sha256 stringlengths 64 64 | language stringclasses 28
values | size int32 1 491k | lines int32 1 23.2k | content stringlengths 1 200k |
|---|---|---|---|---|
5343513331bdbd4e58d6135aae441d9b777bddccfffa20d9d24e09f915f2063c | Jupyter | 13,220 | 319 | # %%
# import relevant packages
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import scanpy as sc
# set figure parameters
sc.settings.set_figure_params(dpi=100)
# %% [markdown]
# Advancements in genomics and machine learning have revolutionized both fields and for instance have culminated in m... |
a08031d4a9c2eacff258e60479cb2855e1b65b2ae5d1460f741dd98e1c5c2d1b | Jupyter | 13,239 | 338 | # %% [markdown]
# ### Imports
# %%
%load_ext autoreload
%autoreload 2
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
from Utilities_and_Loading import *
from Global_parameters import *
from Plotting import *
from Preprocessing import *
from General import *
from Manifolds_analysis import *
fr... |
34e2bdbbb65452a7aedd6729098ac8d11434307a4806007b692b0a4dadb808c9 | Jupyter | 13,250 | 374 | # %%
gene_familly = None
# %%
# Parameters
gene_familly = "Dof"
# %% [markdown]
# <div style="hwidth: 100%; background-color: #ddd; overflow:hidden; ">
# <div style="display: flex; justify-content: center; align-items: center; border-bottom: 10px solid #80c4e7; padding: 3px;">
# <h2 style="position: rela... |
7ad65933125559455b0518c1076ee8deb3500bbc89e8414b131daf58979ac81a | Jupyter | 13,250 | 374 | # %%
gene_familly = None
# %%
# Parameters
gene_familly = "EIL"
# %% [markdown]
# <div style="hwidth: 100%; background-color: #ddd; overflow:hidden; ">
# <div style="display: flex; justify-content: center; align-items: center; border-bottom: 10px solid #80c4e7; padding: 3px;">
# <h2 style="position: rela... |
864d8962c9edadbbb037cc6c1b4708df36f88a5e1bf074c0b85242eae4486e31 | Jupyter | 13,250 | 374 | # %%
gene_familly = None
# %%
# Parameters
gene_familly = "DBB"
# %% [markdown]
# <div style="hwidth: 100%; background-color: #ddd; overflow:hidden; ">
# <div style="display: flex; justify-content: center; align-items: center; border-bottom: 10px solid #80c4e7; padding: 3px;">
# <h2 style="position: rela... |
86a2360e6f42b22da647daf01fe27d9a29de9aaff6e5e2b954940907b0c70a10 | Jupyter | 13,250 | 374 | # %%
gene_familly = None
# %%
# Parameters
gene_familly = "CPP"
# %% [markdown]
# <div style="hwidth: 100%; background-color: #ddd; overflow:hidden; ">
# <div style="display: flex; justify-content: center; align-items: center; border-bottom: 10px solid #80c4e7; padding: 3px;">
# <h2 style="position: rela... |
b61e474b026c1ba40f4749683296fe730f3fba2f63b2c617a0509181cda25de6 | Jupyter | 13,250 | 374 | # %%
gene_familly = None
# %%
# Parameters
gene_familly = "ARF"
# %% [markdown]
# <div style="hwidth: 100%; background-color: #ddd; overflow:hidden; ">
# <div style="display: flex; justify-content: center; align-items: center; border-bottom: 10px solid #80c4e7; padding: 3px;">
# <h2 style="position: rela... |
101c2c65351bcf7b23c84b919b11b519a80b085e8fc4ee86788f5e6976c5cc05 | Jupyter | 13,251 | 374 | # %%
gene_familly = None
# %%
# Parameters
gene_familly = "BES1"
# %% [markdown]
# <div style="hwidth: 100%; background-color: #ddd; overflow:hidden; ">
# <div style="display: flex; justify-content: center; align-items: center; border-bottom: 10px solid #80c4e7; padding: 3px;">
# <h2 style="position: rel... |
8093204289efac3873b7d0efe8555833a5414e7d934eac513bbae799c7a106b7 | Jupyter | 13,251 | 374 | # %%
gene_familly = None
# %%
# Parameters
gene_familly = "GATA"
# %% [markdown]
# <div style="hwidth: 100%; background-color: #ddd; overflow:hidden; ">
# <div style="display: flex; justify-content: center; align-items: center; border-bottom: 10px solid #80c4e7; padding: 3px;">
# <h2 style="position: rel... |
08d2ca4bbfe7cf84c04246aa1e162cdb005601336636c02c1a126495dc69f9ca | Jupyter | 13,252 | 374 | # %%
gene_familly = None
# %%
# Parameters
gene_familly = "CAMTA"
# %% [markdown]
# <div style="hwidth: 100%; background-color: #ddd; overflow:hidden; ">
# <div style="display: flex; justify-content: center; align-items: center; border-bottom: 10px solid #80c4e7; padding: 3px;">
# <h2 style="position: re... |
467c3070d73bf26e4ff0c9a1e85763c8c3d93f53a19dbafaae9888fa04f56e4f | Jupyter | 13,252 | 374 | # %%
gene_familly = None
# %%
# Parameters
gene_familly = "ARR-B"
# %% [markdown]
# <div style="hwidth: 100%; background-color: #ddd; overflow:hidden; ">
# <div style="display: flex; justify-content: center; align-items: center; border-bottom: 10px solid #80c4e7; padding: 3px;">
# <h2 style="position: re... |
c3b14522f45582909e6029f033e440f66ebd7d05d14cd577fb442ce1a611e1cb | Jupyter | 13,253 | 374 | # %%
gene_familly = None
# %%
# Parameters
gene_familly = "E2F/DP"
# %% [markdown]
# <div style="hwidth: 100%; background-color: #ddd; overflow:hidden; ">
# <div style="display: flex; justify-content: center; align-items: center; border-bottom: 10px solid #80c4e7; padding: 3px;">
# <h2 style="position: r... |
4e7cd7dbc5b0a15789e98c3c362ace643628427274a9cfa2611cf3ef1af846ae | Jupyter | 13,254 | 374 | # %%
gene_familly = None
# %%
# Parameters
gene_familly = "CO-like"
# %% [markdown]
# <div style="hwidth: 100%; background-color: #ddd; overflow:hidden; ">
# <div style="display: flex; justify-content: center; align-items: center; border-bottom: 10px solid #80c4e7; padding: 3px;">
# <h2 style="position: ... |
b01f006b9a47e8b05413ee639af94963558ad2138f6f5a37f977eb1160cc0f53 | Jupyter | 13,254 | 374 | # %%
gene_familly = None
# %%
# Parameters
gene_familly = "BBR-BPC"
# %% [markdown]
# <div style="hwidth: 100%; background-color: #ddd; overflow:hidden; ">
# <div style="display: flex; justify-content: center; align-items: center; border-bottom: 10px solid #80c4e7; padding: 3px;">
# <h2 style="position: ... |
96ac0efe33023222e94b24374e8b8ebed86c190298807ebfbd6d839c17269ff9 | Jupyter | 13,273 | 377 | # %%
gene_familly = None
# %%
# Parameters
gene_familly = "B3"
# %% [markdown]
# <div style="hwidth: 100%; background-color: #ddd; overflow:hidden; ">
# <div style="display: flex; justify-content: center; align-items: center; border-bottom: 10px solid #80c4e7; padding: 3px;">
# <h2 style="position: relat... |
5e3ced8dde10be9a4844ae86ab4628f1e2a5bbdab9b6c812b6fdf2ae26942062 | Jupyter | 13,274 | 377 | # %%
gene_familly = None
# %%
# Parameters
gene_familly = "C3H"
# %% [markdown]
# <div style="hwidth: 100%; background-color: #ddd; overflow:hidden; ">
# <div style="display: flex; justify-content: center; align-items: center; border-bottom: 10px solid #80c4e7; padding: 3px;">
# <h2 style="position: rela... |
60927f9bd73da848d8d0bc528e23a492ab1f5263efb1d900e6ae19f056f49e7f | Jupyter | 13,274 | 377 | # %%
gene_familly = None
# %%
# Parameters
gene_familly = "ERF"
# %% [markdown]
# <div style="hwidth: 100%; background-color: #ddd; overflow:hidden; ">
# <div style="display: flex; justify-content: center; align-items: center; border-bottom: 10px solid #80c4e7; padding: 3px;">
# <h2 style="position: rela... |
0cc42bf798ad8c0d64863cafd9cc9c7cb7db30f82198b79e0929fd08abb889d5 | Jupyter | 13,275 | 377 | # %%
gene_familly = None
# %%
# Parameters
gene_familly = "FAR1"
# %% [markdown]
# <div style="hwidth: 100%; background-color: #ddd; overflow:hidden; ">
# <div style="display: flex; justify-content: center; align-items: center; border-bottom: 10px solid #80c4e7; padding: 3px;">
# <h2 style="position: rel... |
39019fdae2ec893507f250f524ba26fd4f4c75f64c0dd4dffa062570d20b4fcb | Jupyter | 13,275 | 377 | # %%
gene_familly = None
# %%
# Parameters
gene_familly = "GRAS"
# %% [markdown]
# <div style="hwidth: 100%; background-color: #ddd; overflow:hidden; ">
# <div style="display: flex; justify-content: center; align-items: center; border-bottom: 10px solid #80c4e7; padding: 3px;">
# <h2 style="position: rel... |
a63ae663caee4737ee054dac924518df3990033d6273a394daa43027d09cc44c | Jupyter | 13,275 | 377 | # %%
gene_familly = None
# %%
# Parameters
gene_familly = "C2H2"
# %% [markdown]
# <div style="hwidth: 100%; background-color: #ddd; overflow:hidden; ">
# <div style="display: flex; justify-content: center; align-items: center; border-bottom: 10px solid #80c4e7; padding: 3px;">
# <h2 style="position: rel... |
a2db1d6bab3434ae7c8ae80c3ad02c9671c6b4b62598e19ca96477df88ea802b | Jupyter | 13,278 | 377 | # %%
gene_familly = None
# %%
# Parameters
gene_familly = "G2-like"
# %% [markdown]
# <div style="hwidth: 100%; background-color: #ddd; overflow:hidden; ">
# <div style="display: flex; justify-content: center; align-items: center; border-bottom: 10px solid #80c4e7; padding: 3px;">
# <h2 style="position: ... |
cf07e2e55d754f41e948a0e81fde26a62812287a717f5aa7765692f6cd7e9b7a | Jupyter | 13,302 | 463 | # %% [markdown]
# # Federated normative modeling (model extension)
#
# In multi-site neuroimaging studies, data often cannot leave the hospital or institution where it was collected, due to privacy regulations such as GDPR. **Federated learning (FL)** addresses this constraint: each site trains a model locally and sha... |
3b24d001e3376c2ccb20a5c942e07e4856170d57995041b6e408c401ae9117d6 | Jupyter | 13,304 | 416 | # %%
import os
import sys
import yaml
import numpy as np
import pandas as pd
from matplotlib import pyplot as plt
from matplotlib.ticker import FormatStrFormatter
sys.path.append('../neurokin/')
from neurokin.utils.neural import processing, importing
from neurokin.utils.experiments import neural_states_helper, neural_c... |
cca982462c9668e13a81ee8a6884f3f205b0f15a5584d78f3bdabd70326a745e | Jupyter | 13,327 | 383 | # %%
gene_familly = None
# %%
# Parameters
gene_familly = "AP2"
# %% [markdown]
# <div style="hwidth: 100%; background-color: #ddd; overflow:hidden; ">
# <div style="display: flex; justify-content: center; align-items: center; border-bottom: 10px solid #80c4e7; padding: 3px;">
# <h2 style="position: rela... |
511905309bf83a8bdcd14b7686395c041eb1e58682512c73055f0b2083cf7abf | Jupyter | 13,363 | 406 | # %% [markdown]
# # Global imports
# %%
import numpy as np
import pandas as pd
import scanpy as sc
import matplotlib.pyplot as plt
from matplotlib import cm
import seaborn as sns
from joblib import Parallel, delayed
from numpy import random
import anndata as ann
import joblib
import gseapy as gp
import gc
from scip... |
cbd49a75e43e3058c500098e721912b46c715517eeabea3efe5bd7f36cfaabf5 | Jupyter | 13,388 | 334 | # %% [markdown]
# In this notebook we're going to improve region coverage which is already built in the metadata.
# Cerebellar lobes will be split in teir leaf regions (Purkinje, Molecular, Granular layers)
# OLF glomerular layer will be created from unassigned regions
# %%
import os
import pickle
import pandas as pd... |
3327e87685ae02a2ab8c843e0f4ca981e89949cd7500c4b4703ebcce7f0e96f2 | Jupyter | 13,400 | 427 | # %% [markdown]
# ## Notebook for figure 1
# 1. The figure of model architecture (.pdf)
# 2. Long native trials/*.csv
# 3. Learning comparison.csv
# 4. mapping = json.load(open("../../data/imagenet_label_to_wordnet_synset.json"))
# 5. behavior_mapping = json.load(open("../../data/wordnet_translation.json"))
# 6. recogn... |
3a058274ec44456844eb8cb86fc73b6dd588648e9a3e65ce4f540e954e393c66 | Jupyter | 13,414 | 421 | # %% [markdown]
# <div style="hwidth: 100%; background-color: #ddd; overflow:hidden; ">
# <div style="display: flex; justify-content: center; align-items: center; border-bottom: 10px solid #80c4e7; padding: 3px;">
# <h2 style="position: relative; top: 3px; left: 8px;">S2 Project: DNA Classification - (part2... |
a78700c76f64afab0bacecc8aea3bffd9fa97de9bceb635c9d133c94cb1ad7ed | Jupyter | 13,446 | 425 | # %%
import math
from collections import OrderedDict
import torch
from torch import nn
from torchvision import transforms
from torchmetrics.functional import pearson_corrcoef, spearman_corrcoef
import torch.utils.model_zoo
import os
from PIL import Image
from torch.utils.data import Dataset, DataLoader
import pandas as... |
4ed8f806a120183440f0a7919988180c5ad5f15d42b4794cb891879487f1bf83 | Jupyter | 13,510 | 422 | # %%
import numpy as np
import matplotlib.pyplot as plt
import os
os.environ["OPENCV_IO_ENABLE_OPENEXR"]="1"
import cv2
from os.path import join
import imageio.v2 as imageio
# %%
path2outs = "/home/alban/Documents/blender_testset/testset/"
#directory = 'images_eevee_patterns'
directory = 'images_cubespattern_1282p9'... |
c20d776a6fd12b76b5900bae30fe9956925487a3a4edfb8b58a2cb04b5cc0fa5 | Jupyter | 13,515 | 427 | # %%
import math
from collections import OrderedDict
import torch
from torch import nn
from torchvision import transforms
from torchmetrics.functional import pearson_corrcoef, spearman_corrcoef
import torch.utils.model_zoo
import os
from PIL import Image
from torch.utils.data import Dataset, DataLoader
import pandas as... |
876d2ff800a2b68d5a16f22fd1dddb215d38ab427dd7284d01430f3c24032aed | Jupyter | 13,517 | 441 | # %%
import pandas as pd
# %%
import pybedtools
pybedtools.set_bedtools_path('/tscc/nfs/home/ekofman/miniconda3/envs/marine_environment/bin/')
# %%
b = pybedtools.BedTool()
# %% [markdown]
# # Integration tests
# %% [markdown]
# ## Todo:
# * Make a test for single-end approach, using this bam: /tscc/projects/ps-ye... |
433ab8a640256e1de1ff38205ea92a3a35daa49fa6879ec4728c16f5f61cd2ec | Jupyter | 13,552 | 374 | # %%
"""
Gabor orientation 2AFC (clockwise vs counterclockwise) -- PyTorch implementation.
Requirements:
- Python 3.8+
- torch
- torchvision (optional)
- numpy
- matplotlib
Run:
python gabor_2afc_mlp.py
"""
import math
import numpy as np
import random
import torch
from torch import nn
from torch.utils.d... |
0b29ae83871c481d7cb07f3be28d691e92977c5fe0f0378b063c8f9284ae7c4c | Jupyter | 13,574 | 245 | # %% [markdown]
# ```{eval-rst}
# .. role:: nge-green
# ```
# {nge-green}`Training a SKOOTS Model`
# ===================================
# %% [markdown]
# This guide is for people unfamiliar with YACS and the command line. The SKOOTS library provides necessary pre-written evaluation functions which make it easy to tra... |
0166ca087e51ce28536c4e9c7312a1c23d7fbe1029fca83298a3e18dd4310904 | Jupyter | 13,584 | 348 | # %% [markdown]
# ### Imports
# %%
%load_ext autoreload
%autoreload 2
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
from Utilities_and_Loading import *
from Global_parameters import *
from Plotting import *
from Preprocessing import *
from General import *
from Manifolds_analysis import *
fr... |
7b8940a3335ba87370c557fb7feb52d6f3b2f12129ea023f5e72472e22c1ddc7 | Jupyter | 13,599 | 345 | # %%
import os
import pyreadr
import json
import pickle
from pathlib import Path
import pandas as pd
import numpy as np
from utils.setup_paths_and_configs import setup_paths_and_config
import ast
# %%
dataset = 'AIBL'
sites = True
samplings = [ 'full', 'representative','left', 'right', '1F4M', '4F1M', '1F10M', '10F... |
cd0b051ecf902785737d0af64ad84dcf27c6b68f887f0eb85e129392f5ee366f | Jupyter | 13,646 | 468 | # %%
library(data.table)
library(dplyr)
library(Matrix)
library(BuenColors)
library(stringr)
library(cowplot)
library(ggpubr)
library(umap)
# %%
# %% [markdown]
# #### define functions
# %%
read_FM <- function(filename){
df_FM = data.frame(readRDS(filename),stringsAsFactors=FALSE,check.names=FALSE)
rownames... |
4d8268d4af4122fdd72fbb9c7a4b74052b4036251b6202917210c7183dde14b8 | Jupyter | 13,699 | 424 | # %% [markdown]
# <div style="hwidth: 100%; background-color: #ddd; overflow:hidden; ">
# <div style="display: flex; justify-content: center; align-items: center; border-bottom: 10px solid #80c4e7; padding: 3px;">
# <h2 style="position: relative; top: 3px; left: 8px;">S2 Project: DNA Classification - (part2... |
24f73944000f74416373226850db48cf9aa63deed2e475ad0b19d68c755a9559 | Jupyter | 13,701 | 510 | # %%
import scipy as sp
import statsmodels
import pandas as pd
import matplotlib.pyplot as plt
import numpy as np
import seaborn as sns
import statsmodels.api as sm
import statsmodels.formula.api as smf
from statsmodels.formula.api import ols
import statsmodels.stats.multitest as multi
import warnings
warnings.filterwa... |
8fa5d51dd60b126636adbb316f6ef172a22565efa5fc8f09c110092b1f9d9571 | Jupyter | 13,748 | 292 | # %% [markdown]
# # Create training data for CellDiscoveryNet
#
# This notebook creates training data for the CellDiscoveryNet model. It assumes you've already run the `make_AutoCellLabeler_input` notebook to create 4D H5 files. Notably, it is important that `θh_pos_is_ventral` is set correctly in that notebook to ali... |
56f1cb4558ec3f0cea7258c5d4a0fcdb9feeda9358c35dcaa878cafc069100a1 | Jupyter | 13,752 | 376 | # %% [markdown]
# ### Installation
# %% [markdown]
# `devtools::install_github("zji90/SCRATdatahg19")`
# `source("https://raw.githubusercontent.com/zji90/SCRATdata/master/installcode.R")`
# %%
# %% [markdown]
# ### Import packages
# %%
library(devtools)
library(GenomicAlignments)
library(Rsamtools)
library(SC... |
a25828f4e65192e6c5a5318bc254698938f4fb623360bb76b78c5c8102bcfc94 | Jupyter | 13,760 | 376 | # %% [markdown]
# ### Installation
# %% [markdown]
# `devtools::install_github("zji90/SCRATdatahg19")`
# `source("https://raw.githubusercontent.com/zji90/SCRATdata/master/installcode.R")`
# %%
# %% [markdown]
# ### Import packages
# %%
library(devtools)
library(GenomicAlignments)
library(Rsamtools)
library(SC... |
9f8f42ebb1bada7b9e7b04c33099d7911c17b1796cc1cad03c1010d645fe6fef | Jupyter | 13,777 | 472 | # %%
library(data.table)
library(dplyr)
library(Matrix)
library(BuenColors)
library(stringr)
library(cowplot)
library(ggpubr)
library(umap)
library(RColorBrewer)
# %%
# %% [markdown]
# #### define functions
# %%
read_FM <- function(filename){
df_FM = data.frame(readRDS(filename),stringsAsFactors=FALSE,check.nam... |
1c19471faa9efcd2980412be7d75a7c701b36bc03a19803c133f76b03777616e | Jupyter | 13,801 | 473 | # %%
library(data.table)
library(dplyr)
library(Matrix)
library(BuenColors)
library(stringr)
library(cowplot)
library(ggpubr)
library(umap)
library(RColorBrewer)
# %%
# %% [markdown]
# #### define functions
# %%
read_FM <- function(filename){
df_FM = data.frame(readRDS(filename),stringsAsFactors=FALSE,check.nam... |
aabc95bda6d36b5d9c15b560c300b16bf4a2e905d475b893717601ff883767ea | Jupyter | 13,806 | 530 | # %%
import pandas as pd
import matplotlib.pyplot as plt
import numpy as np
import seaborn as sns
from statsmodels.formula.api import ols
import statsmodels.api as sm
import statsmodels.stats.multitest as multi
import statsmodels.formula.api as smf
import statsmodels.api as sm
from scipy import stats
plt.rcParams["pdf.... |
e098a8f2155f57309fb7f45ea384ed84d4d5cb047c31cb5285eb0328a4d23e85 | Jupyter | 13,851 | 375 | # %% [markdown]
# ### Installation
# %% [markdown]
# `devtools::install_github("zji90/SCRATdatahg19")`
# `source("https://raw.githubusercontent.com/zji90/SCRATdata/master/installcode.R")`
# %% [markdown]
# ### Import packages
# %%
library(devtools)
library(GenomicAlignments)
library(Rsamtools)
library(SCRATdata... |
1c08540540aacaf75661249aae4371657f9bf72e914edd7822a830dc3dfab36b | Jupyter | 13,877 | 411 | # %% [markdown]
# Parse lentiMPRA data into format for training distilled MPRAnn models with mean+aleatoric+epistemic prediction.
#
# Add the standard deviation of the ensemble predictions for the activity output (epistemic uncertainty) to the .h5 file containing the mean and aleatoric uncertainty (`/home/jessica/ense... |
f76e5a7159499493a9c2175b1b6d25bc57a89774cbb38b6f8c9eaf3e6dca491e | Jupyter | 13,879 | 499 | # %% [markdown]
# ## Figure 4BD
# %%
import numpy as np
import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
import mne
from statannotations.Annotator import Annotator
import os
import sys
import scipy.stats as stats
import matplotlib as mpl
plt.style.use("article.mplstyle")
sys.path.append(... |
1ef1a7d8344318f8d8f99ee73ba5ddf3d4c510c201c03425c07b95136d913039 | Jupyter | 13,896 | 652 | # %% [markdown]
# # B-Cell Receptor (BCR) Visualization for Anti-GAD65 Immune Cell Profiling
#
# This notebook performs comprehensive BCR repertoire visualization and clonotype analysis using single-cell RNA-seq data from GAD65 autoimmune disease patients and controls.
#
# ## Overview
# - **Input**: AnnData objects w... |
73f17d4a3de2529cf0cb94e4f4f8ee673c38745d0bac611ba8566449b437fd99 | Jupyter | 13,946 | 371 | # %% [markdown]
# ### Installation
# %% [markdown]
# `devtools::install_github("zji90/SCRATdatamm9")`
# `source("https://raw.githubusercontent.com/zji90/SCRATdata/master/installcode.R")`
# %% [markdown]
# ### Import packages
# %%
library(devtools)
library(GenomicAlignments)
library(Rsamtools)
library(SCRATdatah... |
71a796c12205b4c6b7b04a140d7079a45a8768aecc2b749a0110056193dfe3d0 | Jupyter | 14,007 | 293 | # %% [markdown]
# ```
# This file is part of connectome-manipulator.
#
# SPDX-License-Identifier: Apache-2.0
# Copyright (c) 2024 Blue Brain Project/EPFL
# ```
# %% [markdown]
# # Connectome wiring example
#
# Working example to wire an empty connectome from scratch.
# %%
# %% [markdown]
# ## Download example cir... |
14f9dc2440bb54a94ccd75d60ab3ccd5a949518d2c2997800d5c6e2c63e75c65 | Jupyter | 14,017 | 478 | # %% [markdown]
# ### Code for Figure 3 plots
# %%
library(Seurat)
library(dplyr)
library(tidyr)
library(ggplot2)
library(edgeR)
library(tximport)
library(data.table)
library(RColorBrewer)
library(ggpubr)
library(enrichR)
library(GO.db)
library(ComplexHeatmap)
library(gprofiler2)
library(circlize)
library(fgsea)
libra... |
34461b12657ae28d272c977c5aa55f9cffdbc0c71ee40bf74d346356c5fe721c | Jupyter | 14,027 | 365 | # %% [markdown]
# # Comparison of BioPathNet and TxGNN
# %% [markdown]
# ## Load data, libraries, some fun
# %%
library(data.table)
library(patchwork)
library(ggplot2)
library(patchwork)
library(purrr)
library(ggthemes)
library(dplyr)
library(grid)
# %%
plot_metric <- function(x_metric, y_disease_area="cell_prolifer... |
e01ff9d82d2a96c27885578ddd8a9f9e6ac88b4d2af7367ecc95988df50f7eca | Jupyter | 14,065 | 377 | # %% [markdown]
# ### Installation
# %% [markdown]
# `devtools::install_github("zji90/SCRATdatahg19")`
# `source("https://raw.githubusercontent.com/zji90/SCRATdata/master/installcode.R")`
# %% [markdown]
# ### Import packages
# %%
library(devtools)
library(GenomicAlignments)
library(Rsamtools)
library(SCRATdata... |
2bf2bfc2ccf1b5a8aaa81f83335b4c9484e39a9e2d75ddc4ae62426d98a945e8 | Jupyter | 14,073 | 326 | # %%
# import relevant packages
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import scanpy as sc
import scipy.stats as ss
import seaborn as sns
from tqdm import tqdm
import torch
import torch_geometric
from torch_geometric.loader import DataLoader
# set figure parameters
sc.settings.set_figu... |
72c4312928c6969a11d08dd7c600c66d32607e7890337f37c7606b5a9d2fb8c0 | Jupyter | 14,077 | 369 | # %% [markdown]
# # Peak Probability Presentation (P3)
#
# To generate **P3**, the first step is processing the spectra and converting the data from the **time domain (FID)** into the **frequency domain (spectrum)**. This transformation can be performed using different software packages such as **TopSpin** or **qMDD**... |
9bf08e23c12baf99b3fd81faac071947988c70e7b757e0be6276d18d7d9e8042 | Jupyter | 14,089 | 405 | # %%
import joblib
import numpy as np
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
from nilearn.connectome import vec_to_sym_matrix, sym_matrix_to_vec
import sys
sys.path.append('../')
from PAINTeR import plot # in-house lib used for the RPN-signature
# %%
# load bochum data
df_bochum = p... |
420d7ccd6067523b7875d78d8c5946b533fd7da16daf72d98617187fc72f72d2 | Jupyter | 14,111 | 392 | # %% [markdown]
# # LookLocker T1mapping
#
# T1-maps may be generated voxelwise through curve-fitting of inversion recovery data acquired through Look-Locker sequences. The curve-fitting procedure works well for tissue that have similar properties as the targeted tissue. However, an MRI image typically includes surrou... |
a6bffa2c2ea009bd3055322a1f708a2a41919b76c064f7a219ac41134ea1a18f | Jupyter | 14,135 | 386 | # %% [markdown]
# # Heuristic T1 grooming classification
#
# ### Brandon Pratt
#
# Refined grooming classifier: 1st pass - use the random forest classifier. 2nd pass - heuristic classifier that uses contact distances.
#
# Dyrad repository: DOI: 10.5061/dryad.fxpnvx153
# %%
# python libraries
import pathlib
import ... |
083b43cb8f5fbee6c7779130e78481ea55ae68f05f79d98b72fd0d0e9afc74d5 | Jupyter | 14,138 | 452 | # %% [markdown]
# ### Clean code and plot for Figure 4
# %%
library(ggplot2)
library(dplyr)
library(tidyr)
library(data.table)
library(rrvgo)
library(org.Mm.eg.db)
library(RColorBrewer)
library(gprofiler2)
library(ComplexHeatmap)
library(circlize)
library(dplyr)
library(tidyr)
library(RColorBrewer)
# %% [markdown]
#... |
376b42d635fa7bdc0d2f524b00efb0f52a84cbe19cb6d1ae09621a6c88aa9d22 | Jupyter | 14,150 | 335 | # %% [markdown]
# ```
# This file is part of connectome-manipulator.
#
# SPDX-License-Identifier: Apache-2.0
# Copyright (c) 2024 Blue Brain Project/EPFL
# ```
# %% [markdown]
# # Adjacency matrix rewiring example
#
# Working example to rewire the connectivity among EXC neurons based on an arbitrary adjacency matrix... |
55648a1334d08624f4b467d13792758de70cb6e6cc94069943d40117e43f02a5 | Jupyter | 14,161 | 422 | # %% [markdown]
# Parse lentiMPRA data into format for training distilled models with mean+aleatoric+epistemic prediction.
#
# Add the standard deviation of the ensemble predictions (epistemic uncertainty) for the activity output to the .h5 file containing the mean and aleatoric uncertainty (`/home/jessica/ensemble_di... |
27f7788e01973f5043b04c58bef7a72c2a4922e8ed3161bb5ef2da26da955f4f | Jupyter | 14,183 | 463 | # %% [markdown]
# # Essential commands
#
# The following describes essential commands for interacting with the AlphaGenome
# API. It is broken into two sections: data and methods.
# %% [markdown]
# ``` {tip}
# Open this tutorial in Google Colab for interactive viewing.
# ```
# %%
# @title Install AlphaGenome
# @mar... |
843f07cd1588f6ada083dcb022dd7a1033e467705ca41bd374747eb3797cf0f0 | Jupyter | 14,207 | 329 | # %%
from infopath.model_loader import load_model_and_optimizer
from infopath.config import load_training_opt
import torch
from infopath.utils.functions import run_with_perturbation
import os
from infopath.utils.plot_utils import plot_with_size, strip_right_top_axis
import numpy as np
import random
import matplotlib.... |
10af843512d247f0afee13d933ad836b59b00a9172a2d834f695bc170967d4ca | Jupyter | 14,211 | 363 | # %%
import os
import numpy as np
import pandas as pd
from sklearn.metrics.cluster import normalized_mutual_info_score
from sklearn.metrics.cluster import adjusted_rand_score
# %%
import seaborn as sns
# import scanpy as sc
import matplotlib.pyplot as plt
import matplotlib as mpl
import matplotlib.font_manager # to ... |
c182ec60afce368adcd0081a54d3f48e34a694fc77a9eb3ee0a7f26da2f4fe0b | Jupyter | 14,244 | 437 | # %%
import re
import subprocess
from pathlib import Path
import dolfin as df
import matplotlib as mpl
import matplotlib.cm as mplcmaps
import matplotlib.colors as mplcolors
import matplotlib.pyplot as plt
import nibabel
import numpy as np
import pyvista as pv
import pandas as pd
import panta_rhei as pr
import seabor... |
6641f30e4ccaababa07b3a98cabcd8a1c4dac9559afd2851ad17977cbf2ccdf0 | Jupyter | 14,246 | 369 | # %% [markdown]
# # T1-Weighted Images
# This notebook describes how to create normalized $T_1$-weighted images.
# T1-weighted images offer an opportunity to study signal intensity increases due to contrast enhancement, without having to convert the signals to concentrations.
# However, as differences in for example ... |
a6b5fbc5c87147e518c222e6ee5180e760335a4d5b5444ad0f01b339b9b38ee5 | Jupyter | 14,290 | 437 | # %%
import brainpy as bp
import brainpy.math as bm
import numpy as np
import matplotlib.pyplot as plt
import matplotlib.gridspec as gridspec
import scipy
from scipy.signal import hilbert
import sys
sys.path.append('..')
from cann import CANN1D
#set backend to cpu
bm.set_platform('cpu')
# %% [markdown]
# ### define... |
32c143126545086a1599e59d2b979a5e5b84beed2d229562051f17e2a9135398 | Jupyter | 14,345 | 266 | # %% [markdown]
# # Comparing Effect Estimators on Fast-food Employment Data
#
# We look at the famous Card and Krueger study[1] of the impact of a minimum wage change on employment levels in fast food restaurants near the border of New Jersey and Pennsylvania. The treatment in this case was whether the restaurant was... |
09d9d0d6e589acb75c6c64d13e441be946e8c0d6c27423264229be60e40a0e8a | Jupyter | 14,347 | 666 | # %% [markdown]
# # T-Cell Receptor (TCR) Visualization for Anti-GAD65 Immune Cell Profiling
#
# This notebook performs comprehensive TCR repertoire visualization and clonotype analysis using single-cell RNA-seq data from GAD65 autoimmune disease patients and controls.
#
# ## Overview
# - **Input**: AnnData objects w... |
0e488410a71b7190c903e33194f941902e75d5755a4cdd7d57cf243be22edc15 | Jupyter | 14,355 | 411 | # %% [markdown]
# ### Clean code and plot for Figure 5
# %%
library(dplyr)
library(tidyr)
library(data.table)
library(ComplexHeatmap)
library(circlize)
library(tibble)
library(ggplot2)
library(ggpubr)
library(Seurat)
library(scales)
# %% [markdown]
# Source functions
# %%
source("Neuron_CROP-seq_functions.R")
# %%... |
0ffee0e4c504330a57ca2d2fe5e6700754de7380177197ffdb8f98af2e06c932 | Jupyter | 14,367 | 485 | # %%
%matplotlib notebook
import os
import matplotlib.pyplot as plt
import seaborn
import numpy as np
from scipy.signal import detrend
import obspy as obs
from obspy.signal.invsim import corn_freq_2_paz
import h5py
import pandas as pd
# %%
file_DASH = os.path.join("data", "DASH", "all.h5")
data_dir_nodal = os.pat... |
4c7fcb510b943c6b307e9e716535556580d85c8c113fb7a87f4374573c322204 | Jupyter | 14,376 | 396 | # %%
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
import numpy as np
from sklearn.linear_model import LinearRegression
import statsmodels.api as sm
from statsmodels.stats.multitest import fdrcorrection as fdr
# %%
df_PD = pd.read_csv('~/MinaRyten/Melissa/MRIxST/stats/04b-4layers_PDpredicti... |
39579eae0b4259ab73563f27bc7592f9c4845397b0080392cca1dff2ac876862 | Jupyter | 14,379 | 344 | # %% [markdown]
# # Supplementary Tables: Model Hyperparameters
# %%
import matplotlib.pyplot as plt
import re
import EIANN as eiann
import EIANN.utils as ut
from EIANN.generate_figures import *
eiann.update_plot_defaults()
# %%
def convert_projection_name(projection_name):
"""
Map full hyperparam key to abbr... |
081e8ed3d0d78bacacbd5199c036ab4228f248eeefab56fe0b967bcb3e2890da | Jupyter | 14,417 | 492 | # %%
import warnings
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import pingouin as pg
import seaborn as sns
from scipy import stats
warnings.filterwarnings("ignore")
# %%
# Loading COHA time series data
ts_df = pd.read_csv("./data/SWOW_prediction/eval/time_series/ts_df.csv")
ts_df = (
... |
c6c508bc27f466e7fb296a2733a9ef2252d222a13b3584e2ae86466fe2dbad0d | Jupyter | 14,443 | 386 | # %%
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
from sklearn.preprocessing import StandardScaler
from sklearn.manifold import TSNE
from sklearn.cluster import KMeans
from scipy.optimize import curve_fit
# ----------------------------
# Configuration
nerves = ["median", "ulnar", "tibial", "p... |
fa3f9f27ba837118eab152b8873082c2cac36df8b4392ca4f7d8b1564acf0989 | Jupyter | 14,447 | 443 | # %%
import numpy as np
import pandas as pd
import os
import plotly.express as px
import plotly.graph_objects as go
import matplotlib.pyplot as plt
import seaborn as sns
import networkx as nx
from scipy.spatial.distance import pdist
import scipy as sp
# %%
from module.convect.entropy_prep import get_ud_all_ent
from mo... |
c8de7685011bf07e5cd2998d6f9cc05fa36d61c269702ff4302888a1acc121fb | Jupyter | 14,494 | 654 | # %%
#python resample.py $IMAGE_TO_RESAMPLE $REFERENCE_IMAGE
# %%
import nibabel as nib
from nilearn.input_data import NiftiMasker , MultiNiftiMasker
# %%
import numpy as np
import os
import scipy.io
import nibabel as nib
from nilearn.input_data import NiftiMasker
from nilearn.masking import compute_epi_mask
from s... |
eb66769b7622a5d4b3c94ec48f021de7c3b8445a32bccf461a0975623318b7df | Jupyter | 14,519 | 255 | # %% [markdown]
# # Why Causal Analysis is Needed
# In this example we will perform a quick causal analysis to estimate the _causal effect_ of smoking cessation on weight gain over a period of a decade.
# We will compare these results to the _observed effect_, arising from simple descriptive statistics.
# We'll th... |
740d715a1564c566d7efb567c3965861d79dd876913cda63cecf0571f78adeda | Jupyter | 14,575 | 489 | # %%
import os
import sys
import yaml
import numpy as np
import pandas as pd
from matplotlib import pyplot as plt
from matplotlib.ticker import FormatStrFormatter
sys.path.append('../neurokin/')
from neurokin.utils.neural import processing, importing
from neurokin.utils.experiments import neural_states_helper, neural_c... |
0305311103e56dff38eb8557e8bdb192d2185507b29338edce1e1c259207fe3a | Jupyter | 14,598 | 283 | # %% [markdown]
# # LaLonde Dataset
#
# Economists have long-hypothesized that training programs could improve the labor market prospects of participants.
# In an attempt to test (or demonstrate) this, the National Supported Work (NSW) Demonstration was initiated using combined private and federal funding. This progra... |
f1cd432adbbf5c7608a5d49c57ef271031b07a9294978ea321142af0741d41e3 | Jupyter | 14,613 | 372 | # %% [markdown]
# # Aperiodic parameters of the fMRI power spectrum associate with preterm birth and neonatal age
#
# Machine learning regression analysis predicting neonatal postmenstrual and postnatal age from aperiodic offset and exponent parameters of the resting-state fMRI BOLD signal
#
# This code is developed... |
f6108367e0818d4359fd8ae1f9fd360a3656f243d59d2e7d86fc4a2b62e3c0d8 | Jupyter | 14,640 | 366 | # %% [markdown]
# ## Perturbation Scoring
# ***
# %%
import numpy as np
import scanpy as sc
import matplotlib.pyplot as plt
import anndata
import pandas as pd
import seaborn as sns
from sklearn.neighbors import NearestNeighbors
from statsmodels.stats.multitest import multipletests
from sklearn.discriminant_analysis i... |
6f0e200112c142c891c7da9ddf1934054ea34cea50c8d6e8169d1574b21ed939 | Jupyter | 14,699 | 414 | # %% [markdown]
# # Figure 5: LMFG and RMTG are linked through PBC
# %%
# Adjust the path to the root data folder containing dataset_description.json, sourcedata/, etc.
DATA_PATH = "/Volumes/External/dataset/"
SUBJECTS = [f"sub-P{k}" for k in range(1, 8)]
# %%
import utils
import numpy as np
import pandas as pd
impo... |
da69cf894bc093a6d81171b68de53bd97561eb8a3049702daaf4615f3c388bbf | Jupyter | 14,714 | 332 | # %%
import os, random, numpy as np, tensorflow as tf
from tensorflow import keras
from tensorflow.keras import layers
from tensorflow.keras.callbacks import EarlyStopping, ModelCheckpoint
from scipy.ndimage import binary_dilation, binary_erosion
import shap
# -------------------- Reproducibility --------------------... |
8155c1431f4f11fa2d2a7fd721cd2d27dd07cb10ed231c52aaa78ec3ab5ddb5c | Jupyter | 14,721 | 425 | # %%
import math
import pandas as pd
import numpy as np
import scipy
import seaborn as sns
import matplotlib.pyplot as plt
import matplotlib.patches as mpatches
from matplotlib import style
import matplotlib
import time
import scanpy as sc
import sklearn
import networkx as nx
import ot
import paste as pst
# %%
def lar... |
6d4d233bfdb145404936f6bac7d9ba98d47b940c95f52ecd17da91cd038e8452 | Jupyter | 14,723 | 563 | # %%
import json
#Pandas for saving datasets
import pandas as pd
#matplotlib for rendering
import matplotlib.pyplot as plt
#numpy for handeling matrix operations
import numpy as np
#time, to, well... keep track of time
import time
#iPython display for making sure we can render the frames
from IPython import display
#se... |
1d3beebcb278658f5a2190f96f2cf5165488c7a91ca80672a50cb318c2fdd8e3 | Jupyter | 14,783 | 386 | # %%
# %%
"""
Import libraries, model, and datasets
"""
import sys
import os
from pathlib import Path
import numpy as np
import matplotlib.pyplot as plt
import shutil
import torch
src_dir = os.getcwd()
src_dir = os.path.abspath(os.path.join(src_dir, '..')) + '/src' # Adjust the path as needed
print(src_dir)
sys.path.... |
92365743543bfd2a2cd142b03302d9f6b82cdaffc53807565bed4a04be388cd2 | Jupyter | 14,796 | 444 | # %%
import numpy as np
import pandas as pd
from nilearn.maskers import MultiNiftiLabelsMasker, MultiNiftiMapsMasker
from nilearn.image import resample_to_img, math_img, concat_imgs, smooth_img
import matplotlib.pyplot as plt
import seaborn as sns
import statsmodels.formula.api as smf
import statsmodels.api as sm
#... |
63b566168a928b6a831012a9b04acbfcd3310f1110095fb5b89b0dc925bc0e61 | Jupyter | 14,824 | 571 | # %% [markdown]
# # Classification of pH range using machine learning
# %%
#libs
from sklearn.model_selection import train_test_split
from sklearn.model_selection import GridSearchCV
from sklearn import linear_model
import pandas as pd
import numpy as np
import scikitplot as skplt
import matplotlib.pyplot as plt
from ... |
943601388749a99ad41ae5e0580be2b772412d9043e1b0f45e4c0b065f5c064a | Jupyter | 14,868 | 373 | # %% [markdown]
# # Reproducibility 6: figure 8
# - Application on data from: Kinsler, Grant, Kerry Geiler-Samerotte, and Dmitri A. Petrov. "Fitness variation across subtle environmental perturbations reveals local modularity and global pleiotropy of adaptation." Elife 9 (2020): e61271.
# %% [markdown]
# ## Import pac... |
b795dee83193976cea43c1139d6d7a96d136cebf46299c62ac2bff7b91cde908 | Jupyter | 14,913 | 410 | # %% [markdown]
# # Leave-K-Studies-Out Analysis
# - This jupyter notebook is available on-line at:
# - https://github.com/spisakt/RPN-signature/blob/master/notebooks/4_leave-k-studies-out.ipynb
# - Input data for the notebook and non-standard code (PAINTeR library) is available in the repo:
# - https://github.com/... |
9e16a896e7e1dbe93d9a0846013178826d504c9114c747be17a1641f558e8abf | Jupyter | 14,922 | 387 | # %%
import os
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import matplotlib.image as mpimg
import matplotlib.pyplot as plt
import seaborn as sns
import scanpy as sc
# %%
import sys
sys.path.append('../../')
from downstream.svg import detect_svg
from downstream.plotting import svgplot, g... |
518582339c6d48116758c98d51742111332085c7df73eeddd8f25b3900e481bb | Jupyter | 14,940 | 387 | # %% [markdown]
# # Medical image segmentation with text-based prompts (Inference; Experimental Functionality)
#
# In this tutorial, we will show how to use text prompts for medical image segmentation. Algothgh SAM has not released the text-based segmentation, we can implement this function by ourselves since there ar... |
f7f5be67eec92d12555c9fb876ca5379ff609651f19e6389986431122e770bea | Jupyter | 14,953 | 590 | # %% [markdown]
# Parse lentiMPRA data into format for modeling.
#
# [Data source](https://www.biorxiv.org/content/10.1101/2023.03.05.531189v1.supplementary-material)
# - Sequences: Supplementary Table 3
# - Target values: Supplementary Table 4 (summary level data, mean column)
# %%
import h5py
import pandas as pd
i... |
1a51377d6ed5e42575a4403ad819421c54b4dc9fad92d005f711d9573469b24c | Jupyter | 14,954 | 428 | # %%
import pandas as pd
import numpy as np
from scipy import stats
import statsmodels.stats.multitest as sm
from sklearn.experimental import enable_iterative_imputer
from sklearn.impute import IterativeImputer
# %%
# Load and tidy data
df = pd.read_csv('../../raw_data/Glycosphingolipid_analysis/Deep-surface_GSLs.c... |
c6bc06fe4c951247455afe7f378635e3cd3fc6786b0d1479e78991c2cc58082d | Jupyter | 15,018 | 470 | # %%
import pickle
# Open the pickle file in binary read mode
with open('./scaled_densities_t_types_as_keys.pickle', 'rb') as file:
# Load the data from the file
data = pickle.load(file)
# Now you can use the data variable which contains the deserialized object
print(data)
# %%
sorted_data = {k: data[k] for ... |
8cf1e0bab8de71f3a4eb1355eceeb2e37195d538090f61400c1c339a23926708 | Jupyter | 15,032 | 293 | # %% [markdown]
# # 5. Sampling (Stage 2b: Sampling)
# This Jupyter notebook demonstrates how generate raw AOSLO frames using a ground truth retina and an eye movement trace. We do not yet add the effects of diffraction, aberrations or noise - that will be shown in notebook "6. Image generation". The images shown are '... |
647c8ee641aea716c501a9834813cef5d13e46171cdd392e41345009d3ebd4f0 | Jupyter | 15,063 | 416 | # %%
gene_familly = "AP2"
# %% [markdown]
# <div style="hwidth: 100%; background-color: #ddd; overflow:hidden; ">
# <div style="display: flex; justify-content: center; align-items: center; border-bottom: 10px solid #80c4e7; padding: 3px;">
# <h2 style="position: relative; top: 3px; left: 8px;">S2 Project: ... |
3a1f7bb0b872ed20ddacfbe67e2a9d3a0200e1d1ad22178f3e7f298b389a4cae | Jupyter | 15,104 | 421 | # %%
gene_familly = "AP2"
# %%
# Parameters
gene_familly = "AP2"
# %% [markdown]
# <div style="hwidth: 100%; background-color: #ddd; overflow:hidden; ">
# <div style="display: flex; justify-content: center; align-items: center; border-bottom: 10px solid #80c4e7; padding: 3px;">
# <h2 style="position: rel... |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.