sha256 stringlengths 64 64 | language stringclasses 28
values | size int32 1 491k | lines int32 1 23.2k | content stringlengths 1 200k |
|---|---|---|---|---|
0f249c3f8d7109983063114567bf188b67e25525c997d07980fa104d399d75ac | R | 13,875 | 555 | # Note - doesn't plot by treatment
# use for baseline plot to show the median for each slice
# ROI medians, colored by slice
# slice medians, larger circle
# median of slice medians for the group +/- IQR
# mean of slice medians for group +/- SEM
mmm_plot <- function(
df
, res
, y_title
, groups
, colorBy = "m... |
1986f25dcfa28d6d5dd49f2b653e66d05b0ca0fe3239f52edb585d59eb98f264 | R | 13,926 | 397 | # logging which server was used to run this script
system("echo \"Running this on ### $(hostname) ### \\(* w *)/\"")
# load library
library(tidyverse)
library(Seurat)
library(scales)
library(scico)
library(tictoc)
library(shades)
library(ComplexHeatmap)
library(circlize)
library(viridis)
# read in annotated data
# da... |
6435a9b74fd7918f68dd7586b4048f4db31685d3fefef5c947d48bf570f83b85 | R | 14,021 | 565 | writeToWorkbook = function(sheetName, df, wb, tableStyle = "TableStyleMedium2"){
addWorksheet(wb, sheetName)
writeDataTable(wb, sheetName, df, tableStyle = tableStyle)
}
saveDFsToExcel <- function(
fileBaseName,
prefix = filePrefix,
addDate = Sys.Date(),
saveFolder = dataOutputFolder,
... # use sheetName... |
ef0a02f48479b124e81506978a9f99445577d63e05954baea20bbc1e60e469bd | R | 14,487 | 394 | library(shiny)
library(dplyr)
library(ggplot2)
library(DT)
library(leafcutter)
library(reshape2)
library(gridExtra)
library(intervals) # needed for pretty strand arrow placement
library(foreach)
library(shinycssloaders)
library(grid)
library(gtable)
library(ggrepel)
#data.table is required
# library(shinyjs)
#### optio... |
59ba86fc24830299fbd11148dbc4070932df74f66bfb45af499b03d8d1b7374a | R | 14,669 | 415 | #' @import dplyr tibble
NULL
#' Fit (regularized) generalized linear model
#'
#' @param formula An object of class \code{formula} with a symbolic description
#' of the model to be fitted.
#' @param data A \code{data.frame} containing the variables in the model.
#' @param method A character string indicating the metho... |
40d9360a0a4b54a08606e0d8af1eeabc9610209bebc3123955b6064fb3a14c1a | R | 14,912 | 279 | # 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)
## read the sctransform-normalized seurat object
selected_celltype <- "Exc"
data_obj_sub <- read... |
4696dd5015398c6e04c0c6aff9d65deb58cfff64ac44958487b036c7f3d4ed62 | R | 15,045 | 361 | #!/usr/bin/env Rscript
library(optparse)
option_list = list(
make_option(c("-i", "--input"), type = "character", default = NULL, help = "Input VCF file"),
make_option(c("-s", "--style"), type = "character", default = "ncbi", help = "[nbci] for 1,2,...X, or [ucsc] for chr1, chr2, ...chrX"),
make_option(c("-g"... |
c4397aac45190f6d16766c1efadd19985b4d75fcbd37316d3b3302de75a663d1 | R | 15,105 | 380 | library(tidyverse)
library(lme4)
library(lmerTest)
install.packages("remotes")
remotes::install_github("stan-dev/cmdstanr")
library(cmdstanr)
install_cmdstan()
library(brms)
library(ggplot2)
#Read data, convert and rename factor variables
dat<-read.delim("Data_Neuroanatomy.txt")
brains <- dat %>%
mutate_at(vars(c('... |
8a76bee1d9d7167ef691a37d8e325e998819cb2c2af14847bdd0a0a8c4d916c4 | R | 15,142 | 466 |
# This is the SCEBE algorithm that we downloaded from -> https://github.com/Myuan2019/SCEBE
####functions
# 计算时间差
timediff <- function(End_time, Start_time){
return(difftime(End_time, Start_time, units = 'secs'))
}
##SPR for NEBE
slmm <- function(y1,y2, X){
n1 <- length(y1)
n<-n2<-n1
y1bar <- mean(y1)... |
713253a4caca095e3c1867f31b099b84d8de3213eb97536cacd6ce1d56d7ac8a | R | 15,262 | 438 | #### Thank you for using our code, wish you all the best! ####
# Note: In this script, our code comments are not very detailed,
# please see the simulation code of Homo-Area for detailed comments.
# The main difference between Compo-Area code and Homo-Area code is to assign-
# different properties to each sub-regi... |
f4bcd0988fec6eb6fba2905c031b1c7ff79070f6412bc44dc38b6442b2dde9cb | R | 15,405 | 367 | library(testthat)
library(gUtils)
setDTthreads(1)
gg.jabba = gG(jabba = system.file('extdata/hcc1954', 'jabba.rds', package="gGnome"))
test_that('gtf2json', {
# download light weight gtf
system('wget http://ftp.ebi.ac.uk/pub/databases/gencode/Gencode_human/release_19/gencode.v19.long_noncoding_RNAs.gtf.gz')
gtf... |
bd15af2dfb69cc710e712e0d91f0809543e279caa15edae0351ec4d08dd656e1 | R | 16,046 | 428 | library(tidyverse)
library(ggrepel)
library(patchwork)
auc_df <- bind_rows(
"ALS_vs_Control_MCX" = read_tsv("augur_out_obj_ALS_vs_Control_MCX_level2_nSubsamples_50_subsampleSize_200_folds_3.tsv"),
"ALS_vs_Control_mFCX" = read_tsv("augur_out_obj_ALS_vs_Control_mFCX_level2_nSubsamples_50_subsampleSize_200_folds_3.ts... |
6ee39042a0326b8fef9a0582ced722907c8f9516d8a5060d6e98302156362bbd | R | 16,465 | 417 | library(JaBbA)
library(gUtils)
library(testthat)
context('JaBbA')
jj = system.file("extdata", "junctions.rds", package = "JaBbA")
empty.jj = system.file("extdata", "empty.junctions.rds", package = "JaBbA")
tier.jj = system.file("extdata", "tier.juncs.rds", package = "JaBbA")
sg = system.file("extdata", "segs.rds", pac... |
0b4967dae16aff9b68e127c27090ad49cfc46ac5351886e9f88d712d980c4955 | R | 16,481 | 425 | # 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(ggrastr)
library(shades)
library(ggbeeswarm)
library(ggpubr)
library(scico)
## read the sctransform-normal... |
d00c119a423605703b75a75bd13066fdfc4475b2aae9d6d6d3ced298eb3c59df | R | 17,654 | 420 |
#' Encode signal into Poisson spikes
#'
#' The 'code' is just a Poisson spike train for a given group of neurons
#' This function will return a data frame with (group_index,spike_time) columns
#'
#' Some other stuff here.
#'
#' @param codebook constructed using construct_codebook function
#' @param symbol symbol to ... |
6f540d1cc90ad352f5dc8644c2361636d7a98329b96230a3f1b4be818b53a8cf | R | 17,858 | 363 | #' @title Bayesian Guided Sparse Factor Analysis on Perturbed Gene Expression Matrix
#' @description Performs GSFA on given gene expression matrix and matching perturbation
#' information using Gibbs sampling.
#' @details Uses functions implemented in Rcpp from \code{GSFA_gibbs_inference.cpp}.
#' For details about the ... |
7a87a0138a88ce9807f9a625ac1ae286bab04e5ab7a912ae5553d15bb1a87334 | R | 18,337 | 555 | ##Using StringTie with DESeq2 analyzing Nasonia RNAseq data
rm(list=ls()) #clear all objects
gc()
#dataset:
#Nasonia RNAseq data
#experimental light treatment and dark control
#4 different time point: 0.5h 1h 2h 4h
#8 sample groups
#3 replicates per experimental sample group, 2 replicates per control sample ... |
efb44fb53dc383f9cb48cb088a5cde9278c31c2d06ccd09eac9c9a66f9f03827 | R | 18,689 | 544 | #' Print diagnostic message
#'
#' @param ... Text to print
#' @param verbose Display messages
log_message <- function(..., verbose=T){
if (verbose){
message(paste0(...))
}
}
#' Find peaks or regions near gene body or TSS
#'
#' @param peaks A \code{GRanges} object with peak regions.
#' @param gene A \... |
208afc4e7d58348ca38fdad1180a2c35d56cfca215aaa4e4768ab4746effdd0c | R | 18,885 | 431 |
#' Make cluster level plot
#'
#' @import ggplot2
#' @importFrom gridExtra grid.arrange
#' @import intervals
#' @export
make_cluster_plot <- function(
cluster_to_plot,
main_title = NA,
exons_table = NULL,
meta = NULL,
cluster_ids = NULL,
counts = NULL,
introns = NULL,
snp_pos=NA){
if( is.null(cluster... |
5975fe41db894b70dbaefb101a39397f0283748d696b6d1ba2ed1ca216840204 | R | 20,217 | 651 | #' @import dplyr tibble ggplot2 ggraph tidygraph pals
NULL
#' Removes legend from plot.
#'
#' @export
no_legend <- function(){
theme(
legend.position = 'none'
)
}
#' Removes margins from plot.
#'
#' @export
no_margin <- function(){
theme(
plot.margin = margin(0,0,0,0,unit='lines')
)
}
... |
39a0fd577c26583eca2ee3d121861bc179790ce1fe06684ab9adc8b0f08f50ea | R | 20,249 | 730 | # logging which server was used to run this script
system("echo \"Running this on ### $(hostname) ### \\(* w *)/\"")
# load library
library(tidyverse)
library(Seurat)
library(scales)
library(scico)
library(tictoc)
library(shades)
# read in annotated data
# data_obj_sub <- readRDS("./seurat_objects/snRNA_cellBneder_co... |
84de971411cb1e18de6f7a3f6a45b365c998494ce38a99fd14c89d62ac5d7234 | R | 20,393 | 542 | #!/usr/bin/env Rscript
#options(echo=TRUE)
#### Jack Humphrey 2017
### annotate the output of differential splicing
## and prepare for visualisation
library(optparse)
option_parser=OptionParser(
usage="%prog [options] <name>_perind_numers.counts.gz <name>_cluster_significance.txt <name>_effect_sizes.txt annotation_... |
591173f406ac32c89a6993f086c4c69629e3f2333961b44b81f78dd9b066464a | R | 21,198 | 430 | ---
title: 'Imputation of splitted UPDRS measures'
author:
- name: "Alejandro M.Carrasco"
affiliation: UCL
output:
html_document:
css: ./font/missingexploration_fontsize.css
code_folding: hide
theme: paper
highlight: kate
df_print: paged
fontsize: 20pt
toc: true
toc_float: true
... |
57675eba1b56a73e00dbccd5c21c02ef5ca8e7a3a30574a9392dc405340c7d9c | R | 21,202 | 481 | #!/usr/bin/env Rscript
####
# load the libraries
###
.nozzleLibraries <- function() {
print('...loading libraries')
RLIBDIR = '/xchip/gistic/Jeremiah/R/x86_64-unknown-linux-gnu-library/3.1/'
GIT.HOME = '/xchip/gistic/Jeremiah/GIT/'
ISVA.HOME = paste(Sys.getenv('GIT_HOME'), '/isva/', sep = "");
.libPaths(c(... |
31d93d2e49aca877b0de27aa3654dc4bcf3f405f3fa88b5be470ecdb1c4f2bc5 | R | 21,964 | 445 | ---
title: 'Sensitivity analysis'
author:
- name: "Alejandro M.Carrasco"
affiliation: UCL
output:
html_document:
css: ./font/missingexploration_fontsize.css
code_folding: hide
theme: paper
highlight: kate
df_print: paged
fontsize: 20pt
toc: true
toc_float: true
number_sections:... |
691113c5a407766d68bf90d321d84cda9bc255824f4b7ee9cb5c4fa56c187fd2 | R | 22,877 | 621 | require(tidyverse)
require(entropy)
setwd('~/git/dissertation/thesis/Chapter 04 - A variable-length code/src/simulations/')
source(file='~/Dropbox/Apps/ShareLaTeX/CogSci 2019 (Camera Ready)/code/transmit/R/transmit.R')
hyman_transmit <- function(
codebook,
signal_power,
noise_power,
timesteps,
time_interval,... |
549bc302a3842a2b2878b563dec7dcebc4e3e69bc1ea974e16ecaf187c5a8612 | R | 23,086 | 799 | library(tidyverse)
library(ggrastr)
library(ggrepel)
library(shades)
library(plotly)
library(htmlwidgets)
library(writexl)
DE_level_2 <- read_tsv("MAST_res_level2_splitControls_summary.txt")
# write_xlsx(DE_level_2, "MAST_res_level2_splitControls_summary.xlsx")
full_DE_level_2 <- read_tsv("./MAST_res_level2_summary.t... |
daa64572d0a6528d463c128492a565adb69964bcec6e7c9f91adb7fede747727 | R | 24,016 | 594 | # logging which server was used to run this script
system("echo \"Running this on ### $(hostname) ### \\(* w *)/\"")
# load library
library(tidyverse)
library(ArchR)
metadata <- read_tsv("metadata_merged.txt")
# full dataset
p <- metadata %>% ggplot(aes(predictedScore_from_snRNA_full_level2))
p + geom_histogram() +
... |
527ab3d5287cdd88179b2b63bc139eac8ec2c6429fcef3e7425ed76d33d5eb69 | R | 24,232 | 831 | library(tidyverse)
library(viridis)
library(scico)
library(pheatmap)
library(plotly)
library(htmlwidgets)
library(ggrastr)
# read full DEG list
full_DE <- read_tsv("./MAST_res_level2_summary.txt", col_types = "cccciicdddddllddd")
# read DEG list generated by separating male and female samples
separate_sex_DE <- read_t... |
527a13566e882700e1d1d9bf4f42df9e3bf8ff5891a77e8c18d63bcac4341dfa | R | 24,630 | 530 | # 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(ggrastr)
library(scattermore)
library(cowplot)
# read in clean dataset with 2nd round annotation (V2, remo... |
3dd3413ff112893973a64bb0d73179c9643c4d95066939263a3ea72a02468229 | R | 25,092 | 734 | library(Matrix)
test_that("Predictor's finalizer should not fail", {
X <- as.matrix(as.integer(iris[, "Species"]), ncol = 1L)
y <- iris[["Sepal.Length"]]
dtrain <- lgb.Dataset(X, label = y)
bst <- lgb.train(
data = dtrain
, params = list(
objective = "regression"
... |
ba115ac3f3d63b0b0d0ff5e781c4b745acb28c900b3607cac4b8db1aced42e74 | R | 26,476 | 614 | ---
title: 'Model generation'
author:
- name: "Alejandro M.Carrasco"
affiliation: UCL
output:
html_document:
code_folding: hide
theme: paper
highlight: kate
df_print: paged
toc: true
toc_float: true
number_sections: true
---
```{r setup, include=FALSE}
library(tidyverse) # For t... |
af6ea8715d35186741ca60d20851c46a5e4420991f3772150f400c24d604652f | R | 27,316 | 576 | # Run before app.R
# Clear workspace
rm(list = ls())
library(shiny)
library(shinyBS)
library(shinyWidgets)
library(tidyverse)
library(stringr)
library(Matrix)
library(Seurat)
library(metacell)
library(scater)
library(Biostrings)
library(igraph)
library(plotly)
library(data.tree)
library(collapsibleTree)
library(phyl... |
14e38e8bb77a5bb62901ff8e017de24b8b4d9d4ab7c2b0a1aeda97f16265af9d | R | 27,317 | 577 | # Run before app.R
# Clear workspace
rm(list = ls())
library(shiny)
library(shinyBS)
library(shinyWidgets)
library(tidyverse)
library(stringr)
library(Matrix)
library(Seurat)
library(metacell)
library(scater)
library(Biostrings)
library(igraph)
library(plotly)
library(data.tree)
library(collapsibleTree)
library(phyl... |
303a310479e46f8b46737ba6847f0cb428148a4a1537ec25e83d9e1ef209d32f | R | 27,317 | 577 | # Run before app.R
# Clear workspace
rm(list = ls())
library(shiny)
library(shinyBS)
library(shinyWidgets)
library(tidyverse)
library(stringr)
library(Matrix)
library(Seurat)
library(metacell)
library(scater)
library(Biostrings)
library(igraph)
library(plotly)
library(data.tree)
library(collapsibleTree)
library(phyl... |
399a7a15d620c6b6d0f17076f0ea9603b3133466a0ce471719d72a3d585d72ec | R | 27,317 | 577 | # Run before app.R
# Clear workspace
rm(list = ls())
library(shiny)
library(shinyBS)
library(shinyWidgets)
library(tidyverse)
library(stringr)
library(Matrix)
library(Seurat)
library(metacell)
library(scater)
library(Biostrings)
library(igraph)
library(plotly)
library(data.tree)
library(collapsibleTree)
library(phyl... |
43c3918189adc1165ab2ebe7382f0615052a887698da9dc888c12eedd06c8a28 | R | 27,317 | 577 | # Run before app.R
# Clear workspace
rm(list = ls())
library(shiny)
library(shinyBS)
library(shinyWidgets)
library(tidyverse)
library(stringr)
library(Matrix)
library(Seurat)
library(metacell)
library(scater)
library(Biostrings)
library(igraph)
library(plotly)
library(data.tree)
library(collapsibleTree)
library(phyl... |
55829bafb53f1a1b045bd286b0fe4564b4d20d73695d596163399104dc6e3ccb | R | 27,317 | 577 | # Run before app.R
# Clear workspace
rm(list = ls())
library(shiny)
library(shinyBS)
library(shinyWidgets)
library(tidyverse)
library(stringr)
library(Matrix)
library(Seurat)
library(metacell)
library(scater)
library(Biostrings)
library(igraph)
library(plotly)
library(data.tree)
library(collapsibleTree)
library(phyl... |
9ef63bd5f04bdf89736ce81793869c2f7646a089be49edaffbaa0b9784b3e8f5 | R | 27,317 | 577 | # Run before app.R
# Clear workspace
rm(list = ls())
library(shiny)
library(shinyBS)
library(shinyWidgets)
library(tidyverse)
library(stringr)
library(Matrix)
library(Seurat)
library(metacell)
library(scater)
library(Biostrings)
library(igraph)
library(plotly)
library(data.tree)
library(collapsibleTree)
library(phyl... |
f347b41491d7f5762f9934d0b2982017177bd2686d43b2efb1926d67880fc06b | R | 27,317 | 577 | # Run before app.R
# Clear workspace
rm(list = ls())
library(shiny)
library(shinyBS)
library(shinyWidgets)
library(tidyverse)
library(stringr)
library(Matrix)
library(Seurat)
library(metacell)
library(scater)
library(Biostrings)
library(igraph)
library(plotly)
library(data.tree)
library(collapsibleTree)
library(phyl... |
ce8fe734ec56a858ddd5fea06abb3a1f7f9704ee4c3c3bfe865dabfaae5ac4cb | R | 27,319 | 577 | # Run before app.R
# Clear workspace
rm(list = ls())
library(shiny)
library(shinyBS)
library(shinyWidgets)
library(tidyverse)
library(stringr)
library(Matrix)
library(Seurat)
library(metacell)
library(scater)
library(Biostrings)
library(igraph)
library(plotly)
library(data.tree)
library(collapsibleTree)
library(phyl... |
8dee478266a738735548c38e4d3c2aef3bfc2b9d39328e5c52324bff22e24563 | R | 27,387 | 579 | # Run before app.R
# Clear workspace
rm(list = ls())
library(shiny)
library(shinyBS)
library(shinyWidgets)
library(tidyverse)
library(stringr)
library(Matrix)
library(Seurat)
library(metacell)
library(scater)
library(Biostrings)
library(igraph)
library(plotly)
library(data.tree)
library(collapsibleTree)
library(phyl... |
4947d54b77b03362cc886b868a2f070bcc90e5976f059f0f3bf1a4496ef15efe | R | 27,523 | 554 | #In this preprocess part, we cite RCTD's preprocess code(Dylan M. Cable, et al. Nature Biotechnology.)
#Citation in our Methods parts.
setClass("Reference",
slots = c(
cell_types = "factor",
counts = "dgCMatrix",
nUMI = "numeric"
),
prototype = list(... |
a998d5fb7e9406858581379761ca528ff9ddfdcd4e5005ed9069ac792610d8e6 | R | 27,547 | 773 | ---
title: "coloc_metaBrain"
author: "AMCalejandro"
date: "18/4/2022"
output:
html_document:
df_print: paged
highlight: kate
code_folding: hide
number_sections: yes
theme: spacelab
toc: yes
toc_float: yes
---
```{r results="hide", warning=FALSE, message=FALSE}
.libPaths("/data/kronos/kron... |
1e9fce46e02576c16c67f88729bfa3dd21a5a3025cc4b0587fb1d6a75ab10480 | R | 27,678 | 669 | ---
title: 'Data wrangling'
author:
- name: "Alejandro M.Carrasco"
affiliation: UCL
output:
html_document:
code_folding: hide
theme: paper
highlight: kate
df_print: paged
toc: true
toc_float: true
number_sections: true
---
```{r setup, hide = TRUE}
library(tidyverse) # For tidy ... |
6fbddb746b65f59386353151c61189b1e56a053ba5f98cbe4619ba9ded51b58c | R | 29,010 | 687 |
#' Make gene level plot
#'
#' Take a gene name or ID, search the counts matrix for all clusters that fall within that gene's boundaries (found from the exon table).
#' @import ggplot2
#' @export
# #
# gene_name <- "ADIPOR2"
# clusterID <- NULL
# min_exon_length <- 0.5
# cluster_list <- clusters
# # for testing - crea... |
afa05c512b847d999548d7e3d6be2462122affd10889cd58b321f9c9dff1f3b8 | R | 29,546 | 774 | #' @import dplyr tibble
NULL
#' Infer a Gene Regulatory Network with \code{Pando}
#'
#' @import Matrix
#' @import sparseMatrixStats
#' @importFrom purrr map_dfr map_lgl map_dbl map
#' @importFrom stringr str_replace_all
#'
#' @param genes A character vector with the target genes to consider for GRN inference.
#' Take... |
86a5bcdc0828e4a5716f8eb2fd8965936a5308a0242a2909eedb0e2a40550d67 | R | 30,070 | 816 | ##########
########## KH additional functions
##########
## #' @name eclusters2
## #' @title gGraph R6 public method eclusters2
## #'
## #' @description
## #' Marks ALT edges belonging (quasi) reciprocal cycles
## #'
## #'
## #' @param juncs GRangesList of junctions
## #' @param mc.cores parallel
## #' @param only_c... |
d0bd13ecef92087306d1d8a3d3f12b44980db76c9a36cee9edc3c4f78dba739f | R | 30,478 | 957 | # logging which server was used to run this script
system("echo \"Running this on ### $(hostname) ### \\(* w *)/\"")
# load library
library(tidyverse)
library(DESeq2)
library(Hmisc)
library(ggpubr)
library(patchwork)
# read in pseudobulk grouping metadata
sample_info <- read_tsv("./pseudobulk_group_metadata.tsv")
# ... |
0dc9cbf004814f2e8ef4cb5db904f8ed80b4b645d1c1b1d739c44d90feed1a59 | R | 31,819 | 774 | #!/usr/bin/env Rscript
## set the right library paths
.libPaths = c("/xchip/gistic/Jeremiah/R", "/broad/software/free/Linux/redhat_6_x86_64/pkgs/r_3.1.1-bioconductor-3.0/lib64/R/library")
require(data.table)
ra_breaks <-
function(rafile, keep.features = T, seqlengths = hg_seqlengths(), chr.convert = T, snowman = FALS... |
967a27143eb3cd39d74b1b961ac41f6bd295ddddbefea2de19af03144ea0610d | R | 33,036 | 775 | # one create PGV instance
# inside we have the add function to initialize the PGV datafile.json and get pointers to where all the files are
# also set up datafiles.json and get pointers to where the data folder and datafiles.json are being stored
# will need to set up datafiles.json and data folder along with backups ... |
8c31d3a436f2e3fcd38d4083f2d446c8e9a7a6f86fda0d41bf697ce9fda1249b | R | 35,953 | 864 | #!/usr/bin/env Rscript
## set the right library paths
.libPaths = c("/xchip/gistic/Jeremiah/R", "/broad/software/free/Linux/redhat_6_x86_64/pkgs/r_3.1.1-bioconductor-3.0/lib64/R/library")
file.dir <-
function(paths)
{
return(gsub('(^|(.*\\/))?([^\\/]*)$', '\\2', paths))
}
require(data.table)
suppressMessages... |
fee05364b2e42e12cd08ac436309ceee2f796a3568a81280fb23bb495b36548d | R | 37,068 | 1,570 | getAD_Pval <- function(result, version = 1, fromPaired = FALSE){
if(fromPaired){
result <- result$test_result
}
tbl <- result$ad
tbl_tibble <- tbl %>% as.tibble()
p_val <- tbl_tibble$` asympt. P-value`[[version]]
return(p_val)
}
getAD_vals <- function(result, version = 1, fromPaired = FALSE){
if(from... |
8f1caacb6bc72f58dbfb0300e6f2dc46123c96bc12fcfd8bf561f9e139977a4b | R | 38,029 | 703 | #setwd("~/LampreyBrainApps/Apps/ad_brain/")
source(file = "prepare_env.R")
# User interface (client)
ui <- navbarPage(title = "AdultEncephalon",
id = "lb",
position = "fixed-top",
theme = "bootstrap_ad_brain.css",
# Taxonomy tab
... |
eb1a41bcbfd57d75665575c1e765c91eb4fef204389e530d71ba8402b56fa1c0 | R | 38,031 | 703 | #setwd("~/LampreyBrainApps/Apps/am_brain/")
source(file = "prepare_env.R")
# User interface (client)
ui <- navbarPage(title = "LarvalEncephalon",
id = "lb",
position = "fixed-top",
theme = "bootstrap_am_brain.css",
# Taxonomy tab
... |
7730fb775643b6c51dda41ddec7e880d067657cfef64a8b6a91054f8c659022f | R | 38,034 | 703 | #setwd("~/LampreyBrainApps/Apps/ad_midb/")
source(file = "prepare_env.R")
# User interface (client)
ui <- navbarPage(title = "AdultMesencephalon",
id = "lb",
position = "fixed-top",
theme = "bootstrap_ad_midb.css",
# Taxonomy tab
... |
c4ddbd5f81879a3ba6f720288020c9aaad7c7f134f7b1aa7db1adbcb9d0594fa | R | 38,035 | 703 | #setwd("~/LampreyBrainApps/Apps/am_midb/")
source(file = "prepare_env.R")
# User interface (client)
ui <- navbarPage(title = "LarvalMesencephalon",
id = "lb",
position = "fixed-top",
theme = "bootstrap_am_midb.css",
# Taxonomy tab
... |
5e5f6f180e8364c0c3c6d083d2c37abdfd9da52485077a71e77651897e9a2400 | R | 38,037 | 703 | #setwd("~/LampreyBrainApps/Apps/ad_hind/")
source(file = "prepare_env.R")
# User interface (client)
ui <- navbarPage(title = "AdultRhombencephalon",
id = "lb",
position = "fixed-top",
theme = "bootstrap_ad_hind.css",
# Taxonomy tab
... |
6642363898cf3bd905065cb9104a1a0ffef82f067e5baf9ec50bcc8c3f110179 | R | 38,038 | 703 | #setwd("~/LampreyBrainApps/Apps/am_hind/")
source(file = "prepare_env.R")
# User interface (client)
ui <- navbarPage(title = "LarvalRhombencephalon",
id = "lb",
position = "fixed-top",
theme = "bootstrap_am_hind.css",
# Taxonomy tab... |
ef8056746d1a2c6b50480ac0b7766b957f7645444d337766ffad381773af5324 | R | 38,179 | 703 | #setwd("~/LampreyBrainApps/Apps/ad_tele/")
source(file = "prepare_env.R")
# User interface (client)
ui <- navbarPage(title = "AdultTelencephalon",
id = "lb",
position = "fixed-top",
theme = "bootstrap_ad_tele.css",
# Taxonomy tab
... |
960d0a53a1c7c8f6acc2bc6a376ce834beec327d5a051a180f77e309ccec07f1 | R | 38,180 | 703 | #setwd("~/LampreyBrainApps/Apps/am_tele/")
source(file = "prepare_env.R")
# User interface (client)
ui <- navbarPage(title = "LarvalTelencephalon",
id = "lb",
position = "fixed-top",
theme = "bootstrap_am_tele.css",
# Taxonomy tab
... |
7a9a7aa809ca7de9f03d5965d90d887a83da7bb08b9c6069cf1c72d539531656 | R | 38,395 | 707 | #setwd("~/LampreyBrainApps/Apps/ad_dien/")
source(file = "prepare_env.R")
# User interface (client)
ui <- navbarPage(title = "AdultDiencephalon",
id = "lb",
position = "fixed-top",
theme = "bootstrap_ad_dien.css",
# Taxonomy tab
... |
3d31311ad071d678688177ceff52e9586d84116f6c98c73ea44883da638673e5 | R | 38,396 | 707 | #setwd("~/LampreyBrainApps/Apps/am_dien/")
source(file = "prepare_env.R")
# User interface (client)
ui <- navbarPage(title = "LarvalDiencephalon",
id = "lb",
position = "fixed-top",
theme = "bootstrap_am_dien.css",
# Taxonomy tab
... |
f011009fe01e86d3f5a623df3b251ebadce14d72acba8289d115636936c94683 | R | 38,910 | 987 | ---
title: "COLOC against PsychEncode and eQTLGen"
author: "AMCalejandro"
date: "18/4/2022"
output:
html_document:
df_print: paged
highlight: kate
code_folding: hide
number_sections: yes
theme: spacelab
toc: yes
toc_float: yes
---
```{r results="hide", warning=FALSE, message=FALSE}
.libPa... |
6646ca2f54c6406e936ec3f14b48edfbebf6b7578cbf00a5cabee3cf7aef2588 | R | 40,596 | 1,183 | #RNA seq analysis
#Written by Rebecca MacPherson as part of MacPherson et al 2022 (Listed in BioRxiv at the time of submission of this work) - Pleiotropic effects of the lncRNA Uhg4 in Drosophila melanogaster
#Last Edited by Rebecca MacPherson 2022-05-21
#####Filtering and normalization of counts data prior to s... |
4ed76de17ed1e5b9f7c792fbe5798ebfe66d1a630a4b9077d5e0d06e04731fee | R | 41,803 | 972 | ####Code used to generate graphs for MacPherson, R.A. et al., "Genetic and Genomic Analyses of Drosophila melanogaster Models of Rare Human Diseases"
####Relevant GEO Accession number: GSE213763
### Fly Models of CSS/NCBRS/CdLS
################## Sleep / Activity Plots #######################
setwd("path\\to\... |
e0a4005a7c84c20611e9e93a7d9d2ef0f17865bc89116fe726ea1f16abf700d1 | R | 45,635 | 898 | ##Using StringTie with DESeq2 for Nasonia RNAseq data
rm(list=ls()) #clear all objects
gc()
#dataset:
#Nasonia RNAseq data
#experimental light treatment and dark control
#4 different time point: 0.5h 1h 2h 4h
#8 sample groups
#3 replicates per experimental sample group, 2 replicates per control sample group
... |
41e9185d48a6079e48ba73af49624d7880f039dcd0c5a9facdbb7ecc06e7e2ff | R | 46,143 | 1,036 |
# ST.FLAG = "-f 0x02 -F 0x10" # former default - DOES NOT EXCLUDE DUPS
#' Samtools flags
#'
#' Samtools flags for fragCounter bam query
#' -F exclude ANY bits set
#' 3868 sets bits
#' 4 - Read unmapped
#' 8 - Mate unmapped
#' 16 - Read reverse strand
#' 256 - Not primary alignment (secondary alignment)
#' 512 - Re... |
24e84c4217db9645f9bd316f31b3d802d0e03ffed3745c11c641b6e380c9f483 | R | 52,761 | 1,556 | library(tidyverse)
library(viridis)
library(scico)
library(pheatmap)
library(plotly)
library(htmlwidgets)
library(ggrepel)
library(ggrastr)
# read DEG list
our_DE <- read_tsv("../MAST_res_level2_summary.txt", col_types = "cccciicdddddllddd")
Manolis_DE <- read_tsv("Manolis_ALS_single_cell_DE_genes_June2021_submission... |
a32f740ca9bc0cf37809579c727d0b7108691f526e7872ee4e6527ab0d4b0a5f | R | 53,230 | 1,166 | #!/nfs/sw/R/R-3.6.1/bin/Rscript
library(dplyr)
require(NanoStringQCPro)
require(ggplot2)
require(EnvStats)
library('pheatmap')
library(RUVSeq)
library(DESeq2)
library(limma)
library(matrixStats)
library(survival)
library(stringr)
library(survminer)
library(limma)
library(lsa)
library(tidyr)
library(biomaRt)
library(... |
d1c7ce28c178ca49c4f4bc01eecda8c90a64a9f886d6e5c68d139fe9199aa633 | R | 53,682 | 1,428 | # Load library
library(ggbiplot)
library(tidyverse)
library(edgeR)
library(magrittr)
library(viridis)
library(gridExtra)
library(ggrepel)
# Read in data and tidy up
# remove subject 908
exp_data <- read_tsv("allSamples_rsem_genes_results_counts_annotated.txt") %>%
select(-starts_with("908"))
tpm_data <- read_tsv("a... |
02c609a67d799c3a4e6f121063f004cdfc11b2b0f6efb2405290ba0e4730e7aa | R | 59,522 | 1,660 | # logging which server was used to run this script
system("echo \"Running this on ### $(hostname) ### \\(* w *)/\"")
# load library
library(tidyverse)
library(ArchR)
library(Seurat)
library(BSgenome.Hsapiens.UCSC.hg38)
library(viridis)
library(scico)
library(ggrepel)
library(broom)
library(future.apply)
library(ggridg... |
c7355d154acd13ccb981260eeab925ac7379b5676d0a2599267b220348329a01 | R | 65,839 | 1,743 | library(testthat)
library(gUtils)
library(gTrack)
setDTthreads(1)
svaba = system.file('extdata', "HCC1143.svaba.somatic.sv.vcf", package = "gGnome")
delly = system.file('extdata', "delly.final.vcf.gz", package = "gGnome")
novobreak = system.file('extdata', "novoBreak.pass.flt.vcf", package = "gGnome")
HGSL = c("1"=24... |
63935c30da4c7265ec46761638668d7bb8b102318b0a7198206beb6cab64d02e | R | 77,304 | 2,154 | ## appease R CMD check vs data.table
sid=side1=side2=side_1=side_2=silent=snid=splice.variant=splicevar=str1=str2=strand_1=strand_2=subject.id=suffix=tag=threep.cc=threep.coord=threep.exon=threep.frame=threep.pc=threep.sc=threep.sc.frame=to=transcript.id.x=transcript.id.y=transcript_associated=transcript_id=twidth=tx.c... |
6cad598e8834c20c38c6af4bc4459b3752637dec017e936f8b8122fa6f2b17f8 | R | 87,471 | 1,806 | ---
title: "[gGnome](https://github.com/mskilab/gGnome) Tutorial"
author: "Marcin Imielinski Laboratory"
output:
html_document:
toc: true
toc_float: true
theme: flatly
---
```{r cache=FALSE,echo=FALSE,results="hide", message = FALSE, warning = FALSE}
knitr::opts_chunk$set(collapse = TRUE, fig.width = 8,... |
4a481734d59364c48e5156aea002e17e071efabc2abc1a21f5b5ba3d12a98ac8 | R | 90,601 | 1,873 | #' Generate a PGV instance
#' @name pgv
#' @description
#'
#' Takes a table with paths to gGraphs, coverage files, and gWalks (optional), and
#' generates an instance of a PGV directory that is ready to visualize using PGV
#'
#' @param data either a path to a TSV/CSV or a data.table
#' @param name.col column name in ... |
928cf16fce7983bdb994a56744eda56cda1defc2c9498d540fae961085e06f20 | R | 93,413 | 2,474 | library(shiny)
library(here)
options(shiny.maxRequestSize = 200 * 1024^2) # 200 MB; files stay local
if (.Platform$OS.type == "windows") windowsFonts(Arial = windowsFont("Arial"))
source(here("01-scripts/01-set-up.R"))
# ---- Constants -----------------------------------------------------------
GROUPS <- c("DI", "... |
b1c2f45b6611ed6dc86596e12a8d8d9992fabaff8a1b3c3c07767db6fd1a1d0f | R | 112,323 | 2,689 | ##############
# STARTUP
##############
overall_start_time = Sys.time()
cat(file=stderr(), 'Loading dependencies...')
options(stringsAsFactors=F)
if(interactive()) setwd('~/d/sci/src/scaso')
suppressMessages(library(tidyverse))
suppressMessages(library(janitor))
suppressMessages(library(weights))
suppressMessages(lib... |
4023e504fa2f296eaf33f9617ae8d7a0e8cdba483ad231159de4b92603ce4259 | R | 115,696 | 2,728 | #' applications of gGraph
#' @name balance
#' @title balance gGnome graphs
#' @description
#'
#' Here we analyze gGraphs with "cn" (copy number) field to enforce integer
#' cn and junction balance, ie sum of incoming (or outgoing) edge
#' cn should be equal to node copy cn.
#'
#' The goal is to find a balaned assignm... |
8c35ddb11c392ab382e27f4920829d631788b6e5994f2719cc8fac7b00118975 | R | 119,332 | 2,911 | # assigning this operator since sometimes R tries to use the ggplot2 operator instead of the gUtils one (depending on the order of libraries loaded in a session)
`%+%` = gUtils::`%+%`
#' @name breakgraph
#' @title breakgraph
#'
#' @description
#' Builds a gGraph by breaking the reference genome at the points specified... |
287bd4e74e7207b9b3b792fcb9ec3984d5866dac189b25cc43d76bbf59541318 | R | 181,705 | 4,316 | #' Proximity analysis of two genomic regions and rearrangment movement
#' @name proximity
#'
#' @description Takes a set of n "query" elements (GRanges object, e.g. genes) and determines
#' their proximity to m "subject" elements (GRanges object, e.g. regulatory
#' elements) subject to set of rearrangement adjacencie... |
c7a739f47dfc804150a6b53e748c40ed4eb929a15b84f0387610c7e5d37039b6 | R | 200,000 | 4,592 | ## appease R CMD CHECK misunderstanding of data.table syntax by declaring these global variables
low.count=high.count=seg=chromosome=alpha_high=alpha_low=beta_high=beta_low=predict.var=dup=psid=.N=es=res=esid=pid=ub=lb=esid=dup=lb=ub=dup=y=dup=pid=lb=es.s.ix=km=es.t.ix=adjusted.ratio=ref.count.t=alt.count.t=depth.norma... |
e91e95cbb3ff463db5f796630519d84e010c7fbc3aaac3e6573346caf67892d3 | R | 200,000 | 4,072 | #' @name gGnome
#' @title gGnome
#' @description
#' Reference-based graph representation of structurally altered genome
#' employing GenomicRanges framework.
#'
#'
#' Copyright (C) 2018 Xiaotong Yao, Joseph DeRose, Marcin Imielinski
#'
#' This program is free software: you can redistribute it and/or modify
#' it ... |
feec56fb8400af7ccfcf3576b797d6176c6a28dad408664536322b0648586993 | R | 200,024 | 4,215 | ---
title: "Different rules for binocular combination of luminance flicker in cortical and subcortical pathways"
author: |
| Federico G. Segala$^1$, Aurelio Bruno$^2$, Joel T. Martin$^1$, Myat T. Aung$^1$, Alex R. Wade$^1$
| & Daniel H. Baker$^1$
output:
bookdown::pdf_document2:
fig_caption: yes
toc: no
... |
9335a8b5a4f3650966bec83474b8ed1546361b2f4e8df446a9c358888879b432 | Rust | 53 | 3 | pub mod coord_shift;
pub mod iloc;
pub mod tile_ref;
|
ad13fd8f2d953a20369dc4d44f730e9c289369e4b324829f0264dbe896e8ddb7 | Rust | 183 | 9 | mod first_sam_file;
mod genome_alignment;
mod new_algo;
mod output_data_types;
mod second_sam_file;
pub mod select_alignments;
pub mod select_reads;
mod split_read;
mod te_alignment;
|
2c097bea6a573d8c7165e60e5407648fbe141526280a17b5f380c99fccdeb957 | Rust | 828 | 22 | use path_abs::PathFile;
use std::ffi::OsStr;
use std::process::Command;
use crate::utils;
pub fn download(url: &str, output_file: &str) {
// first, create the absolute filepath from the relative filepath (create it if it doesn't exist)
let output_path: PathFile = utils::absolute_filepath_unchecked(output_file... |
be340ea966f541a96b1ae7c83017e699c4b614b04ca1b9747bc4e9777bc44485 | Rust | 1,102 | 29 | use lazy_static::lazy_static;
use regex::Regex;
// lazy static regexes to avoid multiple compilations
lazy_static! {
pub static ref SM_REGEX: Regex = Regex::new(r"^(\d+)S(\d+)M$").unwrap();
pub static ref MS_REGEX: Regex = Regex::new(r"^(\d+)M(\d+)S$").unwrap();
pub static ref HM_REGEX: Regex = Regex::new(... |
0d36576a460e52b52aaf5eb479e498a40cf2f262d0bec9281744b42303dbc464 | Rust | 1,486 | 49 | use anyhow::{Context, Result};
use std::collections::HashMap;
pub struct Metadata {
pub delimiter: String,
// a map between positions and headings
// note: the positions are 1-indexed to make it
// easy to look at the file format and generate this struct
// also, not all headings in the file need ... |
88c71d4308cc4f5660c0c537de981dcdbbaa433733d5fd09160d17c6d9624c9b | Rust | 1,716 | 55 | use path_abs::PathFile;
use std::fs::File;
use std::io::BufRead;
use std::io::BufReader;
use std::str::FromStr;
// represent insertion locations (within the reference)
// and read them in from a TSV file
#[derive(PartialEq, Eq, Hash)]
pub struct ILoc {
pub chrom: String,
pub upstream_pos: u64,
pub downstr... |
53c8fb4e208821b32d255d451fa274b0768661b37c6d9bb71200590dc77bb3e6 | Rust | 2,218 | 65 | use path_abs::PathFile;
use rayon::prelude::*;
use std::collections::HashMap;
use std::fs::File;
use std::io::{BufRead, BufReader, BufWriter, Write};
use std::sync::{Arc, Mutex};
use super::first_sam_file;
pub fn select_reads(
te_aligned_path: &PathFile,
selected_reads_path: &PathFile,
only_create_transp... |
a053ddef461a6dec55a13f74812f043f3864c9a7c872595efd90d76553c420d7 | Rust | 2,318 | 68 | use path_abs::PathFile;
use serde_json;
use std::collections::HashMap;
use std::fs::File;
use std::io::{BufReader, BufWriter, Write};
use super::genome_alignment::GenomeAlignment;
use super::output_data_types::OutputInsertions;
use super::second_sam_file;
pub fn select_alignments(
chroms: Vec<String>,
min_ts... |
963ed6ee7b4e8e3d744ecf6a947f6572d0e35edf7fcd3d2847770d0f5683ea09 | Rust | 2,737 | 78 | use std::process::Command;
use crate::utils;
use crate::utils::Reads;
// "fix" alignments by cleaning up read pairing information and flags
// also compress from SAM format to BAM format to save space and
// put the output in result_dir/fixed_alignments.sam
fn samtools_fixmate(result_dir: &str) {
let input_file =... |
c393f825a8e6aa3e202bb78316af203d8bf73232f9d714181daf57a726c2777d | Rust | 3,422 | 117 | // process a split-read using Regex
// CIGAR string [numbers]letter[numbers]letter...
// Each set of numbers modifies the letter after it
// S means "no match", M means "match"
// We are only interested in the 2 letters S and M
// note that H also means "no match" but it uses hard clipping
// Matches with hard clippin... |
92c3d852b6031e6e98009edbbcdc9a31fddf5a7090c75adc9effc7b553e901e4 | Rust | 3,439 | 93 | use super::iloc::ILoc;
// utilities to shift coordinates from normal coords to
// TE-removed coords and vice versa
// the coords may not be translatable
// from the reference genome into the synthetic genome
// because they could be within a transposon
// If within a transposon, we want to store
// the start and end ... |
36c082cf139c16f66788d142313d279fb8a702a42b89295149d97dd9ef09b7aa | Rust | 3,541 | 88 | use path_abs::PathDir;
use std::fs::File;
use std::io::BufRead;
use std::io::BufReader;
use std::io::BufWriter;
use std::io::Write;
use std::str::FromStr;
use crate::utils;
// tile a set of artificial 150-bp-wide artificial "reads" across the reference genome
// this will then be used by the TE mapper to look for tr... |
f93f68b576c91cc2f77e3e7c877cef3f099f9c7dfe396b26a9d0c92250e22960 | Rust | 3,569 | 76 | //! Step 4: orthologous pairs. Within each orthologous group, trees are pruned to
//! the group and every node of the leaf-to-root walk votes ortho or para for the
//! pairs it joins; pairs with ortho / (ortho + para) > ratio_ortho are reported.
use crate::{create, lines, step3::key, Opts, Proteins, R};
use crate::tre... |
da38db4873da9f042b9b21e4315c41fb4b0d2679113dcf3ad53a3222e8cddf62 | Rust | 4,244 | 123 | use path_abs::{PathDir, PathFile, PathOps};
use crate::te_mapper_utils::{select_alignments, select_reads};
use crate::utils;
use crate::utils::Reads;
pub fn map(
ref_name: &str,
reads: &Reads,
transposons_name: &str,
result_dir: &str,
bwa_threads: u16,
output_should_be_json: bool,
phase: u... |
76c3234af8bd0301d029460156adeafe4aa2ff86b9b6cb354447be0f67a48177 | Rust | 4,346 | 122 | mod regexes;
mod sg_utils;
mod sx_app;
mod sx_download;
mod sx_map;
mod sx_variants;
mod tabular;
mod te_mapper_utils;
mod utils;
use std::error::Error;
use crate::utils::Reads;
fn main() -> Result<(), Box<dyn Error>> {
let app = sx_app::app();
let app_matches = app.get_matches();
// handle "download" s... |
5c89cf8f11e301c47f62ae1aba4649f71a5a104bea4c78b500b46c45b2eea19a | Rust | 4,838 | 133 | // a set of common utilities for all StanEx subcommands
use path_abs::PathFile;
use std::ffi::OsStr;
use std::process::Command;
// create an absolute file path from a relative file path
// (file must already exist)
pub fn absolute_filepath_checked(relative: &str) -> PathFile {
match PathFile::new(relative) {
... |
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