sha256 stringlengths 64 64 | language stringclasses 27
values | size int32 1 491k | lines int32 1 21.8k | content stringlengths 1 200k |
|---|---|---|---|---|
54f1a5c1c93d62e336cd2b734a60021b22681597cde6a9e4d4167478413e13fd | R | 4,776 | 101 | library(plotgardener)
library(RColorBrewer)
library(AnnotationHub)
library(TxDb.Mmusculus.UCSC.mm39.refGene)
library(org.Mm.eg.db)
library(data.table)
project = "2024_GRCm39"
assembly_mm39 <- assembly("GRCm39", "TxDb.Mmusculus.UCSC.mm39.refGene", "org.Mm.eg.db",BSgenome = "bsgenome.mmusculus.ucsc.mm39")
my.mat.palette... |
c3fd811f3f15bafda542f9ffbf05ae1cd96353b79365414c76c87e547b292d61 | R | 4,776 | 101 | library(plotgardener)
library(RColorBrewer)
library(AnnotationHub)
library(TxDb.Mmusculus.UCSC.mm39.refGene)
library(org.Mm.eg.db)
library(data.table)
project = "2024_GRCm39"
assembly_mm39 <- assembly("GRCm39", "TxDb.Mmusculus.UCSC.mm39.refGene", "org.Mm.eg.db",BSgenome = "bsgenome.mmusculus.ucsc.mm39")
my.mat.palette... |
9e35ad13a0abc61d498eaccb9810716b3b1a44efdfaba045a3b5f180e40185d9 | R | 4,778 | 101 | library(plotgardener)
library(RColorBrewer)
library(AnnotationHub)
library(TxDb.Mmusculus.UCSC.mm39.refGene)
library(org.Mm.eg.db)
library(data.table)
project = "2024_GRCm39"
assembly_mm39 <- assembly("GRCm39", "TxDb.Mmusculus.UCSC.mm39.refGene", "org.Mm.eg.db",BSgenome = "bsgenome.mmusculus.ucsc.mm39")
my.mat.palette... |
4ee88c12e9cc565096a7f23e797100531f1daf188e2cbc272c9f9f976ea41985 | R | 4,779 | 101 | library(plotgardener)
library(RColorBrewer)
library(AnnotationHub)
library(TxDb.Mmusculus.UCSC.mm39.refGene)
library(org.Mm.eg.db)
library(data.table)
project = "2024_GRCm39"
assembly_mm39 <- assembly("GRCm39", "TxDb.Mmusculus.UCSC.mm39.refGene", "org.Mm.eg.db",BSgenome = "bsgenome.mmusculus.ucsc.mm39")
my.mat.palette... |
61db8c8947e33c5ff0136ff20c55d653121f1711c5ed61b91eff9da41816b28a | R | 4,779 | 101 | library(plotgardener)
library(RColorBrewer)
library(AnnotationHub)
library(TxDb.Mmusculus.UCSC.mm39.refGene)
library(org.Mm.eg.db)
library(data.table)
project = "2024_GRCm39"
assembly_mm39 <- assembly("GRCm39", "TxDb.Mmusculus.UCSC.mm39.refGene", "org.Mm.eg.db",BSgenome = "bsgenome.mmusculus.ucsc.mm39")
my.mat.palette... |
b2f9552913c1bb9c5cf300b0b081a088cc85b66a84027779a45d085ebf323fc4 | R | 4,782 | 101 | library(plotgardener)
library(RColorBrewer)
library(AnnotationHub)
library(TxDb.Mmusculus.UCSC.mm39.refGene)
library(org.Mm.eg.db)
library(data.table)
project = "2024_GRCm39"
assembly_mm39 <- assembly("GRCm39", "TxDb.Mmusculus.UCSC.mm39.refGene", "org.Mm.eg.db",BSgenome = "bsgenome.mmusculus.ucsc.mm39")
my.mat.palette... |
93a3d431eb336e1039583d3eccba22ce3868df392e2e5510b2ab9cb85d2a7f64 | R | 4,783 | 101 | library(plotgardener)
library(RColorBrewer)
library(AnnotationHub)
library(TxDb.Mmusculus.UCSC.mm39.refGene)
library(org.Mm.eg.db)
library(data.table)
project = "2024_GRCm39"
assembly_mm39 <- assembly("GRCm39", "TxDb.Mmusculus.UCSC.mm39.refGene", "org.Mm.eg.db",BSgenome = "bsgenome.mmusculus.ucsc.mm39")
my.mat.palette... |
c69b9fefef85be0b337c7e217c731ab557d83718473a3a34fb4fa85e02e4db54 | R | 4,827 | 68 | #' Preprocess the single-cell raw data using functions in the \code{Seurat} package
#'
#' This function provide a simplified-version of Seurat analysis pipeline for single-cell RNA-seq data. It contains the following steps in the pipeline:
#' \itemize{
#' \item Create a \code{Seurat} object from raw data.
#' \ite... |
c959a444f5c871531dd81a4fca08221cc20da970027bb71a40f648cabe5ef650 | R | 4,843 | 95 | # Generated by using Rcpp::compileAttributes() -> do not edit by hand
# Generator token: 10BE3573-1514-4C36-9D1C-5A225CD40393
scaleC <- function(X) {
.Call(`_Scissor_scaleC`, X)
}
OmegaC <- function(Omega, sgn) {
.Call(`_Scissor_OmegaC`, Omega, sgn)
}
OmegaSC <- function(OmegaS, sgn) {
.Call(`_Scissor_Om... |
75ee236283cdd7c70ebc07ea44d1bd4d9007abe51b55933207d77cc07d71a8cc | R | 5,084 | 102 | #' Cell level evaluations on the Scissor selected cells
#'
#' This function perfroms evaluations for each Scissor selected cell.
#'
#' This function uses two main strategies to evaluate each Scissor selected cell. The first strategy focuses on the correlation values
#' and their corresponding p-values for each selected... |
a0a82890e41c7f1218acf43f9a9b54d23da8dd9902f2ffe75d3d4c0adcfdf726 | R | 5,291 | 157 | library(data.table)
library(ggplot2)
library(reshape2)
#personalized theme
my_theme = theme(
text = element_text(size = 10),
axis.title.x = element_text(size = 14),
axis.title.y = element_text(size = 14),
axis.text = element_text(size = 14, colour = 'black'),
axis.text.x = element_text(vjust = 0.5, size = 1... |
d6b389096b26565d6fa54b798ad8a711d41ced1d17e3b29342a5f915c610c706 | R | 5,296 | 157 | library(data.table)
library(ggplot2)
library(reshape2)
#personalized theme
my_theme = theme(
text = element_text(size = 10),
axis.title.x = element_text(size = 14),
axis.title.y = element_text(size = 14),
axis.text = element_text(size = 14, colour = 'black'),
axis.text.x = element_text(vjust = 0.5, size = 1... |
dcbd06f1ee52c1c1f38ec1f36d42ab41250dfb4584a5b4c1dfaa8f1dce7a05fc | R | 5,296 | 157 | library(data.table)
library(ggplot2)
library(reshape2)
#personalized theme
my_theme = theme(
text = element_text(size = 10),
axis.title.x = element_text(size = 14),
axis.title.y = element_text(size = 14),
axis.text = element_text(size = 14, colour = 'black'),
axis.text.x = element_text(vjust = 0.5, size = 1... |
a15d43a101f72f607a0a2c58638152272151f59ce9b704c265113c1bc95eca23 | R | 5,297 | 157 | library(data.table)
library(ggplot2)
library(reshape2)
#personalized theme
my_theme = theme(
text = element_text(size = 10),
axis.title.x = element_text(size = 14),
axis.title.y = element_text(size = 14),
axis.text = element_text(size = 14, colour = 'black'),
axis.text.x = element_text(vjust = 0.5, size = 1... |
e62049e18ebd0d1abf239a70161346f12fca969f9acc06593657b5129c464c35 | R | 5,298 | 157 | library(data.table)
library(ggplot2)
library(reshape2)
#personalized theme
my_theme = theme(
text = element_text(size = 10),
axis.title.x = element_text(size = 14),
axis.title.y = element_text(size = 14),
axis.text = element_text(size = 14, colour = 'black'),
axis.text.x = element_text(vjust = 0.5, size = 1... |
f8a607e2b7618190b9bbde1fbb8413b86ccccb2da3adfb68c2a0e3accd3a9bbe | R | 5,299 | 157 | library(data.table)
library(ggplot2)
library(reshape2)
#personalized theme
my_theme = theme(
text = element_text(size = 10),
axis.title.x = element_text(size = 14),
axis.title.y = element_text(size = 14),
axis.text = element_text(size = 14, colour = 'black'),
axis.text.x = element_text(vjust = 0.5, size = 1... |
1bf41863d1d23bb1e77251fae1f2c2d75e501dfa6abae4524b2e9af999bfa269 | R | 5,303 | 157 | library(data.table)
library(ggplot2)
library(reshape2)
#personalized theme
my_theme = theme(
text = element_text(size = 10),
axis.title.x = element_text(size = 14),
axis.title.y = element_text(size = 14),
axis.text = element_text(size = 14, colour = 'black'),
axis.text.x = element_text(vjust = 0.5, size = 1... |
36f9b2bd8a036dccd5c9951e88fb6434ddcdd5ff5a6cb6296b3c1df212de3516 | R | 5,303 | 157 | library(data.table)
library(ggplot2)
library(reshape2)
#personalized theme
my_theme = theme(
text = element_text(size = 10),
axis.title.x = element_text(size = 14),
axis.title.y = element_text(size = 14),
axis.text = element_text(size = 14, colour = 'black'),
axis.text.x = element_text(vjust = 0.5, size = 1... |
306894893062c7aa4e0fafd99b84aadd4a205c978b50fdd5373249d7c790fe55 | R | 5,334 | 153 | library(dplyr)
library(ggplot2)
library(scales)
library(tidyverse)
#=======================
#personalized theme
my_theme = theme(
text = element_text(size = 10),
axis.title.x = element_text(size = 14),
axis.title.y = element_text(size = 14),
axis.text = element_text(size = 10, colour = 'black'),
axis.text.x... |
280b18420f90cc2c49f6e0a984decdbf421d161b664a1719468b361976480175 | R | 5,343 | 108 | #!/usr/bin/env Rscript
suppressPackageStartupMessages({
library(data.table)
library(dplyr)
library(exomePeak2)
library(TxDb.Mmusculus.UCSC.mm10.knownGene)
library(BSgenome.Mmusculus.UCSC.mm10)
})
source("R/functions_enrichment_plot.R")
make_dir("output/exomePeak2")
make_dir("output/tables")
make_dir("output/... |
d5a7dc6cd06b142d23431dd50b04521433a2bc3649525b6214d31683a8271626 | R | 5,395 | 124 | ## SparseSmooth2d.R
##
## This is (almost) carbon copy of the matalb function SparseSmooth2d that is part
## of pleiofdr https://github.com/precimed/pleiofdr
##
## This function implements smoothing on a 2d grid, using differencing matrices
## and unrolling of long vectors which I find suggestive, but somehow I can't
#... |
a78022a5e3da1b078222ea2be4d194173685f0228d2b85323c56ebe34987ea8b | R | 5,683 | 163 | require('R.matlab')
require(tidyverse)
require(dplyr)
require(ggplot2)
require(rstatix)
library(philentropy)
library(cluster)
library(jsonlite)
########################################################################
################# 1000 spin tests for occipital coding ################
##############################... |
e8fce59b6f89e160311ddf39fc2d6da9290db20c3d77a146cd3f480181ff489a | R | 5,852 | 140 | library(Seurat)
library(Signac)
library(dplyr)
library(ggplot2)
library(SingleCellExperiment)
library(EnsDb.Hsapiens.v86)
library(BSgenome.Hsapiens.UCSC.hg38)
library(GenomicRanges)
library(parallel)
library(future)
setwd("/home3/ciervo/scMULTIOME/Analisi/")
# Peak calling after cell type annotation ----
load('/home3/... |
14e23ba634bbcf23eea5523d726bfbcf2e58dff1a784a8e420d301de58f8a164 | R | 6,002 | 188 | ################################################################################
# Importación de paquetes
library(dplyr)
library(Seurat)
library(readr)
library(ggplot2)
library(URD)
library(SingleCellExperiment)
library(destiny)
library(tidyverse)
library(dplyr)
library(monocle3)
library(SeuratWrappers)
# Guardo cou... |
173f16f384cf4640cf73489dba5996688b55509e7984572c471795a2e17ea748 | R | 6,054 | 177 | library(Seurat)
library(dplyr)
library(ggplot2)
library(caret)
library(pROC)
source('colori_finali.R')
setwd('/home3/ciervo/scMULTIOME/Analisi/')
# Load malignant subset by Pozniak et al., 2024
pozniak = readRDS('/home/mbesharat/Pozniak/Pozniak_cellannot/Malignant_MP_simp_score_annot_MP_auto_annot.Rds')
dim(pozniak)
... |
5100ba2d6dca6753688e499a54f33ef2dc9ac9d7779d03bf4626c62f640b4057 | R | 6,418 | 150 | ### Functions for subsetting data to just good trials
### Spring 2023
### adam.milton.morgan@gmail.com
###
### Picture naming
###
just.good.pic.naming <- function(.data,
.words = c('chicken','dog','dracula','frankenstein','ninja','nurse'),
.remove.outl... |
31bdb8e79fce888210e18e1c332ca298ca55f2cf0cb35dc52d8bfc409139c448 | R | 6,637 | 163 | ###################
#### Libraries ####
###################
library(Seurat)
library(dplyr)
library(ggplot2)
library(CellChat)
library(ComplexHeatmap)
library(circlize)
setwd("/home3/ciervo/scMULTIOME/Analisi/")
source("colori_finali.R")
load('scRNA/RData/merged_object.RData')
table(seu$Metaprogram_assignment, exclude... |
1a4e5f8646733524676eff8ec5181f4cc0d49281a616f4329a51ed110d2a9924 | R | 6,761 | 163 | ### Get start and end times of windows of adjacent significant samples
### Fall 2021
### adam.milton.morgan@gmail.com
# Given a time series (class: vector) of 1s (significant) and 0s (not significant),
# return either a list or data.frame ("output.class") summarizing the significant windows --
# i.e., temporally adjac... |
42303fe2c2d0f6d11541fbc932fd04d3d2f2d098a1db7c534cf32daf459c17d5 | R | 7,391 | 154 | ###################
#### Libraries ####
###################
library(Seurat)
library(dplyr)
library(ggplot2)
source('colori_finali.R')
simplicity_score = function(df, percentage = 0.1, ncores = 20) {
cclass = parallel::mclapply(1:nrow(df), FUN = function(x) {
tmp = unlist(df[x,])
ifelse( (sort(tmp, decreasing... |
05af19e3483ca5fa186ab3c4dacaa0f75a32e3009c3e76ad5b0d89fd7f50c36d | R | 7,433 | 155 | ###################
#### Libraries ####
###################
library(Seurat)
library(dplyr)
library(ggplot2)
source('colori_finali.R')
setwd('/home3/ciervo/scMULTIOME/Analisi')
simplicity_score = function(df, percentage = 0.1, ncores = 20) {
cclass = parallel::mclapply(1:nrow(df), FUN = function(x) {
tmp = unlist... |
f2e010900852f8281525b5823330bf18a6cf1846041611e2120a980fb5b792b2 | R | 8,153 | 210 | library(openxlsx)
library(ggplot2)
#Base folder
base<- "K:/Lab Member Folders/Villalba Ana/Projects/Collaborations/Isabel Varela - Marta Nieto Callosal projections/Clone analysis"
#list.files(path=".", pattern=NULL, all.files=FALSE, full.names=FALSE)
#### E12.5 CLONES ####
E12clone_df <- read.xlsx(xlsxFile =p... |
fdcd293aca9664bf11229ec5117ce88dbc6b02060f22f59d1918ad5da0dba991 | R | 8,224 | 246 | library(Seurat)
library(Signac)
library(dplyr)
library(ggplot2)
library(EnsDb.Hsapiens.v86)
library(BSgenome.Hsapiens.UCSC.hg38)
library(GenomicRanges)
library(parallel)
setwd("/home3/ciervo/scMULTIOME/Analisi/")
# dir.create("scATAC")
# dir.create("scATAC/RData")
source('colori_finali.R')
# Load objects
# Batch 1
pa... |
2410e99dd592cf493e9878d1832b7e8f29cccce76f865c6f30e97b0b8b62fc81 | R | 8,448 | 201 | ### Chicken Syntax
### Convert parsed psychopy log to 512 Hz log
### May 2020
### adam.milton.morgan@gamil.com
rm(list=ls())
#####################
### Manually edit ###
#####################
patient <- 'Patient010'
####################################
### No manual edits needed below ###
###########################... |
ca19ce87f51e6372f5c38b7f5f14b6749dd0e34178e6bf746d7de564b91f2d6c | R | 8,489 | 194 | # SCENIC using Pozniak et al., 2024 dataset ----
# devtools::install_github("aertslab/SCENIC")
# remotes::install_github("aertslab/SCopeLoomR")
# packageVersion("SCENIC")
# nBiocManager::install("RcisTarget")
library(Seurat)
library(SCENIC)
library(SingleCellExperiment)
library(ggplot2)
library(SCopeLoomR)
library(Rci... |
e6ca684cdb35cb80352af81b06e7be76539ed9ee4cd081a9f674b7916911a040 | R | 8,752 | 233 | require('R.matlab')
require(tidyverse)
require(dplyr)
require(ggplot2)
require(rstatix)
library(philentropy)
library(cluster)
library(jsonlite)
########################################################################
######################## K means clustering ###########################
##############################... |
d978a71ce5ee45c01d5f6b2d787b4a3997dbb8a983f187aae95f7c1ef26e3590 | R | 9,050 | 64 | library(devtools)
library(pkgbuild)
# Bioconductor packages
# sequence manipulation packages
devtools::install_version("Rsubread", version="2.16.1", repos=c("https://bioconductor.org/packages/3.18/bioc","https://cran.uni-muenster.de/"),upgrade = "never", force = TRUE)
devtools::install_version("ShortRead", version="1... |
41fce99ffc802080d3fc6d575ea8989cc6df3d6b627fa277fe76d046e753b8f9 | R | 9,693 | 240 | ### Chicken Syntax
### Add production data to 512 Hz log
### May 2020
### adam.milton.morgan@gmail.com
rm(list=ls())
#####################
### Manually edit ###
#####################
patient <- 'Patient010'
#############################################
### Auto - more manual edits needed below ###
#################... |
ef4e6226dcc2b54770054dff2359ad418af4fbe74c5967a68b6533132ff4f7bc | R | 9,731 | 161 | #' Scissor: Single-Cell Identification of Subpopulations with bulk Sample phenOtype coRrelation
#'
#' \code{Scissor} is a novel approach that utilizes the phenotypes, such as disease stage, tumor metastasis, treatment response, and survival
#' outcomes, collected from bulk assays to identify the most highly phenotype-a... |
c25eed9e5ef9ef2125e00c133c0131fc9e507ee92418768a5116e9d3243839b0 | R | 9,746 | 256 | ### Chicken syntax
### Convert .log file to something legible to then turn into a 512 sampling rate
### adam.milton.morgan@gmail.com
### May 2020
rm(list=ls())
#####################
### Manually edit ###
#####################
patient <- 'Patient010'
####################################
### No manual edits needed bel... |
080c1431ee016d9c325c537dc633cb0e11b964eb6196dedb2865b6091be7f882 | R | 10,683 | 241 | ### Chicken Syntax
### Pre-processing stage 9
### Make sample epochs and remove bad trials
### July 2020
## This whole script is redundant and at some point should be made to be part of preprocessing stage 7, but going for quick and easy right now
rm(list=ls())
### Set paths
if(Sys.info()['sysname'] == 'Darwin'){
... |
940827c80c5724eb5ef784591e374d7034912b84473bbc237bc0abcd19f12d02 | R | 10,785 | 258 | ###################
#### Libraries ####
###################
library(Seurat)
library(SeuratData)
library(dplyr)
library(parallel)
# setwd("/home3/ciervo/scMULTIOME/Analisi/")
# dir.create("/home3/ciervo/scMULTIOME/Analisi/scRNA/RData")
source("/home/caruso/scProject/NYnontumor/package_code/functionScRNAseq.R")
source('c... |
cc5f1d2ab173915b47d68973083da64398e2d4cbf33e986b28d97f1ecf385511 | R | 10,890 | 311 | ### Define MFG axis, model electrodes' peak syntax RSI time as function of projected position along MFG axis
### adam.milton.morgan@gmail.com
###
### Readme
###
###
### Setup
###
### Packages
library('beepr') # for playing beeps
library('dplyr') # for data organization, including bind_rows()
library('parallel')
lib... |
d4bc760fe61c97e3b4233e3be2d1efc4db72367809c9ea521c0ced8e66a30309 | R | 10,973 | 298 |
#################################
##### Logistic Regression #####
#################################
LogL0=function(x, y, Omega=NULL, alpha=1.0, lambda=NULL, nlambda=100, rlambda=NULL, nfolds=1, foldid=NULL, inzero=TRUE, wbeta=rep(1,ncol(x)), sgn=rep(1,ncol(x)), isd=FALSE, keep.beta=FALSE, thresh=1e-6, maxit=... |
39cc4d4dd12bb36f1523576ae708ea88041393a29293b1beeccbe1d7dee0d33a | R | 11,106 | 266 | require('R.matlab')
require(tidyverse)
require(dplyr)
require(ggplot2)
require(rstatix)
library(philentropy)
library(cluster)
library(jsonlite)
options(warn = -1)
#################################################################################
#### In silico experiments on ANNs to simulate varying levels of informati... |
d1c7467ecbe97262e7cdfeca5f82e691bf27d844661f46b4570f14a7c2afcdc2 | R | 11,116 | 74 | # CRAN packages
install.packages("devtools")
install.packages("pkgbuild")
library(devtools)
library(pkgbuild)
# Basic packages
devtools::install_version("commonmark", version = "1.9.1", repos = c("https://cran.uni-muenster.de/", "https://bioconductor.org/packages/3.18/bioc"), upgrade = "never", force = TRUE)
devtools... |
1b9ebe385189738cd05031ef734c6cf003a1bba80adfb8114d8f4b476234bfe1 | R | 11,119 | 309 |
###############################
##### Linear Regression #####
###############################
LmL0=function(x, y, Omega=NULL, alpha=1.0, lambda=NULL, nlambda=100, rlambda=NULL, nfolds=1, foldid=NULL, inzero=TRUE, wbeta=rep(1,ncol(x)), sgn=rep(1,ncol(x)), isd=FALSE, iysd=FALSE, keep.beta=FALSE, thresh=1e-6, m... |
67a943441961627f6d0a3d596f631796d87d8795487204ac894cc2fba1af0afc | R | 11,756 | 348 | ####################################################
# Transposable Elements (TE) Multiome Analysis
####################################################
# --- 0. Setup & Dependencies ---
library(vroom)
library(dplyr)
library(tibble)
library(Seurat)
library(scran)
library(ggplot2)
library(ggrepel)
library(ComplexHeatm... |
47581203d0bad3e01220b9c7bc1b4b012a4b943d0ebb07c7ed26eccdfdb4cdc7 | R | 12,992 | 330 |
#######################
##### Cox model #####
#######################
CoxL0=function(x, y, Omega=NULL, alpha=1.0, lambda=NULL, nlambda=100, rlambda=NULL, nfolds=1, foldid=NULL, inzero=TRUE, wbeta=rep(1,ncol(x)), sgn=rep(1,ncol(x)), isd=FALSE, keep.beta=FALSE, ifast=TRUE, thresh=1e-6, maxit=1e+5) {
# x=xi;... |
f893ce02f175bb977ba0219475d183cdfd603fecdbacda8ed178fc614380da97 | R | 13,053 | 339 | ### Figure out time offsets between ECoG, audio, and PsychoPy log reconstructions
### May 2020
### adam.milton.morgan@gmail.com
rm(list=ls())
cat("\014")
#####################
### Manually edit ###
#####################
patient <- 'Patient010'
#############################################
### Auto - more manual edit... |
cc8e1aac3190be1d4ac8ef4726240a6fc68ae104aa6da38b127a652b65d76a16 | R | 13,768 | 292 | ### Chicken Syntax
### Add production data to 512 Hz log
### May 2020
### adam.milton.morgan@gmail.com
rm(list=ls())
#####################
### Manually edit ###
#####################
patient <- 'Patient010'
#########################################
### Auto - manually check stuff below ###
##########################... |
cd80ac01287c20d29ce0226f8b1c280c3bcc29c573a57cb2fcc0480e9e8e30e5 | R | 14,196 | 395 | ###################
#### Libraries ####
###################
setwd("/home3/ciervo/scMULTIOME/Analisi")
# BiocManager::install('saezlab/decoupleR')
library(decoupleR)
library(tidyr)
library(dplyr)
library(parallel)
library(tibble)
library(caret)
library(pROC)
library(ggplot2)
# 1. NFATC2 activity in bulk melanoma ----
l... |
028cba70916101a28f89da1cdf869aaa65df4a309a60c5988d027a02b3dfbb37 | R | 14,468 | 366 | ### Chicken Syntax
### Pre-processing stage 8
### Make window epochs
### July 2020
## This whole script is redundant and at some point should be made to be part of preprocessing stage 7, but going for quick and easy right now
rm(list=ls())
####################
### Manully edit ###
####################
patient <- 'Pa... |
ffb59bc7e016a6c789c84eaf0224631cf2d1ca77ca1ec79420e0bb1da98730ac | R | 14,712 | 312 | ls()
rm(list = ls())
ls()
setwd("/home/joaquin/Desktop/20250602 - Tul y Dm wt 43a1")
#Libraries
library(tidyverse)
#Data
NN <- read.csv("sc_results.csv") #Data obtained from the automated pipeline
yo <- read.csv("sc_manual.csv") #Data obtained manually
#Joining the dataframes
NN$Method <- "Auto"
yo$Method <- "Manu... |
975eb2ae121b4e591c72349ac17bce8115b6e4668ecd2227be4cbccd7d3301d4 | R | 14,772 | 290 | ############################################################
########## Metaprogram clustering as described by ##########
### Gavish et al, 2023 - DOI: 10.1038/s41586-023-06130-4 ###
############################################################
library(reshape2)
library(NMF)
library(ggplot2)
library(RColorBrewer)
librar... |
d6392fc42bcfaa0cca1e2da2d8aa165959af99315106c9b593ecbf12552a1bb0 | R | 15,108 | 391 | ### Do multiple regression on RSA values - shuffled trial labels for permutation analysis
### June 2023
### adam.milton.morgan@gmail.com
###
### Readme
###
###
### Setup
###
### Packages
library('beepr') # for playing beeps
library('dplyr') # for data organization, including bind_rows()
library('parallel')
library(... |
c724c3ec036f0eff72dce8ae44f680069de5e141d5f2f7d49899cc6cac21f33e | R | 15,639 | 403 | ### Cluster RSIs (zs.adjusted) trained on shuffled data to determine how much signal NMF can find in noise
### adam.milton.morgan@gmail.com
###
### Readme
###
###
### Setup
###
### Packages
library('beepr') # for playing beeps
library('dplyr') # for data organization, including bind_rows()
library('parallel')
libra... |
fcea3d4e8cb5743dde3fa2a42892e1793541f811edecc258728b910855acd079 | R | 15,955 | 352 | # Add AUCell scores SCENICplus ----
library(Seurat)
library(SeuratData)
library(Signac)
library(dplyr)
library(tidyr)
library(ggplot2)
library(readr)
setwd('/home3/ciervo/scMULTIOME/Analisi/')
source('colori_finali.R')
# Load obj ----
load('scRNA_scATAC/RData/seu_multiome_filtered.RData')
seu.multiome = subset(seu.mu... |
f792b7520f55353faa410cd17513ea4bbe346cabb060f1bb02a0de8b09296da9 | R | 16,149 | 397 | ### Do multiple regression on RSA values
### June 2023
### adam.milton.morgan@gmail.com
###
### Readme
###
###
### Setup
###
### Packages
library('beepr') # for playing beeps
library('dplyr') # for data organization, including bind_rows()
library('parallel')
library('foreach')
library('pals')
library('caret')
libra... |
d09784ff1808ab574adda53639111c6cf57a913cd00439146d843feed951866b | R | 17,784 | 233 | ---
title: "Scissor Tutorial"
author: "Duanchen Sun"
date: '`r Sys.Date()`'
output:
html_document:
df_print: paged
toc: yes
vignette: |
%\VignetteIndexEntry{Scissor Tutorial}
%\VignetteEngine{knitr::rmarkdown}
---
# Introduction
This Scissor R package contains the proposed Scissor algorithm (function `... |
1f8e3b6890b859f328153f9b79fb29619be0d562510608a8b8930ce84d130844 | R | 18,262 | 525 | ### Compare RSA outcomes on real data to shuffled data to derive RSIs/z-scored t-values for each representation type
### June 2023
### adam.milton.morgan@gmail.com
###
### Readme
###
###
### Setup
###
### Packages
library('beepr') # for playing beeps
library('dplyr') # for data organization, including bind_rows()
l... |
984556f3187d9a143affe166919942716f76f1a16b537edd322cff83db79a67b | R | 18,295 | 434 | ### Perform task comparisons (e.g., sentence >? list) for each electrode at each time sample
### adam.milton.morgan@gmail.com
###
### Readme
###
###
### Setup
###
### Packages
library('beepr') # for playing beeps
library('pals')
library('viridis')
library('grid')
library('gridExtra')
library('doParallel') # for para... |
37efc58c39f996e574f4d7fbf20fe04db75c24c18a81d7ced206a7d003410329 | R | 18,836 | 484 | ### Plot Venn Diagrams (Euler plots) of overlap between (a) RSIs and (b) active/passive and sentence/list Wilcoxon tests
### June 2023
### adam.milton.morgan@gmail.com
###
### Readme
###
###
### Setup
###
### Packages
library('beepr') # for playing beeps
library('dplyr') # for data organization, including bind_rows... |
7a8a379509dbe59ea311391dc890be960a063a8fab12c6141180b2311eb2288d | R | 19,742 | 523 | ### Shuffle elec-cluster assignments and compare actual cluster means to shuffled cluster means, original RSA model
### October 2023
### adam.milton.morgan@gmail.com
###
### Readme
###
###
### Setup
###
### Packages
library('beepr') # for playing beeps
library('dplyr') # for data organization, including bind_rows()... |
4f4596df13fd87ca32cf4b8ffb69c66ad8eb4d4a61387e716c324fa082114d11 | R | 20,356 | 470 | setwd('/home3/ciervo/scMULTIOME/Analisi/')
library(Seurat)
library(Signac)
library(dplyr)
library(ggplot2)
library(SingleCellExperiment)
library(EnsDb.Hsapiens.v86)
library(BSgenome.Hsapiens.UCSC.hg38)
library(GenomicRanges)
library(parallel)
library(clusterProfiler)
library(org.Hs.eg.db)
library(enrichplot)
library(mo... |
73f01549a2f4bc2381dc46dc0e31e742d1a55f80694d1c77e6c6416d31422feb | R | 20,485 | 344 | ---
title: "Introduction to `cfdr.pleio`: installation and basic usage"
output: rmarkdown::html_vignette
vignette: >
%\VignetteIndexEntry{Installation and basic usage}
%\VignetteEngine{knitr::rmarkdown}
%\VignetteEncoding{UTF-8}
---
The package `cfdr.pleio` implements two forms of inference to leverage and iden... |
76c714cb1c76929a1362b0c56a8e6598f01d638764cb5f7b0a78f2c685028fdd | R | 20,774 | 488 | ### Correlate RSIs and high gamma across electrodes at each time sample
### June 2023
### adam.milton.morgan@gmail.com
###
### Readme
###
###
### Setup
###
### Packages
library('beepr') # for playing beeps
library('dplyr') # for data organization, including bind_rows()
library('parallel')
library('foreach')
library... |
41764fc4041c5b50f5518fb9f3cb7f38781b04d8db89920fa9618ab53ee57d47 | R | 21,768 | 385 | library(Seurat)
library(Signac)
library(dplyr)
library(ggplot2)
library(SingleCellExperiment)
library(EnsDb.Hsapiens.v86)
library(BSgenome.Hsapiens.UCSC.hg38)
library(GenomicRanges)
library(parallel)
library(SingleR)
library(celldex)
setwd("/home3/ciervo/scMULTIOME/Analisi/")
## Add metadata from snRNA-seq (TME annota... |
5fdee0c29de23ee9458cbf742f0f6f260e6c6c87b7e4fa8213b042857fb69c5d | R | 22,139 | 522 | ###################
#### Libraries ####
###################
library(Seurat)
library(Signac)
library(dplyr)
library(ggplot2)
library(SingleCellExperiment)
library(EnsDb.Hsapiens.v86)
library(BSgenome.Hsapiens.UCSC.hg38)
library(GenomicRanges)
library(parallel)
setwd("/home3/ciervo/scMULTIOME/Analisi/")
# dir.create("sc... |
fd7139bd1c49cb54f345c78c4c6139a1ca4ea3bdd49fc17b2ef28acb8065affb | R | 23,012 | 529 | ### Perform task comparisons (e.g., sentence >? list) for each electrode, in time bins (e.g., 250ms windows)
### adam.milton.morgan@gmail.com
###
### Readme
###
###
### Setup
###
### Packages
library('beepr') # for playing beeps
library('pals')
library('viridis')
library('grid')
library('gridExtra')
library('doParal... |
929c14fa9218535f756ebb052051b28f4873634f107b94af24115fee3d14af15 | R | 23,360 | 510 | ### Linearly interpolate the middle section (150ms post stim to 150ms pre speech) of all trials to have the same RT (i.e., median task RT)
### May 2023
### adam.milton.morgan@gmail.com
###
### Readme
###
###
### Setup
###
### Packages
library('dplyr') # for data organization, including bind_rows()
# Clean up
rm(li... |
c71680e921ce3d40e8455cc155513baa88f7ca086a2c64f673a564bc508a9c3d | R | 23,645 | 617 | ### Get cluster significance by comparing clusters of RSA results on real vs. shuffled data
### June 2023
### adam.milton.morgan@gmail.com
###
### Readme
###
###
### Setup
###
### Packages
library('beepr') # for playing beeps
library('dplyr') # for data organization, including bind_rows()
library('parallel')
librar... |
edde2cac61568d1f2f0bc7792f08567fc393914c26c24c685919063d1cc645fc | R | 23,884 | 536 | ### Correlate RSIs and high gamma across electrodes at time when RSI peaks
### adam.milton.morgan@gmail.com
###
### Readme
###
###
### Setup
###
### Packages
library('beepr') # for playing beeps
library('dplyr') # for data organization, including bind_rows()
library('parallel')
library('foreach')
library('caret')
l... |
283a1187d05679d5c785369e305035cd4f40ab820ec8e6224a74d20ccd2312e3 | R | 28,178 | 646 | ### Temporal clustering of syntax electrodes
### adam.milton.morgan@gmail.com
###
### Readme
###
###
### Setup
###
### Packages
library('beepr') # for playing beeps
library('dplyr') # for data organization, including bind_rows()
library('parallel')
library('foreach')
library('pals')
library('caret')
library('doPara... |
6d5b4ff856a6e842c9ffc267ec0cf59d961f571341f48cd237059edd8b749bcd | R | 30,073 | 681 | ### Cluster electrodes using NMF
### adam.milton.morgan@gmail.com
###
### Readme
###
###
### Setup
###
### Packages
library('beepr') # for playing beeps
library('dplyr') # for data organization, including bind_rows()
library('parallel')
library('foreach')
library('pals')
library('caret')
library('doParallel')
libra... |
402cccc61a3f6930b7b4393e569614435f1a607e3a7a36e26eeff3eb674a08c1 | R | 31,244 | 627 | ### Shuffle elecs across clusters to get noise distribution (conservative since it's only significant elecs)
### adam.milton.morgan@gmail.com
###
### Readme
###
## Tried:
# - shuff analysis
# - chi squared across clusters (doesn't capture MFG)
# - chi squared across regions (doesn't capture IFG)
# - define how many y... |
805ae2c7c43e517053ee39227ea05662493e656e3040ec29e22095a219bfc52d | R | 31,411 | 728 | install.packages("tidyverse")
install.packages("gifti")
install.packages("solarius")
install.packages("ggpubr")
install.packages("Cairo")
install.packages("devtools")
#install_github("ugcd/solarius")
install.packages("R.matlab")
install.packages("foreach")
install.packages("doParallel")
library(foreach)
library(doParal... |
2c4c0c3444902b915cb3c1821333a1f1635f2d979520f0cc268849b8d547e289 | R | 32,453 | 724 | ## The master file for package cfdr.pleio
#' @import R6
#' @import Matrix
#' @import data.table
#' @import matrixStats
#' @importFrom stats pnorm qnorm
#' @importFrom interp bilinear
NULL
#' A class for calculating conditional & conjunctional fdr for pleiotropy analysis
#'
#' @description A class that captures all da... |
ee4ae7698877c91d2460b970d80adbf30b4e4a1de3aef32ec28f98dc3fc3fe6a | R | 33,462 | 861 | ls()
rm(list=ls())
ls()
###
# Analisis de recuento manual
setwd("/home/joaquin/Desktop/Poster 2025/20250602 - Tul y Dm wt 43a1")
yo <- read.csv("sc_manual.csv")
str(yo)
summary(yo)
#Overdispersion
mean(yo$Amastigotes)
var(yo$Amastigotes)
library(tidyverse)
#For Binomial modelling
yo$Infectado <- 0
yo$Infectado[... |
a15ba120ac89a66d6f8f79a6524946bdf25aad7155aec818eae23c2f22a33afb | R | 42,570 | 1,047 | ### Test regional makeup of clusters compared to each other and to original dataset distribution
### adam.milton.morgan@gmail.com
###
### Setup
###
### Packages
library('beepr') # for playing beeps
library('dplyr') # for data organization, including bind_rows()
library('parallel')
library('foreach')
library('pals')
... |
c7f41510e2fa5af968a04c9e64f4786296e6e69127aa99529ef7b66d257ba390 | R | 46,827 | 935 | ### Compare tasks (e.g., sentence and list production) by ROI using unwarped data
### adam.milton.morgan@gmail.com
###
### Readme
###
###
### Setup
###
### Packages
library('beepr') # for playing beeps
library('dplyr') # for data organization, including bind_rows()
library('viridis') # for viridis color schemes
lib... |
3102820f0c0aac2db2dad944ffee09a8ec26915e8ada8ac03910f3a227131d05 | R | 50,763 | 1,016 | ### Compare tasks (e.g., sentence and list production) by ROI using warped data
### adam.milton.morgan@gmail.com
###
### Readme
###
###
### Setup
###
### Packages
library('beepr') # for playing beeps
library('dplyr') # for data organization, including bind_rows()
library('viridis') # for viridis color schemes
libra... |
8e118dfa717d10b5e68a75c0d63fa065a97f0a7efcd30b211a81559ebabef5cb | R | 53,920 | 1,400 | library(dplyr)
library(ggplot2)
library(scales)
library(tidyverse)
library(valr)
library(palr)
library(GenomicRanges)
#personalized theme
my_theme = theme(
text = element_text(size = 10),
axis.title.x = element_text(size = 12),
axis.title.y = element_text(size = 12),
axis.text = element_text(size = 10, colour... |
0e46f18b6e7ae5b3e2b488b67a49feb37e2451797af175d0d4757d74bcadea75 | R | 63,456 | 1,543 | # Malanoma bulk - Response (downloaded from TIGER http://tiger.canceromics.org/#/immuneResponse) ----
setwd('/home3/ciervo/scMULTIOME/Analisi/')
library(estimate)
library(readr)
require(org.Hs.eg.db)
source('colori_finali.R')
source('/home/ciervo/Functions/quantileNormalization.R')
tpm_from_rpkm <- function(x) {
rpk... |
813a183abc838afb885867d7b29f9ea5c1af7e8df63ec3637bde019436c86656 | R | 104,801 | 2,642 | ########################
##### Figure Script ####
########################
# setwd('/home3/ciervo/scMULTIOME/Analisi/')
# dir.create('Figures')
###################
#### Libraries ####
###################
library(Seurat)
library(SeuratObject)
library(Signac)
library(parallel)
library(dplyr)
library(tidyr)
library(ggplo... |
68e6e4bc135379b481bf44291c6ac4359245413a9c1af6fc02024fc7177789e5 | R | 104,803 | 2,643 | ########################
##### Figure Script ####
########################
# setwd('/home3/ciervo/scMULTIOME/Analisi/')
# dir.create('Figures')
###################
#### Libraries ####
###################
library(Seurat)
library(SeuratObject)
library(Signac)
library(parallel)
library(dplyr)
library(tidyr)
library(ggplo... |
ca22079c214b61084a043fde846b83cd23cb71c6c4f931b8253278a2a2988c63 | Rust | 20 | 1 | pub mod subcommand;
|
8708d91d46a87513f6fbed6ca1052ac516747d6fff38dac152836ab591787a0a | Rust | 30 | 2 | pub mod subcommand;
mod util;
|
62496f4d6ec12ad9476159a305df07cadc0c1008298ee60c011cf5109782b22f | Rust | 48 | 2 | pub(crate) mod check_tags;
pub mod subcommands;
|
ccc303cfdfa1185394e942ec8fb389359cdcce1df8c56715d20f0b9353e057de | Rust | 56 | 4 | mod args;
pub mod subcommand;
mod util;
pub mod writer;
|
2427f80b31eb4271c5fff6721e90db35a1b96c7117210901c2997456bbd0df95 | Rust | 135 | 8 | mod batcher;
mod feature_loader;
mod features;
mod models;
mod prob_merger;
mod range_feeder;
pub mod subcommand;
pub(crate) mod util;
|
726a14a8914b74de20b0d2262b66a86daab7e2396eeadbec46e0c94939816287 | Rust | 146 | 9 | pub mod bedmethyl;
mod beta_diff;
pub(crate) mod isoform;
mod llr_model;
mod pairwise;
mod single_site;
pub mod subcommands;
mod tabix;
mod util;
|
810f1a6b165ef585d2f617b6467e563b05ed8ed12fdba5c35c25ec1f643cf0d1 | Rust | 288 | 11 | use crate::common::run_modkit;
mod common;
#[test]
fn test_localise_helps() {
let _ = run_modkit(&["localize", "--help"])
.expect("failed to run modkit localize help");
let _ = run_modkit(&["localise", "--help"])
.expect("failed to run modkit localise help");
}
|
305d5916b4294438a5dc9f07839b2844110407fe11effbd7af837937585e7674 | Rust | 413 | 18 | use common::run_modkit;
mod common;
#[test]
fn test_generate_bash_completions() {
let _out = run_modkit(&["generate-shell-completions", "bash"]).unwrap();
}
#[test]
fn test_generate_zsh_completions() {
let _out = run_modkit(&["generate-shell-completions", "zsh"]).unwrap();
}
#[test]
fn test_generate_fish_co... |
73c39a6debe3a7b5b6b13c41ea98a78917983a832f7c7f8015ac2e45378ff1b1 | Rust | 425 | 13 | use crate::common::run_modkit;
mod common;
#[test]
fn test_motif_helps() {
let _ = run_modkit(&["motif", "search", "--help"])
.expect("failed to run modkit motif search help");
let _ = run_modkit(&["motif", "evaluate", "--help"])
.expect("failed to run modkit motif evaluate help");
let _ =... |
d2fee81956726150a36b8635efd6c4254773249387fcd7aa5a872a73e86313de | Rust | 569 | 23 | use crate::mod_base_code::DnaBase;
use derive_new::new;
use std::collections::HashMap;
#[derive(Debug, new)]
pub struct FilterThresholds {
default: f32,
per_base_thresholds: HashMap<DnaBase, f32>,
}
impl FilterThresholds {
pub fn new_passthrough() -> Self {
Self::new(0f32, HashMap::new())
}
... |
ceff051909a919dc9c7b2a1d5349d392cafd647b66983150565a50adec81bb3f | Rust | 729 | 32 | use crate::commands::Commands;
use clap::Parser;
use log::error;
pub mod commands;
#[derive(Parser)]
#[command(version)]
#[command(term_width = 80)]
/// Modkit is a bioinformatics tool for working with modified bases from Oxford
/// Nanopore
struct Cli {
#[command(subcommand)]
command: Commands,
}
fn main() ... |
e067dee80d4808d758c006ab125690f6bb6bb3d3931a911efa75dda78d0570d8 | Rust | 1,169 | 34 | use crate::mod_bam::{CollapseMethod, EdgeFilter};
use crate::monoid::Moniod;
use crate::position_filter::StrandedPositionFilter;
use crate::reads_sampler::record_sampler::RecordSampler;
use rust_htslib::bam;
pub trait RecordProcessor {
type Output: Moniod + WithRecords + Send;
fn process_records<T: bam::Read>... |
6a6c5e185ab0109e7e9d5418422ba51a9924273300d8bfdfca7e36c92e44c442 | Rust | 1,172 | 40 | use crate::features::CountsFeatures;
use burn::data::dataloader::batcher::Batcher;
use burn::prelude::Backend;
use burn::tensor::{Shape, Tensor, TensorData};
use derive_new::new;
#[derive(new, Debug, Clone)]
pub(crate) struct PileupBatcher<B: Backend> {
device: B::Device,
}
#[derive(Debug, Clone)]
pub(crate) stru... |
6a56098ff0b1753eaf86358d5b364da84810b777a9e3b976b00ed5a77e2518e1 | Rust | 1,234 | 36 | use crate::common::run_modkit;
use rust_htslib::bam::{self, record::Aux, Read};
mod common;
#[test]
fn test_update_tags_implicit_no_probabilities() {
let temp_file = std::env::temp_dir()
.join("test_update_tags_implicit_no_probabilities_updated_tags.bam");
let mut reader = bam::Reader::from_path(
... |
c1a558e98b300e01f74a74171ef11bd07c8f534e31d5cc58c2f2f750cbc2fb07 | Rust | 1,286 | 39 | use crate::common::{check_against_expected_text_file, run_modkit};
mod common;
#[test]
fn test_modbam_helps() {
let _ = run_modkit(&["modbam", "--help"])
.expect("failed to run modkit modbam help");
let _ = run_modkit(&["modbam", "check-tags", "--help"])
.expect("failed to run modkit modbam ch... |
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