sha256 stringlengths 64 64 | language stringclasses 28
values | size int32 1 491k | lines int32 1 23.2k | content stringlengths 1 200k |
|---|---|---|---|---|
6558693254098a7ee48fd0650e71428fa8161cec71e0cc52f5ee4cb8b0822f67 | R | 1,686 | 40 | library(Seurat)
library(Matrix)
library(aricode)
# read data matrix
# after reading, suppose the data matrix is saved in 'data'
# the true cell type labels are saved in 'type'
# Seurat preprocessing
data.seurat <- CreateSeuratObject(counts = data, project = "data", min.cells = 3);data.seurat
data.seurat <- No... |
e7c08f2592384ff849a07e62c2c0551702e974dee13192346bb1609464d0111d | R | 1,698 | 50 | my.alpha.col <- function(x, tl.level = 64, from = "#333333", to = "#FFFFFF"){
# alpha <- colorRampPalette(c(from, to))(tl.level + 1)[-1]
# alpha <- gsub(".....", "", alpha)
col.fill <- colorRampPalette(c("#EEEEEE", x))(tl.level + 1)[-1]
# col.fill <- paste(col.fill, alpha, sep = "")
col.fill
} # End of my.alp... |
9a2fb2b2a679bb23a4e3a56c13e18297f2f176f68923779d68491d18a4f02838 | R | 1,702 | 52 | # logging which server was used to run this script
system("echo \"Running this on ### $(hostname) ### \\(* w *)/\"")
# load library
library(tidyverse)
library(Seurat)
library(sctransform)
library(scales)
library(harmony)
library(ggrastr)
## read seurat object
data_obj_map_to_manuscript <- readRDS("./seurat_objects/sn... |
ade41a39a31d8bee3f46e1e0e94a97fdcdbd78d1d5ba443bb841f48937b66542 | R | 1,733 | 72 | test_that("encode a signal", {
codebook <- construct_codebook(c('A', 'B', 'C'))
result <- encode_signal(
codebook=codebook,
symbol = 'A',
signal_power = 10,
duration_in_seconds = 10
)
expect_true('group_index' %in% colnames(result))
expect_true('spike_time' ... |
27f7c919cf5ea4e0a385bc5aad1d23814a483bc2173866b610e45e967737c98e | R | 1,741 | 55 | instLib = commandArgs(T)[1]
r = getOption("repos") # hard code the UK repo for CRAN
r["CRAN"] = "http://cran.uk.r-project.org"
options(repos = r)
rm(r)
ipak <- function(pkg){
new.pkg <- pkg[!(pkg %in% installed.packages()[, "Package"])]
if (length(new.pkg))
biocLite(new.pkg, ask=FALSE, lib=instLib, lib.loc=in... |
87e499596ba3cf5a4da51c9b659abb8376477aeed03768ea1e69db509f922819 | R | 1,746 | 62 |
fbarmax <- function(mu, barom, maxom,
eta = c(0.97675624, 0.01917240, 0.00407136)){
# require(MixSim)
sigma <- array(c(1, 1, 1), dim = c(1, 1, 3))
# eta <- c(0.97675624, 0.01917240, 0.00407136)
mu <- matrix(c(0, mu[1], mu[2]), nrow = 3, ncol = 1)
ommap <- overlap(Pi = eta, Mu = mu, S = sigma)
((barom ... |
03c08bf7decbdb4b8ad79a2250ce2a0f1b23eff70828d5d75ebcb0be298ede0b | R | 1,758 | 59 | library(tidyverse)
library(ArchR)
library(Seurat)
library(tictoc)
set.seed(666)
addArchRThreads(threads = 8)
addArchRGenome("hg38")
proj <- loadArchRProject(path = "Dracheva_ALS_FTD_addRNA_addPseudobulkRep_testDAR/")
peak_se <- readRDS("test_peak_matrix_MCX_Astro_ALS_vs_Control_useAstroPeaksOnly_reproducibility6_SE... |
487e4a31f917ec81acf4cd69552df46fb2107266f6bcfa8e85fcaaa30700fe2b | R | 1,768 | 47 | context('Tests for the patched motifmatchr background conversion')
library(testthat)
library(TFBSTools)
library(Pando)
pfm <- PFMatrix(profileMatrix = matrix(
c(8, 1, 1, 0, 1, 8, 0, 1, 0, 1, 8, 1), nrow = 4,
dimnames = list(c('A', 'C', 'G', 'T'))
))
uniform <- c(A = 0.25, C = 0.25, G = 0.25, T = 0.25)
bg <... |
1811a597b160e2f2b04cc020d9961404a256ef0c43d25408607b3b83b96cb8b4 | R | 1,770 | 56 | #!/usr/bin/Rscript
#!/usr/bin/Rscript
# TODO
# Add a proper example usage on the script.
# The user is expected to pass:
# Mandatory -> gwasFile, Nsamples, OUTCOMEcolname
# Optional mafFile
# if length(args == 0) I run an example usage function
args = commandArgs(trailingOnly=TRUE)
lengthArgs = length(args)
if (l... |
9fc06e6215bac588d1873fe1c651ca9d41c1b6c7eca882e7b74ee97ed87b3b7f | R | 1,823 | 50 | library(ArchR)
library(ggplot2)
############################################################
#------------------------- Figure 3f-----------------------#
#############################################################
###Statastic & Plot
setwd('output path')
list=list.files(pattern = "__") #### Region_Celltype marker l... |
69f107ac62d5cda9c3289dc3c176a117551e9bff110ff7a98785b8c5a6f6d969 | R | 1,829 | 82 |
# facets ------------------------------------------------------------------
# Saving ------------------------------------------------------------------
# Add a basename and figure number for file prefix
# This function creates another function that can be used for a particular
# presentation file
makeBaseName... |
44ca9e8c536043c0a2b3d44699f1a20a529cd4f5af64eb1ef714a3ec60e0eb98 | R | 1,832 | 57 | ### This files provides functions for output.
print.fclust <- function(x, ...){
CHECK <- x$check
PARAM <- x$param
N.CLASS <- x$n.class
Ignore.X <- x$ignore.X
AIC <- x$aic
BIC <- x$bic
ICL.BIC <- x$icl.bic
ETA <- PARAM$ETA
BETA <- do.call("cbind", PARAM$BETA)
if(PARAM$p.X > 0){
MU <- PARAM$MU
... |
665a9c80e73fd59150587f338c5de6615f1020af9911e8c1abadde1b55eb3cea | R | 1,848 | 68 | ### Plot fclust image.
plotpv <- function(da, posterior, PARAM, zlim = c(0, 0.01), plot.mean = TRUE,
xlab = "", ylab = "", main = NULL, xlim = NULL, ylim = NULL,
col = my.YlOrRd(), ignore.bg = FALSE){
da.clust <- da
id <- !is.na(da)
K <- ncol(posterior)
class <- apply(posterior, 1, which.max)
if(is.... |
0bdcbf4a3e110059746184cfeb44bcbfc7877d47b3aeaba6b2a60f56012db468 | R | 1,864 | 62 | ### This is an independent function to provide logL.
### Note that Inf, -Inf, NA, NaN is drop from the summation.
indep.logL.I <- function(PARAM){
nrow <- nrow(.MixfMRIEnv$X.gbd)
p.X <- PARAM$p.X
ret <- matrix(0, nrow = nrow, ncol = PARAM$K)
for(i.k in 1:PARAM$K){
log.a <- dbeta(.MixfMRIEnv$PV.gbd, PARAM$... |
cfcab2e2d8e7140d16e7df7ec13ddd5525c729ef2df7d781b16ff47bc2c72c7a | R | 1,864 | 61 | library(MixfMRI, quietly = TRUE)
source("00-set_condition.r")
### Test 3d data
da <- eval(parse(text = paste(case, sep = "")))
id <- !is.na(da)
PV.gbd <- da[id]
id.loc <- which(id, arr.ind = TRUE)
X.gbd <- t(t(id.loc) / dim(da))
### Load results
fn.in <- paste("./output/ret.", case, ".tp.rda", sep = "")
load(fn.in)
... |
92b7acac1854301c3ffb65f9301d7c757b6114bb2e08612eabfdd0edc8425070 | R | 1,874 | 94 | addSliceIndex <- function(
df
) {
df %>%
# give each slice a unique index
group_by(
group_id
) %>%
mutate(
slice_index = match(slice_id, unique(slice_id))
) %>%
ungroup()
}
groupByROI <- function(
df
) {
df %>%
group_by(
group_idx
, treatment
, slice_id... |
b069086553542d47b0e64dec5296b711a2b7b6db06eb463669e00f6d9a0e11f6 | R | 1,876 | 60 | library(MixfMRI, quietly = TRUE)
source("00-set_condition.r")
### Test 3d data
da <- eval(parse(text = paste(case, sep = "")))
id <- !is.na(da)
PV.gbd <- da[id]
id.loc <- which(id, arr.ind = TRUE)
X.gbd <- t(t(id.loc) / dim(da))
### Load results
fn.in <- paste("./output/ret.", case, ".tp.new.rda", sep = "")
load(fn.i... |
5de2249ea71213af3fce70b84d36d3b2ea2fefd7c23a7dd398c11ffa6a28b7ee | R | 1,919 | 49 | ### Get the parameters
parser = argparse::ArgumentParser(description="Script to Cluster scATAC data by ArchR")
parser$add_argument('-I','--inputpath', help='input arrow directory')
parser$add_argument('-D','--id', help='tissue ID')
parser$add_argument('-O','--out', help='out directory')
parser$add_argument('-C','--Cell... |
1125b27284311773384ac78aa13699f843907cf8903dfb35f546db34d535f881 | R | 1,936 | 51 | ### run_QDNAseq.R
### Wrapper to perform CNV analysis on a set of bam files using the QDNAseq R package.
library(GetoptLong)
library(devtools)
library(Biobase)
# Command line arguments
GetoptLong(c(
"bams|b=s@", "Bam files",
"sampleNames|s=s@", "Sample names, same order as bams",
"qdnaseq_path|qdnaseq=s", "Path t... |
baf8e5228112b5bb1e915ab8b28638d5aa8fc80e66f530c25106ac79988d3404 | R | 1,936 | 55 | ### Get the parameters
parser = argparse::ArgumentParser(description="Script to Cluster scATAC data by ArchR")
parser$add_argument('-I','--inputpath', help='input arrow directory')
parser$add_argument('-D','--id', help='tissue ID')
parser$add_argument('-O','--out', help='out directory')
parser$add_argument('-F','--filt... |
e88627a55055de5390cbecf19b22181f1f3394acb5b2b154e4ce5de0eb6c6c74 | R | 1,940 | 66 | library(MixfMRI, quietly = TRUE)
source("00-set_condition.r")
### Test 2d data
da <- eval(parse(text = paste(case, sep = "")))
id <- !is.na(da)
PV.gbd <- da[id]
id.loc <- which(id, arr.ind = TRUE)
X.gbd <- t(t(id.loc) / dim(da))
### Load results
fn.in <- paste("./output/ret.", case, ".tp.rda", sep = "")
load(fn.in)
... |
1b41c70125b712e0fa652a0b44f1d9d6271277210c2d08917cc84630df7d1254 | R | 1,954 | 58 | #--------------------------------
# Fitting of evolutionary models
# All phenotypes
# 25 January 2023
#
# Warning: this script takes >1h
# to execute.
#--------------------------------
source("./common.R")
Y <- as.matrix(df[c("Log10Area.cb", "Log10Length.cb", "Log10ThicknessMedian",
"Log10PeriodMedian", "Log10WidthM... |
62e39e5cd4246148d6d00ae58e7e87223354379a504c623e7fe69763edbd4d62 | R | 1,981 | 67 | ### Perform H0: alpha/(alpha + beta) = 0.05 vs. Ha: alpha/(alpha + beta) < 0.05.
lrt2.org <- function(PV.gbd, CLASS.gbd, K, H0.mean = 0.05,
upper.beta = .FC.CT$INIT$BETA.beta.max){
if((H0.mean <= 0) || (H0.mean >= 0.5)){
stop("It should be 0 < H0.mean < 0.5.")
}
K.CLASS <- as.integer(max.gbd(CLASS.gbd))... |
64f6193ba25498cbb68e46293a2aafb694a6cb6f9983076def15319888d348ad | R | 1,981 | 81 | # logging which server was used to run this script
system("echo \"Running this on ### $(hostname) ### \\(* w *)/\"")
# load library
library(scDist)
set.seed(1126490984)
simCellType <- function(D, tau, nn = 100, G = 1000, N1 = 5, N2 = 5, J = 30, label = "A") {
beta_true <- rep(0, G)
beta_true[sample(1:G, size = nn... |
ec02785e3a4a43c1a84f12848d1ab61959505fbaf5f754b67ac7dba90b7437b6 | R | 1,985 | 65 |
library(SummarizedExperiment)
library(igraph)
library(bmrm)
T18.ontology <- function(T18,level=2) {
dag <- graph_from_edgelist(unique(cbind(as.character(T18$class),"root")))
if (level>=2) {
dag <- dag + graph_from_edgelist(unique(cbind(as.character(T18$subclass),as.character(T18$class))))
label <- T18$su... |
381d63d47d1b9cefb3cab602cff11654b1be8201b2d946cbc97b968cf828dd90 | R | 1,999 | 76 | ### This implements RndEM algorithm.
initial.RndEM.gbd <- function(PARAM){
logL.save <- -Inf
i.iter <- 1
PARAM.org <- PARAM
PARAM.save <- PARAM
repeat{
if(i.iter > .MixfMRIEnv$CONTROL$RndEM.iter){
break
}
PARAM <- try(initial.em.gbd(PARAM.org), silent = TRUE)
if(.MixfMRIEnv$any(inheri... |
6bd3876a3521103d6c90a9310ee75044088242e6df3b39d9c7f4f084e1d2a42f | R | 2,013 | 86 | addVOAge <- function(df, dobAsDay = 0){
df <- df %>%
mutate(
VO_age = ifelse(
is.na(VO_age) & sex == "F",
as.numeric(VO_day - DOB) + dobAsDay,
VO_age
)
)
return(df)
}
addEstrusAge <- function(df, dobAsDay = 0){
df <- df %>%
mutate(
Estrus_age = ifelse(
... |
4427e0f5627f6a84f7a7261a096ca85b37c2bdbc6956b22a372fe51f1a191c22 | R | 2,016 | 60 | #--------------------------------
# Fitting of evolutionary models
# Brain phenotypes only
# 25 January 2023
#
# Warning: this script takes >1h
# to execute.
#--------------------------------
source("./common.R")
Yb <- as.matrix(df[c("Log10Area.cb", "Log10Length.cb", "Log10ThicknessMedian",
"Log10... |
eae14fe7cb6c2b8d2c5eecce130a654f67ce3d936a795da7f1c170c10005d160 | R | 2,022 | 83 | library(pbdMPI, quietly = TRUE)
init(set.seed = FALSE)
library(MixfMRI, quietly = TRUE)
### Configuration
case <- "ds.pval"
.FC.CT$model.X <- "I"
.FC.CT$ignore.X <- FALSE
.FC.CT$CONTROL$debug <- 1
.FC.CT$CONTROL$RndEM.iter <- 50
.FC.CT$INIT$min.1st.prop <- 0.99
.FC.CT$INIT$max.PV <- 0.05
seed <- 1234
.FC.CT$MPI.gbd <-... |
ece5ceac5083a9e32a8d10ad1c22457d5673c145d71f44cb136a54684696dcbd | R | 2,023 | 48 | library(Seurat)
library(Matrix)
seed <- 2020
set.seed(seed)
# read data matrix
# after reading, suppose the data matrix is saved in 'data'
# add col and row names for seurat object
colnames(data) <- as.character(1:dim(data)[2]); colnames(data)
rownames(data) <- as.character(1:dim(data)[1]); rownames(data)
... |
0ab25041172e4c1f31873c87057f41802e40519a4753438beee6445c2dbf47d4 | R | 2,050 | 43 | ---
title: "Gene-peak association"
author: "Jonas Simon Fleck"
date: "5/18/2022"
output: html_document
---
```{r, echo = FALSE, message=FALSE}
knitr::opts_chunk$set(message = FALSE, warning = FALSE)
```
A crucial step in the Pando workflow is the association of genes with putative regulatory regions inside the funct... |
69058f025c6718f0315b476ddd894b4081bce2aa932e3a02e4af6ce057fb640a | R | 2,069 | 54 | require(tidyverse)
results <- read_tsv('~/git/dissertation/thesis/Chapter 04 - A variable-length code/src/data/response_time_signal_1_noise_15_accuracy_08.tsv')
rt <- results %>%
mutate(correct = ifelse(decoded_symbol == sent_symbol,'Correct','Incorrect')) %>%
group_by(correct) %>%
summarise(rt = mean(st... |
d7ead6559b80179a44acb9454c935b961d2ec0215285396a22572ae44446899e | R | 2,074 | 51 | #' @name testCPLEX
#' @description
#' Tests if CPLEX functionality has been added to gGnome
#'
#'
#' @return returns a message whether the check was successful, else does a fresh installation of gGnome
#' @author Tanubrata Dey
#'
testOptimizationFunction <- function() {
# D... |
b9499c74154436d117037807db2d69d84c59198d32167aa97d6072d8039af03b | R | 2,093 | 54 | "%&%" <- function(a,b) paste(a,b, sep = "")
library(Maaslin2)
library(tidyverse)
#data_path <- "S:/Obesity_Bone/Paul/Data/data_to_O2/"
data_path <- "/n/groups/ifaromics/paul/HRPQCT_16S_Analysis/data/"
res_path <- "/n/groups/ifaromics/paul/HRPQCT_16S_Analysis/16S_results/SPECIES/FHS/both_sex/"
#Load data created on #... |
94027ba13572d44b4ed0c2d646992b54143dd100725f400db90960b5efa5089a | R | 2,099 | 70 | read_bc_count_file_in_list <- function(list_input, filename_input) {
for (i in 1:length(filename_input)) {
list_input[[i]] <- fread(filename_input[i])
}
return(list_input)
}
merging_batch <- function(set1, set2) {
merged_data <- base::merge(set1, set2, by = "barcode", all = T)
merged_data[is.na(merged_d... |
fe5b22a444a6979b688b8a59608dfe1909c17ae3658862c04c715f2233569218 | R | 2,100 | 54 | "%&%" <- function(a,b) paste(a,b, sep = "")
library(tidyverse)
library(Maaslin2)
#data_path <- "S:/Obesity_Bone/Paul/Data/data_to_O2/"
data_path <- "/n/groups/ifaromics/paul/HRPQCT_16S_Analysis/data/"
res_path <- "/n/groups/ifaromics/paul/HRPQCT_16S_Analysis/16S_results/GENUS/FHS/men_only/"
#Load data created on #HS... |
7c204e85f0a8c4a57c1af236002b3cad5249739d20c7508e8ccad70a5340aa4d | R | 2,102 | 52 | "%&%" <- function(a,b) paste(a,b, sep = "")
library(tidyverse)
library(Maaslin2)
#data_path <- "S:/Obesity_Bone/Paul/Data/data_to_O2/"
data_path <- "/n/groups/ifaromics/paul/HRPQCT_16S_Analysis/data/"
res_path <- "/n/groups/ifaromics/paul/HRPQCT_16S_Analysis/16S_results/SPECIES/FHS/men_only/"
#Load data created on #... |
8e2b86ecc292ca7bdf8770edbd0988c10b7dbc64fcbe0d91f2075756965c73d7 | R | 2,102 | 27 | ---
title: "Splicing QTL"
output: rmarkdown::html_vignette
vignette: >
%\VignetteIndexEntry{Vignette Title}
%\VignetteEngine{knitr::rmarkdown}
%\VignetteEncoding{UTF-8}
---
To perform splicing QTL (sQTL) analysis using LeafCutter you'll first need to preprocess your RNA-seq data as described in Steps 1 and 2 und... |
993b6dd1585594cea58a0668a18649dd09eacaf29abb37aa7c33a7dc5cef3810 | R | 2,102 | 67 | library(MixfMRI, quietly = TRUE)
source("00-set_condition.r")
### Test 3d data
da <- eval(parse(text = paste(case, sep = "")))
id <- !is.na(da)
PV.gbd <- da[id]
id.loc <- which(id, arr.ind = TRUE)
X.gbd <- t(t(id.loc) / dim(da))
### Load results
fn.in <- paste("./output/ret.", case, ".tp.new.rda", sep = "")
load(fn.i... |
4cc41a3b3bc7bacc13b5b1e17463b941352d15f01ba788332fece508a0ee553c | R | 2,104 | 52 | "%&%" <- function(a,b) paste(a,b, sep = "")
library(tidyverse)
library(Maaslin2)
#data_path <- "S:/Obesity_Bone/Paul/Data/data_to_O2/"
data_path <- "/n/groups/ifaromics/paul/HRPQCT_16S_Analysis/data/"
res_path <- "/n/groups/ifaromics/paul/HRPQCT_16S_Analysis/16S_results/GENUS/FHS/women_only/"
#Load data created on #... |
e2becdb98da40c58bc9ab1bbb47e0a9898761640ac7e9b714a3a04a3e0ab641c | R | 2,105 | 80 | library(MixfMRI, quietly = TRUE)
prefix <- "simu"
load("new/summary/summary.rda")
### Plot
ret.table <- NULL
for(i.case in 1:nrow(ret.summary.org)){
tmp.phantom <- eval(parse(text = ret.summary.org[i.case, 1]))
tmp.overlap <- eval(parse(text = ret.summary.org[i.case, 2]))
set.seed(1234)
da <- gendataset(phanto... |
c5b2882fc7c523d8b806025ed40c712e358e39f87927703949c755dda86e5f42 | R | 2,106 | 47 | files<-list.files(pattern="Exp")
i<-3
data<-read.table(files[i],header = T)
dat<-data[,c(8:15)]
d<-na.omit(dat)
k3<-d
i<-2
data<-read.table(files[i],header = T)
dat<-data[,c(8:15)]
d<-na.omit(dat)
k2<-d
i<-1
data<-read.table(files[i],header = T)
dat<-data[,c(8:15)]
d<-na.omit(dat)
k1<-d
k1<-k1[,c(1,2,5,6,3,4,7,8)]
i<-... |
3b41b26ac338e7d86bb8bde016f9ae743a62dceb2a0e94563080ef9fc05f6692 | R | 2,108 | 52 | "%&%" <- function(a,b) paste(a,b, sep = "")
library(tidyverse)
library(Maaslin2)
#data_path <- "S:/Obesity_Bone/Paul/Data/data_to_O2/"
data_path <- "/n/groups/ifaromics/paul/HRPQCT_16S_Analysis/data/"
res_path <- "/n/groups/ifaromics/paul/HRPQCT_16S_Analysis/16S_results/SPECIES/FHS/women_only/"
#Load data created on... |
caf5b4cdba022c95307fef914b582abfe32f40e346534efe7e82a9fe452139c7 | R | 2,110 | 80 | library(MixfMRI, quietly = TRUE)
prefix <- "simu"
load("new/summary/summary.rda")
### Plot
ret.table <- NULL
for(i.case in 1:nrow(ret.summary.org)){
tmp.phantom <- eval(parse(text = ret.summary.org[i.case, 1]))
tmp.overlap <- eval(parse(text = ret.summary.org[i.case, 2]))
set.seed(1235)
da <- gendataset(phanto... |
d3be1c43e18c4478e2a603c42cd3e834d8f029935bf6200032684b8776c55d06 | R | 2,110 | 80 | library(MixfMRI, quietly = TRUE)
prefix <- "simu"
load("new/summary/summary.rda")
### Plot
ret.table <- NULL
for(i.case in 1:nrow(ret.summary.org)){
tmp.phantom <- eval(parse(text = ret.summary.org[i.case, 1]))
tmp.overlap <- eval(parse(text = ret.summary.org[i.case, 2]))
set.seed(1235)
da <- gendataset(phanto... |
f2520ee839f2cda22c746ad643b841e5688e81cf2ea953d549b8782f2dd66d29 | R | 2,121 | 51 | library(testthat)
library(gUtils)
setDTthreads(1)
## test_that('read_xmap, read_cmap', {
## # one test for read_xmap is already included in the haplograph tests
## # here we only test compliment functions not tested there
## system('wget https://mskilab.s3.amazonaws.com/gGnome/H838-bionano.tar.gz && tar -... |
4f06c9fca5fa451b71e8373a475d438ca9bc10bc20d0501d9ad09ace48e4defe | R | 2,143 | 78 | library(leafcutter)
test_dirichlet_multinomial_glm_mc <- function() {
N=100 # number of samples
depth=50 # number in each sample
K=4 # number of classes
P=3 # number of covariates
softmax=function(g) exp(g)/sum(exp(g))
beta=matrix(rnorm(K*P),K,P) # true coefficient matrix
x=lapply(1:N, function(g) run... |
8b8696c95f08bbe919a02ad3deef9176be91346b35a577b44561b2a0955b2e40 | R | 2,161 | 68 | #' @name run_gurobi
#' @title run_gurobi
#'
#' @description
#' Rcplex2-like wrapper for calling Gurobi for MIP optimization
#'
#' @name cvec (numeric) linear part of objective function
#' @name Amat (Matrix) sparse matrix of constraints
#' @name bvec (numeric) RHS of constraints
#' @name Qmat (Matrix) sparse matrix, q... |
73c845b36de55437c5c757d22a76f94769fdec84080e8f1e9a6d71301bbcc056 | R | 2,164 | 54 | # apply_reject_table.R
#
# Applies custard-derived rejection verdicts to short- and long-window senktide CSVs
# for both ROI and pensub data sources, producing 16 _clean.csv files.
# Runnable with: Rscript 01-scripts/apply_reject_table.R
# Run from the repo root; here::here() resolves all paths relative to the project ... |
a8f5d9a44cd9e260537b631732e753147de0fe9519bdbad68c18396a343770e5 | R | 2,166 | 55 | library(testthat)
context('JaBbA')
## for testing any function upstream of junction balance
invalid.jjs = system.file("extdata", "invalid.junctions.rds", package = "JaBbA")
test_that(desc = "test filtering of junctions with invalid endpoints",
code = {
jj = readRDS(invalid.jjs)
... |
3f3175fc93e3a5749ada170d1425bd6d2086e6ce416a02f688ea208a43c0feba | R | 2,182 | 47 | #!/usr/bin/env Rscript
library(optparse)
library(leafcutter)
arguments <- parse_args(OptionParser(usage = "%prog [options] counts_file groups_file cluster_name", description="LeafCutter differential splicing plotting tool. Required inputs:\n <counts_file>: Intron usage counts file. Must be .txt or .txt.gz, output from... |
0ad6287a50f1a744d6569d9ba661ff81bcd7898c382359066d5d754971d45755 | R | 2,187 | 85 | setwd("~/Dropbox/Apps/ShareLaTeX/CogSci 2019/code/")
require(tidyverse)
require(cowplot)
source('transmit/R/transmit.R')
codebook <- construct_codebook(c('A', 'B', 'C', 'D'), c(1, 1, 1, 1))
noise_power = 10
timesteps=50
traces <- list()
symbols <- data_frame()
results <- data_frame()
for(signal_power in c(1.5,2,3... |
b17525d5dcefd47a44045eaaa5b19af093d9de6fca59a2166605ba1c8fdcc015 | R | 2,204 | 98 | ### Based on the proecdures 1, 2, and 3 in Benjamini & Heller (2007)
### `False discovery rates for spatial signals', JASA 102(480), 1272-1281
### Author: Wei-Chen Chen Oct. 24, 2014.
### Input:
### p: p-values of length m.
### w: weights for the p-values, default 1/m.
### q: a desired cutoff for adjusting p-val... |
9f24f19e3eea02780b7d89f8e7ed58a5c39ee91f8717c9295a385cc2a49a5d89 | R | 2,227 | 75 | library(tidyverse)
library(ArchR)
library(Seurat)
set.seed(666)
addArchRThreads(threads = 1)
addArchRGenome("hg38")
proj <- loadArchRProject(path = "Dracheva_ALS_FTD_addRNA/")
meta_data <- getCellColData(proj) %>%
as.data.frame() %>%
rownames_to_column("cell_id") %>%
as_tibble()
# Inh
meta_data_inh <- met... |
5c455ab291248f4e0be077b7ba98590e6fc8d03167fb0a14fd8099607fa66e0c | R | 2,234 | 56 | "%&%" <- function(a,b) paste(a,b, sep = "")
library(tidyverse)
library(Maaslin2)
#data_path <- "S:/Obesity_Bone/Paul/Data/data_to_O2/"
data_path <- "/n/groups/ifaromics/paul/HRPQCT_16S_Analysis/data/"
res_path <- "/n/groups/ifaromics/paul/HRPQCT_16S_Analysis/16S_results/GENUS/subsample_FHS/corrected_seed_1234/"
#Loa... |
034a4c00a5370ad5c5be90ab28c386b074879dc7247dc81eecd44a5349188ede | R | 2,242 | 52 | library(Seurat)
library(Matrix)
seed <- 2020
set.seed(seed)
# read data matrix
# after reading, suppose the data matrix is saved in 'data'
# add col and row names for seurat object
colnames(data) <- as.character(1:dim(data)[2]); colnames(data)
rownames(data) <- as.character(1:dim(data)[1]); rownames(data)
... |
7f029bc32e60e8c6893a89e34ff4de7b51e69106252abe255f819b96d3063ad9 | R | 2,250 | 84 | library(MixfMRI, quietly = TRUE)
prefix <- "simu"
load("new/summary/summary.rda")
### Plot
ret.table <- NULL
for(i.case in 1:nrow(ret.summary.org)){
tmp.phantom <- eval(parse(text = ret.summary.org[i.case, 1]))
tmp.overlap <- eval(parse(text = ret.summary.org[i.case, 2]))
set.seed(1234)
da <- gendataset(phanto... |
5ef0e12be41d60910e6930d91ef8aff1a79e8bf5752ec5d087cd51ce0d01d9a8 | R | 2,287 | 49 |
#'Dirichlet multinomial GLM (single overdispersion parameter)
#'
#'We recommend using \code{\link{dirichlet_multinomial_anova_mc}} instead.
#'
#' @param x [samples] x [covariates] matrix
#' @param y [samples] x [introns] matrix of intron usage counts
#' @param concShape Gamma shape parameter for concentration parame... |
d1120ed22f5a29c724c73c3e3ff8ec625356fd180b9a5b88995f979c6bce00c3 | R | 2,290 | 72 | ## from
## https://stackoverflow.com/questions/11215162/how-to-perform-a-fast-dct-discrete-cosine-transform-in-r
##
## 4 down vote favorite
## Using Rprof revealed the dct in the dtt package to be the main offender in a piece of R code that was running quite slowly.
##
## Take your vector and extend it to a vector t... |
4fe8bd408522c174c47bfb4eaa25cd22eb52e1620a1b2add4ca1e65ad9896196 | R | 2,303 | 59 | library(MixfMRI, quietly = TRUE)
set.seed(1234)
phantom <- shepp2fMRI
da.all <- gendataset(phantom = phantom, overlap = 0.01)
da.pval <- da.all$pval
da.tval <- da.all$tval[!is.na(da.all$tval)]
pval.cutoff <- 0.05
qval.cutoff <- 0.05
### Bonferroni
cf.Bonferroni <- Threshold.Bonferroni(pval.cutoff, length(da.tval),
... |
79f7c0cf685a274a935abe50bdc10a0e3a27db7a07054797277dd7d5eec23c0a | R | 2,303 | 59 | library(MixfMRI, quietly = TRUE)
set.seed(1234)
phantom <- shepp1fMRI
da.all <- gendataset(phantom = phantom, overlap = 0.01)
da.pval <- da.all$pval
da.tval <- da.all$tval[!is.na(da.all$tval)]
pval.cutoff <- 0.05
qval.cutoff <- 0.05
### Bonferroni
cf.Bonferroni <- Threshold.Bonferroni(pval.cutoff, length(da.tval),
... |
9d1814b5a2ee75b248b164bd2928b7d3d1b38cd990e72a15c8eafee06738924c | R | 2,318 | 64 |
mle.beta.constr <- function(PARAM, i.k, method = .FC.CT$optim.method[1]){
beta.theta <- PARAM$BETA[[i.k]]
if(method[1] == "BFGS"){
ret <- constrOptim(theta = beta.theta,
f = beta.constrOptim.f,
grad = beta.constrOptim.grad,
ui = rbind(c(1, 0)... |
4f2005df76ba2993d125a3f25aa72492471bca0dde5502527c49fe57ffcdb97d | R | 2,345 | 51 | # load_raw.R
#
# Canonical data loader for FAST/SLOW (and custard) CSVs.
# Sourced by both the Shiny app (05-app/app-plot-nested-stats.R) and the
# precompute scripts (build_reject_table_fastslow.R, build_reject_table_custard.R).
#
# load_raw(path) reads a CSV, drops mouse_id == 81, canonicalizes treatment
# strings, e... |
50e3da9851f128c8b9f3818b42f3ad76f51437ed6e846d702dfdbf77a5657a1d | R | 2,356 | 73 | library(ggplot2)
library(SurvMetrics)
library(caret)
library(survival)
library(pec)
library(tidyverse)
DATA_DIR <- "/data2/MRI_PET_DATA/predicts/"
MODELS <- c("mlp", "cnn") # model file names
OTHER_MODELS <- c("cph", "weibull")
MODEL_TIMES <- list(
"mlp" = c(0, 24, 48, 108),
"cnn" = c(0, 24, 48, 108)
)
####... |
86f270088b10ba75cdbc909ba68b13d908f24a77ae8f4e0488ec2a801d02bf2d | R | 2,358 | 45 | ---
title: "Region selection"
author: "Jonas Simon Fleck"
date: "5/17/2022"
output: html_document
---
```{r, echo = FALSE, message=FALSE}
knitr::opts_chunk$set(message = FALSE, warning = FALSE)
```
The underlying idea of Pando is modeling gene expression by the interaction between TFs and regulatory regions. However,... |
4253fb09f0a6b8e62c4097a1ab83cda6afbc1e6cd1f1898f01c52c510ca09051 | R | 2,371 | 59 | "%&%" <- function(a,b) paste(a,b, sep = "")
library(tidyverse)
library(Maaslin2)
#In Maaslin2 The data file can contain samples not included in the metadata file
#(along with the reverse case). For both cases, those samples not included in both
#files will be removed from the analysis. Also the samples do not need to... |
4efc648b73c3b3971e06801d5081383ba1c53bce35c36d66fa92acf66cc1c10b | R | 2,397 | 57 | # logging which server was used to run this script
system("echo \"Running this on ### $(hostname) ### \\(* w *)/\"")
# load library
library(tidyverse)
library(Seurat)
## set data path and sample info
data_dir <- "/cndd2/junhao/ALS_FTD_singleCell/run_cellBender_on_raw_snRNA/"
sample_meta <- read_tsv(paste0(data_dir, "... |
d9c6af4f1a63e927fb00d3644170b4bb3b91a07e42146b6347a36fea9aa2f984 | R | 2,416 | 49 | # Author: Francois Aguet
# Description: This is a post-processing function for FastQTL. It calculates
# q-values (Storey) and p-value thresholds for all genes in the permutation results file.
suppressMessages(library(qvalue))
suppressMessages(library(tools))
suppressMessages(library(argparser))
# parse inputs
p <- a... |
a3f850c11c07d15c4d0d2aee765ed0c1921508d740eb8e0f139e2c890c6a70cd | R | 2,442 | 80 | ### For LMT.
### Copy from EMCluster.
### For pvalue only.
lmt.pv <- function(fcobj.0, fcobj.a, X.gbd, PV.gbd, tau = 0.5,
n.mc.E.delta = 1000, n.mc.E.chi2 = 1000, verbose = FALSE){
if((!inherits(fcobj.0, "fclust")) ||
(!inherits(fcobj.a, "fclust"))){
stop("fcobj.0 and fcobj.a should be both in \"fclust\... |
e8752f665b49bf5c71074fb3b2a7f860711728b1493d5176e3f5067d45a3666f | R | 2,445 | 80 | ### For LMT.
### Copy from EMCluster.
### For identity cov matrix only.
lmt.I <- function(fcobj.0, fcobj.a, X.gbd, PV.gbd, tau = 0.5,
n.mc.E.delta = 1000, n.mc.E.chi2 = 1000, verbose = FALSE){
if((!inherits(fcobj.0, "fclust")) ||
(!inherits(fcobj.a, "fclust"))){
stop("fcobj.0 and fcobj.a should be both ... |
06b9707e2d7842fb4334122bc81e2b006b5516aefb39ef08498068f40aa964c9 | R | 2,470 | 60 | # logging which server was used to run this script
system("echo \"Running this on ### $(hostname) ### \\(* w *)/\"")
# load library
library(tidyverse)
library(Seurat)
data_obj_sub <- readRDS("./seurat_objects/snRNA_cellBneder_corrected_postQC_scTransformed_clustered_2ndRoundAnnotated_cleanV2_SeuratV4_object.rds")
me... |
48b71c31609f01a0081ed7a31e0d01112c733ce939514b4dee4f80c77275e47e | R | 2,482 | 58 | #' Run a bunch of simulations with a given
#'
#' Some other stuff here.
#'
#' @param codebook constructed using construct_codebook function
#' @param source_distribution source distribution of messages. Messages are generated in simulations according to this distribution.
#' @param prior_distribution initial guess a... |
79d88f4588099f6c4506569762276decdf38d1d8f530dafdf8c18145b529c1fc | R | 2,499 | 56 | #!/usr/bin/env Rscript
library(optparse)
library(leafcutter)
# e.g. Rscript ../scripts/draw_clusters.R brain_liver.txt.gz chrX:clu_8,chrX:clu_9 -g brain_liver_meta.txt -e ../leafcutter/data/gencode19_exons.txt.gz
arguments <- parse_args(OptionParser(usage = "%prog [options] counts_file cluster_name", description="Lea... |
0d498bef122dc4bd75efa211febe69c68340d05d962d8a3b3c626ca9b1e38d16 | R | 2,500 | 79 | #----------------------------
# Ancestral state estimation
# 25 January 2023
#----------------------------
source("./common.R")
# load mvMORPH imputation
load("../data/derived/fit_OU.RData")
dfOU <- imp_2$estimates
rownames(dfOU) <- df$English_name
anc <- anc_2$estimates
# fit OU model
Y <- as.matrix(dfOU[, c("Log10... |
aecc8f2ed9a1e4e27f431eee1ad0976684a7691fa7d54e68e46943d69c836a0f | R | 2,517 | 71 | library(tidyverse)
library(ArchR)
library(Seurat)
set.seed(666)
addArchRThreads(threads = 8)
addArchRGenome("hg38")
proj <- loadArchRProject(path = "Dracheva_ALS_FTD_addRNA_addPseudobulkRep/")
proj <- saveArchRProject(ArchRProj = proj, outputDirectory = "./Dracheva_ALS_FTD_addRNA_addPseudobulkRep_testDAR", load = TR... |
72553c45af86733b9f1800c429460fce28a43e630f714c6de9d5722864e0d316 | R | 2,553 | 75 | # logging which server was used to run this script
system("echo \"Running this on ### $(hostname) ### \\(* w *)/\"")
# load library
library(tidyverse)
library(ggrepel)
library(ggrastr)
rna <- read_tsv("MAST_res_level2_summary.txt")
selected_cell_type <- "Astro"
selected_region <- "MCX"
FDR_threshold <- 0.05
FC_thres... |
e4a1f64a1edc9864b09095fe1e4abb95c808eefe907ae51eca4737c45ba0ee13 | R | 2,566 | 69 | ### Copy some functions from "AnalyzeFMRI/R/threshold.R" since
### "AnalyzeFMRI" is archived by CRAN.
Threshold.Bonferroni <- function(p.val, n, type = c("Normal", "t", "F"), df1 = NULL, df2 = NULL) {
## calculate the Bonferroni threshold for n iid tests to give a p-value of p.val
## type specifies the ... |
35f96052329a1783c4d89423878cfbb8135e36b1d20f22bd473a6375a04c1d60 | R | 2,578 | 73 | library(MixfMRI, quietly = TRUE)
source("00-set_condition.r")
### Test 3d data
da <- eval(parse(text = paste(case, sep = "")))
id <- !is.na(da)
PV.gbd <- da[id]
id.loc <- which(id, arr.ind = TRUE)
X.gbd <- t(t(id.loc) / dim(da))
### Load results
fn.in <- paste("./output/ret.", case, ".tp.rda", sep = "")
load(fn.in)
... |
4ec5029de192253ead5bc577466689e1c8730c8b53c6ec2d191f5c171a841aed | R | 2,578 | 73 | library(MixfMRI, quietly = TRUE)
source("00-set_condition.r")
### Test 2d data
da <- eval(parse(text = paste(case, sep = "")))
id <- !is.na(da)
PV.gbd <- da[id]
id.loc <- which(id, arr.ind = TRUE)
X.gbd <- t(t(id.loc) / dim(da))
### Load results
fn.in <- paste("./output/ret.", case, ".tp.rda", sep = "")
load(fn.in)
... |
762ccc324da334683b754ca9c3f8051a33a63143da4c9ff450028bfc11ec1434 | R | 2,578 | 73 | library(MixfMRI, quietly = TRUE)
source("00-set_condition.r")
### Test 3d data
da <- eval(parse(text = paste(case, sep = "")))
id <- !is.na(da)
PV.gbd <- da[id]
id.loc <- which(id, arr.ind = TRUE)
X.gbd <- t(t(id.loc) / dim(da))
### Load results
fn.in <- paste("./output/ret.", case, ".tp.rda", sep = "")
load(fn.in)
... |
81e4ba86464993107b98292d20bbcb651a103345d2e607275f776248b993b305 | R | 2,578 | 73 | library(MixfMRI, quietly = TRUE)
source("00-set_condition.r")
### Test 2d data
da <- eval(parse(text = paste(case, sep = "")))
id <- !is.na(da)
PV.gbd <- da[id]
id.loc <- which(id, arr.ind = TRUE)
X.gbd <- t(t(id.loc) / dim(da))
### Load results
fn.in <- paste("./output/ret.", case, ".tp.rda", sep = "")
load(fn.in)
... |
7113e2e470e758960fd16a9877294721ec593704f8add725dbd6251870e672fc | R | 2,583 | 73 | library(MixfMRI, quietly = TRUE)
source("00-set_condition.r")
### Test 3d data
da <- eval(parse(text = paste(case, sep = "")))
id <- !is.na(da)
PV.gbd <- da[id]
id.loc <- which(id, arr.ind = TRUE)
X.gbd <- t(t(id.loc) / dim(da))
### Load results
fn.in <- paste("./output/ret.", case, ".tp.rda", sep = "")
load(fn.in)
... |
8e721969e8027710be6c992c9ae0ad43710e6d9b91abe51fdcefa2a9e307f11b | R | 2,583 | 63 | #!/bin/Rscript
#--- Setting up the libpath, loading necesary libraries, and setting up some variable names ----####
args<-commandArgs(trailingOnly=TRUE)
library.path <- .libPaths()
library("tidyverse", lib.loc = library.path)
args = commandArgs(trailingOnly = TRUE)
cohort = args[1]
#---Load principal components gen... |
ab24ff4cc030528dd3b3bcba227dbbcfdebaffd081cff623d1dcb14b0ced537c | R | 2,583 | 73 | library(MixfMRI, quietly = TRUE)
source("00-set_condition.r")
### Test 2d data
da <- eval(parse(text = paste(case, sep = "")))
id <- !is.na(da)
PV.gbd <- da[id]
id.loc <- which(id, arr.ind = TRUE)
X.gbd <- t(t(id.loc) / dim(da))
### Load results
fn.in <- paste("./output/ret.", case, ".tp.rda", sep = "")
load(fn.in)
... |
7aab3cfe5c777295d63ca368c8af6c9e48cd73c85236c5930ccb51a613b617df | R | 2,592 | 104 | # logging which server was used to run this script
system("echo \"Running this on ### $(hostname) ### \\(* w *)/\"")
# load library
library(tidyverse)
# difine samples to check
# selected_region <- "MCX"
selected_region <- "mFCX"
selected_disease_1 <- "ALS"
selected_disease_2 <- "Control"
# read observed scDist resu... |
9fe82aa99c0b6635bad2fa42f484dff372d5ac88b54c4ef536bc57167544fba0 | R | 2,598 | 54 | ####obtain a pseudo bulk expression data from a SRT data by k-means clustering based on the coordinate matrix
defcol1<-c('#1C79B7','#F38329','#2DA248','#DC403E','#976BA6','#8D574C','#D07DB0','#BDBF3B',
'#27BDD0','#B0C7E5','#F9BA79','#A0CC8A','#F09EA1','#C7B0D1','#D4BCB8','#F7CCDD',
'#DBDD8D','#... |
303d491f4dcf0983f85b4c5f9f8b8f83b83006b8b3962319561abb825b888289 | R | 2,609 | 97 | # logging which server was used to run this script
system("echo \"Running this on ### $(hostname) ### \\(* w *)/\"")
# load library
library(tidyverse)
library(Seurat)
library(Augur)
library(tictoc)
tic()
data_obj_sub <- readRDS("../seurat_objects/snRNA_cellBneder_corrected_postQC_scTransformed_clustered_2ndRoundAnnot... |
d9966657260432025ac1558dc02a678a71a4f58510302c9f0ea1a62a88f15f55 | R | 2,626 | 97 | # logging which server was used to run this script
system("echo \"Running this on ### $(hostname) ### \\(* w *)/\"")
# load library
library(tidyverse)
library(Seurat)
library(Augur)
library(tictoc)
tic()
data_obj_sub <- readRDS("../seurat_objects/snRNA_cellBneder_corrected_postQC_scTransformed_clustered_2ndRoundAnnot... |
a3d6891e46ff4b6d6d296be7acc055cfca7f85076b63dff1af1b3e04a17b2cca | R | 2,631 | 87 | ### Perform H0: alpha/(alpha + beta) >= 0.05 vs Ha: alpha/(alpha + beta) < 0.05
lrt2 <- function(PV.gbd, CLASS.gbd, K, H0.mean = .FC.CT$LRT$H0.mean,
upper.beta = .FC.CT$INIT$BETA.beta.max,
proc = c("1", "2", "weight")){
if((H0.mean <= 0) || (H0.mean >= 1.0)){
stop("It should be 0 < H0.mean < 1.0.")
}
... |
796f2e6b1e8eade5ebe6d03dc2461f1dec2408723efbe188d16c3fedc5a44b70 | R | 2,637 | 84 | # logging which server was used to run this script
system("echo \"Running this on ### $(hostname) ### \\(* w *)/\"")
# load library
library(tidyverse)
library(ArchR)
library(Seurat)
library(tictoc)
set.seed(666)
addArchRThreads(threads = 8)
addArchRGenome("hg38")
proj <- loadArchRProject(path = "Dracheva_ALS_FTD_ad... |
6da692796ea0910147b2815e9cfd5d877de0ca9eb106998014904a689bac4cbd | R | 2,654 | 73 | library(tidyverse)
library(edgeR)
count_mat <- read_tsv("peak_Astro_ALS_vs_Control_atac_count_by_major_clusters_by_region_by_disease_by_individual.txt")
pool_libsize <- read_tsv("peak_Astro_ALS_vs_Control_pooled_libSize_by_major_clusters_by_region_by_disease_by_individual.txt")
run_edger <- function(selected_group1, ... |
a1dc4b0acb48fc648f98d9b9a6060604a334d45d4604d9ab121c0ce9d1b1b4ee | R | 2,663 | 57 | library(rtracklayer);library(dplyr);library(Seuart)
gtf.ori <- import('Mus_musculus.GRCm38.102.gtf', format="gtf")
gtf.ori<-as.data.frame(gtf.ori)
data<-Load10X_Spatial('my_data')
mtx=GetAssayData(data,assay = 'Spatial',layer = 'counts')
data=CreateSeuratObject(mtx,min.cells=1)
# dim(gtf)
# unique(gtf$gene_t... |
f0922a4176b701a3290ec9b53589b524ccb600a16312468179675a944b451f44 | R | 2,741 | 85 | library(pbdMPI, quietly = TRUE)
init(set.seed = FALSE)
library(MixfMRI, quietly = TRUE)
source("00-set_condition.r")
.FC.CT$MPI.gbd <- TRUE
### Test 3d data
da <- eval(parse(text = paste(case, sep = "")))
id <- !is.na(da)
PV.gbd <- da[id]
id.loc <- which(id, arr.ind = TRUE)
X.gbd <- t(t(id.loc) / dim(da))
### Load re... |
81b124c396aaccdb4a6cfb8b0df6c503bac1f86b88a1058255c02f18aff57787 | R | 2,745 | 67 | get_angle <- function(a,b){acos( sum(a*b) / ( sqrt(sum(a * a)) * sqrt(sum(b * b)) ) )}
results <- data_frame()
for(i in 1:10000){
signal_power <- 100
n_neurons <- 2
noise_power <- 1
n_samples <- 5000
n_codewords <- 2
# each codeword is mapped to a different allocation
#each column is a neu... |
e9c3eec1faaecc7294b872e28361e353d7b35800a52421f836125947ee182c2a | R | 2,763 | 67 | library(plotly)
library(ggplot2)
library(ggpubr)
library(RUVSeq)
###RNA
data<-read.table("S-Fig1a_Gene_reads_count_table_usedforPCA",header=T)
da<-cpm(data+1)
names<-colnames(da)
m<-t(log2(da))
pca<-prcomp(m,scale. = T)
pca_plot<-as.data.frame(pca$x[,1:3])
pca_plot$name<-rownames(m)
pca_plot$Region<-c(rep("NAc",4),rep(... |
1bf6dfabe8d63f098b63374858dd28f064f1eca7266db5def15a77d7c63c6748 | R | 2,771 | 86 | library(pbdMPI, quietly = TRUE)
init(set.seed = FALSE)
library(MixfMRI, quietly = TRUE)
source("00-set_condition.r")
.FC.CT$MPI.gbd <- TRUE
.FC.CT$CONTROL$max.iter <- 0
### Test 3d data
da <- eval(parse(text = paste(case, sep = "")))
id <- !is.na(da)
PV.gbd <- da[id]
id.loc <- which(id, arr.ind = TRUE)
X.gbd <- t(t(id... |
43f69ff8711a61789c0b9f3e44f24c119a1b180ae9a9f069d58a3b5794630437 | R | 2,779 | 94 | ### Perform H0: alpha_1 == alpha_2 && beta_1 == beta_2 vs
### Ha: alpha_1 != alpha_2 || beta_1 != beta_2
lrt.betaab <- function(PV.gbd, CLASS.gbd, K, proc = c("1", "2")){
K.CLASS <- as.integer(max.gbd(CLASS.gbd))
if(K.CLASS > K){
stop("CLASS.gbd and K are not matched.")
}
### For constrOptim() and... |
8f8a833277f03dc51c6314d10962283adbaa5f7868c1bfe33d40b03bddf796ee | R | 2,788 | 94 | #' Run a mean summary on a dataframe
#'
#' Uses rstatix::get_summary_stats() to return the number, mean, standard error,
#' and standard deviation for selected variables
#'
#' @param df a dataframe to be summarized. Provide a grouped df if desired
#' @param col The column(s) to be summarized. Provide as colName, col1:c... |
e8f1ce8f857aacca6f543add8f54da892df8e9a395672085ccea7fb25dacc295 | R | 2,794 | 98 | library("ggplot2")
library("gtools")
myCols <- c("darkgray","steelblue3","red3","lightgray")
names(myCols) <- c("neutral1","gain","loss","neutral2")
# cat makeKaryotype.R | R --slave --args 2 4 500000 ".$sample_bam_name."_ratio.txt\n";
args <- commandArgs()
# Settings
ploidy <- type.convert(args[4])
maxLevelToPlot ... |
3888506456521c1abd489e684883b4e60e57847eaaff5de706546ca26dc6d113 | R | 2,814 | 95 | # init ----------------------------
library(dplyr)
library(tibble)
library(readr)
library(tidyr)
library(broom)
library(ggplot2)
library(gridExtra)
library(sjPlot)
library(purrr)
if (Sys.info()['sysname'] == "Linux") {
setwd('/home/mfromano/Research/mri-surv-dev/mri_surv')
} else {
setwd('/Users/mromano/resear... |
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