sha256 stringlengths 64 64 | language stringclasses 27
values | size int32 1 491k | lines int32 1 21.8k | content stringlengths 1 200k |
|---|---|---|---|---|
8db2727dc6d791ac6cd7dd109dc24db0ec60f18a045ca8eed32732e9aaae5c9e | R | 63,770 | 1,124 | ---
title: "figure1"
author: "Ben Umans"
date: "2024-07-26"
output: workflowr::wflow_html
editor_options:
chunk_output_type: console
---
## Introduction
This page describes steps used to process cellranger output, cluster and annotate cells, and generate results shown in Figure 1, Figure S1, and Figure S2.
```{r s... |
e86b35347393fdbc0449c5cc619ebe60ee4564b32c9a123813b770d2b5231483 | R | 64,776 | 1,526 | library(Rfast)
library(glmnet)
library(ranger)
library(datasets)
#library(caret)
library(MASS)
# Helper packages
library(dplyr) # for basic data wrangling
library(rootSolve)
library(vtreat)
library(xgboost)
library(fastDummies)
library(nnet)
library(MatchIt)
library(CVXR)
library(ggplot2)
library(reshape2)
##... |
19e8eb8f9c08f36e5981f90316b0ef45018e074cfad5161f301d1f5a0954eba0 | R | 66,075 | 1,578 | #' Create liger object
#' @description This function allows creating \linkS4class{liger} object from
#' multiple datasets of various forms (See \code{rawData}).
#'
#' \bold{DO} make a copy of the H5AD files because rliger functions write to
#' the files and they will not be able to be read back to Python. This will be
... |
eaca9aad0a7c299416919f489d0e7d77133c095012336d025bd4125915d4d93d | R | 73,981 | 2,970 | ---
title: "Modulation of calcium channel in spinal CSF-cNs"
author: "Nicolas Wanaverbecq - Elysa Crozat - Edith Blasco"
date: "`r Sys.Date()`"
output:
word_document:
toc: true
toc_depth: 5
highlight: null
fig_width: 5
fig_height: 3
keep_md: true
html_notebook:
toc: true
code_foldin... |
29a7772ef18b8ee4cc0cee438b6573d71284ed341332cb976f0cab84657b2f40 | R | 74,107 | 1,437 | library(Seurat)
library(SeuratData)
library(ggplot2)
library(patchwork)
library(dplyr)
library(biomaRt)
library(psf)
suppressMessages(library(vesalius))
library(gsdensity)
library(ggrepel)
library(reshape2)
library(CelliD)
# Load curated KEGG signaling pathways
load(system.file("extdata", "kegg_curated_40_signalings... |
7cdcccd8a4bf2c232dc5237b3916fcc4a5985824142361c314312c4e038db294 | R | 75,802 | 1,778 | #%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
# Scatter Plots of DimRed ####
#%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
#' @rdname plotDimRed
#' @param object A \linkS4class{liger} object.
#' @param useCluster Name of variable in \code{cellMeta(o... |
04a4ce3a146b98bf542f0e964e67a2d6272ddc79f3be09e03bdae5065ba6f826 | R | 77,220 | 2,379 |
#### This script is modified from package NbClust
#### We deleted some ouput messages and information in order to make caculation sliently (2021.08.10)
#### We appciate package NbClust and recommend users to cite the paper
#### Malika Charrad, Nadia Ghazzali, Veronique Boiteau, Azam Niknafs (2014). NbClust: An
#### ... |
3b1503e5564c239ad06b2e859386ff9e2f80921d3fbca676a183c8c3531c8c01 | R | 79,442 | 1,885 | #################################### qc ########################################
#' General QC for liger object
#' @description Calculate number of UMIs, number of detected features and
#' percentage of feature subset (e.g. mito, ribo and hemo) expression per cell.
#' @details
#' This function by default calculates:
#... |
7e5f86bbe35b8ede2e64bb380c46b42cb4eb5de74b907f990538e7d95e83c158 | R | 93,677 | 1,956 | #' Calculates PSF formulas for each node in graphNEL object.
#' @param g graphNEL pathway object.
#' @param node.ordering order of nodes calculated with order.nodes function.
#' @param sink.nodes list of terminal (sink) nodes calculated with determine.sink.nodes function.
#' @param split logical, if true then the incom... |
f6dd4c4a4e9557c354d3b92bcc5321d129043708feb16ac28e5c57daf207f95f | R | 96,319 | 2,165 |
library(Rfast)
library(glmnet)
library(ranger)
library(datasets)
library(MASS)
library(dplyr) # for basic data wrangling
library(rootSolve)
library(vtreat)
library(xgboost)
library(fastDummies)
library(nnet)
library(MatchIt)
library(CVXR)
##input: Lagrange multiplier lambda and the calculated estimating fun... |
c470a83753e1b131e1d1b5098530d5c275db602d555e5561fd73ffb603c2f72e | R | 99,169 | 2,377 | #' Integrate scaled datasets with iNMF or variant methods
#' @description
#' LIGER provides dataset integration methods based on iNMF (integrative
#' Non-negative Matrix Factorization \[1\]) and its variants (online iNMF \[2\]
#' and UINMF \[3\]). This function wraps \code{\link{runINMF}},
#' \code{\link{runOnlineINMF}... |
8faa721fbea1a37e91b64425840931ef1184e6c11bc73c92ffba0b6b621f4c97 | R | 106,666 | 1,901 | ---
title: "figure3"
author: "Ben Umans"
date: "2024-08-05"
output: workflowr::wflow_html
editor_options:
chunk_output_type: console
---
## Introduction
This page describes steps used to map eQTLs, fit a topic model, and map topic-interacting eQTLs, corresponding to Figure 3.
```{r}
library(Seurat)
library(tidyverse... |
d65c0ce2298ba9efe918a51d5d122711cfe25144c28b4ac19616098a34235580 | R | 123,489 | 3,759 | # loom.R
#
#*******************# #
# Constants ----
#*******************# #
# Column attributes
CA_CELLID <- "CellID"
CA_DFLT_CLUSTERS_NAME <- "ClusterName"
CA_DFLT_CLUSTERS_ID <- "ClusterID"
CA_EMBEDDING_NAME <- "Embedding"
CA_EMBEDDING_DFLT_CNAMES <- c("_X","_Y")
CA_EXTRA_EMBEDDINGS_X_NAME <- "Emb... |
de5654531cec8e3c33f2b19542138b3b9dc0129d758483c257e24a543e34b01f | R | 137,993 | 3,492 | #### READ_ME
#### Data requirements:
#### Download the following files into a single folder:
#### EcoEvoHippo_Neuron_Numbers_Hippocampus.xlsx
#### From https://doi.org/10.5061/dryad.kd51c5bht
#### EcoEvoHippo_Ecological_Factors.xlsx
#### From https://doi.org/10.5061/dryad.kd51c5bht
#### 4... |
42caaec00940f2aef2aacdd783c93687775715760c77044e1f31f9a1e02de2cd | R | 144,011 | 2,978 | library(magick)
library(shiny)
library(shape)
library(psf)
library(DT)
library(plotly)
library(data.table)
library(shinyjs)
library(visNetwork)
library(shinyjqui)
library(ggplot2)
library(igraph)
library(shinyhelper)
### library(plotrix)
load("whole_data_unit.RData")
protein_drug_interactions <- fread("Protein_drug_in... |
7e4a9a40fcd012912c9d63cd8083aa15470527df8237a5996866239085a285e9 | R | 144,713 | 2,256 | ---
title: "figure2"
author: "Ben Umans"
date: "2024-07-29"
output: workflowr::wflow_html
editor_options:
chunk_output_type: console
---
## Introduction
This page describes steps used to identify differentially expressed genes from pseudobulk data, classify treatment-responsive cells, plot cell data from immunostain... |
2a4c033d2f4487d322c5c0c1703c4a4743d8b5fa25f34d2a79c9d50f498df8e5 | R | 158,985 | 4,005 | ---
title: "RNA-seq analysis from Kallisto output: tximport + DESeq2"
output:
html_document: default
---
```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE)
```
```{r}
# Load required packages for RNA-seq analysis (starting from Kallisto quantification)
# Core analysis
library(DESeq2)
library(tximport)
... |
3a8df7220ae01ccc5e699e26d4c2be7bc0cb874ecc6d5fe2793523999457e3cd | Shell | 56 | 1 | /bin/bash ~/task-taxonomy-331b/tools/script/run_test.sh
|
a5e5a5da30e256808250f82de9c6923f8e3023fee09ed5c7e54723a179a6dcc1 | Shell | 81 | 1 | $PYTHON setup.py install --single-version-externally-managed --record record.txt
|
ea433fac4dbc2adef6431884cb7124a91a3c80f8e4060073ab048b4940e6b146 | Shell | 84 | 2 | #!/bin/bash
curl https://purl.obolibrary.org/obo/go.obo > data/raw/ontologies/go.obo |
08365d87ff5b8b871b54e523ca67b5a5a7e71465ddd593a8a5f14f27c40bb7c2 | Shell | 92 | 1 | python3 ssveptop.py & python3 ssvepbottom.py & python3 ssvepleft.py & python3 ssvepright.py
|
cb497498d9e4edea590326179692fc215be2913439c74a820238086fc67b20ca | Shell | 93 | 6 | #!/bin/bash
for i in {0..99}
do
python Debug_viz_for_transfer.py --idx $i --hs 256
done
|
8783c8c4d6755bfe7d8c3b5a277744168ebfc8d58c0a6bc909fa06bda6aea369 | Shell | 95 | 5 | #!/usr/bin/env bash
pip3 install -e . --break-system-packages
#START250809
#pip3 install -e .
|
d48e251985d03b9defcdd9ea9df17e05df8ed82a8d5cff563909492ff4972d8f | Shell | 105 | 7 | #!/usr/bin/env bash
#ls /home/ubuntu/s3/experiment_models
#if [ "$?" == "2" ]
#then
#reboot now
#fi
|
b0678f9c82879567bbfb23c275d8c2d934d0d9dae9e371da084a357ce4f84dab | Shell | 131 | 6 | #!/bin/bash
cd ./networks/correlation_package
rm -rf *_cuda.egg-info build dist __pycache__
python3 setup.py install --user
cd ..
|
af6bcc39eedc348413a8f0b34146fac95f60c5246c62a0cbd962483bb11ca7b6 | Shell | 134 | 2 | #!/bin/bash
curl "https://ftp.ebi.ac.uk/pub/databases/interpro/releases/latest/entry.list" > "data/raw/interpro/interpro_entries.tsv"
|
2851a47b29f23ef003af9f91de7b35fa8b81538094ecb83069b124cb68105dac | Shell | 144 | 3 | python preprocess.py --raw_dataset_name pancreas \
--raw_dataset_path datasets/pretrain/pancreas/data/pancreas.h5ad \
--n_top_genes 1000 |
e64df03acf1ead4929119004e9806ae2cae95acdd6a9261ec22927fad9e0a387 | Shell | 146 | 7 | #!/bin/bash
source ~/miniconda3/etc/profile.d/conda.sh
conda activate comical-env
python wrapper.py -fo comical_demo_run -bz 32768 -gpu 7 -e 10 |
1b681a24d8ada409c4ccd1ec6e8cbc86fc7865ce31065d572331f22499598c5e | Shell | 147 | 3 | python preprocess.py --raw_dataset_name forebrain \
--raw_dataset_path datasets/pretrain/forebrain/data/forebrain.h5ad \
--n_top_genes 1000 |
0b2fcffc8e2309da7145a0dd4f4754c22cba32220b6e6af6a4a148369db481fd | Shell | 148 | 2 | #!/bin/sh
screen -dm bash -c "source /home/ubuntu/.bashrc; sleep 10; /home/ubuntu/task-taxonomy-331b/tools/run_from_task_pool.sh --resume; exec sh"
|
fbab914ecbe1174ec7f170cb672438cce1554a667c19df49148ea5313095e76b | Shell | 149 | 5 | #!/bin/bash
source ~/anaconda3/etc/profile.d/conda.sh
conda deactivate
conda activate protein_embs
python compute_embeddings.py --pdb_file "1a2b.pdb" |
ab950011b4b130a33971639da5fadc49d2c8af158f044ba973091d74cf5a4925 | Shell | 153 | 15 | #!/bin/bash -l
# Set SCC project
#$ -P ivc-ml
# Request 4 CPUs
#$ -pe omp 6
#$ -m ea
#$ -l h_rt=24:00:00
conda activate py3.11
python skullstrip.py |
0fb8d4a6825d333be42af7a9b9791d8e55b91629d2b96e5232e7ff89d3b19e71 | Shell | 156 | 3 | python preprocess.py --raw_dataset_name dentategyrus \
--raw_dataset_path datasets/pretrain/dentategyrus/data/dentategyrus.h5ad \
--n_top_genes 2000 |
76f8a9bf0377b526704a4a734da3c510d96bc2c9d965fa01b21cdc25089cb961 | Shell | 161 | 12 | #!/bin/bash
#$ -cwd
#$ -j y
#$ -l h_fsize=5G
#$ -l mem_free=2G
#$ -l h_vmem=2G
#$ -l h_rt=144:00:00
#$ -o ./logs
module load conda_R/4.1.x
Rscript Train_Lock.r |
4c41790f8b1538f607b2f0ecf5b4a434ca675769ea99248d54b5f98cca91bb5f | Shell | 162 | 10 | #!/bin/bash
#$ -cwd
#$ -j y
#$ -l h_fsize=1000G
#$ -l mem_free=250G
#$ -l h_vmem=250G
#$ -l h_rt=96:00:00
#$ -o ./logs
cat ./final_kmers/* > ../kmers_all.fasta
|
29066c1351a763dc7d56db617230754c5612107bf38f67b2719a783b5f9b4585 | Shell | 163 | 11 | #!/bin/bash
#$ -cwd
#$ -j y
#$ -l h_fsize=1000G
#$ -l mem_free=250G
#$ -l h_vmem=250G
#$ -l h_rt=96:00:00
#$ -o ./logs
module load conda_R
Rscript x1_split_bed.r
|
146daa5f34c4e75215cbcffc9c2366702f92db819ccc780f321d3ead1880ec56 | Shell | 164 | 7 | #!/usr/bin/env bash
DOWNLOAD_DIR=$1
DATA_ROOT=$2
tar -zxvf $DOWNLOAD_DIR/OpenDataLab___LaPa/raw/LaPa.tar.gz -C $DATA_ROOT
rm -rf $DOWNLOAD_DIR/OpenDataLab___LaPa
|
920d437d8c6d4bbecb407b3c7fff657fefe725bcd56f86b9702b1de1dbd42c3a | Shell | 164 | 5 | #!/bin/bash
# Passes
smc++ posterior -v model.final.json out.npz chr11_5subjs.smc.gz
# Fails
smc++ posterior -v model.final.json out.npz chr11_5subjs_broken.smc.gz
|
898df0e20b9985dd472d8e1f61ed44ce17dc8cb9d6d18286aa89261c334e8a8e | Shell | 167 | 8 | #!/bin/sh
bowtie_root="${PWD%%bowtie*}/bowtie"
docker run -t -i --rm \
-v $bowtie_root:/io \
phusion/holy-build-box-64:latest \
bash /io/scripts/bowtie-hbb.sh
|
e4ea871605ea4f6a442b2c1fefa2a0e9b75954b5bbbcebceb6b1029f1d1a84c6 | Shell | 170 | 11 | #!/bin/bash
#$ -cwd
#$ -j y
#$ -l h_fsize=1000G
#$ -l mem_free=10G
#$ -l h_vmem=10G
#$ -l h_rt=96:00:00
#$ -o ./logs
module load conda_R
Rscript x4_featureMatrix_unsc.r
|
87cfc61092509e33a9b4886687ee491e1799e0eccec4f96517aa6a17db12b32c | Shell | 175 | 11 | #!/bin/bash
#$ -cwd
#$ -j y
#$ -l h_fsize=1000G
#$ -l mem_free=250G
#$ -l h_vmem=250G
#$ -l h_rt=96:00:00
#$ -o ./logs
module load conda_R
Rscript x9_format_altemose_kmers.r
|
e7a6d0b0d0feaab7466a7b6f47a032ebfcf9fb3933417fc27dd3fbd97e8a7836 | Shell | 176 | 7 | #!/usr/bin/env bash
DOWNLOAD_DIR=$1
DATA_ROOT=$2
tar -zxvf $DOWNLOAD_DIR/OpenDataLab___FreiHAND/raw/FreiHAND.tar.gz -C $DATA_ROOT
rm -rf $DOWNLOAD_DIR/OpenDataLab___FreiHAND
|
c492d94a512cf8d4fbe3e012322314e72ca2800af6e4fceda6659609518c7d77 | Shell | 177 | 5 |
CUDA_VISIBLE_DEVICES=0 python tools/train.py configs/recognition/hardvs_ESTF/hardvs_ESTF.py --work-dir work_dirs/hardvs_ESTF --validate --seed 0 --deterministic --gpu-ids=0
|
262b2133bbd70cbc9803ae48c85796ba266a4324a02f410b5d412196903ca0bc | Shell | 179 | 7 | #!/usr/bin/env bash
DOWNLOAD_DIR=$1
DATA_ROOT=$2
tar -zxvf $DOWNLOAD_DIR/OpenDataLab___CrowdPose/raw/CrowdPose.tar.gz -C $DATA_ROOT
rm -rf $DOWNLOAD_DIR/OpenDataLab___CrowdPose
|
f8ac4f94cd40db36c5acf9c5bc2b7b3d034e90205d5e001678a22100d242bb75 | Shell | 181 | 6 | #!/bin/bash
echo "User: $(id -un "$USER")" && echo "Group: $(id -gn "$USER")" && \
export && \
echo "SHELL: $SHELL" && \
echo "PATH: $PATH" && \
xvfb-run -a python /app/run.py "$@"
|
d94234f7c335b639baf7c518f6bfd55aa69940d46efadbe92a50aa258051ad2d | Shell | 182 | 14 | #!/bin/bash
#$ -cwd
#$ -j y
#$ -R y
#$ -l mem_free=10G
#$ -l h_vmem=10G
#$ -l h_fsize=10G
#$ -l h_rt=24:00:00
#$ -o ./logs
module load conda_R/4.0.x
Rscript x2_aggregate_matrix.r
|
6379ddb581c945252db428281c5628f6e3e5e9fe179384bbecc4347d3233599a | Shell | 183 | 10 | feature_dir=clip_feat
for DATASET in OxfordPets
do
python linear_probe.py \
--dataset ${DATASET} \
--feature_dir ${feature_dir} \
--num_step 8 \
--num_run 3
done
|
58d6241fc99424b66e20ed3b3095a34c32765d33b9f09cd23d738938b5bb2e9f | Shell | 185 | 3 | #/bin/bash
#CUDA_VISIBLE_DEVICES=0 python tools/test.py --cfg experiments/awa/w48_384x288_1.yaml
CUDA_VISIBLE_DEVICES=0 python tools/test.py --cfg experiments/awa/w48_384x288_sup_5.yaml |
403490339a3e2b9f8db1d25f5873620ba3416522b01a266f330e4e5f596bab8d | Shell | 191 | 7 | #!/usr/bin/env bash
DOWNLOAD_DIR=$1
DATA_ROOT=$2
tar -zxvf $DOWNLOAD_DIR/OpenDataLab___AI_Challenger/raw/AI_Challenger.tar.gz -C $DATA_ROOT
rm -rf $DOWNLOAD_DIR/OpenDataLab___AI_Challenger
|
d3a47ce328334600a5919ba5d1ab01b813a64f7eee97101b6d6169aeca6fc3fc | Shell | 194 | 21 | #!/bin/bash
# bash script_tensorboard.sh
tmux new -s tensorboard -d
tmux send-keys "source activate benchmark_gnn" C-m
tmux send-keys "tensorboard --logdir out/ --port 6006" C-m
|
c0660502abc0f83b48d606b6dbd4e90d343236e6a1a5125843b9253bfd9aa6a6 | Shell | 197 | 7 | #!/usr/bin/env bash
DOWNLOAD_DIR=$1
DATA_ROOT=$2
tar -zxvf $DOWNLOAD_DIR/OpenDataLab___MPII_Human_Pose/raw/MPII_Human_Pose.tar.gz -C $DATA_ROOT
rm -rf $DOWNLOAD_DIR/OpenDataLab___MPII_Human_Pose
|
3239e3250aeddd548e15fad5af834c7a1827f273406f7cb020d112f3af0fb941 | Shell | 203 | 8 | #!/bin/bash
#SBATCH --job-name=fmriprep_build
#SBATCH --ntasks=1
#SBATCH --cpus-per-task=4
#SBATCH --time=120:00:00
#SBATCH --mem=32G
singularity build fmriprep.simg docker://nipreps/fmriprep:latest |
09d1e37af9ac244b1dfe0fe12d1359781a1139d2774be6009767e02e041b31b3 | Shell | 207 | 5 | export OUTPUT_PATH=outputs/experiment/perturb/pancreas/saliency
export WANDB_API_KEY=YOUR_WANDB_KEY
python perturb.py pancreas-saliency-inverse-loss \
--pert.perturbation-num 10 \
--slurm.mode slurm
|
c2122898f4113534ac794f3c1ed4284d6c5fa5467df9d5f1fd7834c49006b30c | Shell | 207 | 10 | #!/bin/bash
#SBATCH --account=def-lpenacas
#SBATCH --time=2:00:00
#SBATCH --mem=64G
#SBATCH --cpus-per-task=4
module load python
source env/bin/activate
python model.py hyp.json OpDetect data_processed.npz |
d55944fd5f28a7a2bd562301f98e40c456ced5951f3743ebb255022f09c89e19 | Shell | 208 | 5 | export OUTPUT_PATH=outputs/experiment/perturb/pancreas/mean_importance
export WANDB_API_KEY=YOUR_WANDB_KEY
python perturb.py pancreas-mean-importance \
--pert.perturbation-num 10 \
--slurm.mode slurm
|
1692833dd1291fca27fb0d35bf7023ba647926a7ba1150af92cc230e828c9dc8 | Shell | 210 | 5 | export OUTPUT_PATH=outputs/experiment/perturb/pancreas/permutation_test
export WANDB_API_KEY=YOUR_WANDB_KEY
python perturb.py pancreas-permutation-test \
--pert.perturbation-num 10 \
--slurm.mode slurm
|
1b760cf170548dd9f622f74ba828119a550d31235259dd09df2837aac5522531 | Shell | 210 | 5 | export OUTPUT_PATH=outputs/experiment/perturb/pancreas/feature_ablation
export WANDB_API_KEY=YOUR_WANDB_KEY
python perturb.py pancreas-feature-ablation \
--pert.perturbation-num 10 \
--slurm.mode slurm
|
0714dba0e7601d4876332b95245a16ae99fac7c904ff2b47bb5ed84f9e211011 | Shell | 211 | 5 | export OUTPUT_PATH=outputs/experiment/perturb/bonemarrow/saliency
export WANDB_API_KEY=YOUR_WANDB_KEY
python perturb.py bonemarrow-saliency-inverse-loss \
--pert.perturbation-num 10 \
--slurm.mode slurm
|
ebeccdb44fe441ffc0e20fe6ac614b8858d98dd6dd7020d1ec41eb612e89a9d6 | Shell | 212 | 5 | export OUTPUT_PATH=outputs/experiment/perturb/bonemarrow/mean_importance
export WANDB_API_KEY=YOUR_WANDB_KEY
python perturb.py bonemarrow-mean-importance \
--pert.perturbation-num 10 \
--slurm.mode slurm
|
9958d26fab4046148ec6dc6f67cdfc5f591f3e882b23cbdcfff75ea4d867288e | Shell | 213 | 5 | export OUTPUT_PATH=outputs/experiment/perturb/pancreas/permutation
export WANDB_API_KEY=YOUR_WANDB_KEY
python perturb.py pancreas-permutation-inverse-loss \
--pert.perturbation-num 10 \
--slurm.mode slurm
|
903469c2e93170ae82a722804f99c3199c4e8796c5048843796ccce28fbb9af8 | Shell | 214 | 5 | export OUTPUT_PATH=outputs/experiment/perturb/bonemarrow/feature_ablation
export WANDB_API_KEY=YOUR_WANDB_KEY
python perturb.py bonemarrow-feature-ablation \
--pert.perturbation-num 10 \
--slurm.mode slurm
|
e08d7e70086c12e6c09af84b06a5971ad5a2bb49c9cf557571d26033e332e1ba | Shell | 214 | 5 | export OUTPUT_PATH=outputs/experiment/perturb/bonemarrow/permutation_test
export WANDB_API_KEY=YOUR_WANDB_KEY
python perturb.py bonemarrow-permutation-test \
--pert.perturbation-num 10 \
--slurm.mode slurm
|
a8ad25ad733ef2d17205afffe418a99610db508b111249c0982f1ebfceb4c674 | Shell | 215 | 5 | export OUTPUT_PATH=outputs/experiment/perturb/dentategyrus/saliency
export WANDB_API_KEY=YOUR_WANDB_KEY
python perturb.py dentategyrus-saliency-inverse-loss \
--pert.perturbation-num 10 \
--slurm.mode slurm
|
e4026d676f1ad4e3e8f4d867362b62e4fee784513dd63ff4840f1ad5f0b69df8 | Shell | 216 | 5 | export OUTPUT_PATH=outputs/experiment/perturb/dentategyrus/mean_importance
export WANDB_API_KEY=YOUR_WANDB_KEY
python perturb.py dentategyrus-mean-importance \
--pert.perturbation-num 10 \
--slurm.mode slurm
|
34f33d4c38f4b35776dfc92f6f30b09d9fbc90f75a6cc79261f99defd53e46d1 | Shell | 217 | 5 | export OUTPUT_PATH=outputs/experiment/perturb/pancreas/approximation
export WANDB_API_KEY=YOUR_WANDB_KEY
python perturb.py pancreas-approximation-inverse-loss \
--pert.perturbation-num 10 \
--slurm.mode slurm
|
442d8c4e167ff27be782f11a758a6004ce7e09b44d463420d6b87a16ce18020d | Shell | 217 | 5 | export OUTPUT_PATH=outputs/experiment/perturb/bonemarrow/permutation
export WANDB_API_KEY=YOUR_WANDB_KEY
python perturb.py bonemarrow-permutation-inverse-loss \
--pert.perturbation-num 10 \
--slurm.mode slurm
|
4aef078da26e63b5df211e9ab94d9d1f4b062f2a9a4a85da5510c877863706ec | Shell | 218 | 5 | export OUTPUT_PATH=outputs/experiment/perturb/dentategyrus/permutation_test
export WANDB_API_KEY=YOUR_WANDB_KEY
python perturb.py dentategyrus-permutation-test \
--pert.perturbation-num 10 \
--slurm.mode slurm
|
9591202fe3a340878459773e042bd83aebc793a632d5aecd2645e26a285f1f2f | Shell | 218 | 10 | #!/bin/sh
SCRIPTSDIR=$(cd "$(dirname "$0")"; pwd)
BASEDIR="$(dirname "$SCRIPTSDIR")"
cd "$BASEDIR"
echo "Building documentation in $BASEDIR/documentation/_build/html"
cd "$BASEDIR/documentation"
make clean
make html
|
99342c5003783088178daeb2b411572f9af6c1bb4836c0435ea46325c78e4a6a | Shell | 218 | 5 | export OUTPUT_PATH=outputs/experiment/perturb/dentategyrus/feature_ablation
export WANDB_API_KEY=YOUR_WANDB_KEY
python perturb.py dentategyrus-feature-ablation \
--pert.perturbation-num 10 \
--slurm.mode slurm
|
85a81d3e8f453df96ba01d87d1383b4eff3cd5357a757b7678792b0ee493846d | Shell | 220 | 14 | #!/bin/bash
#$ -cwd
#$ -j y
#$ -l h_fsize=200G
#$ -l mem_free=250G
#$ -l h_vmem=250G
#$ -l h_rt=96:00:00
#$ -o ./logs
#$ -t 1-1287
module load conda_R
mkdir -p ./unique_kmer_type
Rscript x6_select_kmers.r $SGE_TASK_ID
|
9cbf9f16ca51b23a18adb5c0afa7783bc96233cef96bdab88e5434719591c107 | Shell | 220 | 13 | #!/bin/bash
#$ -cwd
#$ -j y
#$ -l h_fsize=1000G
#$ -l mem_free=250G
#$ -l h_vmem=250G
#$ -l h_rt=96:00:00
#$ -t 1-1287
#$ -o ./logs
module load conda_R
mkdir -p ./final_kmers
Rscript x8_finalize_kmer_set.r $SGE_TASK_ID
|
007bbc44243e9ef4373bc0ccce9e011a713be2b9aa02a02243605fb3cef8d781 | Shell | 221 | 5 | export OUTPUT_PATH=outputs/experiment/perturb/bonemarrow/approximation
export WANDB_API_KEY=YOUR_WANDB_KEY
python perturb.py bonemarrow-approximation-inverse-loss \
--pert.perturbation-num 10 \
--slurm.mode slurm
|
39baf665c207ed634a49f91affd3294d9ad8436f4452cc55cd46e407207cd71e | Shell | 221 | 5 | export OUTPUT_PATH=outputs/experiment/perturb/dentategyrus/permutation
export WANDB_API_KEY=YOUR_WANDB_KEY
python perturb.py dentategyrus-permutation-inverse-loss \
--pert.perturbation-num 10 \
--slurm.mode slurm
|
769fe7ba8c87530d33e91a03c6ce84016b06bb033c312f504574dfb8672f95d7 | Shell | 224 | 8 | #!/usr/bin/env bash
DOWNLOAD_DIR=$1
DATA_ROOT=$2
cat $DOWNLOAD_DIR/OpenDataLab___HaGRID/raw/*.tar.gz.* | tar -xvz -C $DATA_ROOT/..
tar -xvf $DATA_ROOT/HaGRID.tar -C $DATA_ROOT/..
rm -rf $DOWNLOAD_DIR/OpenDataLab___HaGRID
|
943343221198f3d37d7f7885a34cc056c008958fc79852e0988c52fc1fdda758 | Shell | 225 | 5 | export OUTPUT_PATH=outputs/experiment/perturb/dentategyrus/approximation
export WANDB_API_KEY=YOUR_WANDB_KEY
python perturb.py dentategyrus-approximation-inverse-loss \
--pert.perturbation-num 10 \
--slurm.mode slurm
|
b212492efd5d9f53cdbb2e52cdd40a1ab9d1a7bc1b923022b671bbe174bd32b8 | Shell | 225 | 5 | #/bin/bash
#conda activate hand
#module load gcc/8.1.0
#CUDA_VISIBLE_DEVICES=7 python tools/train.py --cfg experiments/atrw/w48_384x288.yaml
CUDA_VISIBLE_DEVICES=2 python tools/train.py --cfg experiments/awa/w48_384x288.yaml
|
7b17161d9413138025c89b10dadba1641f08bf67efe0b44afeaa3b862e8f5389 | Shell | 228 | 13 | #!/bin/bash
#$ -cwd
#$ -j y
#$ -l h_fsize=1000G
#$ -l mem_free=200G
#$ -l h_vmem=200G
#$ -l h_rt=96:00:00
#$ -t 1-1287
#$ -o ./logs
module load conda_R
mkdir -p ./kmers_non_masked
Rscript x7_select_nonmask_kmers.r $SGE_TASK_ID
|
0d19af4671ad369f864541be506317d1f56843ab757db8b18bcd1a78685ef4d1 | Shell | 229 | 15 | #!/bin/bash
#$ -cwd
#$ -j y
#$ -l h_fsize=5G
#$ -l mem_free=2G
#$ -l h_vmem=2G
#$ -l h_rt=144:00:00
#$ -t 1-226
#$ -o ./logs
module load conda_R/4.1.x
#fold="fold"$SGE_TASK_ID
Rscript Ensemble_ARTEMIS1_delfi_LOO.r $SGE_TASK_ID |
128591ac6483eafff8d45e37b2968927c00036916defe998a87aaf917376cd6c | Shell | 229 | 15 | #!/bin/bash
#$ -cwd
#$ -j y
#$ -l h_fsize=5G
#$ -l mem_free=2G
#$ -l h_vmem=2G
#$ -l h_rt=144:00:00
#$ -t 1-423
#$ -o ./logs
module load conda_R/4.1.x
#fold="fold"$SGE_TASK_ID
Rscript Ensemble_ARTEMIS1_delfi_LOO.r $SGE_TASK_ID |
4860536499f2e0260087b68f13adccf1db31496d2a4c950a26d26758bd23ee16 | Shell | 229 | 15 | #!/bin/bash
#$ -cwd
#$ -j y
#$ -l h_fsize=5G
#$ -l mem_free=2G
#$ -l h_vmem=2G
#$ -l h_rt=144:00:00
#$ -t 1-287
#$ -o ./logs
module load conda_R/4.1.x
#fold="fold"$SGE_TASK_ID
Rscript Ensemble_ARTEMIS1_delfi_LOO.r $SGE_TASK_ID |
d03494a6298f45533c29bae59943e2ee88d035076ffd07886a9831e425b885fe | Shell | 229 | 15 | #!/bin/bash
#$ -cwd
#$ -j y
#$ -l h_fsize=5G
#$ -l mem_free=2G
#$ -l h_vmem=2G
#$ -l h_rt=144:00:00
#$ -t 1-208
#$ -o ./logs
module load conda_R/4.1.x
#fold="fold"$SGE_TASK_ID
Rscript Ensemble_ARTEMIS1_delfi_LOO.r $SGE_TASK_ID |
c0d249d193136085df20a5572cfd4504efcd9d3fc32ff911c935b9e8326696d1 | Shell | 231 | 8 | #! /bin/bash
for number in {1..20}
do
echo "Welcome to spot fleet, No.$number... "
aws ec2 request-spot-instances --spot-price "0.40" --instance-count 1 --launch-specification file://launch_specs_2.json
sleep 30
done
|
fe97aee76089b061110385f39cadeff3a48ce1dacee1464bc2b34566b7465eda | Shell | 231 | 5 | cat /path/to/folder/prok_pfam_filepaths.txt | while read line #collected pfam filepaths
do
acc=$(echo "$line" | awk -F'/' '{print $9}')
awk -v str="$acc" '{print $0 "\t" str}' "$line" >> "/path/to/folder/prok_pfam_comb.pfam"
done |
4b48e6a46e9d99d7843aefbd4850f3a72740b3d51b0200d2018f8abf18139cc7 | Shell | 235 | 3 | #/bin/bash
#CUDA_VISIBLE_DEVICES=0 python tools/test.py --cfg experiments/awa/w48_384x288_1.yaml
CUDA_VISIBLE_DEVICES=0 python tools/demo.py --cfg experiments/awa/w48_384x288_sup_5.yaml --imFile 'Saint-Aignan_(Loir-et-Cher)._Okapi.jpg' |
90795672bb06adf7475d6370d7e8a0e6544475d8788b083ba0b52d110218d14f | Shell | 237 | 3 | #!/bin/bash
wget -P data/raw/uniref/uniref50 https://ftp.uniprot.org/pub/databases/uniprot/uniref/uniref50/uniref50.fasta.gz
wget -P data/raw/uniref/uniref90 https://ftp.uniprot.org/pub/databases/uniprot/uniref/uniref90/uniref90.fasta.gz |
f5099a31854e7b5a6d50badaf259b7ffcec3f49e4b59da8120078da034b554af | Shell | 238 | 10 | #!/bin/sh
# liftOver hg38 to hg19
liftOver=./04_Softwares/liftOver/liftOver
chain=./04_Softwares/liftOver/hg38ToHg19.over.chain.gz
for samp in `ls *bed`
do
$liftOver $samp $chain ${samp%.*}".hg19.bed" ${samp%.*}".hg19.unlifted.bed"
done |
2d6ed0f6d714b05ee95f8fa487ad96263878289e4772b3965afdb1cfeb70dd70 | Shell | 245 | 8 | #!/usr/bin/env bash
DOWNLOAD_DIR=$1
DATA_ROOT=$2
tar -zxvf $DOWNLOAD_DIR/OpenDataLab___WFLW/raw/WFLW.tar.gz.00 -C $DOWNLOAD_DIR/
tar -xvf $DOWNLOAD_DIR/WFLW/WFLW.tar.00 -C $DATA_ROOT/
rm -rf $DOWNLOAD_DIR/WFLW $DOWNLOAD_DIR/OpenDataLab___WFLW
|
89b1f50c9947bc22e3f0f9785ab50a03635079f1b3ca394a8c17588f8b76799e | Shell | 245 | 8 | #!/usr/bin/env bash
DOWNLOAD_DIR=$1
DATA_ROOT=$2
tar -zxvf $DOWNLOAD_DIR/OpenDataLab___300w/raw/300w.tar.gz.00 -C $DOWNLOAD_DIR/
tar -xvf $DOWNLOAD_DIR/300w/300w.tar.00 -C $DATA_ROOT/
rm -rf $DOWNLOAD_DIR/300w $DOWNLOAD_DIR/OpenDataLab___300w
|
24435e5ccb45a869f38d0353e92a8a3d21b79ec7f2cd4a1dc44d660cfe4f14e5 | Shell | 249 | 9 | #!/bin/bash
Xvfb :0 -screen 0 1600x1200x24 & export DISPLAY=:0
if [[ "$1" == "notebook" || "$1" == "jupyter" || "$1" == "jupyter-notebook" ]]; then
exec jupyter notebook --allow-root --no-browser --ip=0.0.0.0 --port=9999
else
exec "$@"
fi
|
697b0e6e1979493ca2462b90684cdef99703bd3e28c8655e697c138aaf18b1fc | Shell | 251 | 8 | #!/usr/bin/env bash
DOWNLOAD_DIR=$1
DATA_ROOT=$2
tar -zxvf $DOWNLOAD_DIR/OpenDataLab___Halpe/raw/Halpe.tar.gz.00 -C $DOWNLOAD_DIR/
tar -xvf $DOWNLOAD_DIR/Halpe/Halpe.tar.00 -C $DATA_ROOT/
rm -rf $DOWNLOAD_DIR/Halpe $DOWNLOAD_DIR/OpenDataLab___Halpe
|
4a1c1a895d06e57011e653e10097c3a6262a8b78bf3c6ae62b7c73b7dac14910 | Shell | 254 | 6 | export OUTPUT_PATH=outputs/experiment/perturb/pancreas/saliency
export WANDB_API_KEY=YOUR_WANDB_KEY
python perturb.py pancreas-smooth-saliency-inverse-loss \
--pert.perturbation-num 10 \
--pert.saliency.smooth-number 256 \
--slurm.mode slurm
|
9982edf4dd05f8ca078b9d38415101eef04ca173ed3260a8b4049d57f708f693 | Shell | 254 | 10 | # Evaluates predictors with different training data sizes
dataset=$1
predictor=$2
n_seeds=20
kwargs=$3
for n_train in 24 48 72 96 120 144 168 192 216 240; do
sbatch scripts/evaluate_predictor.sh $dataset $predictor $n_seeds $n_train "$kwargs"
done
|
6c72fff6f45bc01223cb7256b0e5b728683ee83a914920007b07dc92b7ef886f | Shell | 257 | 8 | #!/usr/bin/env bash
DOWNLOAD_DIR=$1
DATA_ROOT=$2
tar -zxvf $DOWNLOAD_DIR/OpenDataLab___AP-10K/raw/AP-10K.tar.gz.00 -C $DOWNLOAD_DIR/
tar -xvf $DOWNLOAD_DIR/AP-10K/AP-10K.tar.00 -C $DATA_ROOT/
rm -rf $DOWNLOAD_DIR/AP-10K $DOWNLOAD_DIR/OpenDataLab___AP-10K
|
e60f80a448b3c2b2924d05ba2091c705031bc6f2f1a549155de0d006671ab2dd | Shell | 258 | 6 | export OUTPUT_PATH=outputs/experiment/perturb/bonemarrow/saliency
export WANDB_API_KEY=YOUR_WANDB_KEY
python perturb.py bonemarrow-smooth-saliency-inverse-loss \
--pert.perturbation-num 10 \
--pert.saliency.smooth-number 256 \
--slurm.mode slurm
|
7aaf137188a2d2d1ea561a407aab5e2382f8a86cbc0abefec8dc8a11612f999a | Shell | 262 | 6 | export OUTPUT_PATH=outputs/experiment/perturb/dentategyrus/saliency
export WANDB_API_KEY=YOUR_WANDB_KEY
python perturb.py dentategyrus-smooth-saliency-inverse-loss \
--pert.perturbation-num 10 \
--pert.saliency.smooth-number 256 \
--slurm.mode slurm
|
5d0095ee51b63f44d576a67fd9f450a7672e93920fc03e7a308f731ac683d4f5 | Shell | 264 | 14 | #!/bin/sh
#
# Run this from the bowtie directory to sanity-check the bowtie index
# builder
#
make bowtie-build-debug
if ./bowtie-build-debug -s -v genomes/NC_008253.fna .build_test ; then
echo Build test PASSED
else
echo Build test FAILED
fi
rm -f .tmp*.ebwt
|
dcda87bac2243e485e266ef152ce427039a78fb33ed15e07fdb9eb24cace1d22 | Shell | 274 | 8 | #!/bin/bash
proj_dir=/mnt/lustre/working/lab_lucac/sebastiN/projects/OCD_modeling/
redis_ip=`awk '{print $1}' ${proj_dir}traces/.redis_ip`
python $proj_dir/code/OCD_modeling/mcmc/abc_hpc.py
abc-redis-manager stop --port 6379 --host ${redis_ip} --password bayesopt1234321
|
b2dc0dbd24c2dc499c1c6c6851ffd82462416c0fa27f9a1d03d8b320390bf723 | Shell | 279 | 13 | # Command to download dataset:
# bash script_download_TSP.sh
DIR=TSP/
cd $DIR
FILE=TSP.pkl
if test -f "$FILE"; then
echo -e "$FILE already downloaded."
else
echo -e "\ndownloading $FILE..."
curl https://data.dgl.ai/dataset/benchmarking-gnns/TSP.pkl -o TSP.pkl -J -L -k
fi
|
34b48d22fd2a5b235355a401b3e018078fb7e0e7b4d9f0bcaf14e1aaf98dd162 | Shell | 281 | 8 | #!/usr/bin/env bash
DOWNLOAD_DIR=$1
DATA_ROOT=$2
tar -zxvf $DOWNLOAD_DIR/OpenDataLab___OneHand10K/raw/OneHand10K.tar.gz.00 -C $DOWNLOAD_DIR/
tar -xvf $DOWNLOAD_DIR/OneHand10K/OneHand10K.tar.00 -C $DATA_ROOT/
rm -rf $DOWNLOAD_DIR/OneHand10K $DOWNLOAD_DIR/OpenDataLab___OneHand10K
|
eab090bbd7ffd091d87d4af435fc686525afc6d955dd01409ea4021be322b1c6 | Shell | 295 | 10 | #!/usr/bin/env bash
# Copyright (c) OpenMMLab. All rights reserved.
CONFIG=$1
GPUS=$2
PORT=${PORT:-29500}
PYTHONPATH="$(dirname $0)/..":$PYTHONPATH \
python -m torch.distributed.launch --nproc_per_node=$GPUS --master_port=$PORT \
$(dirname "$0")/train.py $CONFIG --launcher pytorch ${@:3}
|
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