sha256 stringlengths 64 64 | language stringclasses 28
values | size int32 1 491k | lines int32 1 23.2k | content stringlengths 1 200k |
|---|---|---|---|---|
634644cb6ae78aa26aeb09aa7b9831495082c58f139a89eaab73876d372859fa | R | 675 | 21 | #' spatial_adjacency_matrix()
#'
#' Creating spatial-driven adjacency matrix using cell spatial coordinates
#'
#' @param obj stCCA S3 Object
#'
#' @return A matrix that describes the adjacency relationships between cells. The entries in the matrix are binary values (0 or 1) that indicate whether cells are adjacent to e... |
42d35b27b2e443daaf4372c9d7d395ddac748377b9b945a0121142c999e28c55 | R | 681 | 25 | library(dplyr)
library(Seurat)
library("ggplot2")
library(monocle3)
library(patchwork)
library(SeuratWrappers)
library(harmony)
library(ggplotify)
library(rmarkdown)
subsets <- readRDS("rObjects/subsets_list.rds")
for (i in 1:9){
outName <- names(subsets)[i]
cds_subset_cc <- sobjToCds(seuratObj_subset)
saveRD... |
c54c9128e4776bd708b68c3ae3a843542e2a77952eaf598521ecf4f52dc2626e | R | 686 | 41 | # Remove cache if you prefer to recalculate all the alignments
# unlink("tmp", recursive=T, force=T)
source("01-replication_fork_nn.R")
tzNN_prepare_training_data()
tzNN_train()
tzNN_evaluate()
gc()
source("02-detect_offtargets.R")
detect_offtargets()
gc()
source("03-detect_rdc.R")
detect_rdc()
gc()
source("04-rdc... |
b74089fe00c844ee36fd068cb9aff7ae72819e7cb0a2cf168c71cfe42e84a9fd | R | 695 | 16 | spatial_predict = function(model, df, class_ID) {
## predict() in XGBoost uses as many threads as set in `xgb.Booster` (all by default)
## additionally, conversion matrix to `xgb.DMatrix` uses multiple threads too
pred = predict(model, as.matrix(df), reshape = TRUE)
pred_class = max.col(pred) - 1L
prob_class ... |
bcef954b2433f7087d2ee75a91d6b72b7155c6d481b37285c6e9d75ee28d113a | R | 697 | 20 | ### DATA
d <- read.table("data.txt", header = T)
genes <- read.csv("human_gene_annotation.csv", header = T)
rownames(d) <- genes[,2]
d <- d[,2:ncol(d)]
### ANNOTATIONS
ann <- read.table("human_islet_cell_identity.txt", header = T, sep = "\t", stringsAsFactors = F)
rownames(ann) <- ann[,1]
rownames(ann) <- gsub(" ", "_... |
0093d99732f4ad9fcc2dd51ecd2f9b228c0cd559d31e789eb409605534d737e9 | R | 719 | 29 | ---
title: "GO_Term"
output: html_notebook
---
```{r}
library(enrichR)
```
```{r}
dbs <- listEnrichrDbs()
dbs <- c("GO_Molecular_Function_2018", "GO_Cellular_Component_2018", "GO_Biological_Process_2018")
# enriched <- enrichr(module_genes$'0'[1:50], dbs) #input module genes
```
```{r}
E16_proj_DE <- read_csv("/dat... |
9a0ac6ba74f97b73e125ba70dcf8c0e2230c065ead2b8a1c7d1e78733dcfb42e | R | 744 | 13 | # Figure 3 in situ heatmaps
situgenes <- c("Gsg1l", "Sstr2", "Chrna3", "Tfap2b", "Cbfa2t2", "Fbxl7", "Smoc1", "Gsx1", "Prmt8")
for (i in situgenes) {
compareheatmap(trajectories, trajectorynames, i, span = 0.3) %>% ggsave(file = paste("figures/fig 3/updated_", i, ".png", sep = ""), height = 1, width = 4)
}
f... |
10932e74ba0cd74cb90ed98390cdef33501617329b7298c76f92f9d589239870 | R | 747 | 14 | #' Pruning data and a tree to a particular subset
#'
#' Prunes a comparative dataset down to a particular clade
#' @param data a vector of tip values for species; should be in the same order as tiplabels in the tree
#' @param tree an object of class "phylo".
#' @param targetGroup vector listing the tip labels to which ... |
45e0058612e57aab974ddef0ce74a862da568c6c989f2c82ea41dd0b56c7ffc5 | R | 753 | 22 | inputFile = "raw/jointData.txt"
outputFile = "results/macrophage_DoRothEA.txt"
minNrOfGenes = 5
# Load requeired packages
library("dorothea")
dorotheaData = read.table('annotation/dorothea.tsv', sep = "\t", header=TRUE)
confidenceFilter = is.element(dorotheaData$confidence, c('A', 'B'))
dorotheaData = dorotheaData[co... |
f8acde480bfa45a9947d39df296bd05e8c92892c6b3ca1637f0a7edf89603a3b | R | 761 | 20 | #!/usr/bin/env Rscript
# This little script runs the demux comparisons notebook for each library
# present in the data folder. Note that the main text will not change,
# even though results can vary among libraries, sometimes widely.
rmd_template <- "15-demux-comparisons.Rmd"
outdir <- file.path("results", "noteboo... |
6de18ac5aabe049799f7f3067fb0fe64c9288cd2b88a10ec5e1c7f3418a2abbc | R | 765 | 23 | setwd('/work/AnneSophiaHartebrodt#6698/ms-project/')
require(rhdf5filters)
require(rhdf5)
require(Rhdf5lib)
renv::activate('/work/AnneSophiaHartebrodt#6698/ms-project/r-single-cell')
renv::repair()
set.seed(11)
require(data.table)
require(stringr)
atac.de.dir<-file.path('/work/AnneSophiaHartebrodt#6698/ms-project/sub... |
a1e0ac13faf10895f0b17ec03cf1f532a74c65bab47e37f6b0d0db4959eaa3ae | R | 765 | 11 | library(DiffBind)
Control_K27ame3_cluster <- dba(sampleSheet = "Control_K27me3_1-6_7-19.csv",dir=system.file("extra", package="DiffBind"))
Control_K27ame3_cluster_count <- dba.count(Control_K27ame3_cluster)
Control_K27ame3_cluster_contrast <- dba.contrast(Control_K27ame3_cluster_count,categories = DBA_CONDITION, m... |
4c0a86e2a342a425c152e11b83bd2d41d0298658ee54782677fe4a72636fe516 | R | 772 | 35 | pretty_path_label <- function(pathways, wrap = NULL){
split_path <- str_replace_all(pathways, "_", " ") %>%
str_split(" ")
split_NA <- function(x){
term <- str_c(x, collapse = " ") %>%
str_extract("[A-Z]*[D,R]+NA")
term1 <- str_remove(term, "[D,R,T]+NA") %>% str_to_lower()
... |
23fedc049312d54d9dac697703c247b55ce26d1d499200dea4972d51cc039873 | R | 785 | 20 | #' dlpfc
#'
#'
#' The data provided is a list of Data set containing LIBD human dorsolateral pre-frontal cortex (DLPFC)
#' and spatial transcriptomics data (sample id 151673) generated with the 10x Genomics Visium platform.
#'
#' This data is provided as a sample data to understand the package and not a mandatory req... |
62f8cdad5d2be6d887e90cee556ec1c3d1be9a299291ee95432c8df65da46629 | R | 790 | 25 | library(ggplot2)
# Sample data
data <- data.frame(
model = rep(c("DNN", "XGB"), each = 4),
metric = rep(c("accuracy", "f1", "precision", "recall"), 2),
value = c(0.8945, 0.9326, 0.9358, 0.9294, 0.9049, 0.7519, 0.6556, 0.8815)
)
# File path to save the plot
file_path <- "mlmodel_metric_compare.png"
# Open a PNG... |
7c770530f69ea5386ec97f0587d29cf1ca9ac0f7d5bde0d63c2aac7204a5012c | R | 794 | 22 | library("ggplot2")
data = read.csv("results/variable_importance.csv")
data$Feature[2] = "Median 1000 m"
data$Feature[3] = "Median 500 m"
data$Feature[5] = "St. Dev."
data$Feature[9] = "Multi-scale TPI"
ggplot(data, aes(x = Gain, y = reorder(Feature, Gain))) +
xlab("Information gain") +
ylab("Geomorphometric varia... |
4ec0c459e37751027f4491c5a08f8c9beaf259f9aa210381f5502e180e169a5e | R | 795 | 17 | #' Sample dataset to test UCell installation
#'
#' A sparse matrix (class "dgCMatrix") of single-cell transcriptomes
#' (scRNA-seq) for 30 cells and 20729 genes.
#' Single-cell UMI counts were normalized using a standard log-normalization:
#' counts for each cell were divided by the total counts for that cell and
#' mu... |
a3d8c85971800bf8360dbdd730a935c8f84e189e403d62796bb5934618c308aa | R | 798 | 11 | # Execute the original authors' inference function and fitted parameter object.
# Usage: Rscript generate_miage_references.R /tmp/MiAge-original-mirror /tmp/miage_fixture_input.csv output.csv
args <- commandArgs(trailingOnly=TRUE)
source(file.path(args[1], 'function_library.r'))
load(file.path(args[1], 'site_specific_p... |
58e5b8e892461e4df70c475ed58f79941be87a76f0ca93241e43aa6d67b5e64a | R | 803 | 22 | ### DATA
d0 <- read.csv("klein/GSM1599494_ES_d0_main.csv", header = FALSE)
d2 <- read.csv("klein/GSM1599497_ES_d2_LIFminus.csv", header = FALSE)
d4 <- read.csv("klein/GSM1599498_ES_d4_LIFminus.csv", header = FALSE)
d7 <- read.csv("klein/GSM1599499_ES_d7_LIFminus.csv", header = FALSE)
d <- cbind(d0, d2[,2:ncol(d2)], d4[... |
bcb391be1eef85b293f8982cc77cf15fef0ab1a5cf58e88af6c2e0a708f16b2b | R | 809 | 32 | ## ---Libraries ------
library(Signac)
library(Seurat)
library(GenomeInfoDb)
library(EnsDb.Hsapiens.v86)
library(ggplot2)
library(dplyr)
library(patchwork)
library(rtracklayer)
library(cowplot)
#library(BSgenome.Hsapiens.UCSC.hg38)
library(JASPAR2020)
library(TFBSTools)
library(patchwork)
##--- Functions --------
howM... |
d4e4d3dbd22876b2a6fcce39c78c76e1e029f530b16d1c7a268e6e6877452cfc | R | 810 | 12 | # Original-author R execution, independent of the pyaging implementation.
# Usage: Rscript generate_references.R /tmp/CellDRIFT-original /tmp/celldrift_fixture_input.csv /tmp/celldrift_expected.csv
args <- commandArgs(trailingOnly=TRUE)
suppressPackageStartupMessages(library(glmnet))
for (f in list.files(file.path(args... |
5714d4b599164b7653932dcd7ef7354fb1a0f29e9873f55f6fa8d75027c9a620 | R | 812 | 32 | ## ---Libraries ------
library(Signac)
library(Seurat)
library(GenomeInfoDb)
library(EnsDb.Hsapiens.v86)
library(ggplot2)
library(dplyr)
library(patchwork)
library(rtracklayer)
library(cowplot)
#library(BSgenome.Hsapiens.UCSC.hg38)
library(JASPAR2020)
library(TFBSTools)
library(patchwork)
##--- Functions --------
howM... |
b187dc748eaba12c23f03df3c3fecf1e75c5fde7915f9fb78bd65fa1eb2ddb70 | R | 815 | 17 | #' Extract number of variants for each chromosome in the VCF file
#'
#' This function extract the number of variants for each chromosome from the modeled VCF file
#'
#' @param SAMPLE_GT_CLEAN the modeled VCF file
#' @param CHR_NAMES (optional) vector with chromosme names to plot
#' @return A vector with number of varia... |
c2ff90d7584a0a220a13f1afda6e200b0623f39a645753d75f2f7c523eedf2c2 | R | 817 | 29 | ## need package minpack.lm
fitSinCurve <- function(xx, observed, parStart = list(A=3,phase=0, offset=0)){
getPred <- function(parS, xx) {
parS$A * sin(2*pi/24 * (xx + parS$phase)) + parS$offset
}
residFun <- function(p, observed, xx) observed - getPred(p,xx)
nls.out <- nls.lm(par=parStart, fn = residFun, obser... |
76319ad3d5db7617efb15804e3e395fa0473256fe2aefa5495237595889fe9fd | R | 826 | 37 | ### _gprofiler.R ###
suppressPackageStartupMessages({
library(gprofiler2)
})
gene_loader <- function(path){
stopifnot(is.character(path))
return(rownames(read.csv(path, header=T, row.names=1)))
}
egost <- function(
gene,
organism="rnorvegicus",
correction_method="fdr",
user_threshold=0... |
c98e5261c1ff88439f2b45e2971e592f885954522158475a65bffbd69bd87615 | R | 828 | 26 | ### DATA
zeisel <- read.table("zeisel.txt", sep = "\t", header = T)
rownames(zeisel) <- zeisel[ , 1]
zeisel <- zeisel[ , 3:dim(zeisel)[2]]
zeisel <- as.matrix(zeisel)
### ANNOTATIONS
labs <- read.table("zeisel.txt", nrows = 1, stringsAsFactors = F)
labs <- as.character(labs[2:length(labs)])
ann <- data.frame(clust_id ... |
5e6eec4113615a6e392c22710f555e6f0c2fe058c2aa02e255c86487a53a3f4d | R | 831 | 21 | library(dplyr)
library(Seurat)
### merge datasets
sObj_List1 <- readRDS(paste0("rObjects/sObj_List_", "run1", ".rds"))
sObj_List2 <- readRDS(paste0("rObjects/sObj_List_", "run2", ".rds"))
sObj_List <- c(sObj_List1, sObj_List2)
saveRDS(sObj_List, paste0("rObjects/sObj_List_", "all", ".rds"))
sObj_Filtered1 <- readRDS... |
00c8935a4ff127fad8c23fdb9bdcfa58a7a2449cd2c8787f72b1b8b9151451d2 | R | 841 | 32 | library(Signac)
library(Seurat)
library(GenomeInfoDb)
library(EnsDb.Hsapiens.v86)
library(ggplot2)
library(dplyr)
library(patchwork)
library(rtracklayer)
library(cowplot)
set.seed(1234)
sObj_Filtered <- sObj_cluster <- readRDS("rObjects/sObj_cluster.rds")
sampleInfo <- readRDS("rObjects/SampleInfo.rds")
## add gene... |
c188e880215acf2149cc78b916ed1a51a5dc10d70997c57c7c8c39d26b41dc93 | R | 843 | 22 | # required external packages for SIMLR
library("Matrix")
library("parallel")
# load the SIMLR R package
source("./R/SIMLR_Estimate_Number_of_Clusters.R")
source("./R/compute.multiple.kernel.R")
source("./R/network.diffusion.R")
# load the datasets
load(file="./data/Test_4_Usoskin.RData")
# in this example we test th... |
7e56f42aaf97b47275e8de5851d0c0e51abc30d0424f3cbe9c43f5b46b7bd57b | R | 848 | 25 | ### DATA
d <- read.table("Goolam_et_al_2015_count_table.tsv", header = T)
### ANNOTATIONS
labs <- colnames(d)
labs[grep("4cell",labs)] = "4cell"
labs[grep("8cell",labs)] = "8cell"
labs[grep("16cell",labs)] = "16cell"
labs[grep("32cell",labs)] = "blast"
labs[grep("2cell",labs)] = "2cell"
ann <- data.frame(cell_type1 = ... |
9843e4e16768a49c5475b3faa55718cfa4c238315e473f79f308fac4548a38d4 | R | 866 | 26 | ### DATA
d <- read.table("pancreas_refseq_rpkms_counts_3514sc.txt", stringsAsFactors = F)
d <- d[!duplicated(d[,1]), ]
rownames(d) <- d[,1]
d <- d[,3:ncol(d)]
d <- d[,3515:7028]
labs <- read.table("labels.txt", stringsAsFactors = F, header = F)
labs <- as.character(labs)
colnames(d) <- labs
d <- d[,order(colnames(d))]
... |
7d45fb0e4e751e6d34ed547c8108aa0b96e3491c51bc669dd0685185b91b8988 | R | 870 | 14 | source("/home/XXXXX/utils/R/run_scvi_integration.R")
run_SCVI_integration(object = "/projects/b1038/Pulmonary/rgrant/microglia_aging_flu/scRNAseq/analysis/230118_human_microglia_penultimate.rds",
python_path = "/projects/b1038/Pulmonary/rgrant/microglia_aging_flu/scRNAseq/human_microglia_venv/bin... |
4c6aa47d83d93a9c36748cf75b69ccd9631eba3444d48696b32927728104533e | R | 873 | 24 | # Observational Analysis - Body Shape Phenotypes -> Colorectal cancer
## Load the file with the information on colorectal cancer cases
table(cc_data_crc$colorectal)
dd <- datadist(cc_data_crc)
options(datadist='dd')
## Example of Cox regression
### Model was adjusted by socio-demographic and lifestyle factors as we... |
eeb6290315666715749e7f125b9e588cb69d747178aa7a14e6744e50d4da2c33 | R | 874 | 27 | ### DATA
d <- read.table("counttable_es.csv")
d <- d[1:(nrow(d) - 5), ]
### ANNOTATIONS
ann <- data.frame(
cell_type1 = unlist(lapply(strsplit(colnames(d), "_"), "[[", 3)),
batch = paste(
unlist(lapply(strsplit(colnames(d), "_"), "[[", 3)),
unlist(lapply(strsplit(colnames(d), "_"), "[[", 4)),
... |
7766849e060b417b72b38ca5fd559304391269fb8ede916c0427bf3a4adceec8 | R | 887 | 28 | #' Isler et al. (2008) dataset of primate brain and body mass.
#'
#' A dataset containing the brain and body mass of 74 primate species
#'
#'
#' @format A data frame with 74 rows and 2 variables:
#' \describe{
#' \item{Brain}{brain mass, in grams}
#' \item{Body}{body mass, in grams}
#' ...
#' }
#' @source Isler ... |
988d5f236d272420993e2970226e1208a606cbb0e8722b95511a1062d82257a0 | R | 887 | 28 | #' FindClusters()
#'
#' Perform cell clustering using different resolutions
#'
#' @param obj stCCA S3 Object
#' @param resolution_list List of resolutions to use for clustering
#'
#' @export
#'
#' @import future.apply
#' future
#' Seurat
FindClusters <- function(resolution_list, obj) {
future::plan(multisession, wor... |
87f840a8e436b673bf024b548216cb6091898b7fb5bd7744bfb2197d7507cb49 | R | 893 | 27 | ## Setup
setwd("/cluster/p/p33/cluster/users/elizabept/multimodal/discovery/phenos/data/diffusion")
library(ggplot2)
library(data.table)
library(nFactors)
## Load data
PC = c(c(1:1000),c(1:1000),c(1:1000))
metric = c(rep("N0", 1000), rep("NF", 1000), rep("ND", 1000))
N0 = fread("N0_Eigenvalues.txt",data.table = F,head... |
86a63d190d9b0a60a6f7edb54e8994b8aa4edbcdac76e458c334d87b5235fbf2 | R | 895 | 18 | # Usage: Rscript generate_reference.R ORIGINAL_HPS_DIRECTORY FIXTURE_DIRECTORY
# Source and execute the original author's function without modification.
args <- commandArgs(trailingOnly=TRUE)
source(file.path(args[1], "HPS.R"))
input <- read.csv(file.path(args[2], "input_npx.csv"), row.names=1, check.names=FALSE)
resul... |
29bad9403be78aad9da64284ff77d205024b1aa6d7995cf74f82927ce580929f | R | 899 | 26 | ### DATA
d <- read.csv("Usoskin+et+al.+External+resources+Table+1.csv", stringsAsFactors = F)
exprs_data <- data.matrix(d[11:nrow(d), 10:ncol(d)])
ann <- t(d[c(1:3, 5:8), 10:ncol(d)])
colnames(ann) <- d[c(1:3, 5:8), 9]
colnames(ann)[5:7] <- c("cell_type1", "cell_type2", "cell_type3")
# filter bad cells
filt <- !grepl("... |
7965745d6a24ccc6a12268b5303f2baf4dacdcb327344395242e048bd2597a9b | R | 900 | 29 | ### DATA
d <- read.csv("nsmb.2660-S2.csv")
rownames(d) <- d[,1]
d <- d[,3:92]
### ANNOTATIONS
ct2 <- colnames(d)
ct2[grepl("Oocyte", ct2)] <- "oocyte"
ct2[grepl("Zygote", ct2)] <- "zygote"
ct2[grepl("X2.cell", ct2)] <- "2cell"
ct2[grepl("X4.cell", ct2)] <- "4cell"
ct2[grepl("X8.cell", ct2)] <- "8cell"
ct2[grepl("Morul... |
c244910d574083b557190abdf776e5c60975253bf532ebd06cd0468af8484a3f | R | 910 | 23 | # Shared fixtures for the UCell test suite.
# Random counts, with ties, as a plain matrix
make_counts <- function(nfeat = 60, ncell = 40, seed = 42) {
set.seed(seed)
m <- matrix(rpois(nfeat * ncell, lambda = 3), nrow = nfeat, ncol = ncell)
dimnames(m) <- list(paste0("gene", seq_len(nfeat)),
... |
501bf27b146836bf9a01e64a8a92062bdfa7b9024a8a7159f9281ad55df27981 | R | 919 | 17 | #' Load a FASTA file and get chromosomes characteristics
#'
#' This function loads the FASTA file and returns a Seqinfo object with length for each chromosome
#' or with only specified chromosomes, if CHR_NAMES is given
#'
#' @param FASTA_FILE Path to the input FASTA file
#' @param CHR_NAMES (optional) vector with chro... |
699bcf42ac2d1c79a7c18ec25d297075a708c2b164d1483f522e8a3a71d73df6 | R | 919 | 18 | ---
title: "R Notebook"
output: html_notebook
---
This is an [R Markdown](http://rmarkdown.rstudio.com) Notebook. When you execute code within the notebook, the results appear beneath the code.
Try executing this chunk by clicking the *Run* button within the chunk or by placing your cursor inside it and pressing *Ct... |
434d7d9c4476b637a9bdd95503b1e98a8a877ffb4a2aa4cad160554c08de0ceb | R | 920 | 27 | #Circadian Drawing
circadianDrawing <- function(tod, expr, apar, labels, specInfo=NULL){
getPred <- function(parS, xx) {
parS$A * sin(2*pi/24 * (xx + parS$phase)) + parS$offset
}
geneName <- apar$genes
peak <- round(apar$peak)
if(peak==18) peak <- -6
#pvalue <- signif(apar$pvalue,3)
# amain <-... |
ee9f0f41cb5d396dc2950f64e37989c97c207c726734b267d4a93500e6fe6969 | R | 925 | 25 | # install.dependencies.gruffi.R
# Install standard dependencies ------------------------------------------------
install.packages('pdist')
install.packages('raster')
install.packages('rgl')
BiocManager::install("clusterProfiler")
BiocManager::install("org.Hs.eg.db")
# Install standard dependencies -----------------... |
109ad8f31c92059732da83506e48802469c2fc06a38f5908cb9d0b48da4fbe5b | R | 931 | 30 | ## Deseq2 for DGE ##
cts <- as.matrix(read.delim("sum_rawcount_wt-axxc.txt", sep="\t", row.names="gene_name"))
head(cts)
coldata <- read.delim("coldata_AXXC-WT.txt")
coldata
coldata$condition <- factor(coldata$condition)
library(DESeq2)
dds <- DESeqDataSetFromMatrix(countData = cts,
... |
c3b80531ad13c008aa4e3543ba5fd4cb892d3fef58c8acd9d6353995c28f9e1f | R | 941 | 18 | ### Figure 4A
rm(list = ls())
library("ggplot2")
setwd("/Users/chenguangji/Desktop/05.内源性病毒/LTR/Solo-LTR_report/Solo-Final/Source_Data/Figure_4/Figure_4A/")
ClosestGene<-read.table("Figure_4A.txt",sep="\t",header=T)
ClosestGene$term<-factor(ClosestGene$term,levels=rev(c("all","H3K27ac","H3K4me3","H3K27me3")))
Closest... |
64c241099d9f79428025896be53d5006ae2e5467186ffcfed9b96f67f4ac5a93 | R | 946 | 35 | source('code/misc/miscfxns.R')
rank_INT_base <- function(x,k=3/8){
mask <- x %in% c(-Inf,Inf,NaN,NA)
x[!mask] <- qnorm((rank(x[!mask])-k)/(length(x[!mask]) - 2*k + 1))
return(x)
}
rank_INT <- function(x,k=3/8){
# INPUTS:
# x: any R object
# OUTPUTS:
# that object normalized using rank inverse normal transfo... |
97599ae2e862ca714566d9f458a4e05c3ad6c0e002024d4fbe82ab436ecfb723 | R | 955 | 18 | # Usage: Rscript generate_reference.R ORIGINAL_PAC_DIRECTORY FIXTURE_DIRECTORY
# Execute the pinned original author's function, not a rewritten formula.
args <- commandArgs(trailingOnly=TRUE)
source(file.path(args[1], "pac_proteomic_age.R"))
input <- read.csv(file.path(args[2], "input_npx.csv"), row.names=1, check.name... |
d6eb3dae169e42e0d282cc18089cbae133ad1bcb470b3f8f79fff11e148b2b7c | R | 957 | 16 | #' Lineage-specific rate estimation using multiple variance Brownian motion
#'
#' Computes lineage-specific rates using mvBM
#' @param tree an object of class "phylo".
#' @param tree_mvBM an object of class "phylo". The rescaled tree from an mvBM procedure.
#' @param branches vector listing the branch numbers ('edge' ... |
c043ac04439f1c7c77eb13f5d7dcdef0c575f8b3261ea301003c858eecec2c75 | R | 967 | 26 | ### This code creates fig. S3
library(ggplot2)
library(tidyr)
library(gridExtra)
# read in the results from Dinvestigate
Data <- read.table("P1_P2_P3_localFstats__100_50.txt",as.is=T,header=T)
p1 <- ggplot(Data, aes(x=windowStart, y=D)) +
geom_point() + xlim(c(21200000,21900000)) +
xlab(paste("Scaffold 12")) +
... |
528be821bcfc7657031d25bc1c4eeb3344dc69b8fa18b87dec9e1909dff0f848 | R | 976 | 37 | ## ---Libraries ------
library(Signac)
library(Seurat)
library(GenomeInfoDb)
library(EnsDb.Hsapiens.v86)
library(ggplot2)
library(dplyr)
library(patchwork)
library(rtracklayer)
library(cowplot)
#library(BSgenome.Hsapiens.UCSC.hg38)
library(JASPAR2020)
library(TFBSTools)
library(patchwork)
##--- Functions --------
howM... |
7043a0cfdaba947ac3e5ffccb637a2a5e7a84517b6d26c4ae5afa1c7ab32f822 | R | 984 | 38 | library(dplyr)
library(VennDiagram)
library(grid)
# Read data
dnn <- read.csv("feature_importance.csv")
xgb <- read.csv("xbg_importance_matrix.csv")
logit <- read.csv("logit_features.csv")
# Calculate the 95th percentile for Importance and Gain
dnn_percentile_95th <- quantile(dnn$Importance, probs = 0.95)
xgb_percen... |
7ebe2326d38fecfdf0ae62eed4850f6fdfb35a745878a914a130e2959ca8b97b | R | 988 | 25 | ### DATA
deng_rpkms <- read.table("deng-rpkms.txt", check.names = F, header = T)
deng_reads <- read.table("deng-reads.txt", check.names = F, header = T)
genes <- deng_rpkms[ , 1]
deng_rpkms <- as.matrix(deng_rpkms[ , 2:ncol(deng_rpkms)])
rownames(deng_rpkms) <- genes
deng_reads <- as.matrix(deng_reads[ , 2:ncol(deng_re... |
29c8a2a572c97f142d538fcf8e40e0bfe0817772a4a27046f87f488e670ad72e | R | 994 | 30 | require("biomaRt")
counts = read.table('data/counts.tsv', sep ='\t', stringsAsFactors = FALSE, header=TRUE, row.names=1)
originalNames = rownames(counts)
geneNames <- gsub("\\.[0-9]*$", "", originalNames)
mart <- useMart("ENSEMBL_MART_ENSEMBL")
mart <- useDataset("hsapiens_gene_ensembl", mart)
annotLookup <- getBM(... |
f6b2c080536f2f3b13ac0836de91f615f845e8e265333c76d8247d4f60fd2277 | R | 997 | 31 | #' Create a vector with height to plot each variant
#'
#' This function defines the Y axis coordinate to plot each variant, based on the total variants in the VCF file
#'
#' @param CHR_N the vector with number of variants for each chrosomome (as returned by get_chr_num() function)
#' @param ORDERED (optional) if user w... |
0ee22965e54fca16c79fac7319dc82d842a7b73a72cdf036822e750ac3c9ae66 | R | 998 | 26 | ### DATA
d <- read.table("data.csv", header = T, stringsAsFactors = F)
ann <- read.table("cell_type_annotation_Cels2016.csv", stringsAsFactors = F)
### ANNOTATIONS
d <- d[,colnames(d) %in% rownames(ann)]
ann <- ann[rownames(ann) %in% colnames(d),,drop = FALSE]
d <- d[,order(colnames(d))]
ann <- ann[order(rownames(ann)... |
f199dd8fb82d767c9d797497a55e4ff8167fe1d9f39c630f63d500a3a362c54f | R | 1,001 | 24 | # Figure 1B
rm(list = ls())
require("writexl")
library("dplyr",quietly=T)
library("ggplot2",quietly = T)
library("ggpubr",quietly = T)
setwd("Source_Data/Figure_1/Figure_1B/")
data.df.1B<-read_excel("Figure_1B.xlsx")
### Figure_2B
p.Fig1B<-ggscatter(data.df.1B %>% mutate(clade=factor(clade,levels=c("Aves","Reptilia... |
2cce21adf9bb24dbc80992776c945bd9e2447933f9f74db47e9155e5cc05cde5 | R | 1,016 | 38 | library(Spaniel)
library(scran)
library(scater)
library(batchelor)
library(dplyr)
library(Seurat)
library(SCFunctionsV3)
library(cowplot)
library(RColorBrewer)
library(SCFunctionsV3)
library(harmony)
allData <- "/data/rachel/Linda_Lako/Spatial/spaceranger_outs_2021_102"
tissue <- "AD3"
projDir <- paste0("/data/rache... |
3561552deca0b4893ca6d5064abe895b412a20bd0cede87a4ab541c77dfbfe84 | R | 1,023 | 43 |
############################################################################################################
########## R script for:
##########
########## Statistical indices of masculinity-femininity: A theoretical and practical framework.
##########
########## Marco Del Giudice
##########
########## Script for Fi... |
b40d561d903005a867eab23c120248a778f94b181e7c82edcf12ae406df624d3 | R | 1,025 | 38 | library(Spaniel)
library(scran)
library(scater)
library(batchelor)
library(dplyr)
library(Seurat)
library(SCFunctionsV3)
library(cowplot)
library(RColorBrewer)
library(SCFunctionsV3)
library(harmony)
allData <- "/data/rachel/Linda_Lako/Spatial/spaceranger_outs_2021_102"
tissue <- "fetal_retina"
projDir <- paste0("/d... |
0d400796e9648a9e664f492e0319dd8485c64917a9bcb1ec3ac3395a5df93da7 | R | 1,026 | 27 | library(Seurat)
library(SeuratObject)
library(dplyr)
library(ggplot2)
# Read the Seurat object
dat0 <- readRDS("PlateletsOnly_cluster_Anno.rds")
# Update metadata within the Seurat object
dat0 <- dat0 %>%
mutate(seurat_clusters = paste0("C", seurat_clusters),
seurat_clusters = factor(seurat_clusters,
... |
43b741b1de66c60f8658905e5efe7617d60f245105c369371b0c5f50834de8ee | R | 1,030 | 30 |
# Go through all sequence files and check whether they are ok.
# Does each sequence have 16 images?
# Does each sequence consist of unique images?
library(tidyverse)
sequence_length <- 10
# Identify sequence files
# Juli: I changed the path because my project is in the main folder
all_files <- list.files("./categor... |
94cc3c6314e0ee5eea3313430b6e895a0d67ddd631f4724ddebf22ac66465efb | R | 1,043 | 36 | #
#
#
require(tidyverse)
require(tidygenomics)
cyto=read_tsv("ucsc_mm10_cytoBand.txt.gz") %>%
rename(chrom=`#chrom`) %>%
mutate(chrom=gsub("^chr","",chrom))
cyto=cyto %>% mutate(CID=paste0("c",row_number())) %>%
rename(start=chromStart,end=chromEnd) %>%
mutate(band=paste0(chrom,name))
ar... |
b137620e786aa8fb5fc41cb23c3034ee6caa1d939c9807beabed8274b4587e9d | R | 1,044 | 28 | ### DATA
# Human
x1 = read.delim("GSE75330_Marques_et_al_mol_counts2.tab", "\t", header=T, stringsAsFactors=FALSE)
ann <-read.table("Marq_Ann.txt", header=T, stringsAsFactors=FALSE)
rownames(x1) <- x1[,1];
x1 <- x1[,-1];
x1 <- x1[, colnames(x1) %in% colnames(ann)]
### ANNOTATIONS
ann <- ann[, colnames(ann) %in% colnam... |
288906023eea01346fe8d6de9f56c98c3251847864918414547d8ed610dd2e2b | R | 1,057 | 33 | #! /usr/bin/env Rscript
rm(list=ls())
library(data.table)
Path = "/cluster/projects/p33/users/elizabept/multimodal/discovery/phenos/data"
GenPath <- "/cluster/projects/p33/groups/imaging/ukbio/genetics"
Batch="42k"
Date="160320"
######################
# Merge all measures #
######################
DM <- fread(paste... |
a6481a52dc3e45a07a708dd4dbe2fdc601e081f6e528248f88a558f4125e81d9 | R | 1,077 | 22 | # File for creating some UMAPs for figure 2
library(plotly)
reticulate::py_run_string("import sys")
genes <- c("Atoh1", "Neurog1", "Ascl1", "Prdm13")
for (i in genes) {
Feature3D(data.combined, i) %>% layout(scene = list(camera = list(eye = list(x = .5, y = -1, z = 1.6)),
... |
41962b4cc223541f33d6db6fe20d677b3fd004f0b3bc57bf38ed59f2f8b4d240 | R | 1,084 | 20 | # Figure_4C
require( ggplot2 );
require( scales );
require( ggrepel );
require( dplyr )
setwd("Source_Data/Figure_4/Figure_4C/")
Brian_compare.DEGs.df<-read.csv("Figure_4C.csv")
p.Volcano<- ggplot(Brian_compare.DEGs.df,
aes(log2FoldChange,-1*log10(padj)))+
geom_point(aes(color = diffexpressed),al... |
05a66634b5190ce7ff68e533bc51c00595fe51fde19ee78dbe3c1d50b76bb949 | R | 1,092 | 39 | #! /usr/bin/env Rscript
library(DropletUtils)
mtx_dir <- "${input_mtx_dir}"
sce <- read10xCounts(mtx_dir) # Read to SingleCellExperiment object
print(sce)
# Convert to matrix
count_matrix <- counts(sce)
rownames(count_matrix) <- rownames(sce)
colnames(count_matrix) <- colData(sce)\$Barcode
if ("${write_csv}" == "... |
499f8e8156901edcf1cab9ac150f69a4d99743af570e278a80d47ad6dce948ed | R | 1,094 | 31 | #' plot_cluster()
#'
#' Visualizing clusters in human brain image
#'
#' @param coordis Spatial location of cells with cell barcode as rows and coordinates as columns.
#' @param label Cluster label as a column of data frame.
#' @param colors Color palette for clusters.
#' @param t Plot Title. Null in default.
#' @param ... |
4d2eb8b29cfad5959a2d4b52e76fd1bac10c2b0b5026bfbd77b59872bd660962 | R | 1,099 | 34 | CCAAlgorithm <- function(x,z,v,penaltyx,penaltyz,K,niter,trace,upos,uneg,vpos,vneg){
if(K>1) v.init <- v[colSums(z^2) != 0]
if(K==1) v.init <- v[colSums(z^2) != 0]
v.init <- matrix(v.init,ncol=K)
u=v=d=NULL
xres <- x; zres <- z
xres <- x[, colSums(x^2) != 0]
zres <- z[, colSums(x^2) != 0]
... |
351a2781497b2487d6195fb1edbecd0a74661584ba04c9057515e2be35845646 | R | 1,101 | 32 | #' CheckVS
#'
#' @param v v
#' @param x x
#' @param z z
#' @param K K
#'
#' @import RSpectra
CheckVs <- function(v,x,z,K){ # If v is NULL, then get v as appropriate.
##print(list(v=v, x = x, z = z, K = K))
if(!is.null(v) && !is.matrix(v)) v <- matrix(v,nrow=ncol(z))
if(!is.null(v) && ncol(v)<K) v <- NULL
if(!is... |
ebdc08e1166b2509303211d991ba1e764eeb81f3c4bbd107146adc3dd8f138c7 | R | 1,101 | 27 | ### DATA
DATA = read.table("data.txt", header=T)
expr_mat = as.matrix(DATA[,2:length(DATA[1,])]);
keep = colSums(expr_mat> 0) > 100; # Some cell QC (avoid all zero cells)
expr_mat = expr_mat[,keep];
gene_names = DATA[,1];
gene_names = matrix(unlist(strsplit(as.character(gene_names),"__")),ncol=2, byrow=T )
dups = dupli... |
4c0aea988b97e4f209900957137fe8a0d1e8e27717f4325eafa4f3b7d7ff4a42 | R | 1,111 | 31 | compareplots <- function(nameofref, nameoflookup, samplesize) {
reflocation <- which(colnames(ReadsTable) == nameofref)
lookuplocation <- which(colnames(ReadsTable) == nameoflookup)
reference <- ReadsTable[,reflocation]
lookup <- ReadsTable[,lookuplocation]
referenceprop <- reference/sum(reference... |
4084f295b1846a8c0d458d78aa1e56f2a86112c311e1f146adf25f5536e01856 | R | 1,128 | 33 | library("stars")
variables = list.files("data/variables", pattern = ".tif", full.names = TRUE)
n = length(variables)
df = data.frame(min = double(n), max = double(n), mean = double(n), sd = double(n))
for (i in seq_along(variables)) {
cat(i, "/", n, "\n")
r = read_stars(variables[i], proxy = FALSE)
df[i, "min"]... |
348a8ea7eec8cdff6211f555c9ed42bf05a7e700b50e02b7cd50de7e113ccfc1 | R | 1,133 | 36 | setwd('/work/AnneSophiaHartebrodt#6698/ms-project/')
require(rhdf5filters)
require(rhdf5)
require(Rhdf5lib)
renv::activate('/work/AnneSophiaHartebrodt#6698/ms-project/r-single-cell')
renv::repair()
set.seed(11)
require(Seurat)
require(data.table)
require(ggplot2)
require(ggrepel)
require(dplyr)
require(tidyr)
require(c... |
06be487512d388aad9b5ebef50aff59d57072c447d20aab7be5de6878481e90e | R | 1,140 | 18 | #' Lineage-specific rate estimation using multiple variance Brownian motion
#'
#' Plots lineage-specific rates using mvBM
#' @param tree an object of class "phylo".
#' @param tree_mvBM an object of class "phylo". The rescaled tree from an mvBM procedure.
#' @param branches vector listing the branch numbers ('edge' num... |
e2957371b87c07d3e9f695ab3a0fe167c19acbff70804aa411223a1b52744b87 | R | 1,143 | 40 | ---
title: "License"
output:
workflowr::wflow_html:
toc: false
theme: cosmo
highlight: textmate
code_folding: hide
df_print: paged
editor_options:
chunk_output_type: console
---
This repository contains code and output of the analysis of single-nuclei
RNA sequencing data for the article [Hevesi... |
df7a87db16164b096080b78539476e98a3242c82007961af46fadf35c3d31570 | R | 1,147 | 34 | library(dplyr)
library(EnhancedVolcano)
# Set parameters
dpi <- 300
# Create Volcano Plot
png(file = "NS_S_volcano_ML_features.png", width = dpi * 12, height = dpi * 6, units = "px", res = dpi, type = 'cairo')
EnhancedVolcano(markers,
lab = markers$gene,
x = 'avg_log2FC',
... |
491e21a9ba405e0af731224506af4e9138f21ee745e32c2d3216fbc690eb3cdd | R | 1,156 | 33 | library(monocle3)
library(ggplot2)
# Load the CellDataSet
cds <- readRDS("pla_cds_C0_C1_C4_C6.rds")
dpi = 300
# Generating Monocle 3 Trajectory plot colored by Seurat clusters
png(file = "Monocle3_Traject_by_cluster.png", width = dpi * 6, height = dpi * 4, units = "px", res = dpi, type = 'cairo')
plot_cells(cds,
... |
015e4371664d6e31ae4650c78e70160de9f6767416d71a581a6ae860d4e165fc | R | 1,177 | 42 | #' sct()
#'
#' Apply variance stabilizing transformation to UMI count data using a regularized Negative Binomial regression model. This will remove unwanted effects from UMI data
#'
#' @param n_genes Number of genes
#' @param obj stCCA S3 Object
#' @param transpose Boolean of Matrix transpose
#'
#' @export
#'
#' @impor... |
628a53e040d1aa1fd5d9fac4731521263ec51d42ca4b4abe428f20cd57609276 | R | 1,178 | 31 |
# Go through all sequence files and check whether they are ok.
# Does each sequence have 16 images?
# Does each sequence consist of unique images?
library(tidyverse)
# Identify sequence files
all_files <- list.files("./graph", full.names = TRUE)
sequence_files <- all_files[grepl("sequence_", all_files)]
# Empty dat... |
08ddf44a4f35f5cdeb0fd52a92d92ef2fa1f068add2e37f7a2c6c99e601473ed | R | 1,181 | 31 |
# Go through all sequence files and check whether they are ok.
# Does each sequence have 16 images?
# Does each sequence consist of unique images?
library(tidyverse)
# Identify sequence files
all_files <- list.files("./category", full.names = TRUE)
sequence_files <- all_files[grepl("sequence_", all_files)]
# Empty ... |
4dd8bd0a9a53fef0095003ba0da86135935479ef4bf16229ad7a05de0720b5a4 | R | 1,191 | 31 | #' return a vector of chromosome names in proper genomic order
#'
#' This function takes in a vector of chromosome names and return them in proper order
#'
#' @param CHR_VECTOR vector with chromosme names
#' @return ordered vector of CHR_VECTOR
chr_order <- function( CHR_VECTOR ){
CHR_NAMES38 <- c("chr1","chr2","chr3... |
4429ffa9481472e95a4d70fa621a44a96b68062c38ec2cb399ac7017eaa1566e | R | 1,198 | 33 | library(Gviz)
library(GenomicRanges)
data(geneModels)
library(TxDb.Mmusculus.UCSC.mm10.knownGene)
library("org.Mm.eg.db")
library(rtracklayer)
geneRanges = import.gff("gencode.vM25.annotation.gtf")
itrack <- IdeogramTrack(genome = "mm10", chromosome = "chr19")
plotTracks(list(itrack),from = 10950000, to = 11650000)
... |
e850d82603154459fa570e7385a112892fa573cbe2d8e38ba05457f436f4b99b | R | 1,200 | 41 | library(data.table)
library(dplyr)
############
## Load data
############
# Load Phenotypes
PCs = fread("/path/ABCD11904_diffusion_QCed_230322.txt",data.table = F)
PCs$IID = PCs$FID = sapply(strsplit(PCs$Row,"_"), `[`, 1)
PCs$Row = paste0("NDAR_",PCs$IID)
# Recommended inclusion
inclusion = fread("/path/ABCDStudyNDA... |
efb8d00767d138c57f8062d470b1ccf9c1ce2fe10583fa7771fda365f9029693 | R | 1,208 | 48 | library(Signac)
library(Seurat)
library(GenomeInfoDb)
library(EnsDb.Hsapiens.v86)
library(ggplot2)
library(dplyr)
library(patchwork)
library(rtracklayer)
library(cowplot)
set.seed(1234)
sampleInfo <- readRDS("rObjects/SampleInfo.rds")
sObj_predicted <- readRDS("rObjects/sObj_predictions.rds")
anno_all <- read.csv(... |
1894ff47116f340f7d741259c188c7f1d35d22c358d1954ab69cbe65eb13aa4f | R | 1,224 | 40 | #' spatial_gradient()
#'
#' Spatial-informed cell gradient plots
#'
#' @param t A character string to define plot title. Null in default.
#' @param obj stCCA S3 Object
#'
#' @export
#'
#' @import ggplot2
#' ggpubr
spatial_gradient <- function(obj, t = NULL) {
coordis <- obj$spatial
joint_embedding <- obj$joint_emb... |
abcd466b36354614ae45d700556b508e3df686d76bba9a0595536f4f83993cbc | R | 1,224 | 44 |
# Mendelian Randomization Analysis - Body Shape Phenotypes -> Colorectal cancer
## Load the file with summary data for the body shape phenotype and colorectal cancer
### In this, there are
### Genetic variants that surpass the 10^-8 threshold for each body shape and their match in GECCO consortium summary statistics... |
108003aa8b085305c655121d4f0832b3dc9f91da666bfe6dd6c79cff0142cd8e | R | 1,233 | 36 | #' BuildSNNSeurat()
#'
#' Build SNN matrix by inputting canonical variates of spatial-driven canonical loading
#'
#' @param k.param Maximum number of nearest neighbors to compute. Default - 30.
#' @param prune.SNN Sets the cutoff for acceptable Jaccard index when computing the neighborhood overlap for the SNN construc... |
ee766f1e398409955a7bf12375147af94d419d40f7fce79e9ed8a21f464bfe76 | R | 1,242 | 36 | ### DATA
x1 = read.delim("GSE74672_expressed_mols_with_classes.csv", ",", header=T, stringsAsFactors=FALSE)
ann <-x1[1:11,]
x1 <- x1[-1*(1:11),]
rownames(x1) <- x1[,1];
x1 <- x1[,-1];
tmp <- ann[,1]
ann <- ann[,-1]
### ANNOTATIONS
SOURCE <- rep("hypothalamus", times=length(x1[1,]))
#BATCH <- as.character(unlist(ann[,4... |
38a917cca0090c1a19aabd20530e47340af0545072a1f3dc9a255c0081932864 | R | 1,253 | 24 | # Configuration for the Package
DESCRIPTION <- list(
package.name = "gruffi",
version = "1.5.6",
title = "Gruffi identifies and removes stressed cells from brain organoid single-cell datasets.",
description = "The Gruffi R package helps you (1) to identify stressed cells in single-cell
RNA-seq datasets usin... |
008bac29ed0db81ac5106d6732cd92655fd49dae83c1086c01c7134c883ec22b | R | 1,257 | 37 | # StoreRankings_UCell and the rankings2Uscore() scoring path
test_that("StoreRankings_UCell returns a gene x cell sparse rank matrix", {
m <- make_counts()
ranks <- StoreRankings_UCell(m, ncores = 1)
expect_s4_class(ranks, "dgCMatrix")
expect_equal(dim(ranks), dim(m))
expect_equal(rownames(ranks), rownames(... |
3a51439f673fcadd46e4b6872ab6e941f1dca240d63042fa907eb9cd976d7732 | R | 1,258 | 35 | ### Figure 3C
rm(list = ls())
library("UpSetR")
library("ComplexHeatmap")
### upset R
setwd("Source_Data/Figure_3/Figure_3C/")
upset.df<-read.csv("Figure_3C.csv",header = T,row.names=1)
names(upset.df)[1]="taeGut1"
m = make_comb_mat(upset.df)
m2 = m[comb_size(m) <= 60]
m3 = m2[comb_size(m2) >= 2]
pdf("Figure_3C.pdf... |
895277801166261300b6c94492a8a82d92ce130521aff94b40af6873a75822c9 | R | 1,259 | 50 | # Load required libraries
library(Seurat)
library(dplyr)
library(ggplot2)
library(cowplot)
library(tidyr)
library(ggpubr)
library(stringr)
# Load the dataset
dat0 <- readRDS("Platelets_cluster_Anno.rds")
DefaultAssay(dat0) <- "RNA"
# Function to perform operations and generate plots for a given group.by parameter
g... |
8059c1526667e3617b7948e81bdc598da6dcb7d11ae94a6d799bbdac93decd85 | R | 1,269 | 38 | #! /usr/bin/env Rscript
rm(list=ls())
library(data.table)
library(readr)
Path = "/cluster/projects/p33/users/elizabept/multimodal/discovery/phenos"
GenPath <- "/cluster/projects/p33/groups/imaging/ukbio/genetics"
Batch="42k"
Date="160320"
######################
# Merge all measures #
######################
daniel_... |
573ce3055cc464ea78231047b3d91be1f37aa1455c767bd1bca9160e798bc08d | R | 1,273 | 39 | CalibrateNr <- function(Standards, SamplesToCalibrate, outputfilename) {
standards <- read.csv(Standards)
samples <- read.csv(SamplesToCalibrate, row.names = 1)
cfu <- log10(standards[,1])
nr <- log10(standards[,2])
fit <- smooth.spline(x = cfu, y = nr, df = 7)
xvals <- seq(from = min(cfu), to = 7... |
cf8908d2c3cb4f6e27b0b83c8e616ea0d7117511479a3aa5517dc307fe9c876c | R | 1,273 | 39 | CalibrateNs <- function(Standards, SamplesToCalibrate, outputfilename) {
standards <- read.csv(Standards)
samples <- read.csv(SamplesToCalibrate, row.names = 1)
cfu <- log10(standards[,1])
ns <- log10(standards[,2])
fit <- smooth.spline(x = cfu, y = ns, df = 7)
xvals <- seq(from = min(cfu), to = 7... |
1c64c7ac705184d51df86e4298d78d9abb1e2e0a53c950c216ecac2de36c6047 | R | 1,284 | 36 | library(ggplot2)
setwd("R:/WeijieZheng/Social_defeat_csv")
data <- read.csv("I_O_difference_level6.csv")
ggplot(data, aes(log2FoldChange, -log10(pValue)))+
geom_hline(yintercept = -log10(0.05), linetype = "dashed", color = "#999999")+
geom_vline(xintercept = c(-0.5, 0.5), linetype = "dashed", color = "#999999")... |
554a5e2e9304b1a510645ce90ed1ce1f8529635ea3f5ef69be49e19186aee22d | R | 1,284 | 24 | library(dplyr)
devtools::load_all('breaktools/')
common_motif = function()
{
rdc_df = readr::read_tsv("data/rdc.tsv") %>%
dplyr::filter(tlx_group=="APH-Inter" & rdc_subset=="Wei+DKFZ" & rdc_is_significant) %>%
dplyr::select(-tlx_group, -rdc_subset) %>%
dplyr::do(as.data.frame(get_seq("genomes/mm10/mm10.f... |
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