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@CnrLwlss
CnrLwlss / StripchartOpacity.R
Last active Jun 14, 2019
R script demonstrating a few things: 1) drawing boxplots with notches roughly indicating significance of differences 2) stripcharts using transparency to highlight values of high density 3) overlaying boxplot on stripcharts and 4) writing plots as multi-page .pdf reports
View StripchartOpacity.R
# Info about boxplot notches https://sites.google.com/site/davidsstatistics/home/notched-box-plots
# Article about why not to use barplots https://doi.org/10.1371/journal.pbio.1002128
# Article about being wary of summary statistics and why raw data plots are better than boxplots https://www.autodeskresearch.com/publications/samestats
# Generate some fake data
concs = seq(0,10,1)
concobs = rep(concs,each=500)
mdel = function(x) -x^2+10*x+20
vals = mdel(concobs) + rnorm(length(concobs),0,12)
dat = data.frame(conc=concobs,val=vals)
@CnrLwlss
CnrLwlss / mtDNASpecies.R
Created Mar 14, 2019
Testing whether small deletions are under-represented within fibres containing multiple deletion species
View mtDNASpecies.R
# Testing whether small deletions are under-represented within fibres containing multiple deletion species
# Assume 200 mtDNA molecules per fibre section
Nassume = 200
# P7, P15 and P16 are the proportions of smaller mtDNA species in fibres with two or more mtDNA species
P7 = c(0.6,0.2,0.49,0.33,0.57,0.75,0.47,0.29,0.27,0.23,0.51,0.54)
N7 = rep(Nassume,length(P7))
Ndel7 = c(2,2,3,2,2,2,2,3,2,2,2,2)
P15 = c(0.39,0.73,0.37,0.17,0.43,0.53,0.54,0.57)
View EM_mito.R
#install.packages(c("mixOmics","RVAideMemoire"))
library(mixOmics)
library(RVAideMemoire)
# Calculate whether measure is greater in control group after scaling
# Used to colour points in VIP plots
direction=function(dt,measure){
dts = as.data.frame(scale(dt[,-1]))
dts$Group = dt$Group
res = median(dts[[measure]][dts$Group=="Control"],na.rm=TRUE) > median(dts[[measure]][dts$Group!="Control"],na.rm=TRUE)
@CnrLwlss
CnrLwlss / CustomErrorBars.py
Last active Nov 12, 2018
Script for plotting stripplot or swarmplot with barplot overlay, including custom error bars, with matplotlib/Seaborn.
View CustomErrorBars.py
import seaborn as sns
tips = sns.load_dataset("tips")
print(sns.__version__)
print(tips.head())
ax=sns.swarmplot(x="day", y="total_bill", hue="sex", data=tips,split=True)
sns.barplot(x="day", y="total_bill", hue="sex", data=tips,capsize=0.1,errwidth=1.25,alpha=0.25,ci=None)
xcentres=[0,1,2,3]
delt=0.2
xneg=[x-delt for x in xcentres]
@CnrLwlss
CnrLwlss / mutationLoadMeasures.R
Last active Sep 19, 2018
Comparing measures of mtDNA mutation load in single cells.
View mutationLoadMeasures.R
library(grDevices)
library(gtools)
dat = read.delim("data/RTdata.txt",sep="\t",stringsAsFactors=FALSE)
dat$PNUM = as.numeric(gsub("P","",dat$Patient))
dat$ID = sprintf("P%02d_%04d",dat$PNUM,dat$Cell.number)
dat$PAT = sprintf("P%02d",dat$PNUM)
colfunc = colorRamp(c("blue","yellow","red"),space="Lab")
colfun = function(x, alpha=1.0) {
@CnrLwlss
CnrLwlss / explore_mitocyto.R
Created May 25, 2018
Generating some exploratory plots of mitocyto data.
View explore_mitocyto.R
# Read data and rename columns
dat = read.delim("mitocyto_merged_results.csv",sep=",",stringsAsFactors=FALSE)
colnames(dat) = c("value","id","channel","patient_id","patient_type")
# Specify which ids correspond to patients, and which to control
dat$patient_type = ifelse(dat$patient_id=="M1105","patient","control")
dat$patient_id = paste(toupper(substr(dat$patient_type,1,1)),dat$patient_id,sep="_")
# Specify some colours for plotting
dat$colour = "black"
@CnrLwlss
CnrLwlss / estimate_mu.R
Created May 22, 2018
How many replicate samples do we need to estimate the mean of a distribution?
View estimate_mu.R
mu = 5
stdev = 2
N = 100
data = rnorm(N,mu,stdev)
pdf = function(x) dnorm(x,mu,stdev)
bestmu = function(N,x) sum(x[1:N])/N
op=par(mfrow=c(1,2))
@CnrLwlss
CnrLwlss / circleObjective.py
Created Jan 19, 2013
Functions for packing N circles into a rectangle of width W and height H, together with a function for plotting solution and some example code fitting 13 circles into a square. http://cnr.lwlss.net/CircleObjectivesPython/ #python #opimisation #circle #packing #svg #function #closure
View circleObjective.py
import random, numpy
def genGuess(N,W,H):
'''Generate sensible initial guess vector (random circle coordinates and radii)'''
z=numpy.zeros(3*N)
for i in xrange(0,N):
z[i*3]=random.random()*W
z[i*3+1]=random.random()*H
z[i*3+2]=0.001*min(W,H)+random.random()*(0.009*min(W,H))
return(z)
@CnrLwlss
CnrLwlss / checktiff.py
Created Feb 9, 2018
Converting non-image .tiff data to pseudo-images (8-bit greyscale, high contrast).
View checktiff.py
from PIL import Image
import numpy as np
import os
for fname in os.listdir("."):
if fname.endswith(".tiff"):
im=Image.open(fname)
arr=np.array(im,dtype=np.uint16)
maxval = np.max(arr[arr>0])
minval = np.min(arr[arr>0])
@CnrLwlss
CnrLwlss / CompareCompanies.R
Last active Jan 31, 2018
siRNA screen scatterplots
View CompareCompanies.R
getReps = function(df,avedef){
dfA = df[df$RepeatNumber%in%c(1,3,5),]
dfB = df[df$RepeatNumber%in%c(2,4,6),]
dfaveA = aggregate(dfA,by=list(dfA$Gene),FUN=avedef)
dfaveA$Gene = dfaveA$Group.1
dfaveB = aggregate(dfB,by=list(dfB$Gene),FUN=avedef)
dfaveB$Gene = dfaveB$Group.1
dfreps = merge(dfaveA,dfaveB,by="Gene")
return(dfreps)
}
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