sha256 stringlengths 64 64 | language stringclasses 28
values | size int32 1 491k | lines int32 1 23.2k | content stringlengths 1 200k |
|---|---|---|---|---|
1088ce3f12877f89307e6a0ff89353f5a612cca4ee745a5dd8cadaaa095a8fb6 | R | 628 | 19 |
#' Utility function to get the plotted values
#'
#' Get the plotted values from an output matrix if \code{return.value = T}.
#'
#' @param output The output from \code{plot_FRmatch()} or \code{plot_FRmatch()} if \code{return.value=T}.
#'
#' @return A data frame of row names, column names, and values.
#'
#' @seealso \co... |
8069157a8f5b768a868ff3c376d16d756e51f6c50cd436f6ba670343f0464e44 | R | 630 | 23 | rm(list = ls())
library(lme4)
library(lmerTest)
library(sjstats)
library(psycho)
# data preparation
data = read.csv(file = "D:/Workspace/EEG_Data/data/neural_features.csv",fileEncoding="UTF-8-BOM")
as.factor(data$parti)
as.factor(data$task)
as.factor(data$gender)
trans_data = data
trans_data$alpha = lo... |
7afb3ff4aea303866ad0289bf023e9d4f9706729521c1cedddbed09e81be4d4f | R | 637 | 23 | #!/usr/bin/env Rscript
library( "ANTsR" )
library("extremevalues" )
args<-commandArgs(TRUE)
aslImg <- args[1]
m0Img <- args[2]
outname <- args[3]
#init <- cbf_pasl_robust( asl_img, m0_img )
#cbf <- cbf_pasl_quantify( init$delta, init$m0 )
pasl <- antsImageRead( aslImg, 4 )
m0 <- antsImageRead( m0Img, 3 )
pasl.proce... |
7c7978524bfebd7a0624a22c1ed594ffb3c29a323947a43715ef8207b3aeacab | R | 659 | 21 | # Extract Coefficients for different learning models from glm objects
extractCoefficients_TP <- function(df){
unnest(df, cols = coef_TP) |>
select(-c("statistic", "p.value")) |>
pivot_wider(names_from = term, values_from = c("estimate", "std.error"))
}
extractCoefficients_IF <- function(df){
unnest(df,... |
bc4e3a77556fddae4d9e4831644ed817af8c3c6826894cfa827f0c57f6423b22 | R | 684 | 13 | library(EnsDb.Mmusculus.v79)
library(GenomeInfoDb)
annotations <- GetGRangesFromEnsDb(ensdb = EnsDb.Mmusculus.v79)
annotations@seqnames@values=paste0('chr',annotations@seqnames@values)
annotations@seqinfo@seqnames=paste0('chr',annotations@seqinfo@seqnames)
annotations@seqinfo@genome=rep('mm10',length(annotations@seqin... |
3140e5282b557335967fbf9aa10a251a35696fa314103186b23525f1ab5e7aa7 | R | 695 | 30 | Pb_F <- function(SampleData){
Pb_vector <- rep(NA, 43)
for (i in 1:nrow(SampleData)) {
if(SampleData[i,5] == "E1" ){
Pb_vector[i] <- "control"
}
if(SampleData[i,5] == "E2") {
Pb_vector[i] <- "control"
}
if(SampleData[i,5] == "E3" ){
Pb_vector[i] <- "control"
}
if(Sample... |
589dd2622b66f2c89e0a283629da2ffb248d71109c8d9dc4eecd9164682f35c9 | R | 695 | 30 | Zn_F <- function(SampleData){
Zn_vector <- rep(NA, 43)
for (i in 1:nrow(SampleData)) {
if(SampleData[i,5] == "E1" ){
Zn_vector[i] <- "control"
}
if(SampleData[i,5] == "E2") {
Zn_vector[i] <- "treated"
}
if(SampleData[i,5] == "E3" ){
Zn_vector[i] <- "treated"
}
if(Sample... |
c94d85bf7af4b268aa9b0dc57f77817e460d2b6867387586c7aba163dc4e1b87 | R | 695 | 30 | FH_F <- function(SampleData){
FH_vector <- rep(NA, 43)
for (i in 1:nrow(SampleData)) {
if(SampleData[i,5] == "E1" ){
FH_vector[i] <- "control"
}
if(SampleData[i,5] == "E2") {
FH_vector[i] <- "treated"
}
if(SampleData[i,5] == "E3" ){
FH_vector[i] <- "control"
}
if(Sample... |
06d7c0986477ad81f58fe4cdcd0537f702c388e3a571ad7b98849861d87763ed | R | 706 | 30 | BPA_F <- function(SampleData){
BPA_vector <- rep(NA, 43)
for (i in 1:nrow(SampleData)) {
if(SampleData[i,5] == "E1" ){
BPA_vector[i] <- "control"
}
if(SampleData[i,5] == "E2") {
BPA_vector[i] <- "control"
}
if(SampleData[i,5] == "E3" ){
BPA_vector[i] <- "treated"
}
if(S... |
aa60a8df3a87ff9e063c2b13a36f9eec4bbdf526a1efbc622b7127987f64f192 | R | 706 | 30 | Eth_F <- function(SampleData){
Eth_vector <- rep(NA, 43)
for (i in 1:nrow(SampleData)) {
if(SampleData[i,5] == "E1" ){
Eth_vector[i] <- "control"
}
if(SampleData[i,5] == "E2") {
Eth_vector[i] <- "treated"
}
if(SampleData[i,5] == "E3" ){
Eth_vector[i] <- "control"
}
if(S... |
b0ab02fdabc0965048e8a1ad7c1e60beee4836ce7810a1866c0c3c9a407f26b8 | R | 706 | 30 | VPA_F <- function(SampleData){
VPA_vector <- rep(NA, 43)
for (i in 1:nrow(SampleData)) {
if(SampleData[i,5] == "E1" ){
VPA_vector[i] <- "control"
}
if(SampleData[i,5] == "E2") {
VPA_vector[i] <- "control"
}
if(SampleData[i,5] == "E3" ){
VPA_vector[i] <- "treated"
}
if(S... |
ddd678fdd20e9cc4ba94e22d48465149ab19ba061896670a22bd895a578409be | R | 781 | 42 |
#/home/toolkit/tools/R4.2.0/bin/R
#A=readRDS('compare_real.rds')
A=readRDS('compare_real_scaleCicero.rds')
B=readRDS('compare_random.rds')
a=apply(A,2,median)
b=apply(B,2,median)
length(which(b<a[4]))/500
pdf('p11_compare_boxplot.pdf',width=4,height=5)
boxplot(A,col=c(rep('grey60',4),rep('indianred1',2)),pch... |
0ccd7bceebd88e506569c2d9c1608acf7674ba0a4b0d23b3e39eb33a9597d3db | R | 864 | 41 | # Functions: Probit regression for stimulus and respective learning model or
# stimulus-only model
# Transition Probability
modelTP <- function(df) {
glm(response ~ stimulus + TP,
family = binomial(probit),
data = df)
}
# Item Frequency
modelIF <- function(df) {
glm(response ~ stimulus + IF,
fam... |
e2c74a0d7b833cd3f0e32de2e00a2a4f1531591f2e11c7be2594c5d1c36d81a7 | R | 887 | 52 |
#/home/toolkit/tools/R4.2.0/bin/R
library(Seurat)
library(Signac)
source('https://gitee.com/jumphone/BEER/raw/master/BEER.R')
library(data.table)
library(stringr)
pbmc=readRDS('./eQTL/scAtlas_pbmc.rds')
SNP_TSS=read.table('./eQTL/SNP_TSS_500k.bed.loop.txt',sep='\t',row.names=NULL,header=FALSE)
SNP_TSS_TAG=SNP_TSS... |
fb8067e150c2229f12d84bc34dacbe4041c55434e86b55c9d2d30619639cab95 | R | 898 | 36 | context("Test linear algabra")
## .cor_vectors is correct
test_that("(Code) .cor_vectors works with len is 0", {
res <- .cor_vectors(c(1,0),c(0,0))
expect_true(is.na(res))
})
######################
## .angle_from_vector is correct
test_that(".angle_from_vector works", {
res <- .angle_from_vector(rep(1/sqrt(2... |
e5be20cb92438916644d960f2f1af582cecb275998de7d398c0ef94e3892a245 | R | 932 | 30 |
# MARJAN BIRIA December 2022
# Hierarchical Linear Regression of spectroscopy data in OCD and HV groups, and three voxels: ACC, SMA, OCC
# Box Cox transformation
library(MASS)
x = df$GABA
b <- boxcox(lm(x ~ 1))
# Exact lambda
lambda <- b$x[which.max(b$y)]
lambda # = -0.1010101which is used in the next formula to tra... |
53799232eb1a40a141ae6aaf8f0a651d68e831320ead5ebe87f0b936929496a3 | R | 940 | 41 |
#/home/toolkit/tools/R4.2.0/bin/R
library(Seurat)
library(stringr)
library(data.table)
LIST=read.table('LIST.txt',sep='\t')
pbmc=readRDS('./eQTL/scAtlas_pbmc.rds')
#ORGAN=t(matrix(unlist(str_split(colnames(pbmc),'_')),nrow=2))[,1]
#TYPE=colnames(pbmc)#t(matrix(unlist(str_split(colnames(pbmc),'_')),nrow=2))[,2]
... |
3d9ccd5a7d187e3a88ee848ca5f138d182de8025ae7f34f55021c727a6ca4cdf | R | 950 | 28 | test_that("pmQueryTotalCount rejects empty query", {
result <- pmQueryTotalCount(query = "[Title/Abstract]", api_key = NULL)
expect_equal(result$total_count, 0)
expect_equal(result$query_translation, "Not valid search terms")
})
test_that("pmQueryTotalCount rejects blank query", {
result <- pmQueryTotalCount(q... |
fbe80a0815f70a2c6e2837f9e1f9395202a07fa1bd215a5c59bcf4f4c5b6a37b | R | 981 | 36 | #!/usr/bin/Rscript
# benchmarks
df <- read.csv("./data/Petrovski_DatasetS1.csv", sep="\t", head=TRUE)
omim <- subset(df, OMIM.Haploinsufficiency != "")
omimd <- subset(df, OMIM.de.novo...Haploinsuficciency != "")
mgil <- subset(df, MGI.Lethality.orthologs != "")
mgis <- subset(df, MGI.Seizure.orthologs != ... |
39740be3b03ecf036e88a92d1f6a5c2a8db3fd3334d7657e6a6b384477b9e5ac | R | 996 | 73 |
# /home/toolkit/tools/R4.2.0/bin/R
library(Signac)
library(Seurat)
library(GenomeInfoDb)
#library(EnsDb.Hsapiens.v86) #hg38
library(EnsDb.Mmusculus.v79)
library(ggplot2)
library(patchwork)
set.seed(1234)
#############################################################i
library(monocle)
library(cicero)
pbmc=readR... |
641de6e8560971041a0e127014a212f8f5c3977814d4e2b6e873b918abccc594 | R | 1,006 | 21 | # Shared setup for the PACNet web-app tests. Run from the repo root with:
# Rscript -e 'testthat::test_dir("tests/testthat")'
# Packages are attached by webApp/global.R, exactly as in the app.
repo_root <- normalizePath(file.path(testthat::test_path(), "..", ".."))
app_file <- function(f) file.path(repo_root, "webApp... |
3247f77cf2f0bae7378d38dbb9421d5f59f8a236bca0e4759bfb92a2dec6127e | R | 1,025 | 42 | # Function to create a predictor for repetiton preference
# Add a simple preference for repetition (0 == 0.4 | 1 == 0.6)
library(tidyverse)
# Add function to shift down vector n rows and replace with NA
shiftdown <- function(x, n){
c(rep(NA, n), head(x, -n))
}
CountRepetitionPreference <- function(df){
# Sto... |
55da8417affd13e055fc843b340c9b2aa88132988b236355dcdce54ade664ef7 | R | 1,039 | 25 |
#' Plot minimum spanning tree (MST)
#'
#' This function is a wrapper function for plotting MST of two interested clusters.
#'
#' @param sce.query,sce.ref Query and reference data objects.
#' @param query.cluster,ref.cluster Query and reference cluster names to plot.
#' @param nsamp Number of randomly selected cells to... |
1f76cf62e043628ab2ccf272b56ffd4ad50c302e32998991f016e60338e18aa1 | R | 1,040 | 43 | # Functions: Probit regression with stimulus, intensity, and respective
# learning model (interaction)
# Transition Probability
fullModelTP <- function(df) {
glm(response ~ stimulus + intensity_ord * TP,
family = binomial(probit),
data = df)
}
# Item Frequency
fullModelIF <- function(df) {
glm(respons... |
29aea771517638d99423bd3b5a43fa6336c77448f5c667ca5c284ec345692d8d | R | 1,041 | 36 | # Function for quantifying the conditional probabilities
# Input:
# - s: Binary sequence
# - intensity: Stimulus intensities (optional, needs to be a factor)
# Output: Function returns a dataframe containing the conditional probabilites
library(tidyverse)
# Function to shift down vector n rows and replace with... |
24eaaff4a32d724b1548b0c0d0fdb4869b040f954d7b4a4fd55904ad8cab0696 | R | 1,043 | 54 |
#/home/toolkit/tools/R4.2.0/bin/R
library(Seurat)
library(Signac)
source('https://gitee.com/jumphone/BEER/raw/master/BEER.R')
library(data.table)
library(stringr)
pbmc=readRDS('./eQTL/scAtlas_pbmc.rds')
SNP_TSS=read.table('./eQTL/SNP_TSS_500k.bed.loop.txt',sep='\t',row.names=NULL,header=FALSE)
SNP_TSS_TAG=SNP_TSS... |
ebc288efdbf026230bdd22aaa513ffbebd008ca78d4cea30da4ec939374ccb12 | R | 1,086 | 38 | # /home/toolkit/tools/R4.2.0/bin/R
library(Signac)
library(GenomicRanges)
library(Seurat)
source('https://gitee.com/jumphone/BEER/raw/master/BEER.R')
#args <- commandArgs(TRUE)
#opath=args[1]
pbmc.isloop=readRDS('./scATAC_pbmc.rds')
CLST=readRDS(file='./scATAC_clst.rds')
pbmc.isloop[['EXP']]=pbmc.isloop[['RNA']]
De... |
a9626fc80ba7ffd4e9b9661b3fe0627396dd57167ccae223607d9dca57ddc4de | R | 1,092 | 27 | library('biomaRt')
library('DESeq2')
library(readxl)
library(openxlsx)
setwd('/Volumes/aronow/Kang/induced_neuron/ChangHui_RNAseq_fastq/isoform_RSEM/rsem_transcripts/differential_v2_DEG/')
# prepare gene / transcript metadata for conversion
ensembl = useEnsembl(biomart = 'ensembl', dataset = 'hsapiens_gene_ensembl', ... |
1c7550f24cff40ff56bbc7ccab1f2eb339e6889720b4a4e32912db4ceca0a676 | R | 1,101 | 29 | ##' Utility function to test whether there are any curves missing from specimens
#'
#' @export
#' @param lm.tibble a tibble of landmark data
#' @return just used inside of \code{import_chkpt_data} really
check_curves <- function(lm.tibble,ncurves,filenames){
uniques <- lm.tibble %>% group_by(spec.id) %>% filter(cla... |
63cb43ad20d754a16791d96846205c613a984431a6bfe4aafd786587328d6c90 | R | 1,108 | 34 | library(ggplot2)
library(dplyr)
library(plyr)
library(waffle)
library(ggthemes)
library(RColorBrewer)
setwd(dirname(rstudioapi::getSourceEditorContext()$path))
mitoVGP <- read.csv("NOVOPlasty_results.tsv", header = TRUE, sep = "\t", na.strings="")
mitoVGP$status <- revalue(mitoVGP$status, c("Out of memory!"="Memory ... |
daf0aa0ef73791f1185c18cb3d87a025092d9169e8b89858a276661df47fc2e6 | R | 1,115 | 33 | # Run the following script before this analysis: Code_for_Microinjection_Dataset.Rmd
# Computing contribution of each condition (DMSO/EPZ)to proportion of cells in each cluster
clust <- sce_hvg.seurat$seurat_clusters
clust_pts <- data.frame(names=names(clust),cluster=clust)
clust_pts$condition <- substr(clust_pts... |
6d96f1112e31715020b000c0ef2fee4d2c04d1ee05a0cf9e902ae07b7cf02fbe | R | 1,118 | 33 | # Run the following script before this analysis: Code_for_Electroporation_Dataset.Rmd
# Computing contribution of each condition (DMSO/EPZ)to proportion of cells in each cluster
clust <- sce_hvg.seurat$seurat_clusters
clust_pts <- data.frame(names=names(clust),cluster=clust)
clust_pts$condition <- substr(clust_pt... |
1cb4fa6a9d7702106e1c2d715a4365d404c9559361d98dc861360891e4918d31 | R | 1,135 | 25 | #'
#' Example data object
#'
#' This is an example of input data object for \code{\link[FRmatch]{FRmatch}}.
#' The data object is of the \link[SingleCellExperiment]{SingleCellExperiment} class.
#'
#' @format
#' Please follow the naming convention and data formats of this example when constructing your own data object.
... |
697699ad9a752edf4ff89d81cc098ffabc4643be0e79617cdb7eba951786b81c | R | 1,148 | 62 | # Function: Log-Likelihood analysis for the three learning models
# Functions for TP, IF, AF, and Stimulus-Only
# Important notes:
# (1) Because for all the other analyses, data is transformed
# using qlogis(). For this analysis, we transform it back, using plogis()
# (2) for loglike_Stim: Data is transformed ... |
b1c6521365b51be3cf553083bbdacfbf69e1f4a3ccf97da8337c6c4419770913 | R | 1,152 | 31 | #!/usr/bin/env Rscript
library(readr)
library(DESeq2)
args <- commandArgs(trailingOnly = TRUE)
df_counts <- read_csv(args[1])
sampleTable <- data.frame(condition = factor(sapply(strsplit(colnames(df_counts)[-c(1)], "_"), head, 1)))
row.names(sampleTable) <- colnames(df_counts)[-c(1)]
dds <- DESeqDataSetFromMatrix(as.d... |
d455449f5f3e18a5d4fcc704cf23617cc22f62c4c221aaff7ff7c62de99d7473 | R | 1,153 | 20 | library(Seurat)
library(ggplot2)
# combined = readRDS('/data/aronow/Kang/single_cell_projects/10X-Pak/integration/patient_derived_engineered/v2_filtered/combined_all_organoids_filtered.rds')
# DimPlot(combined, group.by = 'integrated_snn_res.2', raster = F, label = T) + theme(legend.position = 'none')
# QC of donors
... |
f2e84499c5d5008c98c55174dea8feeb8896029d126baeeffa6f33a09db61cea | R | 1,162 | 30 | # This function collates the CSV files for each individual vision ROI for each
# subject (PRL, URL, LPZ) into a single csv file. This also adds mean thickness
# for each subject and calculates normalized thickness for each vertex as well
# Load libraries
library(janitor)
library(tidyverse)
# read in the individual vi... |
635f31fdc79d915c3968d1e2f7d3e1ed365e10c54f8bafdbb13e2d6769809732 | R | 1,164 | 35 | # Loaded once before ui.R and server.R (locally and under Shinylive).
suppressPackageStartupMessages({
library(shiny)
library(shinythemes)
library(shinycssloaders)
library(shinyWidgets)
library(shinyjs)
library(shinyalert)
library(shinybusy)
library(ggplot2)
library(gridExtra)
library(RColorBrewer)
... |
ed51209abbe605e1e77154fc33423fe555362f580ad0472c75c52b7052b576c0 | R | 1,168 | 39 | ---
title: "SplicingEnrichmentPlot"
author: "Marika Oksanen"
date: "4/5/2022"
output: html_document
---
Import data, filter and order by enrichment
```{r}
GOBP <- read.delim("enrichment_results_wg_BPA.txt")
GOBP <- GOBP[(GOBP$FDR < 0.05),]
GOBP <- GOBP[order(GOBP$enrichmentRatio, decreasing = TRUE),]
GOBP$description <... |
b6912251618c8895385c93f42f5f401014aa5a87d4fe4c12e970505ecb906e23 | R | 1,172 | 33 | # This function collates the CSV files for each individual vision ROI for each
# subject (PRL, URL, LPZ) into a single csv file. This also adds mean thickness
# for each subject and calculates normalized thickness for each vertex as well
# Load libraries
library(janitor)
library(tidyverse)
# read in the individual vi... |
2502efe5edb464b53690b1e4f5dd31d5efd002246959873464e217c6366712f6 | R | 1,175 | 22 | # set your working directory
studydir <- paste0("Desktop/Reference genomes/Reference gene files/")
# import gtf
Human_gene <- rtracklayer::import('Desktop/Reference genomes/Reference gene files/human_genes.gtf')
Human_gene_df=as.data.frame(Human_gene)
marmoset_gene <- rtracklayer::import('Desktop/Reference genomes/Re... |
ae6c4f395f201642f573d7fd287d27c437f93b663712914ddc4f9f41de33cae5 | R | 1,200 | 24 | ###The following script was used to test if the proportion of the cells in each cell states/clusters was different between the control and Atoh1S193A animals
#Import package
library(speckle)
library(limma)
#Run test
propeller(clusters = obj$cell.type, sample = obj$ID, group = obj$genotypes)
#Plot the compositio... |
3d86a9fee2481a6d29486834eb9f3e283c9d12be63116487433fe1dbe43b5a3b | R | 1,207 | 33 | # Save human data as Seurat
library(dplyr)
library(Seurat)
library(SeuratDisk)
source("./src/preprocessing_utils.R") # function recode_genes & save dirs
datadir <- "./data/raw/bakken/"
nrows = -1
metadata <- read.csv(paste0(datadir, "metadata.csv"), header = TRUE, sep=',',
nrows = nrows)
counts ... |
6b7fdee24f3a279537aaff36856cecd7c156a49596952bcd44f6289f16b3a949 | R | 1,207 | 45 | #!/usr/bin/env Rscript
#
# Copyright © 2017 Charles E. Vejnar
#
# Use of this source code is governed by an MIT-style license that can be
# found in the LICENSE file or at https://opensource.org/licenses/MIT.
#
library('DESeq2')
args = commandArgs(trailingOnly = TRUE)
path_count = args[1]
path_condition = args[2]
pa... |
a763bcf752db5f4db9ac5d5d1125e9005db01048bbd430fb6a6797b54995b66a | R | 1,221 | 69 |
#/home/toolkit/tools/R4.2.0/bin/R
library(Seurat)
library(Signac)
source('https://gitee.com/jumphone/BEER/raw/master/BEER.R')
library(data.table)
library(stringr)
pbmc=readRDS('./eQTL/scAtlas_pbmc.rds')
SNP_TSS=read.table('./eQTL/SNP_TSS_500k.bed.loop.txt',sep='\t',row.names=NULL,header=FALSE)
SNP_TSS_TAG=SNP_TSS... |
d8065e4c0976c4d64ee7b21c9f652fcc2124f167321ab669148656775d4962eb | R | 1,230 | 72 |
#/home/toolkit/tools/R4.2.0/bin/R
library(Seurat)
library(Signac)
source('https://gitee.com/jumphone/BEER/raw/master/BEER.R')
LIST=read.table('LIST.txt',sep='\t')
MAT=c()
i=1
while(i<=nrow(LIST)){
this_name=LIST[i,1]
this_file=paste0('/home/disk/database/data/scATAC_ATLAS/raw/FRAG/',this_name,'.format/m... |
74471cfb6147e3a95e9fd344d0f6bd0f2ed2c39da62936e18c70d55a02a66b22 | R | 1,243 | 23 | # This plots the MEAN SuperLearner coefficients values for each algorithm used in the SL.library
# across the total runs (i.e., M for the TRAINING step, ~50-100 for the VALIDATION step)
barplot(colMeans(m_validation))
barplot(colMeans(m_training))
# This plots the MEDIAN SuperLearner coefficient values for each algorit... |
b0147c0d2c71a25031f427fd484b418ea6f7ee519041fca8d2b2b306c5223ba3 | R | 1,250 | 72 |
# /home/toolkit/tools/R4.2.0/bin/R
p
p
p
p
p
library(Signac)
library(GenomicRanges)
library(Seurat)
source('https://gitee.com/jumphone/BEER/raw/master/BEER.R')
.getRNA<-function(pred.ref){
#pred.ref=readRDS('./ics_out/COM_pred.ref_r0.rds')
LOOP2GENE=read.table("./RNA/LoopAndTSS.bed",header=F,sep='\t',row.names=NU... |
d15d5ee176de3503bbd213ade0b03aa385b4b16d2aaccd543f5a59598822f3bc | R | 1,277 | 36 | ### conduct age regression model for all davi regions ###
### print out signifiant regions ###
library("dplyr")
library("readr")
library("tidyr")
library("lsr")
library("broom")
library("reshape2")
# read in Davi ROI data #
# data is a csv file with cols - Name, Age, Sex, Scanner, TIV & davi region acron... |
2ba560e62a917331343539bcf2f4684d7b5085a5ed2a786029c2982dee8c6983 | R | 1,318 | 34 | # Add Factor Significance to respective dataframe
addSignificance_TP <- function(x){
x <- x |>
mutate(Significance = ifelse(estimate_TP < low_quantile_TP | estimate_TP > high_quantile_TP,
"significant",
"non-significant"),
Significa... |
efce129fc0d226cd749276cd5d5a8024b3a7016b54d87e511421a105cbb05155 | R | 1,318 | 42 | #!/usr/bin/env Rscript
args = commandArgs(trailingOnly=TRUE)
label=as.array(args[1])
workspace_directory=as.array(args[2])
filename_specs <- file.path(workspace_directory,paste0(label,"_info.RData"))
#filename_specs <- file.path(workspace_directory,paste0("_validation_info.RData"))
print(filename_specs)
load(filename... |
38cc612eeff8e28ce0819ac4ac53b2f6a45fb1448739616cf6d3c48cb71cd109 | R | 1,322 | 45 |
library("devtools")
install_github("Danko-Lab/BayesPrism/BayesPrism")
suppressWarnings(library(BayesPrism))
setwd("/content")
dataset_zip = 'more_than_50_dummy'
unzip(paste(dataset_zip, 'zip', sep='.'))
main_dir = paste("/content/", dataset_zip, sep="")
setwd(main_dir)
folders <- as.vector(list.dirs())
folders <- gr... |
55896cf8f0c61a5dd568d9296eaa92210f6abe8373d6e429271d29c6ba30e6df | R | 1,335 | 42 | # Save raw bird data as Seurat
library(dplyr)
library(Seurat)
library(SeuratDisk)
source("./src/preprocessing_utils.R") # function recode_genes
datadir <- "./data/raw/tosches/"
species_list <- c("turtle", "lizard")
for (species in species_list){
# Load data
load(paste0(datadir, paste0(species, ".neurons.Robj")... |
23f5acd12c5dcb97680bf092582b4f85aab849ebe4907a9ff474aed4c07dc3fc | R | 1,338 | 40 | # /home/toolkit/tools/R4.2.0/bin/R
library(Signac)
library(GenomicRanges)
library(Seurat)
source('https://gitee.com/jumphone/BEER/raw/master/BEER.R')
#args <- commandArgs(TRUE)
#opath=args[1]
pbmc.isloop=readRDS('/home/disk/database/data/scHIC/Blood/analysis/scATAC_pbmc.rds')
#pbmc.isloop$type[which(pbmc.isloop$seura... |
cfff3ee2028af3dca7aabca953d4370817b9d155f863543f7093df04d8a0b502 | R | 1,352 | 32 | library('biomaRt')
library('DESeq2')
library(readxl)
library(openxlsx)
setwd('/Volumes/aronow/Kang/induced_neuron/ChangHui_RNAseq_fastq/isoform_RSEM/rsem_transcripts/differential_v2/')
# prepare gene / transcript metadata for conversion
ensembl = useEnsembl(biomart = 'ensembl', dataset = 'hsapiens_gene_ensembl', mirr... |
7e32f52244900bf2dd71ca3b638962344a4b85aa4f1fc25c8fe02fd96bd1807b | R | 1,402 | 36 | # Install missing libraries
required_packages <- c("ggplot2", "msa", "ggmsa")
missing_packages <- required_packages[!(list.of.packages %in% installed.packages()[,"Package"])]
if(length(missing_packages) > 0) {
install.packages(missing_packages)
}
# Load them
library(msa) # Alignment
library(ggmsa) # Alignment viewer
... |
d1802fa9c6a688f8c6a1761256cc67fa6bab4d7b0b0073cd78877a7c111369f8 | R | 1,402 | 15 | ########Outlier function: (I need to work out how to save function files then just call them whenever I want)
outlierSummary<-function(variable, digits = 2){zvariable<-(variable-mean(variable, na.rm = TRUE))/sd(variable, na.rm = TRUE)
outlier95<-abs(zvariable) >= 1.96
... |
5d78d0561cc36aef4bfab89998762661570c628c2002c8556b490154020141db | R | 1,403 | 38 | # Reference data access for the PACNet web app.
#
# Native R (runApp / runGitHub): files are read from ../data relative to the
# app directory (option pacnet.data_dir overrides).
# webR (Shinylive, in the browser): files are downloaded from the data/
# directory next to the exported site, once per session, then read.
... |
cf7ff642028e3d02bb96a664a8f96b509948ecc28265fbbe1ef43249a8512523 | R | 1,416 | 40 | #' @title
#' CBDA Initialization function for Compressive Big Data Analytics
#'
#' @description
#' This CBDA function installs and attaches all the packages needed to run the CBDA.
#' A user-defined list of packages can be passed as argument. Itis recommended to first execute
#' the function without any arguments.
#... |
c473cd2822b252986f6df9d67bcca2f9cebff8d3fc8c8e86cb6fc297a71f6eaf | R | 1,430 | 58 | # /home/toolkit/tools/R4.2.0/bin/R
library(Signac)
library(GenomicRanges)
library(Seurat)
source('https://gitee.com/jumphone/BEER/raw/master/BEER.R')
#args <- commandArgs(TRUE)
#opath=args[1]
pbmc.isloop=readRDS('./scATAC_pbmc.rds')
#pbmc.isloop$type[which(pbmc.isloop$seurat_clusters %in% c(9))]='Unclear'
CLST=readRD... |
10a14f5e5674522e82ddaf14d41ca520ce7f9d893888af6fe03e735688bb8c70 | R | 1,441 | 51 |
#/home/toolkit/tools/R4.2.0/bin/Rscript
#/home/toolkit/tools/R4.2.0/bin/R
source('https://gitee.com/jumphone/VISA/raw/master/VISA.R')
mat=read.table('signal_mat.txt', sep='\t', row.names=1, header=TRUE,check.names=FALSE)
a=t(mat)[,1]
b=t(mat)[,2]
type=readRDS('signal_type.rds')
setwd('/home/disk/database/data/ICS... |
f9d273c8dd03d49f4dd01e71a36b25eb38dc6e2fee7a68fe3c0a42412469fcb6 | R | 1,449 | 56 | ---
output: github_document
---
<!-- README.md is generated from README.Rmd. Please edit that file -->
```{r, include = FALSE}
knitr::opts_chunk$set(
collapse = TRUE,
comment = "#>",
fig.path = "man/figures/README-",
out.width = "100%"
)
```
# SURGE
<!-- badges: start -->
<!-- badges: end -->
The SURGE pac... |
1b4f9f566e3e04530621a8179b412feda91c1c4e650ff25c51ad4c39368e30cb | R | 1,456 | 40 | #' @title
#' CBDA Initialization function for Compressive Big Data Analytics
#'
#' @description
#' This CBDA function installs and attaches all the packages needed to run the CBDA.
#' A user-defined list of packages can be passed as argument. Itis recommended to first execute
#' the function without any argume... |
aebb16bff4d3eb05f9b7c969fb35ed6518c21aebac04afcd87187a9ec67c0420 | R | 1,462 | 48 |
# /home/toolkit/tools/R4.2.0/bin/R
library(Signac)
library(GenomicRanges)
library(Seurat)
#args <- commandArgs(TRUE)
#opath=args[1]
.single=function(TAG){
d1=read.table(paste0('./coolCount_',TAG,'/Astro.coolCount.txt'),sep='\t',header=FALSE,row.names=NULL)
d2=read.table(paste0('./coolCount_',TAG,'/Neuron.Ex.coolCou... |
177f766468e9005a5d39aab3100981aa104de3f7cb7074f6351f79109c973dbc | R | 1,476 | 64 | ---
title: "Contact us"
# author:
# - name: Xiaotao Shen PhD (https://www.shenxt.info/)
# - affiliation: School of Medicine, Stanford University
date: "Created on 2021-02-28 and updated on `r Sys.Date()`"
output:
html_document:
df_print: paged
toc: false
pdf_document:
toc: false
pkgdown:
as_is: false
... |
c91d186cf344e8728dbb0c6899dd44284e0a71e6f7b865edc988873633bf81b6 | R | 1,478 | 40 | # Save raw bird data as Seurat
library(dplyr)
library(Seurat)
library(SeuratDisk)
source("./src/preprocessing_utils.R") # function recode_genes & save dirs
datadir <- "./data/raw/tasic/"
# The files to load
filename <- "mouse_VISp_2018-06-14_exon-matrix.csv"
filename.meta <- "mouse_VISp_2018-06-14_samples-columns.csv"... |
510258e5fd88abf9758abdcd7e5704a6b8cd3f064ae096868eaa6bc12a8a5880 | R | 1,485 | 20 | # Small UI helpers shared by ui.R and server.R (renderUI inputs).
# Input label followed by an info icon that shows `tip` on hover or focus.
label_tip <- function(label, tip) {
tags$span(
label,
bslib::tooltip(
tags$span(shiny::icon("circle-info"), class = "text-muted ms-1", tabindex = "0"),
tip,... |
5bf345e25427c3554f4403ed2e7552056dd18eb5566466057f9ea095449d1be1 | R | 1,491 | 51 | test_that("pmApi2df handles empty data", {
P <- list(data = list())
result <- pmApi2df(P)
expect_true(is.data.frame(result))
expect_equal(nrow(result), 0)
})
test_that("pmApi2df output has expected columns in bibliometrix format", {
skip_on_cran()
skip_if_offline()
skip_if_not(
nzchar(Sys.getenv("PUB... |
86a8632dd280d4f28741f5e56fb17a3a3cdac11d934bb156868e95833f400340 | R | 1,493 | 96 |
library(Signac)
library(GenomicRanges)
library(Seurat)
source('https://gitee.com/jumphone/BEER/raw/master/BEER.R')
#args <- commandArgs(TRUE)
#opath=args[1]
pbmc=readRDS('./scATAC_pbmc.rds')
#pbmc.isloop$type[which(pbmc.isloop$seurat_clusters %in% c(9))]='Unclear'
#pbmc$type=readRDS('/home/disk/database/data/scHIC/Bl... |
94cd20abcb50139252b9d05ec6f2d4f295d4a153f442d18fd6ca371ac67de543 | R | 1,494 | 36 | context("Test .compute_unnormalized_scores")
## .compute_unnormalized_scores is correct
test_that("(Math) .compute_unnormalized_scores computes the correct scores", {
trials <- 20
bool_vec <- sapply(1:trials, function(x){
set.seed(x)
n <- 150; K <- 2
common_space <- scale(MASS::mvrnorm(n = n, mu = ... |
f2292895d2789a4f994fac012d0727184557ea4cbb6b31e2fbcc8467a703a068 | R | 1,497 | 42 | require(remotes)
system("apt install libgsl-dev", intern=TRUE)
install.packages("topicmodels")
remotes::install_github('JEFworks-Lab/STdeconvolve')
library(STdeconvolve)
my_working_dir = '/content'
setwd("/content")
dataset_zip = 'dense'
unzip(paste(dataset_zip, 'zip', sep='.'))
main_dir = paste("/content/", dataset_... |
49ac643f7718c16009a72ebcc71666f08bd811112e5ef07f7b75111c61fb312b | R | 1,512 | 35 | library(Seurat)
library(ggplot2)
# donor
donor = readRDS('donor_reintegrated.rds')
# make 3d umap
original_umap = donor@reductions$UMAP@cell.embeddings
donor = RunUMAP(donor, reduction = 'pca', dims = 1:30, n.components = 3, reduction.name = 'UMAP_3d')
df_meta = donor@meta.data
df_umap = donor@reductions$UMAP_3d@cel... |
0d60c11d3d1c7fe1601a2125cfeac65ecd5434203a32c56c3cae0e0738b4d3b2 | R | 1,520 | 44 | args<-commandArgs(TRUE)
out_name=paste("mash.", args[[1]], ".png", sep="")
png(out_name, res=300, width=9.6, height=9.6, units="in", pointsize=8, type="cairo", bg="white")
par(mar=c(3.2,3.2,0,0))
par(mgp=c(2.2,1,0))
# get names
key=read.table("key")
labels=key[,1]
# make dendrogram
x = read.table("combined.tbl")
y=x... |
38d86a99a4f45f0583a03e168a81bf89b26bda2141497133b5f419580ef740ce | R | 1,523 | 43 | # Load and Clean Data
library(readxl)
fp<-file.path
dp<-"data/original"
dreadds<-read.csv(fp(dp,"cfos_dreadds_behavior.csv"), header=TRUE, na.strings=c("NA","N.A.","<NA>"))
clearmap<-read.csv(fp(dp,"cfos_imaging_and_clearmap_params_20201125.csv"), header=TRUE)
counts122<-as.data.frame(read_excel(fp(dp,"cfos_total_and... |
42d88db5243f8d045d6bd539f3e5375490619cce81c596864fc5f0a1b58372b5 | R | 1,524 | 70 | ---
title: "SiT - Figures"
author: '[Kevin Lebrigand](mailto:lebrigand@ipmc.cnrs.fr)§, Joseph Bergenstråhle §, Kim Thrane §, Annelie Mollbrink, Pascal Barbry, Rainer Waldmann and Joakim Lundeberg'
date: 'Compiled: `r format(Sys.Date(), "%B %d, %Y")`'
abstract: 'The spatial landscape of gene expression isoforms in tissu... |
501c9a410e8345913ce7f9c6241ec076117decede73f8057a874cc175b40bbda | R | 1,530 | 23 | getBackgroundReads = function(snap_obj, clusters, snap_file, peak_file, bin_size, genome_file,sample_prefix, output_folder, path_to_bgreads){
genome=read.table(genome_file, stringsAsFactors = FALSE)
chr_size=genome[,2]
names(chr_size)=as.character(genome[,1])
tiles=tileGenome(seqlengths=chr_size, tilewidth=bin_... |
6bed5f7a00c7fa38ba46bf606770a510d24640d81f09fb34f1d27588b65c6e59 | R | 1,531 | 75 |
# /home/toolkit/tools/R4.2.0/bin/R
library(Signac)
library(GenomicRanges)
library(Seurat)
source('https://gitee.com/jumphone/BEER/raw/master/BEER.R')
pbmc=readRDS('./scATAC_pbmc_final.rds')
#pbmc.isloop$type[which(pbmc.isloop$seurat_clusters %in% c(9))]='Unclear'
pbmc$type=readRDS('./scATAC_type.rds')
library(data... |
5be2fed844b954f3bf540bcd85646663762bddaae7978a1b103b0e03cd82ae86 | R | 1,535 | 59 | #' Forestplot Theme
#'
#' A custom theme used in \code{\link{forestplot}} that builts upon
#' \link[ggplot2]{theme_minimal}.
#'
#' @inheritParams ggplot2::theme_minimal
#' @import ggplot2
#' @importFrom grid unit.c
#' @export
#' @author Maria Kalimeri
#' @seealso \code{\link{forestplot}}, \code{\link{geom_effect}}, \co... |
f5ecc071678fc6901847f499100b1284ea9889d06e3ecf2834e26688b226b3a7 | R | 1,545 | 57 | # /home/toolkit/tools/R4.2.0/bin/R
library(Signac)
library(GenomicRanges)
library(Seurat)
source('https://gitee.com/jumphone/BEER/raw/master/BEER.R')
#args <- commandArgs(TRUE)
#opath=args[1]
pbmc.isloop=readRDS('/home/disk/database/data/scHIC/GSE130711_analysis/scATAC_pbmc.rds')
pbmc.isloop$type[which(pbmc.isloop$se... |
96d973a3d9cb0d379d65cd61fb65ebde2b4638f01cc95757490d044ada7a8a79 | R | 1,554 | 46 | # This function collates csv files containing cortical thickness values of each
# vertex in V1 for each participant into a single csv file. This also adds mean
# thickness for each subject and calculates normalized thickness for each vertex
# as well
# Load libraries
library(janitor)
library(freesurferformats)
library... |
cd4a70bf9d679a00661c8f7ec44f87f03b263f6b2f4f30dd8e99320f0978aeb5 | R | 1,559 | 51 | library(rstatix)
# for visualization
library(ggpubr)
library(ggplot2)
dat <- read.table('../Output/Logratio_fraction_in_behavior_controls.txt', header = TRUE, sep = ",")
# preprocess data
dat$group <- as.factor(dat$group)
dat$mouse <- as.factor(dat$mouse)
dat$behavior <- as.factor(dat$behavior)
# visualize the data... |
5249c178f2c97ef13e1319a9b23e8674b8c1263c52da9d94b91ec79a8464156f | R | 1,560 | 34 | # Save raw bird data as Seurat
library(dplyr)
library(Seurat)
library(SeuratDisk)
rm(list=ls())
source("./src/preprocessing_utils.R") # function recode_genes & save dirs
datadir <- "./data/raw/colquitt/"
# Load the data: single csv with meta data and counts
counts_and_metadata <- read.table(paste0(datadir, "HVC_RA_RNA... |
99dd75d0ac909c4b9c5cfadb4dfd2820513490e78886b833fde62191b1ceff40 | R | 1,576 | 57 | ---
title: "Compare effect sizes of different c-Fos contrasts"
author: "Will Townes"
output: html_document
---
```{r}
library(tidyverse); theme_set(theme_bw())
fp<-file.path
mkdir_p<-function(x){
if(!dir.exists(x)){dir.create(x,recursive=TRUE)}
}
rp<-fp("results/bwas")
mkdir_p(rp)
source("utils.R")
```
data loadi... |
895dc85a5f1678e73dbc040ae2211d3b9b4a0742b308de94d456e42bc5cf31bc | R | 1,581 | 96 |
library(Signac)
library(GenomicRanges)
library(Seurat)
source('https://gitee.com/jumphone/BEER/raw/master/BEER.R')
#args <- commandArgs(TRUE)
#opath=args[1]
pbmc=readRDS('/home/disk/database/data/scHIC/Blood/analysis/scATAC_pbmc.rds')
#pbmc.isloop$type[which(pbmc.isloop$seurat_clusters %in% c(9))]='Unclear'
pbmc$type... |
f9d3e42b9dcf5f9286d1b2240aa4d12fe4286b6de19db986c7b0d2cd6cfb0f21 | R | 1,594 | 36 | test_that("cnlib has no parallel or snow code paths", {
src <- readLines(app_file("cnlib.R"))
expect_false(any(grepl("detectCores|snow::", src)))
})
test_that("quick-pairs scoring gives the same pairs whether or not it is sliced", {
set.seed(1)
genes <- paste0("g", 1:30)
expDat <- matrix(rexp(30 * 40), nrow ... |
61e96eeac85c7325202042c72572769729f7b36d1f7c64ae84e0cf8659d0a0d8 | R | 1,606 | 38 | library(tximport)
library(DESeq2)
library(stringr)
library(openxlsx)
setwd('/data/aronow/Kang/induced_neuron/ChangHui_RNAseq_fastq/isoform_RSEM/rsem_transcripts/differential_v2')
source_folder = '/data/aronow/Kang/induced_neuron/ChangHui_RNAseq_fastq/isoform_RSEM/rsem_transcripts/transcripts/'
# load data
df_samples ... |
4b7ed79a56881e4916b1dda6a97bb8ecba378211fa14b81937a7089e475e6ac7 | R | 1,623 | 70 | # Observer Model (Item Frequency) - 2022
library(tidyverse)
# Add function to shift down vector n rows and replace with NA
shiftdown <- function(x, n){
c(rep(NA, n), head(x, -n))
}
# Main function
CountEventBernoulli <- function(s, omega = Inf){
# Store input sequence in dataframe
df <- enframe(s, name = "t... |
c3d8c299781089f988a7fa97171f8223504a514605d341b386ff6ba58bd00595 | R | 1,623 | 40 | #' @title
#' CBDA Clean up function for Compressive Big Data Analytics
#'
#' @description
#' This CBDA cleans the current directory where all the intermediate workspaces have been created.
#' @param label This is the label appended to RData workspaces generated within the CBDA calls
#' @param workspace_directory Dir... |
36a3ca29dc84a4249e1f25fc949b66d8cde8cdfadfc4a5ca1f994d57ae35ed41 | R | 1,641 | 45 | # Multiple plot function
#
# ggplot objects can be passed in ..., or to plotlist (as a list of ggplot objects)
# - cols: Number of columns in layout
# - layout: A matrix specifying the layout. If present, 'cols' is ignored.
#
# If the layout is something like matrix(c(1,2,3,3), nrow=2, byrow=TRUE),
# then plot 1 will... |
34bd7d8df9111c6c0b0493529e6807190d65957c343e326a4161513df234ea06 | R | 1,663 | 40 | #' @title
#' CBDA Clean up function for Compressive Big Data Analytics
#'
#' @description
#' This CBDA cleans the current directory where all the intermediate workspaces have been created.
#' @param label This is the label appended to RData workspaces generated within the CBDA calls
#' @param workspace_direc... |
0b51be77b3ec9a1736aa2bb7b0e90250691ec4b5f61d7fd159995203ddf5bd69 | R | 1,674 | 74 |
# /home/toolkit/tools/R4.2.0/bin/R
library(Signac)
library(Seurat)
library(GenomeInfoDb)
#library(EnsDb.Hsapiens.v86) #hg38
library(EnsDb.Mmusculus.v79)
library(ggplot2)
library(patchwork)
set.seed(1234)
pbmc=readRDS('scATAC_pbmc.rds')
image=Read10X_Image('/home/disk/database/data/ICS_scHIC/fxx_spatial/download/G... |
b306daadf74c685936862551682f3d7b46da3be4c0b6b0ef282bcc66142c08f4 | R | 1,681 | 59 | # /home/toolkit/tools/R4.2.0/bin/R
library(Signac)
library(GenomicRanges)
library(Seurat)
source('https://gitee.com/jumphone/BEER/raw/master/BEER.R')
#args <- commandArgs(TRUE)
#opath=args[1]
pbmc.isloop=readRDS('/home/disk/database/data/scHIC/Blood/analysis/scATAC_pbmc.rds')
#pbmc.isloop$type[which(pbmc.isloop$seura... |
9f36c26f559c5c521f58f9ad8652a670d463cf125de87d2835aefa407a1ea11b | R | 1,696 | 41 | library(Seurat)
library(ggplot2)
source('../functions/_global.R')
source('../functions/_visualize.R')
# load data
organoid = readRDS('../../../v2_filtered/combined_all_organoids_filtered_v3.rds')
organoid = organoid[, organoid$cellclass_draft_v2 %in% cellclass_ordered_v2]
organoid$cellclass_draft_v2 = factor(organoid... |
1931c4e21f96f2dd200e322c87f899f9001f3ef320dce81c862dac74a74c3e22 | R | 1,697 | 40 | panel <- c("heart", "hspc", "intestine_colon", "liver", "lung", "neuron", "skeletal_muscle")
other <- c("esc", "endothelial_cell", "fibroblast", "kidney", "b_cell", "t_cell", "monocyte_macrophage")
data_dir <- file.path(repo_root, "data")
rd <- function(name) readRDS(file.path(data_dir, name))
test_that("all expected ... |
a43cc33ff82d31ed56db25fe9d3c9d03e26ec34dd244592197ceaeee325e054c | R | 1,707 | 68 | ---
title: "SiT - Supplementary Figure 09"
author: '[Kevin Lebrigand](mailto:lebrigand@ipmc.cnrs.fr)§, Joseph Bergenstråhle §, Kim Thrane §, Annelie Mollbrink, Pascal Barbry, Rainer Waldmann and Joakim Lundeberg'
date: 'Compiled: `r format(Sys.Date(), "%B %d, %Y")`'
abstract: 'The spatial landscape of gene expression i... |
e858d1a2bf35fee38593b11b3067ca0cfc6f9c679761a8a3b35010a95c8b1f5c | R | 1,709 | 43 | context("Test consensus PCA")
## consensus_pca is correct
test_that("consensus_pca works", {
# load("tests/assets/test_data4.RData")
load("../assets/test_data4.RData")
mat_1 <- test_data$mat_1
mat_2 <- test_data$mat_2
res <- consensus_pca(mat_1 = mat_1, mat_2 = mat_2,
dims_1 = 1:2,... |
c1f4ed27d0a1758c24114f70c736b6d7daba12074aad46b3903252922f7f9293 | R | 1,710 | 60 | test_that("list2char flattens nested lists correctly", {
x <- list(a = list(b = "hello", c = "world"), d = "foo")
result <- pubmedR:::list2char(x)
expect_type(result, "character")
expect_true(length(result) == 3)
expect_equal(unname(result[1]), "hello")
})
test_that("get_api_key returns explicit key first", ... |
c0850b5392b24e3df5fb04f133649a93adc055fb2e73c8950031f9a4c3293665 | R | 1,713 | 79 | ---
title: "MetNormalizer"
author:
- name: Xiaotao Shen (https://www.shenxt.info/)
- affiliation: School of Medicine, Stanford University
url: http://shenxt.info/
date: "`r Sys.Date()`"
output:
html_document:
df_print: paged
toc: no
pdf_document:
toc: no
vignette: "%\\VignetteIndexEntry{Vignette Title... |
f7e8adee3b855c9df45d328907f39c2d2de13f91bf295b3f8e94f44b09db65ea | R | 1,718 | 71 | ---
title: "SiT - Figures"
author: '[Kevin Lebrigand](mailto:lebrigand@ipmc.cnrs.fr)§, Joseph Bergenstråhle §, Kim Thrane §, Annelie Mollbrink, Pascal Barbry, Rainer Waldmann and Joakim Lundeberg'
date: 'Compiled: `r format(Sys.Date(), "%B %d, %Y")`'
abstract: 'The spatial landscape of gene expression isoforms in tissu... |
b2951c7e02046ce5e1816f32f1aaf586eb41d378b77b7b63a797ba856dbd553f | R | 1,722 | 38 | #' @title
#' CBDA Spectrum plot function for Compressive Big Data Analytics
#'
#' @description
#' This CBDA function generates histograms of the feature counts/densities as returned
#' by the Accuracy and MSE metrics after the Learning/Training step.
#'
#' @param top Top ranked predictive models from the Learning/Tra... |
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