sha256 stringlengths 64 64 | language stringclasses 27
values | size int32 1 491k | lines int32 1 21.8k | content stringlengths 1 200k |
|---|---|---|---|---|
35e1d033c2e1d8fabfa41dc9e992b32489975283424b4910a197a210d7164ea8 | Python | 99,376 | 2,449 | import os
import warnings
import numpy as np
from numba import njit
from scipy.optimize import curve_fit
from scipy.signal import medfilt, find_peaks
from scipy.stats import norm, chi2
import matplotlib.pyplot as plt
from bombcell.extract_raw_waveforms import path_handler
from bombcell.default_parameters import che... |
18280604207a0e0fb6009c7506163aa5487cfcbb7dab7a6f40e2ccf14cf37877 | Python | 99,463 | 2,182 | import sys
import os
import time
code_dir = os.path.abspath(os.path.join(os.path.dirname(__file__), '..', 'hippie'))
sys.path.append(code_dir)
try:
from dataloading import ( # type: ignore[import]
DATASET_TECHNOLOGY, TECHNOLOGY_IDS, LAYER_IDS, LAYER_NORMALISE,
)
_HIPPIE_VOCAB_AVAILABLE = True
exc... |
594e955ec36d3bcbf169185e2be1ba95304540feeee0f3ee59b9332cccd10a00 | Python | 101,284 | 2,028 | """
This module contains all the classes and code to collect data and calculate
statistics from the output files of various calculation types. Each calculation
type needs its own class.
All data is stored in a special class derived from the list.
"""
# #################################################################... |
b58e974c15a41bb17817e12b538d5b54abfd8d959ab538eb77022fd9a10cab07 | Python | 101,753 | 3,339 | from __future__ import annotations
import json
import re
import shutil
from pathlib import Path
from typing import TYPE_CHECKING
from typing import Any
from typing import cast
import pytest
from cleo.io.buffered_io import BufferedIO
from cleo.io.inputs.input import Input
from cleo.io.null_io import NullIO
from cleo... |
3a8d3b04bf23639ce95c09793d6f728798e7cbded51149e73bf2c8a2ecfe7e6d | Python | 102,166 | 3,365 | import warnings
from collections import OrderedDict
from shutil import get_terminal_size
from unittest.mock import patch
import numpy as np
import numpy.testing as np_test
import pytest
from skbase.utils.dependencies import _check_soft_dependencies
from pgmpy import config
from pgmpy.example_models import load_model
... |
d11a20f50d320e46e071b2d1a2dbf639b529736c4e0a65979b402dc743660a7d | Python | 103,138 | 9,412 | """
Copyright (c) Facebook, Inc. and its affiliates.
This source code is licensed under the MIT license found in the
LICENSE file in the root directory of this source tree.
Original CGCNN k-hot elemental embeddings.
"""
KHOT_EMBEDDINGS = {
1: [
0,
1,
0,
0,
0,
0,
... |
f981d86d04d0a7ac346c4bf6797c2d5f064d6cda1795a79db5d38363f8746036 | Python | 108,977 | 2,536 | """Base model class and the helpers shared by every model in this package.
`Model` provides base transformer loading, AMP and gradient checkpointing,
token embedding lookup and logit pooling; the concrete subclasses live in their
own modules and import from here. See the models page of the documentation for
the poolin... |
bc660f4f96bd0e4c81249de1816c8ab8ee4ac18adf9811415da9564c19545285 | Python | 111,165 | 2,519 | import torch
import torch.nn as nn
import torch.nn.functional as F
from copy import deepcopy
from torch_geometric.nn import GATConv, EdgePooling, GCNConv, VGAE
from torch_geometric.nn.models import GIN
from .gvp_utils import GVP, GVPConvLayer, LayerNorm
from torch_scatter import scatter_mean
import os
from torch_s... |
3f7da9a75cab78576ad158c7e1642d5364bda0b276aa9d3a3f27c15dd107def2 | Python | 115,586 | 2,242 | from pathlib import Path
import joblib
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
from matplotlib_venn import venn3
from pingouin import kruskal
from scipy.optimize import curve_fit
from scipy.stats import gaussian_kde
from scipy.integrate import quad
from analysis.ca... |
06ceb15810074a45cbb082e7680b7758ea8e029d0384611953b12535772b6f13 | Python | 120,010 | 1,789 | import os
import numpy as np
import pandas as pd
import nibabel as nib
from vtk import vtkPolyDataNormals
from ..mesh.mesh_io import read_surface
from ..mesh.mesh_operations import combine_surfaces
from ..vtk_interface import wrap_vtk, serial_connect
def load_mask(name='midline', surface_name="fsa5", join=False):
... |
d3d2a68b0df2d0f73a5205c050bea46b4988904263d49df238d2722def86eaef | Python | 121,706 | 2,692 | # written by Floris van Breugel, with some help from Andrew Straw and Will Dickson
# dependencies for LaTex rendering: texlive, ghostscript, dvipng, texlive-latex-extra
# general imports
import matplotlib
##print matplotlib.__version__
##print 'recommended version: 1.1.1 or greater'
##################################... |
009156202fc4fe8e89a8963a5ca503bcfa1150fefb0cae3421b692fef3141351 | Python | 123,050 | 2,714 | # written by Floris van Breugel, with some help from Andrew Straw and Will Dickson
# dependencies for LaTex rendering: texlive, ghostscript, dvipng, texlive-latex-extra
# general imports
import matplotlib
##print matplotlib.__version__
##print 'recommended version: 1.1.1 or greater'
##################################... |
f6a2c18e3a16afae072c316cb07792d882ed5a72e5e8d079ca8628b1c6cdf548 | Python | 124,398 | 2,692 | # written by Floris van Breugel, with some help from Andrew Straw and Will Dickson
# dependencies for LaTex rendering: texlive, ghostscript, dvipng, texlive-latex-extra
# general imports
import matplotlib
##print matplotlib.__version__
##print 'recommended version: 1.1.1 or greater'
##########################... |
2c9cc6bae7cdbde5defbf5848770cf11345989cb7a4e224f447b578a35e62253 | Python | 125,342 | 2,297 | """
Generate Amber topology files from GROMACS files
"""
# ##############################################################################
# GPLv3 LICENSE INFO #
# #
# Copyright (C) 20... |
b28b19130ba9e5efa25f5f0c1eb3b7ddbe0afd3299628d6b45b34d59942752d6 | Python | 126,547 | 3,345 | #!/usr/bin/env python
import io
import unittest
import xml.etree.ElementTree as etree
from pgmpy.readwrite import PomdpXReader, PomdpXWriter
class TestPomdpXReaderString(unittest.TestCase):
def setUp(self):
string = """<pomdpx version="1.0" id="rockSample"
xmlns:xsi="http://www.w3.org/2001/XMLSche... |
b90f68f59ae9e704908fec7ff2e8a87cae284d08ee27156bc9e3cf33a01b5df3 | Python | 131,461 | 2,299 | #!/usr/bin/env python
'''
(c) 2016-2018 Oleksandr Frei and Alexey A. Shadrin
Various utilities for GWAS summary statistics.
'''
from __future__ import print_function
import pandas as pd
import numpy as np
from scipy import stats
import scipy.io as sio
import scipy.sparse
import os
import time, sys, traceback
import ar... |
3e8fc26bc373c85e630ac9022e820a62113ccad9b10dcf6858105f94b4d5dd56 | Python | 133,786 | 2,982 | import logging
from collections.abc import Mapping
from textwrap import dedent
import copy
import collections
import numpy as np
from mdt.configuration import get_active_post_processing
from mdt.lib.deferred_mappings import DeferredFunctionDict
from mdt.lib.exceptions import DoubleModelNameException
from mdt.model_buil... |
a6c5e2eac2d17c62a9826d6502a9043d45e897e7023c18f764d1ccdd8429eff6 | Python | 135,447 | 3,148 | # -*- coding: utf-8 -*-
"""
致密砂岩数字岩石物理平台 - Streamlit Web Application
===================================================
核心功能：五步完整工作流 (RAW预处理 → CNN预测 → 3D预览 → 物理验证 → 气体输运模拟)
Author: Claude Code Assistant
"""
# ========================================================
# 0. OMP 冲突修复 - 必须放在第一行 (import os 之后)
# ==========... |
073c5794e350c8400f51f3adcbe01e57e827fa26b2fe7f9e961246701fab44e0 | Python | 138,152 | 2,497 | """
This module can perform enrichment analyses on a given set of genomic features and visualize their intersections. \
These include gene ontology/tissue/phenotype enrichment, enrichment for user-defined attributes, \
set visualization ,etc. \
Results of enrichment analyses can be saved to .csv files.
"""
import func... |
ab615dde554fbcc88744a955c9060fb1673fd6bd8b9bc641698aa7f98c535092 | Python | 140,788 | 2,805 | """
The *fastq* module provides a unified programmatic interface to external tools that process FASTQ files.
Those currently include the *CutAdapt* adapter-trimming tool, the *kallisto* RNA-sequencing quantification tool,
the *bowtie2* alignment tool, and the *featureCounts* feature counting tool.
"""
import abc
impor... |
ab1a5d388dcd2085ad79909ea15f3666740d76486f8e4b43d25fdf6fb5553091 | Python | 144,685 | 2,686 | #################################################
### PHAGE GENOME DESIGN // FILTERING PIPELINE ###
#################################################
"""
Usage:
eval "$(conda shell.bash hook)"
conda activate genome_design
CONFIG_FILE="/path/to/config.yaml"
python /path/to/genome_design_filtering_pipeline.py $CONFIG_F... |
fc72288fb496422468fbe4299c53f4fb24f73621d27a0d5e39f75eaa9760728d | Python | 146,512 | 4,002 | import os
from typing import Optional, Literal
import math
import torch
from torch import nn
import torch.nn.functional as F
from einops import rearrange
from .utils import expand_key_padding_mask
from .modules import BaseClassifier, CNNMixer, CNNMixerConfig, BaseClassifier_reg
import pdb
from .position_embeddings im... |
95bbf2a6b18550574d11adef5d1238972df3d80fbd2015396127ce32bde8261e | Python | 146,883 | 3,552 | import json
import shutil
from enum import Enum, IntEnum
from unittest.mock import Mock, patch
import matplotlib
import pytest
import yaml
from rnalysis import __version__
from rnalysis.exceptions import InvalidTypeError, InvalidValueError, RNAlysisInputError
from rnalysis.filtering import *
from tests import __attr_... |
bc75b2236646d2c9b93fb5a0f032cec18078a42ff5e60a32d0b66fbe614edf03 | Python | 153,102 | 3,474 | #!/usr/bin/env python3
"""
TSNFA Monte Carlo Simulator v1.2 (Deliverable 2, revision)
==========================================================
VERSION HISTORY
v1.1 Original Deliverable-2 simulator. ProposedMethod implemented the
EMA variant reverse-engineered from deployed STM32 hardware
(max-acros... |
85356033cf1619c08ade8f91f8a01a931c549d9d369fc1a10ee90ba8ffc2b7ae | Python | 156,893 | 3,735 | import json
import os
import re
import tkinter as tk
import tkinter.font as tkf
import uuid
import copy
import math
import datetime
from tkinter import colorchooser, filedialog, messagebox, ttk
import matplotlib
import traceback
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
from matplotlib.back... |
409ee9e1ceb9253211556365a448207606e9c77e451bc36f1dd943536e1872e2 | Python | 157,445 | 3,442 | import asyncio
import sys
if sys.platform == 'win32':
asyncio.set_event_loop_policy(asyncio.WindowsSelectorEventLoopPolicy())
import concurrent.futures
import contextlib
import csv
import ftplib
import functools
import gzip
import hashlib
import inspect
import json
import os
import queue
import random
import re
i... |
72cfe37eca6abee73bae6fd981902527e34161d846d19bd5436643ff5f6e7dd9 | Python | 165,318 | 4,564 | import os
from typing import Optional, Literal
import math
import torch
from torch import nn
import torch.nn.functional as F
from einops import rearrange
from .utils import expand_key_padding_mask
from .modules import BaseClassifier, CNNMixer, CNNMixerConfig, BaseClassifier_reg
import pdb
from .position_embeddings im... |
772f1762aea7e535a3ad608fcf6a3e954b84f3c88a5c647f1e44abab86d47b2e | Python | 180,298 | 4,578 | import logging
import re
from unittest.mock import Mock, patch
import matplotlib
import polars.selectors as cs
import pytest
import rnalysis.gui.gui_report
matplotlib.use('Agg')
from rnalysis.gui.gui import *
LEFT_CLICK = QtCore.Qt.MouseButton.LeftButton
RIGHT_CLICK = QtCore.Qt.MouseButton.RightButton
@pytest.fix... |
815ef45604172d628e6ed67f4214da6a66925c5f1ff7a8573ee8bd6d7b293c1b | Python | 185,303 | 4,046 | import random
import re
import stat
import warnings
import zipfile
from unittest import mock
from unittest.mock import MagicMock, Mock
import platformdirs
import pytest
import requests_mock
from rnalysis.exceptions import (
CorruptSessionError,
IDMappingJobFailedError,
IDMappingTimeoutError,
InternalE... |
bf61f662f4a81d2ab899b79da960ec94fcfd46c756d2e984351f19e5d86a2e1f | Python | 190,627 | 5,914 | from __future__ import annotations
import re
import shutil
import sys
from pathlib import Path
from typing import TYPE_CHECKING
from typing import Any
from typing import Literal
import pytest
from cleo.io.buffered_io import BufferedIO
from packaging.utils import canonicalize_name
from poetry.core.packages.dependenc... |
045255a77741f6d39f640ded0b339192f9f6515dac98436b546ca2835783858e | Python | 200,000 | 4,591 | import builtins
import copy
import functools
import hashlib
import importlib
import itertools
import os
import platform
import sys
import time
import typing
import warnings
from collections import OrderedDict
from pathlib import Path
from queue import Queue
from typing import Callable, List, Tuple, Type, Union
import ... |
0ed91600c1352e6f37a727e49d7587f43c668fabad1f9043c3d176aa14fa8ba8 | Python | 200,000 | 2,999 | # -*- coding: utf-8 -*-
# Resource object code
#
# Created by: The Resource Compiler for PyQt5 (Qt v5.15.2)
#
# WARNING! All changes made in this file will be lost!
from PyQt5 import QtCore
qt_resource_data = b"\
\x00\x00\x2d\xc6\
\x89\
\x50\x4e\x47\x0d\x0a\x1a\x0a\x00\x00\x00\x0d\x49\x48\x44\x52\x00\
... |
118a3a46ab27a257fcaf2959746547ce9c0999334bbb6a9073e31b595a8caccd | Python | 200,000 | 3,837 | from __future__ import annotations
import cffi
import _cffi_backend
import datetime
import numpy as np
import os
import platform
import re
import signal
import subprocess
import sys
import threading
import warnings
from typing import Any, Literal
__version__ = '0.2.0'
class ignore_sigint:
"""
Ignore Ctrl + ... |
2c965fb63a6d1b29243af037087e7cb50ada2850ead8f0cb5cc02f95e6aa90f6 | Python | 200,000 | 362 | """Data downloaded from NCBI Gene data converted into Python data."""
# Copyright (C) 2014-2018, DV Klopfenstein. All rights reserved
downloaded = "2016_02_22" # 13919 items
# Downloaded as a tsv from NCBI Gene. Then translated to Python:
#
# http://www.ncbi.nlm.nih.gov/gene/?term=genetype+protein+coding%5BPropertie... |
4dc60f91c8d6d94f3cccbeb3a08c286e1ff1ca362751b3a33d4384f2f44736c7 | Python | 200,000 | 7,961 | import numpy as np
score_stack = np.array([
[0, 0, 0, 0, 0, 0, 0, 0, ],
[0, -240, -330, -210, -140, -210, -210, -140, ],
[0, -330, -340, -250, -150, -220, -240, -150, ],
[0, -210, -250, 130, -50, -140, -130, 130, ],
[0, -140, -150, -50, 30, -60, -100, 30, ],
[0, -210, -220, -140, -60, -110, -90, -60, ],
... |
535d004dd5c64d9b944751a53c05179218392d1e00a62908303b9eb1624a7f2a | Python | 200,000 | 2,592 | #!/usr/python
'''
For questions about running the script or for reporting bugs, please contact either:
Brian Baker (brian-baker[at]nd.edu)
or
Esam Abualrous (e.abualrous[at]fu-berlin.de)
DESCRIPTION
Calculates the geometrical parameters (r, theta, phi) of TCR on top of MHC proteins
USAGE
For peptide-MHC class I-T... |
a63d6f7cdd50e24c998d7facc69eab63a190d867139419e5ffe8a426f5ec5f75 | Python | 200,000 | 450 | """Data downloaded from NCBI Gene data converted into Python data."""
# Copyright (C) 2014-2018, DV Klopfenstein. All rights reserved
downloaded = "2016_02_22" # 28212 items
import collections as cx
NtData = cx.namedtuple('NtData', 'tax_id Org_name GeneID CurrentID Status Symbol Aliases description other_designatio... |
c5b371d92d679f54bc6c00be4e91833fcb2ae5865d481301198c0174a6ddc2bc | Python | 200,000 | 392 | """Data downloaded from NCBI Gene data converted into Python data."""
# Copyright (C) 2014-2018, DV Klopfenstein. All rights reserved
downloaded = "2016_02_22" # 21955 items
import collections as cx
NtData = cx.namedtuple('NtData', 'tax_id Org_name GeneID CurrentID Status Symbol Aliases description other_designatio... |
bcc767ef0d4344a2b35cc21937be33c218438f18a451f8c7511c272c4ea03fad | Python | 200,001 | 4,576 | import tkinter as tk
from tkinter import ttk, filedialog, simpledialog
from tkinter import messagebox
import tkinter.font as tkf
import os
import re
import json
import copy
import pandas as pd
import numpy as np
import math
from scipy.signal import find_peaks
import matplotlib.pyplot as plt
import matplotlib.cm as cm
... |
3d67f0c967f439414b7116ea1f94c82d5e3447dad767bf57ecac3a643736567d | Python | 200,004 | 3,957 | from __future__ import annotations
import os
import re
import signal
from decimal import Decimal
from datetime import date, datetime, time, timedelta
from itertools import chain, islice
from packaging import version
from pathlib import Path
from textwrap import fill
from typing import Any, Callable, Dict, Ite... |
5ede6aba2c2d51e5459649a4ef5fef27ee75ef52f9305137d6fe9b27bbea16ff | Python | 200,021 | 4,351 | from __future__ import annotations
import os
import polars as pl
import signal
import subprocess
import sys
from contextlib import contextmanager
from functools import cache, reduce, wraps
from typing import Any, Iterable
pl.enable_string_cache()
########################################################################... |
2d62c5df77b036ae287ffc0b27424f663cc9e6cf1233eb49bce7baeade1b73a9 | Python | 200,137 | 3,774 | import numpy as np
import matplotlib.pyplot as plt
from matplotlib.widgets import Button
import matplotlib.gridspec as gridspec
from pathlib import Path
import pandas as pd
import pickle
import os
from bombcell.ccg_fast import acg, ccg
try:
import ipywidgets as widgets
from IPython.display import display, cle... |
156062cdf22b313ebc6f230e57f42c20cab867d6fb6940f891e1b7ffae521d7c | Python | 204,474 | 3,774 | """
This module can filter, normalize, intersect and visualize tabular data such as read counts and differential expression data.
Any tabular data saved in a csv format can be imported. \
Use this module to perform various filtering operations on your data, normalize your data, \
perform set operations (union, interse... |
7313cf394489979d2f52c7f45bfedb45df62b39a391a5412820e56977493b525 | Python | 206,596 | 5,177 | __version__ = "1.3.2"
import sys
import os
import re
import io
import json
import zipfile
from pathlib import Path
def segref3d_product_name() -> str:
edition = os.environ.get("SEGREF3D_EDITION", "").strip().lower()
if edition == "local-gpu":
return "SegRef3D Local GPU"
if edition == "local-cpu"... |
e3eca180c306b575c34b4650bdddcc43c36c1a6cd752753bb5a05329bba24141 | Python | 210,023 | 5,374 | import sys, torch, warnings
print("=== SegRef3D Local GPU Diagnostic ===")
print("Python:", sys.executable)
print("Torch:", torch.__version__, "CUDA", torch.version.cuda)
print("ARCH:", torch.cuda.get_arch_list())
print("CUDA available:", torch.cuda.is_available())
if torch.cuda.is_available():
try:
... |
a4bccb361599bf2420da460942da515f5a92cebb85f6e4d5ac8222df4cd61021 | Quarto | 253 | 17 |
```{python}
import polars as pl
from src.single_cell import SingleCell
import genome_kit as gk
```
```{python}
lr_bulk = SingleCell("results/long_read/pbid_filtered.h5ad")
genome = gk.Genome("SFARI")
```
```{python}
genome.transcripts["PB.4.1"]
``` |
033417db628b98ddd2c4b19ab8ff0337039f117858029d0b4f731f95d3124953 | Quarto | 560 | 32 | ```{python}
from src.ryp import r, to_py, to_r
```
```{r}
library(VariantAnnotation)
library(GenomicFeatures)
library(TxDb.Hsapiens.UCSC.hg19.knownGene)
library(BSgenome.Hsapiens.UCSC.hg19)
```
```{r}
txdb <- makeTxDbFromGFF("SFARI.gtf", format = "gtf")
```
```{r}
gr <- rtracklayer::import("SFARI.gtf")
```
```{r}
f... |
11839cf99beb3e8b7529d2f93718458309fa7d93282af13cbad6215fe5383f3a | Quarto | 3,748 | 123 | # Preparation
## Import packages
```{python}
from pathlib import Path
import polars as pl
from src.single_cell import SingleCell
from src.ryp import r, to_r
import json
import polars.selectors as cs
```
```{r}
library(pheatmap)
library(dplyr)
library(pheatmap)
library(RColorBrewer)
library(stringr)
```
## Prepare s... |
82a27e468bd3e6c316a8686baa68840095a790761863ebfc04fb302a53c65e64 | Quarto | 7,957 | 218 | # Import packages
```{python}
#| label: import-python
import polars as pl
from src.ryp import r, to_r
from src.single_cell import SingleCell
```
```{r}
#| label: import-r
library(ggpubr)
library(pheatmap)
library(RColorBrewer)
library(stringr)
library(scales)
library(dplyr)
```
# Import data
```{python}
#| label: i... |
ed56cf30d7ee5d48af74a71638854233fb9c3a5a263ded9f74c28d4f10cd5cfc | Quarto | 9,556 | 442 | ---
title: "analysis of sMRI data"
format: html
editor: source
echo: false
warning: false
---
```{r}
rm(list = ls(all = TRUE))
library(data.table)
library(tidyverse)
library(afex)
library(emmeans)
library(ggsignif)
emm_options(opt.digits = TRUE)
source("R/plotting.R")
source("R/rainclouds.R")
source("R/utils.R")
``... |
c71eb29501c6b6a4ad6bb55e6275d1828a64e9e89e0796ed1ba1b76cf4761eea | Quarto | 10,518 | 327 | ---
title: |
Code for Figure 4 + Supp Figs
format:
html:
toc: true
self-contained: true
highlight-style: github
code-line-numbers: true
code-fold: true
editor: source
editor_options:
chunk_output_type: console
---
## Define parameters
```{r display-params}
datadir <- "data/"
``... |
7f5227c848d1bdafbd63829ea67d47d033f732db368539dbc4cd87f3742640eb | Quarto | 10,675 | 459 | ---
title: "analysis of noddi data"
format: html
editor: visual
echo: false
warning: false
---
```{r}
rm(list = ls(all = TRUE))
library(data.table)
library(tidyverse)
library(extrafont)
library(afex)
library(emmeans)
source("R/plotting.R")
source("R/rainclouds.R")
source("R/preprocess.R")
source("R/utils.R")
noddi <... |
b7597b60b3c0d9b1e0e7e36b5a06ec84ccf8ac645366b0be6b85314c45ac2acb | Quarto | 12,768 | 372 | # Run Transdecoder on full_nt.fasta
Run transdecoder to generate ORFs
```{bash}
#| label: run-transdecoder
cd ~/tools
wget https://data.broadinstitute.org/Trinity/CTAT_SINGULARITY/MISC/TransDecoder/transdecoder.v5.7.1.simg
cd ${SCRATCH}/SFARI
module load apptainer
apptainer shell -B="/scratch/nxu/SFARI:/scratch/nxu... |
cc5abf25e0676e773b0e4b653abbe2093cc2bcf9de42209258a7604696c9bc58 | Quarto | 14,408 | 534 | ---
title: "KRK016 analysis"
format: html
editor: visual
echo: false
warning: false
---
```{r}
rm(list = ls(all = T))
# load libraries
library(data.table)
library(tidyverse)
library(afex)
library(emmeans)
# load internal libraries
source("R/plotting.R")
source("R/rainclouds.R")
source("R/utils.R")
dwi <- readRDS("..... |
7478833619efc284091f2576abcb8be2868fc80c437e23ff40128fd10aa82649 | Quarto | 16,749 | 655 | ---
title: "analysis of qMRI at 7T"
format: html
editor: visual
echo: false
---
```{r}
rm(list = ls(all = T))
library(data.table)
library(tidyverse)
library(extrafont)
library(afex)
library(emmeans)
library(ggsignif)
source("R/plotting.R")
source("R/rainclouds.R")
source("R/preprocess.R")
source("R/utils.R")
mpm0p5... |
d266f03e659d0adfb24e875e60b155dc6f23d2ff3b7ba27a9f3bc28aa01ee335 | Quarto | 18,199 | 512 | # Preparation
```{python}
#| label: import-python
import polars as pl
from src.single_cell import SingleCell
from src.utils import read_gtf
import numpy as np
import polars.selectors as cs
```
```{python}
#| label: read-gencode_V39
gencode_V39 = read_gtf("/Users/xunuo/Genomic_references/GENCODE/gencode.v39.annotatio... |
330e8ed3e9f20aefb9c4fbf242ec15e03ee7e8820e64277bdde2dcd9a0a09379 | Quarto | 18,889 | 719 | ---
title: "KRK016_mpm_analysis"
format: html
editor: source
echo: false
---
```{r}
rm(list = ls(all = T))
# import libraries
library(data.table)
library(tidyverse)
library(afex)
library(emmeans)
library(ggsignif)
emm_options(opt.digits = TRUE)
# import local functions
source("R/plotting.R")
source("R/rainclouds.R")... |
1f8225b68d72d8fe530f910f4c4653732d7325671df4611ead314d2d602e895a | Quarto | 19,758 | 706 | ---
title: "Code for Figure 3 + Supp Figs"
format:
html:
toc: true
self-contained: true
highlight-style: github
code-line-numbers: true
code-fold: true
editor: source
editor_options:
chunk_output_type: console
#params:
# datadir: 'test'
# nmethod_range:
# ground_truth:
# nns: 6... |
3d79e212ba330e743eb2c7af841d8a9a3b53017889f156bbf2f2e765a9d49ceb | Quarto | 26,095 | 1,000 | ---
title: "analysis of microstructure using PCA"
format: html
editor: source
---
# load libraries
```{r}
rm(list = ls(all = TRUE))
# import libraries
library(data.table)
library(tidyverse)
library(afex)
library(emmeans)
emm_options(opt.digits = TRUE)
library(tidytext)
library(ggseg)
library(ggsegGlasser)
library(gg... |
a0c22dc5d16db10ca0e3d99859ffccb2b4d536b21d6788cfbe2d2cfac60e8117 | R | 22 | 1 | print("Hello world!")
|
89385650ce68da2ef3bf89064f985cf88ccd83097d2a2b6b0aa407e4686bbbde | R | 51 | 3 | library(RUnit)
runTestFile("test/runitModules.R")
|
83b42e105a6a9d893eef996a4ae25b1442cc5212f70e6d6e638fd9cbf3ff0826 | R | 53 | 3 | loadDefaultDB <- function() {
loadDB("GMMs.v1.07")
} |
97d3bef73090632c301280556f77e9fe5a909ee31d766354970c80088323a750 | R | 66 | 4 | library(testthat)
library(sctransform)
test_check("sctransform")
|
b44e24e22b27d528689bc77fe436b96e15758f45e63ae9f68d8f7280c45ce790 | R | 71 | 3 | .First.lib <- function(lib, pkg) {
library.dynam("peer", pkg, lib)
}
|
330c37facdcc6fb9eda48c97269defae2fba05376befa2348f78aed9765a9d70 | R | 73 | 5 | library(withr)
library(testthat)
library(ggrepel)
test_check("ggrepel")
|
8febb6b9aebb23dc2ae317aa516556159f2b7c6da7fc87664c94deb4035a4895 | R | 80 | 4 | library(testthat)
library(DropSeq.eqtl.susie)
test_check("DropSeq.eqtl.susie")
|
c110528106ec0a48184de391d68fc89252cc0ddccbdaad8fa74751bc171a75ff | R | 84 | 4 | # get session info
sessionInfo()
# Close the sink to stop redirecting output
sink()
|
45deb21f74d0c76023e8b17137aba369d408fe0686c0971381949554fa2cef2a | R | 93 | 6 | #' @keywords internal
"_PACKAGE"
## usethis namespace: start
## usethis namespace: end
NULL
|
935bfed5d5e0709223d65a1d7de254f426afb50adfd0b7ee7dae60d5d24d0c5a | R | 124 | 6 | #! /usr/bin/env Rscript
#This script takes some data from a DIAGRAM GWAS file and \
"rs1937888"
"rs17155745"
"rs62626328"
|
4fc55271f95ad9ef4b48fc1511bad739a90eadbf17041d66558dc10c8b63f20a | R | 149 | 5 | listDB <- function() {
db.path <- system.file("extdata", package = "omixerRpm")
db.list = gsub(".txt", "", dir(db.path, pattern=".txt"))
db.list
} |
5a10cabc17fe7b49a2d40ea397592f07d3c3e9d5de60c30f6814e0a8f3a04ffc | R | 156 | 6 | # Function to calculate y_C given x_C
calculate_y_c <- function(xA, yA, xB, yB, Cx) {
m <- (yB - yA) / (xB - xA)
yC <- yA + m * (Cx - xA)
return(yC)
} |
43bbfb45baff64a27ebf5d53d7e8a2b8dfd333aea26ea5b68de65220e95d2305 | R | 161 | 3 | res <- results(dds_res, contrast=c$CONTRAST, cooksCutoff=$COOKS)
res_ordered <- res[order(res$padj),]
write.csv(as.data.frame(res_ordered),file="$OUTFILE_NAME")
|
42607563d96564a3af935f8ce8ccbe8094e41999352d55fe7cb0c54566b2e6a7 | R | 175 | 3 | cov_res <- results(dds_res, name="$COVARIATE", cooksCutoff=$COOKS)
cov_res_ordered <- cov_res[order(res$padj),]
write.csv(as.data.frame(cov_res_ordered),file="$OUTFILE_NAME")
|
7f260ab2e20aeb14badc7d11ba17a929a31f5f85538ad43a25ffe0b4a9d77713 | R | 175 | 5 | # Reset ggplot2 theme to default for consistent test snapshots
# This prevents ~/.Rprofile custom themes from affecting vdiffr tests
library(ggplot2)
theme_set(theme_gray())
|
29327a4a3aa8cdaeff1adbaf56bbd539199bde16afb479ad722675850cd4ef07 | R | 177 | 11 | #!/usr/bin/env Rscript
# build_site.R
#
# Build the pkgdown site and then make a few additional tweaks.
library(pkgdown)
build_site(lazy = TRUE)
# build_site(lazy = FALSE)
|
ba33753d36354ccf3045addfe0ef11ab8b16627724cab35ea6a04aaeaa4730e8 | R | 178 | 5 | # Open a connection to a log file
logfile <- file("$LOGFILE", open = "a")
# Redirect both output and messages to the file and console
sink(logfile, append = TRUE, split = TRUE)
|
8c2b7d51b244b3530cb068e563e122b1f36b69a8ddf555469483dac840c45183 | R | 194 | 5 |
bayes_fit <- eBayes(fit)
lrt_res <- topTable(bayes_fit, n=Inf, coef=$COEFS)
lrt_res_ordered <- lrt_res[order(lrt_res$adj.P.Val),]
write.csv(as.data.frame(lrt_res_ordered),file="$OUTFILE_NAME")
|
a7cade5b763119f0b53202e558acb5ecb10fe8b0ba6b664115e9cf1b9b2630d8 | R | 194 | 9 | ## usethis namespace: start
#' @useDynLib CellChat, .registration = TRUE
## usethis namespace: end
NULL
## usethis namespace: start
#' @importFrom Rcpp sourceCpp
## usethis namespace: end
NULL
|
0dfa40182f21c0c198b0caa741719d273f56887a3a4ff7251f97784c76584254 | R | 195 | 5 |
bayes_fit <- eBayes(fit)
cov_res <- topTable(bayes_fit, n=Inf, coef="$COEF")
cov_res_ordered <- cov_res[order(cov_res$adj.P.Val),]
write.csv(as.data.frame(cov_res_ordered),file="$OUTFILE_NAME")
|
5f72eaf18bbe3e115fc068b4df5f73d971129a160ba2439b8e8460f210f618d6 | R | 212 | 4 | dds_lrt <- DESeq(dds, test="LRT", reduced=$REDUCED)
lrt_res <- results(dds_lrt, cooksCutoff=$COOKS)
lrt_res_ordered <- lrt_res[order(lrt_res$padj),]
write.csv(as.data.frame(lrt_res_ordered),file="$OUTFILE_NAME")
|
0dea58c087c7a5c2e6857adbce7e05376502d4e0aa87cd37ea3475e4838634cb | R | 225 | 7 | # Generated by using Rcpp::compileAttributes() -> do not edit by hand
# Generator token: 10BE3573-1514-4C36-9D1C-5A225CD40393
ComputeSNN <- function(nn_ranked, prune) {
.Call(`_CellChat_ComputeSNN`, nn_ranked, prune)
}
|
db8e932bb222a70cb395b93e7358ddbb6cfcd4095c880b88cca4d3ba3596e781 | R | 242 | 8 | library(dplyr)
library(readr)
All_variants <- read_tsv("export/variant/All_variants_used_in_project.tsv")
All_variants %>%
filter(variant_type == "denovo") %>%
select(-is_conserved) %>%
write_tsv("export/variant/Table_S6.tsv") |
49402212633c82c89fb9b7a40810203e6f054512aafbe1e1e50d0aad5c02c764 | R | 264 | 12 | ####
library(EBImage)
tifs = paste0("/dcl01/lieber/ajaffe/Maddy/test", 1:6, ".tiff")
x = readImage(tifs[1], info = TRUE)
display(x, method = "raster", all = TRUE)
## make grey
xGrey = x
colorMode(xGrey) = Grayscale
display(xGrey, method = "raster", all = TRUE)
|
2841220f690d6ba17da586cad425904476a1a3250acb2f57d3ff477d9de22081 | R | 270 | 7 | require("DESeq2")
count_data <- read.table("$COUNT_MATRIX", header=TRUE, sep= ",", row.names = 1)
design_matrix <- read.table("$DESIGN_MATRIX", header=TRUE, sep= ",")
dds <- DESeqDataSetFromMatrix(count_data, design_matrix, $FORMULA)
$NORMFACTORS
dds_res <- DESeq(dds)
|
e783b644fe7e50448718a2ff194f71bebe44b8d78b718904042e10367ec53861 | R | 279 | 7 |
contrast <- makeContrasts($CONTRAST, levels = design)
contrast_fit <- contrasts.fit(fit, contrast)
contrast_bayes <- eBayes(contrast_fit)
res <- topTable(contrast_bayes, n=Inf)
res_ordered <- res[order(res$adj.P.Val),]
write.csv(as.data.frame(res_ordered),file="$OUTFILE_NAME")
|
aa83e29e14ef7213b9139206b1502e4e85d64af2072e6d3e780b166de6edebfc | R | 282 | 7 | library(IsoformSwitchAnalyzeR)
IsoseqsSwitchList <- readRDS("results/long_read/IsoseqsSwitchList.rds")
IsoseqsSwitchList <- analyzeAlternativeSplicing(IsoseqsSwitchList, onlySwitchingGenes=FALSE)
saveRDS(IsoseqsSwitchList, "results/long_read/full_analyzeAlternativeSplicing.rds") |
6480a4b4eccb8c8f3fc644c1a13f479d8949aad0090489a7e0283d3e4f017afa | R | 299 | 13 |
#' check JSD score
true <- read.csv('./DSTG_Result/true_output.csv',header=F)
predict <- read.csv('./DSTG_Result/predict_output.csv',header=F)
source('R_utils.R')
jsd.score <- JSD_performance(
spots_true_composition = as.matrix(true),
spots_predicted_composition = as.matrix(predict))
|
031d9636acfe620e1acaa15f747d952acede749604d801f64d98b7d5946aae58 | R | 309 | 7 | rmarkdown::render('batch_correction.Rmd')
rmarkdown::render('correcting.Rmd')
rmarkdown::render('differential_expression.Rmd')
rmarkdown::render('seurat.Rmd')
rmarkdown::render('variance_stabilizing_transformation.Rmd')
rmarkdown::render('theta_regularization.Rmd')
rmarkdown::render('method_comparison.Rmd')
|
fa09a39e4ae5110c3abe4d216fdfef8edf263271c87480a6bdd03cb383bdbbb5 | R | 335 | 7 | library("BSgenome.Hsapiens.UCSC.hg38")
library(IsoformSwitchAnalyzeR)
bsg <- BSgenome.Hsapiens.UCSC.hg38
IsoseqsSwitchList_part1 <- readRDS("proc/IsoseqsSwitchList_part1.rds")
IsoseqsSwitchList_part1_Analyzed <- analyzeORF(IsoseqsSwitchList_part1, bsg)
saveRDS(IsoseqsSwitchList_part1_Analyzed, "IsoseqsSwitchList_part1... |
dcd0f1e8e50c02992137914fb00c692d5a40d3f44c076db4b1c64e613004c7d4 | R | 354 | 11 | require("Rsubread")
fc <- featureCounts($KWARGS)
counts <- fc$counts
annotation <- fc$annotation
stats <- fc$stat
write.csv(as.data.frame(counts),file="$OUTPUT_DIR/featureCounts_counts.csv")
write.csv(as.data.frame(annotation),file="$OUTPUT_DIR/featureCounts_annotation.csv")
write.csv(as.data.frame(stats),file="$OUT... |
0f2ba2169a759be979514a9c16a3e7c427d2ebb6aee580e8ce25804a83723c06 | R | 368 | 12 | loadDB <- function(name) {
db.path <- system.file("extdata", package = "omixerRpm")
db <- ModuleDB(directory = db.path, modules = paste0(name, ".txt"), module.names.file=paste0(name, ".names"))
db@module.names <- read.table(
file.path(db@directory, db@module.names.file),
row.names=1,
sep="\t",
header=F, ... |
014c1f953297ba435630def10adfd908dd15a21e79321cac3b1a6a6e93bb8e2b | R | 385 | 8 | # Three-way ANOVA of swim gain (speed ratio) by genotype, stimulus speed, and stimulus frequency.
args <- commandArgs(trailingOnly = FALSE)
script_dir <- dirname(sub("^--file=", "", args[grep("^--file=", args)]))
source(file.path(script_dir, "anova_common.R"))
input_csv <- file.path(getwd(), "processed_data_speed_rat... |
0cfee86cc908a0ec7d248332877f57e296d190e559a67280482aba7f6a785cd9 | R | 388 | 8 | # Three-way ANOVA of trial duration (stimulus time) by genotype, stimulus speed, and stimulus frequency.
args <- commandArgs(trailingOnly = FALSE)
script_dir <- dirname(sub("^--file=", "", args[grep("^--file=", args)]))
source(file.path(script_dir, "anova_common.R"))
input_csv <- file.path(getwd(), "processed_data_st... |
9e5d4335e7c8057dedcd97d2d5286ec2c3797e0a803f358fe4abfeb9e049e1e9 | R | 392 | 11 | library(tidyverse)
###Must set working directory
setwd("D:/PCB-MB Astrocyte Imaging/P35 Timepoint/Analysis Files/Somatosensory/GFAP_C3+ Counts")
files <- list.files(pattern = "\\.csv$", full.names = TRUE)
files <- files[file.info(files)$size > 0]
compiled_dataset <- map_dfr(files, read_csv, show_col_types= F... |
29fb4a1eff498fa187c172e2c70157d7a77b01caea4b891dac01d57229ed2582 | R | 474 | 14 | ###
pd = read.delim("hafner_SraRunTable.txt",as.is=TRUE)
table(pd$Sample_Name, pd$tissue)
fqPath = "/dcs04/lieber/lcolladotor/with10x_LIBD001/HumanPilot/Analysis/hafner_vglut/FASTQ/"
man = data.frame(leftRead = paste0(fqPath, pd$Run, "_1.fastq.gz"),
leftMmd5 = 0, sampleID = pd$Run, stringsAsFactors=FALSE)
all(file.... |
d8c8cb13cda1b12be518ae6ffadc3402f8b55c40bbf985b16f6bd0e226064c9e | R | 482 | 11 | setwd("/groups/stark/vloubiere/projects/DeepATAC_shenzhi/")
devtools::load_all("/groups/stark/vloubiere/vlite/")
# Screenshot
pdf("pdf/0_paper/ATAC_screenshot_MEF2B_locus.pdf", height = 4)
vlite::bwScreenshot(bed = "chr8:70,145,000-70,175,000",
tracks = "/groups/stark/vloubiere/projects/DeepATAC_sh... |
51c3034e07806115d03b987cf1884c5a0397fc103eecd5428c3e951c008a0552 | R | 504 | 20 | setwd("/groups/stark/vloubiere/projects/DeepATAC_shenzhi/")
devtools::load_all("/groups/stark/vloubiere/vlite-dev/")
# Import peaks ----
peaks <- readRDS("Rdata/revision_ChIPseq_peaks.rds")
# Import peaks ----
vl_par(mfrow= c(2,2))
dat <- peaks[signalValue>5]
dat[, {
.c <- collapseBed(.SD)
.c[, name:= paste0("pea... |
8200d3d640a624b972c5c71355544ad5d8c40469618d086e559c3a877d675f09 | R | 511 | 21 |
## ----Libraries ------------------
library(SummarizedExperiment)
library(Matrix)
## load rse list
load("Human_DLPFC_Visium_processedData_rseList.rda")
## collect sample names and clusters
dfList = lapply(rseList, function(rse) {
d = as.data.frame(colData(rse)[,c("sample_name", "Cluster")])
d = d[!dup... |
fff3867779548e271afa25292949446868166b5074214d0b05ddfa8a27b034e1 | R | 519 | 12 | txdb <- makeTxDbFromGFF("/project/s/shreejoy/Genomic_references/GENCODE/gencode.v47.annotation.gtf")
tx2gene <- select(txdb, keys = transcripts(txdb)$tx_name, columns = "GENEID", keytype = "TXNAME")
files <- Sys.glob("nextflow_results/salmon_GENCODE47/*/quant.sf")
names(files) <- str_split(files, "/") %>%
map_ch... |
437d1b017736aea98e7a94bdbd92ad117754bb478b44f689d8b5c7f1c57fc2ca | R | 520 | 12 | require("limma")
design_matrix <- read.table("$DESIGN_MATRIX", header=TRUE, sep= ",")
$DEFINE_FACTORS
design <- model.matrix($FORMULA, design_matrix)
colnames(design)[1] <- "Intercept" # rename the intercept column from "(Intercept)" to "Intercept"
colnames(design) <- make.names(colnames(design)) # make coefficient na... |
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