sha256 stringlengths 64 64 | language stringclasses 28
values | size int32 1 491k | lines int32 1 23.2k | content stringlengths 1 200k |
|---|---|---|---|---|
190c610d591d950fc746d68473a2782663453b1cf8116b2ab9931ec25dc461c9 | R | 7,712 | 273 | ---
title: "Tutorial: Repeated Measures"
output: rmarkdown::html_vignette
vignette: >
%\VignetteIndexEntry{Tutorial: Repeated Measures}
%\VignetteEngine{knitr::rmarkdown}
%\VignetteEncoding{UTF-8}
---
```{r, include = FALSE}
knitr::opts_chunk$set(
collapse = TRUE,
comment = "#>"
)
```
This vignette document... |
865d8024a33cc08ee1dd122ffb77e929ec567b3986aca65bd4b90066d4a898f4 | R | 7,741 | 163 | ### LIBRARIES ###
libs <- c('ggplot2','RColorBrewer','reshape2','devtools',
'pheatmap','flowCore','plyr','scales','coin','grid',
'gridExtra','tidyr','randomcoloR','viridis','tidyverse')
for (L in libs){
if(!require(L, character.only = T)) {
install.packages(L, dep = T, quiet = T)
if (... |
28ba25ce39c913fb7061f7b5d104550c6544cbbaad866a22f79700a8772f4c14 | R | 7,748 | 147 | # universe_policy (SPEC-RICHR-005 REQ-017): an opt-in ORA option counting
# tested-but-unannotated universe genes in N and n. The default ("annotated")
# must stay identical to the omitted argument on every entry point. Fixtures:
# F-AUD inline, F-ORA-C from the committed characterization fixtures.
fc_read <- function... |
1e0c0719dca6503a1f487ddfa8286bd30be74c703332b4dd72fa3a3a0db29ef5 | R | 7,771 | 125 | #### Neighborhood Analysis ####
#### Initial Data Carpentry ####
# prepare quantile normalized neighborhood data (note: neighborhood counts are summed from each object within each neighborhood)
master_neighborhood_c <- master_neighborhood
names(master_neighborhood_c)[80] <- c('de_novo_regions')
mas... |
a0f99a5f845150473bc8297a91f31a0dcdd5946d01dc3fbb163c98bf7bb6c3fe | R | 7,779 | 156 | ##' Dotplot for enrichment results
##' @importFrom ggplot2 ggplot
##' @importFrom ggplot2 aes
##' @importFrom ggplot2 geom_point
##' @importFrom ggplot2 element_text
##' @importFrom ggplot2 geom_text
##' @importFrom ggplot2 theme
##' @importFrom ggplot2 scale_color_gradient
##' @importFrom ggplot2 xlab
##' @importFrom ... |
943b6c125ef30b8d6e132bc0c86a8991c9926acdaf43d59d0200a0a2d1c4dddc | R | 7,782 | 182 | ---
title: "Running estimate_cycle_position from tricycle on Seurat Objects"
date: 'Compiled: `r format(Sys.Date(), "%B %d, %Y")`'
output:
github_document:
html_preview: true
toc: true
toc_depth: 3
fig_width: 16
html_document:
df_print: kable
theme: united
fig_height: 5
fig_width: 1... |
a14feb0bd9148ea0d025c9472c43f4c9c7bcdcf8f77fe78cf624f7bb0ec981d9 | R | 7,798 | 190 | #' Prune Cy3 spots from spatial transcriptomics (ST) array
#'
#' This takes segmented contours form Cy3 ST array and prunes it from aritfacts
#' and detects spots thar are missing.function loads a file as a matrix.
#'
#' @param segmentation a segmentation list obtained from segment()
#' @param array.dim the dimensions ... |
f0ca6d4dae313221b840bd368c745e3aa5d17023a02fa8c3a21d022d5b764951 | R | 7,808 | 162 | #' Stitch image using grid layout
#'
#' Stitches multiple image tiles.
#' @param input input a character vector consisting of the full path name to 16-bit raw tif image files. If length(input)==1 then its a folder and it will get tiffs inside.
#' @param stitched.image.name filename of the stitched output, default is th... |
cbed86a75d51c26f9a51a7fe0de649e95f70b5a0d11af6c54745c7573ee94811 | R | 7,817 | 160 | ### LIBRARIES ###
libs <- c('ggplot2','RColorBrewer','reshape2','devtools',
'pheatmap','flowCore','plyr','scales','coin','grid',
'gridExtra','tidyr','randomcoloR','viridis','tidyverse', 'ggpubr')
for (L in libs){
if(!require(L, character.only = T)) {
install.packages(L, dep = T, quiet = T)
... |
48b2c0e422d14f1ac00e20be6dd38c320c450e8007cb56058549fb1791b2d78a | R | 7,920 | 245 | #R/Natverse code for Video 2 of the Jasek et al 2022 desmosomal connectome paper
#Gaspar Jekely, Sanja Jasek 2022
source("code/Packages_and_Connection.R")
# colorblind-friendly color palette
Okabe_ito_noblack <- c("#E69F00", "#56B4E9", "#009E73", "#F0E442", "#0072B2", "#D55E00", "#CC79A7")
# load landmarks
yolk <- c... |
854b78bb7820fe5b6cc94f4841c68a11bfb175d98506696d7e971b82713de3ca | R | 7,923 | 156 | ##### object_import ##### - Repeat for Hi, Med, and Ctrl Conditions
# Import post-ez_segmenter data or deepcell data (MATLAB generated) for single object + cell analysis, respectively
# Author: Bryan Cannon 2020 (multiple code snippets taken or built from DM, FH, EFM, DT)
##### INSTALL / LOAD PKGS #####
# instal... |
e06589f68fa0d7b2594b241de0d8769bf9ea2982791ca7a5f726d2b9fdefc820 | R | 7,970 | 246 | options(warn=0)
args <- commandArgs(TRUE)
# What post-hoc comparison we want to do
mode <- c(no_ph = "No post-hoc test", co_deco = "Disconnected versus spared",
deco_ctr = "Disconnected versus controls", co_ctr = "Spared versus controls")
# ph_test <- c(kw = "Kuskal-Wallis", mw = "Mann-Whitney", ttest = "t-test",
# k... |
88c0a12889c4ca6ac89d7c6b828e017af67dbddb6a641733264169d77f54a54f | R | 7,993 | 169 | # Fixtures for the plot tests of SPEC-RICHR-006 (M6.1, pure plots):
# test-plot-selection.R, test-plot-fabrication.R and test-plot-reference.R.
# fixtures/make-plot-reference.R sources this file too, so the AC-009 reference
# and the test that compares against it run the same calls on the same inputs.
# Inputs are buil... |
b7f8130a04833c8322b7c014acebe85f51436ba5f43c414d9dc9c822c05ba662 | R | 8,011 | 139 | ##### object_image_writer ##### - Repeat for Hi, Med, and Ctrl Conditions
# Working with post-ez_segmenter data ann deepcell (MATLAB GUI generated) for single object image creation
# Author: Bryan Cannon 2020 (multiple code snippets taken or built from DM, FH, EFM, DT)
# install and / or load ez_pkgs if you have... |
af09909df39d93a3a4db89fb29f482450e35051e9776b558da96af485e6a69cb | R | 8,028 | 139 | ##### object_image_writer ##### - Repeat for Hi, Med, and Ctrl Conditions
# Working with post-ez_segmenter data ann deepcell (MATLAB GUI generated) for single object image creation
# Author: Bryan Cannon 2020 (multiple code snippets taken or built from DM, FH, EFM, DT)
# install and / or load ez_pkgs if you have... |
7fa6a92fcfabf0b934fd795d1c4c82e92617fa9f6c0e2712f74d0120b181b046 | R | 8,114 | 161 | ---
title: "Benchmarking Visualization"
author: "Will Townes"
output: html_document
---
```{r}
library(tidyverse)
theme_set(theme_bw())
fp<-file.path
pth<-"scrna/visium_brain_sagittal"
plt_pth<-fp(pth,"results/plots")
if(!dir.exists(plt_pth)){
dir.create(plt_pth,recursive=TRUE)
}
```
```{r}
d0<-read.csv(fp(pth,"r... |
9c470854a392edccf5490bbf2713a489f8d2e63f48e6034c7312b8e69fd8d0f2 | R | 8,165 | 170 | #!/usr/bin/env Rscript
################################################
################################################
## LOAD LIBRARIES ##
################################################
################################################
library(optparse)
library(ggplot2)
library(reshape... |
f8ea3ddb6e460060f561de343c7bb72afaa37190ce91d13543b3696131e238ec | R | 8,255 | 139 | ##### object_image_writer ##### - Repeat for Hi, Med, and Ctrl Conditions
# Working with post-ez_segmenter data ann deepcell (MATLAB GUI generated) for single object image creation
# Author: Bryan Cannon 2020 (multiple code snippets taken or built from DM, FH, EFM, DT)
# install and / or load ez_pkgs if you have... |
3c4cf97447e790f7d59905646a92c9d3a6b2f64b47e2b0760cd62be8c112d920 | R | 8,337 | 251 | ---
title: "Running CoGAPS on Seurat Objects"
date: 'Compiled: `r format(Sys.Date(), "%B %d, %Y")`'
output:
html_document:
df_print: kable
theme: united
highlight: null
github_document:
html_preview: true
toc: true
toc_depth: 3
---
This vignette demonstrates the use of the CoGAPS package o... |
ebb8317464145d02325dd05b3382916a66c3b2680fa9dec1abe202f5caabf82f | R | 8,357 | 179 | # Alpha Diversity adaptable
##### Alpha Diversity Boxplots Script
######## Load Data & functions
source("src/load_packages.R")
source("src/metadata_prep_funcs.R")
source("src/miscellaneous_funcs.R")
source("src/community_composition_funcs.R")
source("src/stats_funcs.R")
alpha_diversity_summary <- function(x, z, tag){... |
be4bfc1b2bea061b5cfa661ab397e530bfa76513b8c89402267ec5146c107c3a | R | 8,409 | 188 | # ORA plot display labels (REQ-009 .. REQ-011). Upstream-portable: the results
# are built by hand (no enrich() labelling), only exported plot functions are
# called, and only testthat edition-2/3 expectations are used.
#
# Two identifiers sharing a label must be drawn as two categories
# ("label [ID]"), never merged a... |
5e4a54e60241f526a7e7d9bc328db0f661cc150069ad86f99ace64560f6906b7 | R | 8,438 | 235 | ---
title: "Tutorial: Basics"
output: rmarkdown::html_vignette
vignette: >
%\VignetteIndexEntry{Tutorial: Basics}
%\VignetteEngine{knitr::rmarkdown}
%\VignetteEncoding{UTF-8}
---
```{r, include = FALSE}
knitr::opts_chunk$set(
collapse = TRUE,
comment = "#>"
)
```
This vignette documents the basic functional... |
d1239dac46b52ac21b5b39cc37e2eeb02999fc4635e732b95e611201ad953dc8 | R | 8,503 | 166 | ---
title: "Benchmarking Visualization"
author: "Will Townes"
output: html_document
---
```{r}
library(tidyverse)
theme_set(theme_bw())
fp<-file.path
pth<-"scrna/sshippo"
plt_pth<-fp(pth,"results/plots")
if(!dir.exists(plt_pth)){
dir.create(plt_pth,recursive=TRUE)
}
```
Convergence failures
* MEFISTO with L>6 or ... |
50c8d1f50a3f274eaee4c68b915d0a9302d802a7596a0b5d200d27eee6d09934 | R | 8,504 | 202 | library(Seurat)
library(patchwork)
library(dplyr)
library(ggplot2)
library(glmGamPoi)
### This file will do the Immune integration and output files needed for the integration section of the Shiny
### These 5 RDS files can be downloaded at GEO Accession = GSE198582
wd <- getwd()
setwd("Y:/CDBLabs-Bioinformatics/Some_Pl... |
5b8c7fec5f5eb43f308ff39c6ab7da02f7772eeb2be38a8debcf1effde170fab | R | 8,506 | 199 |
#### analysis ####
# combine all three datasets - Ctrl, MedAD, HiAD
master_obj_data_Ctrl_together <- master_obj_data_Ctrl
levels(master_obj_data_Ctrl_together$run_type_id) <- c('Healthy Control', 'Healthy Control', 'Healthy Control')
master_obj_data <- rbind(master_obj_data_Ctrl_together, master_obj_data_Med, master_o... |
38595f5eacd623ab5db7ddef25e6149b3454ccd01cbb44802ba2d7aff0e0fc13 | R | 8,521 | 211 | #' @include internal.R
#'
NULL
#' Run miQC on a Seurat object
#'
#' @param object Seurat object
#' @param percent.mt (character) Name of the column in the Seurat metadata that
#' contains the percent of reads attributed to mitochondrial genes.
#' Defaults to "percent.mt".
#' @param nFeature_RNA (character) Name ... |
993d7374db51e83c313e4aec08fcdb0139a0a90e63ab8c99941b45a2ecd24b17 | R | 8,528 | 278 | #Harmonisation functions
##############################
# #
# Cross-sectional Combat #
# #
##############################
get_crosscombat <- function(forcombat, from = "Ventricles", to = "Brainstem", is_dti = "no", prior = "parametric"){
#we nee... |
df36ccf6c7ecc6fbfa58bd0017881ca33a56850bc62a13c311424d7a5de46cfc | R | 8,672 | 182 | # set up ------------------------------------------------------------------
rm(list = ls())
library(tidyverse)
library(data.table)
library(plyr)
setwd("/home/eli/Desktop/Neurobiologia/projects/ongoing/verbal.irony/facilitators/experiment.2/")
# general -----------------------------------------------------------------
... |
ce8af90c5c052078ddf388260d9df998c290d8fb0ddca8d8e7c5eadd64f43f09 | R | 8,733 | 322 | ---
title: "GLMM Script for iScience"
author: "Kirthana"
date: "2023-01-19"
output: html_document
---
```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE)
```
## Import Libraries
```{r include=FALSE}
rm(list = ls())
graphics.off()
gc()
# Package names
packages <- c("ggplot2", "rea... |
363fe0800242f6d55940d1f16af72d433d3d9686edb381359355149fae571f9f | R | 8,795 | 243 | #' Plot KEGG Cluster Visualization
#'
#' This function generates a KEGG GESA cluster plot, showing pathways and their relationships
#' across different clusters with customizable aesthetics.
#'
#' @param data A data frame containing KEGG cluster data. Must include columns: `pathway`, `Level2`, `group`, `Padj`, `Signifi... |
96052b7ff888e1c2998fd31e4ec31ec1ffa96466de7d7381076820bd5d9a3a3f | R | 8,797 | 223 | #' Finding statistically significant differences in cell type proportions
#'
#' Calculates cell type proportions, performs a variance stabilising
#' transformation on the proportions and determines whether the cell type
#' proportions are statistically significant between different groups using
#' linear modelling.
#'
... |
f34f156420b2038267eae79572b7bdb021cb485e504ddef5913c299aceb7fc66 | R | 8,803 | 209 | wd <- getwd()
library(Seurat)
library(dplyr)
library(ggplot2)
WT_1 <- Read10X("Y:/CDBLabs-Bioinformatics/Some_Place_Where_Big_Files_Can_Sit/Giger_2022_CellRangerFiles/Day_3/Sample_WT_sciatic_3_days_1/filtered_feature_bc_matrix")
WT_2 <- Read10X("Y:/CDBLabs-Bioinformatics/Some_Place_Where_Big_Files_Can_Sit/Giger_2022_C... |
59b2cfd1b1eeb4acc88dc08acc48158cb1f941c263ccab970831b7876550316e | R | 8,804 | 148 | ##### object_image_writer ##### - Repeat for Hi, Med, and Ctrl Conditions
# Working with post-ez_segmenter data ann deepcell (MATLAB GUI generated) for single object image creation
# Author: Bryan Cannon 2020 (multiple code snippets taken or built from DM, FH, EFM, DT)
# install and / or load ez_pkgs if you have... |
2a997689280a9a7e062d90e2247690ffe5b440ba5bd43324046fc2f23d6561b5 | R | 8,807 | 148 | ##### object_image_writer ##### - Repeat for Hi, Med, and Ctrl Conditions
# Working with post-ez_segmenter data ann deepcell (MATLAB GUI generated) for single object image creation
# Author: Bryan Cannon 2020 (multiple code snippets taken or built from DM, FH, EFM, DT)
# install and / or load ez_pkgs if you have... |
cf7e312a5b4b850c41a69cf138c1625462573a7dc3ebe73e1b6db1c7a2bdea83 | R | 8,896 | 164 | #' Run PaCMAP (Pairwise Controlled Manifold Approximation)
#'
#' Runs PaCMAP, a method for dimensionality reduction for scRNA-seq data.
#' data. Constructs three kinds of pairs of points: neighbor pairs (pair_neighbors),
#' mid-near pair (pair_MN), and further pairs (pair_FP) based on positional relationship
#' in the ... |
d8e7cbc744b99a89b51e70bbfcc07410535b367d515790b31352028cbc27d682 | R | 8,923 | 241 | #' Find dimensions
#'
#' Find dimensions of stitched image from number of tiles.
#' @param x an integer value which give sthe number of tiles.
#' @keywords grid
#' @export
#' @examples
#' find.dim(63)
#find.dim<-function(x){
# width<-ceiling(sqrt(x))
# height<-floor(sqrt(x))
# if( (x-width*height)>0){
# ... |
38f02f5372a724a8cef7e2c6cceb382e841c4ac02a52e8e346139acbce7478c1 | R | 8,961 | 237 | ---
title: "Controlling Plot Aesthetics"
output: rmarkdown::html_vignette
description: |
Loading in of sample datasets for dabestr.
vignette: >
%\VignetteIndexEntry{Controlling Plot Aesthetics}
%\VignetteEngine{knitr::rmarkdown}
%\VignetteEncoding{UTF-8}
---
```{r setup, include = FALSE}
knitr::opts_chunk$set(... |
4ece8b73175c3910426bfa7dbca414e6a2160210243cf9a786a77cc7a9020ef8 | R | 8,994 | 225 | setwd(
"~/Dropbox (ASU)/Placenta/BATCH2_PLACENTA_DECIDUA_ANALYSIS/HISAT_FeatureCounts/batch1_and_batch2/"
)
library(reshape)
library(ggpubr)
library("gridExtra")
# read in placenta CPM data
df_merged_placenta <-
read.delim ("genelists/placentas_batch_1and2/cpm_placenta_df_genes.txt")
df_merged_placenta$group <- "t... |
c688eb06e1eeb9bb69e570674f1cc0f267044be9b9fd4eb43ddbb2ac92a2a8a7 | R | 9,027 | 166 | ---
title: "Align biological replicate data with blotIt"
author:
- name: Severin Bang
affiliation: Uni Freiburg
email: severin.bang@physik.uni-freiburg.de
- name: Svenja Kemmer
affiliation: Uni Freiburg
- name: Marcus Rosenblatt
affiliation: Uni Freiburg
- name: Daniel Kaschek
affiliation: Uni Freiburg
output... |
95e5cda69c22c2cfb398c5291234a80589a8894cc715b88f5bd69697b0e5204a | R | 9,052 | 148 | ##### object_image_writer ##### - Repeat for Hi, Med, and Ctrl Conditions
# Working with post-ez_segmenter data ann deepcell (MATLAB GUI generated) for single object image creation
# Author: Bryan Cannon 2020 (multiple code snippets taken or built from DM, FH, EFM, DT)
# install and / or load ez_pkgs if you have... |
9a7a096ab3ad01ce6b8658e78b9e67acd1303d1c1841946d3f959c0668c08750 | R | 9,069 | 301 | # This include the Knockoff and Knockoff plus fliter to computer FDR and Power
# This master R file includes all the R functions for implementing the knockoff filter and
# knockoff filter plus in high-dimensional linear regression with precision matrix of
# covariates estimated by the innovated scalable efficient ... |
1de436e42c433f76c06077bb15bc02ea2af51b18ee0ab022e320433564f138cc | R | 9,075 | 189 | library(EBImage)
library(nnet)
library(reshape2)
library(readr)
setwd("/Volumes/KausaliaHD 1/R4R/General/")
#Run DeepCellAnalysis.R then DeepCellAnalysisUmapTMAQuntile before using this Rscript to get "gated_UMapData" or "all_data" clusters!!!!!!
#DeepCell Segmentation Paths
fcsPath = "/Volumes/KausaliaHD 1/AllProje... |
658cba65c116e1fc53ca71d5cf909af1f0bac790682bae0b191e894c142fdf3e | R | 9,084 | 213 | # title: "decidua MDS plots"
# author: "Kimberly Olney"
# date: "02/14/2020"
# output:
# pdf_document: default
# html_document:
# df_print: paged
library(limma)
library(edgeR)
library(RColorBrewer)
library(dplyr)
library(ggplot2)
library(VennDiagram)
library(mixOmics)
library(devtools)
library(dplyr)
library(str... |
533a4d9c2af2c159dadf7e016c8caa6c81a012162e1b8377a8e93fbed7c63f02 | R | 9,112 | 185 | # Function to generate a permutation map from a set of cortical regions of interest to itself,
# while (approximately) preserving contiguity and hemispheric symmetry.
# The function is based on a rotation of the FreeSurfer projection of coordinates
# of a set of regions of interest on the sphere.
#
# Inputs:
# c... |
a10e83e6fb3126434ccdf1b517f3d22cc0061d64e51146332c1c52a1b030c43b | R | 9,117 | 211 | # ----------------------------------------------------------------------
# ----------------------------------------------------------------------
#
#
# ----------------------------------------------------------------------
closeAllConnections()
rm(list=(ls()[ls()!="v"]))
gc(reset=TRUE)
library(raster)
library(ggplot2... |
71b8f855276b201e76404d833b4e39de0fa9c65d02fb85153b2da86829ec7c63 | R | 9,156 | 182 | # Load data
source(here::here("code","01_load_data.R"))
load(here::here("data","langrecov.RData"))
load(here::here("data","concur_acute.RData"))
load(here::here("data","concur_subacute.RData"))
load(here::here("data","lagged_subacute.RData"))
load(here::here("data","lagged_chronic.RData"))
load(here::here("dat... |
a2476194cc376c9703a362a7e94650fc7c453513354a48eda1c9f98832d018ea | R | 9,221 | 184 | ---
title: "Cell-type and tissue specificity"
author:
- name: "Regina H. Reynolds"
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(data.tabl... |
d8c0b321490e3421d12d6756f3834b650f802e30ae9f84d6a005824119a9aef5 | R | 9,384 | 221 | wd <- getwd()
library(Seurat)
library(dplyr)
library(ggplot2)
RG_5 <- Read10X("Y:/CDBLabs-Bioinformatics/Some_Place_Where_Big_Files_Can_Sit/Giger_2022_CellRangerFiles/Day_7/2405-RG-5/filtered_feature_bc_matrix")
RG_6 <- Read10X("Y:/CDBLabs-Bioinformatics/Some_Place_Where_Big_Files_Can_Sit/Giger_2022_CellRangerFiles/Da... |
28998cc0ec7ba103ca4bd8d2995bd5e433d174b1da2887410e7137a746352da1 | R | 9,438 | 246 | # Differentially Abundant Features (DAF) Functions
#_______________________________________________________________________________
fit_models <- function(obj.name,
df_input,
df_metadata,
cores = 10,
plot_scatter = F){
... |
04a70539bf795bcb05e60853bd48808c557f69efcb4e054e066f5ac0f7f54cfe | R | 9,480 | 386 | # @DESCRIPTION: de
# Library -----------------------------------------------------------------
library(magrittr)
library(ggplot2)
library(patchwork)
library(rlang)
library(DESeq2)
# Load data ---------------------------------------------------------------
se <- readr::read_rds(
file = file.path(
"analysis/202... |
95b3cace07d12ce89e19fd0696a06f0ac9bf170f846d524a9db9edc198826cbf | R | 9,501 | 162 | # Read in RGB mask TIF(s) and plot a heatmap of mean expression values per region (and per sample)
#### Import All ####
# Read in csv with pixel per mask, run info (generated in MATLAB)
pixel_regions_info <- read_csv('/Volumes/BryJC_Stanford/other/ForDmitry/fig6_carpet_voronoi/separated_masks_voronoi/pixel_info.cs... |
74b5ac19f0d25be17dcf01102bcdb6b3b1d33804180502cbfdaafc23b7c72c82 | R | 9,505 | 291 | # Sex differences in placenta compared to sex differences in adult GTEx tissues.
# Lopes-Ramos et al. 2020 gene lists per tissue
# Set working directory
setwd("~/Dropbox (ASU)/Placenta/BATCH2_PLACENTA_DECIDUA_ANALYSIS/HISAT_FeatureCounts/batch1_and_batch2/sexDiff_GTEx_version6/")
# load libraries
library(dplyr)
libra... |
9d1c92baa9bd80a4224ec1c2ccc5b660c0444c0643cc6a217be5ae950edcff49 | R | 9,518 | 216 | # The tested table (SPEC-RICHR-004 REQ-001 .. REQ-004): what an analysis
# tested, not only what it reported. Fixtures F-ORA and F-GSEA are built in
# helper-tested.R with the public API.
# ---- ORA (AC-001, AC-002) ------------------------------------------------------
test_that("ORA records every tested term with o... |
66699450d9399924c00f0d6182fccb11637d313d49e0f8e4fee454cd5828715c | R | 9,586 | 85 | library(tidyverse)
library(lme4)
source("scripts/R/ReadDataMTurk.R")
# read the tables from python code
# Distance of each child to adults
all_dists = read.table("results/csv/all-dist-to-adults.csv", header=T, sep=",")
# Analysis and plots for ICDL20 paper
### Statistics ###
# which layer to use (0-6)
for (l in 1:6... |
4d14e84d49afdc38d1fbbb5b039a0501ce6710d8ead5d2772f6a897da7fc4f4b | R | 9,591 | 292 | ---
title: "Tutorial: Proportion Plots"
output: rmarkdown::html_vignette
vignette: >
%\VignetteIndexEntry{Tutorial: Proportion Plots}
%\VignetteEngine{knitr::rmarkdown}
%\VignetteEncoding{UTF-8}
---
```{r, include = FALSE}
knitr::opts_chunk$set(
collapse = TRUE,
comment = "#>"
)
```
This vignette documents ... |
8bf3075ff66c72c4d2598910d397d72d1aca54b3d5ae8752c0d52b63ec3fef24 | R | 9,665 | 218 | library(limma)
library(edgeR)
library(RColorBrewer)
library(ggplot2)
library(tidyverse)
library(matrixStats)
library(reshape)
library(ggpubr)
library("gridExtra")
#--------------
# set working directory
#--------------
#setwd("~/Dropbox (ASU)/Placenta/BATCH2_PLACENTA_DECIDUA_ANALYSIS/HISAT_FeatureCounts/batch1_and_ba... |
19c088fde8e5097e9136786d7a2825f9896076e4340e21afc1788d2f8c4771d7 | R | 9,692 | 206 | # Gene-set fingerprints (REQ-015 .. REQ-018), canonical form richR-geneset-v1.
# Catches: a fingerprint that is missing, R-version-dependent, computed over the
# wrong columns, unstable under row order, blind to membership changes, or copied
# from a stale Annot into an analysis result. Offline; providers are mocked.
... |
2f89cb1499ab3dde6ed9decdf6166be519fd575fa54549f5fe89d6342198547a | R | 9,711 | 288 | #' @include internal.R
#'
NULL
#' @inheritParams Seurat::CreateSeuratObject
#' @param default.assay Name or index of matrix to use as default assay;
#' defaults to name of first matrix in list
#' @param slot Name of slot to store matrix in; choose from 'counts' or 'data'
#'
#' @importFrom methods new
#' @importFrom ut... |
c8d5372471038bb72094326b5d48dddbbe71fc7b2c67348398e2cc4dff5c95f5 | R | 9,720 | 252 | ---
title: "Running miQC on Seurat objects"
date: "Compiled: `r format(Sys.time(), '%B %d, %Y')`"
output:
html_document:
df_print: kable
theme: united
github_document:
html_preview: no
toc: no
---
This vigettte demonstrates the use of the miQC package in Seurat. Vignette is
based off of the [miQC v... |
88c83ae7307174fa0da09800203d0f75dfec7c74e0c47e628af046515b8b8f98 | R | 9,752 | 192 | WD = getwd( )
setwd(WD)
source("mirror.R")
CHR = "8"
FID = "201" # at the end of this code you can find the list of significant IDPs for both Chr 8 and 17
data <- read.csv(file=paste(WD , "/data/Chr_",CHR,"/myData_",FID,".csv",sep=""),head=TRUE,sep=",")
Data_Mirror <- data.frame(data)
save(Data_Mirror , file = "... |
c3cd63492499f2e985c137988c61f0bed01053c092b7ccbd3dd08bbeefe9b733 | R | 9,757 | 276 | #' Apply Effectsize
#'
#' @description Generates the dabest effectsize objects given the specified effect size
#'
#' @param contrasts A list of contrast objects.
#' @param effect_size Type of effect size (e.g., "mean_diff", "median_diff").
#'
#' @return The list of updated dabest effectsize objects
#' @noRd
#'
apply_ef... |
1a9a4bd9b79255f9b549182f3e7aa18b07aa45e87727c0b7f1dacacc5143797d | R | 9,778 | 256 | #
# This is an example of applying the graphical knockoff filter.
#
# The master file "isee_all.R" includes all the R functions for implementing the innovated
# scalable efficient estimation (ISEE) procedure introduced in Fan and Lv (2015) for
# scalable estimation of both the innovated data matrix (n x p matrix X ti... |
97b07c4ef1b2a5e1333ff3c6127fc7beaee0607952ae57ffc78352fa427714f4 | R | 9,860 | 255 | #code to generate Video1 of the Jasek et al desmosomal connectome paper
#Gaspar Jekely
source("code/Packages_and_Connection.R")
# read cells and meshes --------------------------------------------------------------
{
#read some meshes and cells from catmaid for plotting an anatomical background
outline <- catmaid_ge... |
3cd8a13a24f4f25731501e349419d3de87ae74e272b8468824f91010af1351c7 | R | 9,861 | 347 | # @DESCRIPTION: enrichment.R
# Library -----------------------------------------------------------------
library(magrittr)
library(ggplot2)
library(patchwork)
library(rlang)
library(DESeq2)
# Load data ---------------------------------------------------------------
se_group_de <- readr::read_rds(
file = file.pat... |
c647b4a8bd575b3a65df37dd2c9286cbc2e6519029c8ea209529282c54ad77bc | R | 9,903 | 339 | # Obtains bootstraps as well as bca intervals
#
# Contains functions `effsize_boot`, `bootstrap`, `bca` and `boot_weighted_row`.
effsize_boot <- function(
data,
effect_size_func,
reps = 5000,
paired = FALSE) {
s <- c(
rep(1, length(data$control)),
rep(2, length(data$test))
)
bootboot <- ... |
5ed0d2d51c7830a683262f8e6e0c3f1f1e96ae5afc0020d5d1916b976118382d | R | 9,958 | 201 | # mapGenes() (SPEC-RICHR-005 REQ-001 .. REQ-005): explicit identifier
# conversion with one recorded decision per input entry. Fixture F-MAP is
# inline; only the OrgDb leg needs org.Hs.eg.db (a Suggests package).
fmap_table <- function() {
data.frame(
from = c("a", "a", "b", "b", "d", "e", "g", "h", "k", paste0... |
8a790b4d2d8256a3d93866d635cb0713e10284937008b20c1060143319bf7661 | R | 9,989 | 372 | # @DESCRIPTION: de
# Library -----------------------------------------------------------------
library(magrittr)
library(ggplot2)
library(patchwork)
library(rlang)
library(SummarizedExperiment)
# Load data ---------------------------------------------------------------
se <- readr::read_rds(
file = file.path(
... |
d62ffdf7a7680d5097bfbf44a163a05b51560b82e456a903e2e39037c38857d0 | R | 10,011 | 272 | calculate_delta_corthick <- function(longdata){
tlong <- longdata
tlong <- tlong %>%
mutate(across(c("Frontal",
"Parietal",
"Insular",
"Occipital",
"Temporal",
"Hippocampal",
"W... |
fd1ef19ca58dec45c2e6fe537091365a8c72c5ddad0449278ffd033407c015b4 | R | 10,032 | 206 | library(EBImage)
library(pheatmap)
library(RColorBrewer)
library(ggplot2)
library(reshape2)
library(ggdendro)
library(viridisLite)
library(scales)
allTifsData = data.frame()
outputFolder = ("/Volumes/KausaliaHD/AllProjects/BRAIN DATA/MIBI/MIBIData Matlab/May_June2019/TMA/MedResScan_TMA/RResults/BoxScatterplotADScaled... |
b32f7ba9cc45e647a60ca0043e597b2a4e1b43c857f9d70b60b397603873bc88 | R | 10,184 | 227 | #' Read enrichment diagnostics and provenance
#'
#' Metadata is recorded on newly created Annot, richResult, and GSEAResult
#' objects. Legacy objects return an explicit unavailable record.
#'
#' The \code{testing} element names the family that \code{padj.method}
#' adjusted over (\code{family_policy}) and its size (\c... |
41bc029250d8e4657e9e32d14a5fc204ba1502b25f04825f9bcacfdae189bc1c | R | 10,221 | 390 | library(lmerTest)
library(multcomp)
library(reshape)
library(oro.nifti)
library(doParallel)
library(foreach)
##############################################################################
# Example
# basic inputs (see function for more options)
# IMAGING_FILE: 4-D file with VBM images
# OUTPUT_DIR: output directory
#... |
cfb2dd680b7102b293633019801111a157591a05e833e1dc4e505d76e6a17284 | R | 10,233 | 212 | # AC-005 .. AC-008 (SPEC-RICHR-006 REQ-002 .. REQ-005): plots draw only values
# the result carries, titled by what they are.
# Discrete-axis label of each row of a layer's data (the scale's limits, in
# level order, indexed by the mapped position).
.m61_axis_values <- function(p, layer, aes = "x") {
b <- ggplot2::g... |
c5d4af24c2554534032c8199166ff0aa9e65560515f19fd978cdcddd47ece1c8 | R | 10,244 | 172 | ##### object_image_writer ##### - Repeat for Hi, Med, and Ctrl Conditions
# Working with post-ez_segmenter data ann deepcell (MATLAB GUI generated) for single object image creation
# Author: Bryan Cannon 2020 (multiple code snippets taken or built from DM, FH, EFM, DT)
# install and / or load ez_pkgs if you have... |
60939e72bb88e614fba373df60d46e097c0db759bc1358edefa33f61f7c1182f | R | 10,277 | 261 | # Shared fixtures and base-R readers of the report tests (SPEC-RICHR-008),
# built from base R, testthat and the public API, with no network and no
# package outside DESCRIPTION (report.html is read with regular expressions,
# which is exact because richReport() double-quotes every attribute value and
# escapes every t... |
185413040a727cedd0810e835f396f7b5f31db95957be88399385c3ce1ca3a2f | R | 10,282 | 172 | ##### object_image_writer ##### - Repeat for Hi, Med, and Ctrl Conditions
# Working with post-ez_segmenter data ann deepcell (MATLAB GUI generated) for single object image creation
# Author: Bryan Cannon 2020 (multiple code snippets taken or built from DM, FH, EFM, DT)
# install and / or load ez_pkgs if you have... |
c71e88a2d91a67d04d29b4cfaf546651cab87c72ba6c53e921a8086a4c73bcf0 | R | 10,292 | 182 | # Load data
source(here::here("code","01_load_data.R"))
# Create subset of data for missing data imputation
PWAWM_miss <- PWAWM %>% dplyr::select(sex, age, education,
stroke_type, stroke_size, stroke_laterality, stroke_history, old_lesion_load, NIHSS_total, NIHSS_languag... |
16f12be6e3ba63e4173ea9156bfc6fc4f2d82dfd8f4e2919be112e0f0e1ce588 | R | 10,335 | 333 | ---
title: "Projection predictive feature selection for the MCED GAGome score"
author: "Sinisa Bratulic"
date: "`r format(Sys.time(), '%d %B, %Y')`"
output:
html_document:
toc: true
toc_depth: 2
toc_float: true
number_sections: true
df_print: paged
editor_options:
chunk_output_type: console
--... |
17a48a2af932e2a44ab0a540946bc40d6ddac2537ad9b888f763dfa8dd2c1819 | R | 10,336 | 186 | library(Seurat)
library(patchwork)
library(ggplot2)
library(cowplot)
library(scatterpie)
library(BiocParallel)
library(dplyr)
library(stringr)
library(Matrix)
library(scales)
library(RCurl)
## total cells analysis
# import data
data_dir <- "D:/personal_user/Shyr Shyang/00 Tbx3-CreER; Ai14 lineage tracing... |
34c0ed55062437ad7614cc75ae1f1bdd88ed07f71c240127b3ee552420323ee3 | R | 10,336 | 242 | # set up ------------------------------------------------------------------
#rm(list = ls())
library(tidyverse)
library(webr)
library(rstatix)
library(ggstatsplot)
library(ggpubr)
setwd("/home/eli/Desktop/Neurobiologia/projects/ongoing/verbal.irony/facilitators/experiment.2/")
# data ----------------------------------... |
dbdaf1c4ba7cd36cafe83fde8a504bbefc812dfd3075548b314454be1df54241 | R | 10,379 | 214 | install.packages("renv")
library("renv")
renv::init("/Volumes/BryJC_Stanford/Analysis/KV_BJC_MIBI_Brain_Paper_1/Fig_MedRes/single_object_pipeline")
setwd("/Volumes/BryJC_Stanford/Analysis/KV_BJC_MIBI_Brain_Paper_1/Fig_MedRes/single_object_pipeline")
install_ez_packages(T)
load_ez_packages(T)
#Seed
seed <- 123
set.se... |
154da41aaba339e42883eff296d871038592670aa5efd35e1638b43f34bc70fd | R | 10,386 | 371 | # @DESCRIPTION: UV
# Library -----------------------------------------------------------------
library(magrittr)
library(ggplot2)
library(patchwork)
library(rlang)
# Load data ---------------------------------------------------------------
all_expr <- readr::read_rds(
file = file.path(
"analysis/2022-07-20/r... |
6930e9f2afeccee8549c8f5dc6983965873caddc4ba4f6b2c86a752216b45982 | R | 10,427 | 146 | #' Pathway Enrichment analysis for different level function
#' @importFrom dplyr filter left_join
#' @importFrom rlang sym
#' @param x vector contains gene names or dataframe with DEGs information
#' @param kodata KEGG annotation data
#' @param level pathway level ("Level1", "Level2", or "Level3")
#' @param pvalue cuto... |
698f69bd9c7130451b9a28b1f7118d3fb31f1b345fbacae94cd9c08dab94939a | R | 10,483 | 251 | gsea_test_annotation <- function() {
data.frame(GeneID = c("A", "B", "C", "D"),
Term = c("T1", "T1", "T2", "T2"))
}
gsea_recording_backend <- function(...) {
args <- list(...)
if (!exists(".gsea_backend_args", envir = .GlobalEnv, inherits = FALSE)) {
assign(".gsea_backend_args", list(), envir = ... |
c66290c97b312597d637d7791e45db41b41b3a362fa1248fc4e212ad199bd17f | R | 10,569 | 385 | #!/usr/local/bin/Rscript
#install.packages(c('argparser', 'R.matlab', 'sp', 'gstat'), repos="http://cran.r-project.org")
library(argparser, quietly=TRUE)
library(sp, quietly=TRUE)
library(gstat, quietly=TRUE)
library(R.matlab, quietly=TRUE)
NAME <- "cokrige.r"
DESCRIPTION <- "Cokriging of a csv dataset, where a head... |
07732840052c2bd22d65c252983ab905f81d6a97f38ece89a3cfc63fc75672f7 | R | 10,589 | 304 | # Helper functions that generates plot components.
#
# Contains functions `create_rawplot_components`, `create_deltaplot_components` and `create_violinplot_components`.
#' Generates list of TRUE/FALSE for raw plot components that will be built
#'
#' This function generates a list of booleans determining whether certai... |
9a700e51babcb65afaeceda9c5e82ff8f0e84a5b1eda4e6ce109cd8ebdbbc33a | R | 10,636 | 174 | ##### object_image_writer ##### - Repeat for Hi, Med, and Ctrl Conditions
# Working with post-ez_segmenter data ann deepcell (MATLAB GUI generated) for single object image creation
# Author: Bryan Cannon 2020 (multiple code snippets taken or built from DM, FH, EFM, DT)
# install and / or load ez_pkgs if you have... |
99208b61e98ea5aaecb467676e032a9c603ec73eae6f6ce014fcb722b44769be | R | 10,672 | 174 | ##### object_image_writer ##### - Repeat for Hi, Med, and Ctrl Conditions
# Working with post-ez_segmenter data ann deepcell (MATLAB GUI generated) for single object image creation
# Author: Bryan Cannon 2020 (multiple code snippets taken or built from DM, FH, EFM, DT)
# install and / or load ez_pkgs if you have... |
4d85c884edbc3b663ba826d46c72387384c599215bfe760582578ac175eb7271 | R | 10,709 | 340 | # TODO Add documentation
effect_size_mean_func <- function(control, test, paired) {
mean_diff <- mean(test) - mean(control)
if (paired == TRUE) {
mean_diff <- mean(test - control)
}
mean_diff
}
# TODO Add documentation
effect_size_median_func <- function(control, test, paired) {
median_diff <- stats::med... |
5beabef0be25e8e213eb5a236f89807064c13508063dc743798a0c8802a4d2d6 | R | 10,756 | 167 | ##RUN DeepCellAnalysis_HiAD_Gated_NormAllChannel.R to generate all_data ###
# Import existing all_data from the enviroment using Import Dataset. Rename simply into all_data
# Need to pool all the new Mantis corrected csv into one folder.
library(readr)
dataPath = "/Volumes/KausaliaHD/AllProjects/BRAIN DATA/MIBI/MIBID... |
2378e3a211e249bdea69713d6f3d2e7d5d373d29311755aa8a5118b02852c570 | R | 10,766 | 182 | ##### object_image_writer ##### - Repeat for Hi, Med, and Ctrl Conditions
# Working with post-ez_segmenter data and deepcell (MATLAB GUI generated) for single object image creation
# Author: Bryan Cannon 2020 (multiple code snippets taken or built from DM, FH, EFM, DT)
# install and / or load ez_pkgs if you have... |
d28c40493582408b156ec6529c4ad87ce49c3d62739b7beb1bba653f148327e5 | R | 10,770 | 182 | ##### object_image_writer ##### - Repeat for Hi, Med, and Ctrl Conditions
# Working with post-ez_segmenter data and deepcell (MATLAB GUI generated) for single object image creation
# Author: Bryan Cannon 2020 (multiple code snippets taken or built from DM, FH, EFM, DT)
# install and / or load ez_pkgs if you have... |
d3288d381d6c782e914e18dd072a5ac0cbe3b61227de3222d4ef9ea032ca660e | R | 10,798 | 182 | ##### object_image_writer ##### - Repeat for Hi, Med, and Ctrl Conditions
# Working with post-ez_segmenter data and deepcell (MATLAB GUI generated) for single object image creation
# Author: Bryan Cannon 2020 (multiple code snippets taken or built from DM, FH, EFM, DT)
# install and / or load ez_pkgs if you have... |
67053176e924eaba370fc1067c4f9d6278383ae02fa5e1a9acab244ee955e334 | R | 10,799 | 182 | ##### object_image_writer ##### - Repeat for Hi, Med, and Ctrl Conditions
# Working with post-ez_segmenter data and deepcell (MATLAB GUI generated) for single object image creation
# Author: Bryan Cannon 2020 (multiple code snippets taken or built from DM, FH, EFM, DT)
# install and / or load ez_pkgs if you have... |
085a6cc463023bff3a4fbb11723bf3ed1fcc52ec1331393e972c89d153085085 | R | 10,804 | 182 | ##### object_image_writer ##### - Repeat for Hi, Med, and Ctrl Conditions
# Working with post-ez_segmenter data and deepcell (MATLAB GUI generated) for single object image creation
# Author: Bryan Cannon 2020 (multiple code snippets taken or built from DM, FH, EFM, DT)
# install and / or load ez_pkgs if you have... |
853f09efc0463c0375d09b44e519d1d3988ca37f5084090b6a2ccc0db1da93e5 | R | 10,806 | 182 | ##### object_image_writer ##### - Repeat for Hi, Med, and Ctrl Conditions
# Working with post-ez_segmenter data and deepcell (MATLAB GUI generated) for single object image creation
# Author: Bryan Cannon 2020 (multiple code snippets taken or built from DM, FH, EFM, DT)
# install and / or load ez_pkgs if you have... |
559e10f15000e8221fcceee79bea75a5470b03eaf410e3ce604062eb20a7a1e1 | R | 10,812 | 182 | ##### object_image_writer ##### - Repeat for Hi, Med, and Ctrl Conditions
# Working with post-ez_segmenter data and deepcell (MATLAB GUI generated) for single object image creation
# Author: Bryan Cannon 2020 (multiple code snippets taken or built from DM, FH, EFM, DT)
# install and / or load ez_pkgs if you have... |
f86fb45b735832ae39a64a11b82a3ac4ee77fd58ee4b31d4cba87a563011443b | R | 10,814 | 182 | ##### object_image_writer ##### - Repeat for Hi, Med, and Ctrl Conditions
# Working with post-ez_segmenter data and deepcell (MATLAB GUI generated) for single object image creation
# Author: Bryan Cannon 2020 (multiple code snippets taken or built from DM, FH, EFM, DT)
# install and / or load ez_pkgs if you have... |
f98bf4292269ea9ed40d8ed90384296414afc36d3b1e363c033d47f19951749b | R | 10,877 | 274 |
## BART: Bayesian Additive Regression Trees
## Copyright (C) 2018 Robert McCulloch and Rodney Sparapani
## mc.gbart.R
## This program is free software; you can redistribute it and/or modify
## it under the terms of the GNU General Public License as published by
## the Free Software Foundation; either version 2 of the... |
905cef5621b04b2ac0bbfa745785d0c75f63813a07a91b239e1b8f44a9bb1d6a | R | 10,883 | 189 | # Read in RGB mask TIF(s) and plot a heatmap of mean expression values per region (and per sample)
#### Import All ####
# Read in csv with pixel per mask, run info (generated in MATLAB)
pixel_anat_info <- read_csv('/Volumes/BryJC_Stanford/Analysis/KV_BJC_MIBI_Brain_Paper_1/Fig_MedRes/data_outputs/mask_expansion_pi... |
3fbd18f23beed4ee662cdd118752cdfc61c3ca16207accbc0f398c2f467cbc5f | R | 10,929 | 381 | #' @include internal.R
#'
NULL
#' Run optimizeALS on a Seurat object
#'
#' @inheritParams rliger::optimizeALS
#' @inheritParams RunFastMNN
#' @param object A merged Seurat object
#' @param split.by Attribute for splitting, defaults to "orig.ident"
#' @param ... Arguments passed to other methods
#'
#' @return A Seurat ... |
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