = 5 | |
| sd_x = 2.5 | |
| intercept = 0.1 | |
| slope_normal = 1.0 | |
| slope_deficient = 0.0 | |
| sd_err = 1.0 | |
| x_normal = rnorm(N/2,mu_x,sd_x) |
CnrLwlss
/ gillespie.txt
Last active
June 2, 2021 11:57
Gillespie algorithm mtDNA population dynamics
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| RATE OR STOICHIOMETRY | |
| REACTIONS HAZARD (W) (M) PROBABILITIES | |
| Synthesis W -> 2W ks*W +1 0 ks*W/CH | |
| M -> 2M ks*M 0 +1 ks*M/CH | |
| Degradation W -> NULL kd*W -1 0 kd*W/CH | |
| M -> NULL kd*M 0 -1 kd*M/CH | |
| __________ | |
| Combined Hazard (CH): (ks+kd)*(W+M) |
CnrLwlss
/ index.html
Last active
May 28, 2021 15:05
Annotating images with annotorious. Live version here: http://mito.ncl.ac.uk/imserv/
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| <!DOCTYPE html> | |
| <html> | |
| <head> | |
| <meta charset="utf-8" /> | |
| <title>Annotation|IMC images</title> | |
| <link rel="stylesheet" href="https://cdn.jsdelivr.net/npm/@recogito/[email protected]/dist/annotorious.min.css"> | |
| <script src="https://cdn.jsdelivr.net/npm/@recogito/[email protected]/dist/annotorious.min.js"></script> | |
| <script language="javascript" type="text/javascript"> | |
| var key = 2; |
CnrLwlss
/ Table S2_List of genetic interactions.csv
Last active
May 16, 2021 09:19
Data for interactive SGA plot on Observable
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| R package version: 0.0-39 | |||||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Summary type: median | |||||||||||||
| Test type: wilcoxon | |||||||||||||
| x-axis treatment: 20 | |||||||||||||
| x-axis medium: SDM_rhlk_TGNH | |||||||||||||
| x-axis screen ID: SGA0188 | |||||||||||||
| x-axis screen name: CONTROL | |||||||||||||
| x-axis libraries: SDLV4_1536 | |||||||||||||
| x-axis client: MMA | |||||||||||||
| x-axis user: AAB |
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| from PIL import Image | |
| import numpy as np | |
| # Generate a 100x100 array of random floats between 0.0 and 1.0 | |
| random_floats = np.random.rand(100,100) | |
| # Scale these up to lie in the range of 8-bit integers (between 0.0 and 255.0) | |
| random_floats_8bit = random_floats * 255.0 | |
| # Round these float values to the nearest integer |
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| # Sampling noise | |
| simulateSample = function(mtDNAPerWell=1000, mutantFraction=0.95, fracVolumeSampled=1.0/3.0){ | |
| # Create a synthetic population of mtDNA molecules | |
| moleculeMutant = rbinom(n = round(mtDNAPerWell*fracVolumeSampled), size = 1, prob = mutantFraction) | |
| # Calculate mutant fraction in sampled molecules | |
| sum(moleculeMutant)/length(moleculeMutant) | |
| } | |
| simulateUncertainty = function(mtDNAPerWell=1000, mutantFraction=0.95, fracVolumeSampled=1.0/3.0, reps = 5000){ |
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| N = 10000 | |
| Ncurve = 1000 | |
| png("Walk.png",width=3000,height=1000,pointsize=12,type="cairo-png") | |
| plot(NULL,ylim=c(-200,200),axes=FALSE,xlim=c(N/2,N),xlab="",ylab="") | |
| for(i in 1:Ncurve) points(cumsum(rnorm(N,0,0.5)),type="l",col=rgb(0,0,0,0.1),lwd=2) | |
| dev.off() |
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| nA = 8 | |
| meanA = 48 | |
| sdA = 3 | |
| nB = 10 | |
| meanB = 53 | |
| sdB = 4 | |
| set.seed(42) | |
| nBoot = 100000 |
CnrLwlss
/ laser_neighbours.R
Last active
August 2, 2020 22:24
Calculates and visualises proportion of fibres that can be extracted/sampled, from a skeletal muscle section leaving all immediate neighbours of sampled fibres in place.
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| library(deldir) | |
| library(png) | |
| dat = read.delim("results.csv",sep=",",stringsAsFactors=FALSE) | |
| im = readPNG("AVE_mitocyto.png") | |
| h = dim(im)[1] | |
| w = dim(im)[2] | |
| td = data.frame( | |
| xCoord = dat$Value[dat$Channel=="xCoord"], |