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|---|---|---|---|---|
92f0daef47a9fdae1391cd6963400f59722be76340df3294f8ca6374078483ca | Jupyter | 3,544 | 147 | # %% [markdown]
# ## Inputs
# ---
# %%
# reading data and working with arrays
import h5py, nrrd
import numpy as np
# data paths
data_path = '/groups/scicompsoft/home/fleishmang/rnr-exm-work/data/c_elegan_pair2.h5'
hdf5_file = h5py.File(data_path, 'r')
fix = hdf5_file['/fixed'][...]
mov = hdf5_file['/move'][...]
# as... |
60e7b292516965d06a6dae0effad02dd7ce7aba8e12567ebf19b3a9567d97c5b | Jupyter | 3,593 | 135 | # %%
import sys
import os
from os.path import dirname, realpath, join
base_dir = dirname(dirname(os.getcwd()))
import itertools
import pandas as pd
from os.path import join
base_dir
sys.path.insert(0, base_dir)
from config_path import PROSTATE_DATA_PATH, PLOTS_PATH, GENE_PATH, PROSTATE_LOG_PATH, BASE_PATH
from data.da... |
15514df8ef9d41cc59fc9fbc9f968546643e0b6720d935b958beaf96c6806972 | Jupyter | 3,594 | 117 | # %%
import torch
from utils import *
import matplotlib.pyplot as plt
import numpy as np
# Set the device to GPU if available, otherwise use CPU
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
print(f"Using device: {device}")
# %% [markdown]
# ### Identifying the slice above the lateral ventricl... |
a4c7284469796fa1e26aa2afd96d0cfdb95d60ba293ab87d9dc6509fbc1fe7a6 | Jupyter | 3,595 | 98 | # %% [markdown]
# ## Predicting exons with Evo 2 Embeddings
#
# As a result of extensive pretraining, we expect Evo 2 embeddings to capture details of eukaryotic gene architecture. By training a lightweight model on top of Evo 2 embeddings, we can build classifiers to predict genomic annotations. Annotation prediction... |
5aa132e019b2346879be38abec83d216807c355c58bcda740f22be580b7221e2 | Jupyter | 3,613 | 86 | # %%
### Annotating Different Clusters
# %%
library(dplyr)
library(Matrix)
library(data.table)
library(Seurat)
library(ggplot2)
library(RColorBrewer)
library(cowplot)
library(future)
library(viridis)
library(SeuratObject)
library(tidyr)
# %%
seuobj = readRDS("/data/Bin200.merge.debatch.rds")
# %%
### read anno info
... |
3897b3081caf3c7ac33ef11d6985a7b05fa0c9dbbb0a95a56edbcfb3422ba9e8 | Jupyter | 3,620 | 111 | # %% [markdown]
# ## Using scMulan to annotate cell types in Heart, Lung, Liver, Bone marrow, Blood, Brain, and Thymus
# %% [markdown]
# #### we provide a liver dataset sampled (percentage of 20%) from Suo C, 2022 (doi/10.1126/science.abo0510)
# you can download the sampled dataset for this tutorial from: https://clou... |
87bb4ce7e6626d1caf44a1acf4aac8b044cf8c88182f9cfd81c7b16959345aa6 | Jupyter | 3,651 | 123 | # %% [markdown]
# # Plot benchmark results
# This notebook reads metrics from `baseline_metrics` and produces barplot akin to those in the paper. **Note** that these results are based on the retrained checkpoints and **MatterSim** relaxation & energy (**not DFT**), so results may vary from those in the paper.
# %%
imp... |
cbabff2f329b2b5499cdf210d084e72c4589ac16516e400f8d1d17eb41eeb731 | Jupyter | 3,661 | 94 | # %% [markdown]
# # Keras LSTM for IMDB Sentiment Classification
#
# This is simple example of how to explain a Keras LSTM model using DeepExplainer.
# %%
# This model training code is directly from:
# https://github.com/keras-team/keras/blob/master/examples/imdb_lstm.py
'''Trains an LSTM model on the IMDB sentiment... |
dd965db19cc8041fb6c49dfb2969cc2c9056760d1ab0b7646539e0bcd2e2dacd | Jupyter | 3,669 | 138 | # %%
# This notebook loads in the data from the marmoset and computes a histogram of the spatial frequency selectivity
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
# %%
marm_data = pd.read_hdf('../data/parker/marm.h5')
# %%
def get_group_h5_keys(savepath):
""" Get the keys of a group h5... |
05e57f0d4de3fc15cf4dc8d19e930ffc4ee91e4c195ac4daaee33eec0b7c91c6 | Jupyter | 3,693 | 112 | # %%
### Merge single-slice RDS objects and perform batch-corrected clustering using BayesSpace.
# %%
### library
suppressMessages({
library(Seurat)
library(data.table)
library(ggplot2)
library("stringr")
library("dplyr")
library(ggrepel)
library(SingleCellExperiment)
library(BayesSpace... |
4f9862d4e1b733c66aed53c71763c7bdadcd76c06e035e88b5db52908e51fac6 | Jupyter | 3,702 | 113 | # %%
# running geneformer and deepSem
import anndata as ad
import torch
import scanpy as sc
from bengrn import BenGRN, get_perturb_gt
from grnndata import utils as grnutils
%load_ext autoreload
%autoreload 2
FOLDER = "../../../Geneformer/"
NUM_GENES=5000
# %%
import os
import requests
# Create a directory to sto... |
6dd835afe28b0fc8999ab8ae82ad1185353027984a64b055cf39680801f4522d | Jupyter | 3,708 | 115 | # %% [markdown]
#
#
# # Tutorial 1: Quick Start
#
# This tutorial gives a quick overview of ``surfplot`` before diving into more
# detail in subsequent tutorials. The aim here is to get a flavour of how
# ``surfplot`` works and what can be plotted.
#
# ## Getting surfaces
#
# First, we need to get some brain sur... |
8f3dda423938a554c8553b89939d3cece7a4e4b35bf67b53a7a5db7bb00ab2be | Jupyter | 3,722 | 95 | # %%
###
library(dplyr)
library(Matrix)
library(data.table)
library(Seurat)
library(ggplot2)
library(RColorBrewer)
cols = c(brewer.pal(9, "Set1"),brewer.pal(8,"Set2")[1:8],brewer.pal(12,"Paired")[1:12],brewer.pal(8,"Dark2")[1:8],brewer.pal(8,"Accent"),brewer.pal(12, "Set3"),brewer.pal(9,"Pastel1"),brewer.pal(8,"Pastel2... |
62c07eb6e286ce53d2408e1ad392a8d1d30b86d0686e30987f367953d24cc255 | Jupyter | 3,728 | 100 | # %%
% matplotlib inline
import numpy as np
import pandas as pd
import matplotlib as mpl
import matplotlib.pylab as plt
import seaborn as sns
from mpl_toolkits.axes_grid1 import make_axes_locatable
from scipy.io import loadmat
mpl.rcParams['svg.fonttype'] = 'none'
SpecificTasks = loadmat('gradient_data/brainmap/Spec... |
42b3a768d31dc4164119c9e67079644d7ba6248cf6fe52b8e903ae1dfb16cf4d | Jupyter | 3,740 | 167 | # %%
from pylab import *
import seaborn as sns
# cmap = cm.get_cmap('seismic', 5) # PiYG
cmap = sns.color_palette(None, 10)
for c in cmap:
# print (c)
# for i in range(cmap.N):
# rgba = cmap(i)
# # rgb2hex accepts rgb or rgba
print(matplotlib.colors.rgb2hex(c))
# %%
import pandas as pd
import numpy... |
07ce3ea4b95e27fb373d34e7e0a36bda1946ab765c2e03379d2491df88061f4c | Jupyter | 3,781 | 108 | # %% [markdown]
# # Usage on proteins
#
# Although Novae was mainly designed for spatial transcriptomics, it can also be used for spatial proteomics.
#
# The main difference with the [normal tutorial](../main_usage) is that we will not be able to use the pretrained model, so we'll re-train a Novae model from scratch.... |
676b128b0e29030e6d97499e49fa6f811da5781fbf1a20ce8c1ec2034cf722ce | Jupyter | 3,781 | 96 | # %% [markdown]
# # Inference in Discrete Bayesian Network
# %% [markdown]
# In this notebook, we show a simple example for doing Exact inference in Bayesian Networks using pgmpy. We will be using the Asia network (http://www.bnlearn.com/bnrepository/#asia) for this example.
# %% [markdown]
# ### Step 1: Define the m... |
5945623dd9e87bc06f1f02ea46986678097c5553bb47773a7aedafa51eca08a5 | Jupyter | 3,811 | 118 | # %% [markdown]
# # PyTorch Deep Explainer MNIST example
#
# A simple example showing how to explain an MNIST CNN trained using PyTorch with Deep Explainer.
# %%
import torch, torchvision
from torchvision import datasets, transforms
from torch import nn, optim
from torch.nn import functional as F
import numpy as np
... |
a9236f4af2365bb8efc964917039d74c979588df18a8b31e60e589b5a22baa20 | Jupyter | 3,821 | 137 | # %%
import os
import json
from os.path import join
import matplotlib.pyplot as plt
import matplotlib as mpl
import numpy as np
import pandas as pd
import seaborn as sns
from statsmodels.stats.anova import AnovaRM
from dn_utils.misc import tidy_data
from dn_utils.plotting import plot_matrix
from dn_utils.style impor... |
b33f338a0431008ce81467cebae256de50caa849269194c1db1611c9c53bf049 | Jupyter | 3,824 | 117 | # %%
#!/usr/bin/env python
# coding: utf-8
# In[1]:
# import matplotlib.pyplot as plt
# import pytorch_lightning.callbacks
# get_ipython().run_line_magic('load_ext', 'autoreload')
# get_ipython().run_line_magic('autoreload', '2')
# get_ipython().run_line_magic('matplotlib', 'widget')
# In[2]:
print('=========Loadin... |
de40c8397cd8ba9ccf90aa7c47e47ba21ca7a07a22578ceb783f305e386aa0a5 | Jupyter | 3,844 | 80 | # %%
library(dplyr)
library(Matrix)
library(data.table)
library(Seurat)
library(ggplot2)
library(RColorBrewer)
cols = c(brewer.pal(9, "Set1"),brewer.pal(8,"Set2")[1:8],brewer.pal(12,"Paired")[1:12],brewer.pal(8,"Dark2")[1:8],brewer.pal(8,"Accent"),brewer.pal(12, "Set3"),brewer.pal(9,"Pastel1"),brewer.pal(8,"Pastel2"))
... |
f65622bcdd644609ba5dc87d0bb6403c10beacefebc7ae40fa1f5edb8d8a1744 | Jupyter | 3,941 | 150 | # %%
# Script that analyses area fold changes in transcribing and non-transcribing NCs shown in Figure 5
# Imports
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
from scipy.stats import ttest_rel
# Load dataset
df = pd.read_csv('AreasInWindowsNaNMean.csv')
# Identify window columns
windows... |
d394eac14f8282a0e4105dec256672c92c6380221bfab4ecbd92f4f3d0a28586 | Jupyter | 3,951 | 145 | # %% [markdown]
# # Figure S4
# %%
import sys
import os
# GeoJax should use the CPU
os.environ["JAX_PLATFORMS"] = "cpu"
# Get the absolute path to the project root (one directory up from notebooks/)
root_path = os.path.abspath("..")
sys.path.append(root_path)
import pandas as pd
import numpy as np
import matplotl... |
78dedb2f9a5a2934f87a5b68e1686ea69c2769857e3490f556b44457bca054f3 | Jupyter | 3,977 | 139 | # %%
%pylab inline
import numpy as np
# %% [markdown]
# # Enhancement
# %%
tgtres5 = np.load('../enhancement/baron_human_samp_19264_fromsaver_50M-0.1B-res_tgthighres5_embedding.npy')
# %%
demoa5 = np.load('./examples/enhancement/Baron_01B-resolution_singlecell_cell_embedding_a5_resolution.npy')
# %%
tgtres5,demoa5 ... |
e6e83efce5178dcf7647628614746d356b0af1c58131be0d540f8d1bbb7af4c1 | Jupyter | 4,006 | 108 | # %% [markdown]
# # Combine parcellations
#
# Here, two parcellations are combined into one. These parcellations are:
# - base parcellation: whole-brain network functional parcellation from [Power et al., 2011](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3222858/)
# - meta-analytic parcellation: only regions involved... |
1ef34b1bdbb2ac884f4c89bef19a64cb2946a10e51e0ee61fe87f91796965e40 | Jupyter | 4,018 | 104 | # %% [markdown]
# # Census income classification with scikit-learn
#
# This example uses the standard <a href="https://archive.ics.uci.edu/ml/datasets/Adult">adult census income dataset</a> from the UCI machine learning data repository. We train a k-nearest neighbors classifier using sci-kit learn and then explain the... |
32b80c182c629ad9fab621115f97afef0e4e0e43c0882a93303088e9ee20a8bc | Jupyter | 4,041 | 165 | # %%
%matplotlib inline
%reset
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import pickle
import skfda
import sys
from scipy.integrate import simpson
from skfda.preprocessing.dim_reduction import FPCA
from skfda.representation.basis import BSplineBasis
sys.path.append("[insert root path]/f... |
5f758313a64a56c537fb9ec54c6b501766a9f205fc6047efb677937c2a6a22de | Jupyter | 4,042 | 152 | # %% [markdown]
# # Creating Linear Gaussian Bayesian Networks
# %% [markdown]
# Similar to defining a Discrete Bayesian Network (BN), defining a Linear Gaussian BN also involves specifying the network structure and its parameterization.
#
# There are a few ways to define a Linear Gaussian BN in pgmpy:
# 1. Load an e... |
3ccfe8be514a647e9081a5f3f8c0c569a9eee54f0664b2896d8bff1d50f2f01d | Jupyter | 4,047 | 96 | # %% [markdown]
# # Consensus partitions calculation
#
# Here representative module partitions are calculated for entire task and condition classes. First, agreement matrix $D$ between all partitions of interest is calculated. An element of agreement matrix $D_{ij}$ represents the number of times that nodes $i$ and $j... |
095288a7124cedfe897225bf91afabd2c835bcc50783cfdd6404dc52975f32cb | Jupyter | 4,063 | 132 | # %% [markdown]
# ### Tutorial of spatial imputation on slice #151673 of DLPFC using CPS
# %%
import os,sys
sys.path.append(os.path.realpath(os.path.join(os.getcwd(), '..')))
import warnings
warnings.filterwarnings("ignore")
# %%
import scanpy as sc
import pandas as pd
import CPS
# %%
opt = CPS.config()
args = opt.p... |
cdc23473615dea07d3b2b66656338e2732f2e77509627d132d21e89adf3c05e3 | Jupyter | 4,066 | 124 | # %% [markdown]
# # CaLM
# ## The Codon adaptation Language Model
#
# Implementation of the [Codon adaptation Language Model (CaLM)](https://www.biorxiv.org/content/10.1101/2022.12.15.519894v1.abstract), a large protein language model has been trained on coding DNA, instead of amino acids. Embeddings from CaLM can be ... |
3209e98c56a37a3059ee2bd6830004447ec44b2084ebaef828786a0f1df85c5c | Jupyter | 4,121 | 157 | # %% [markdown]
# # Using FISSA with SIMA
#
# [SIMA](http://www.losonczylab.org/sima/) is a toolbox for motion correction and cell detection.
# Here we illustrate how to create a workflow which uses SIMA to detect cells and FISSA to extract decontaminated signals from those cells.
#
# **Reference:**
# Kaifosh, P., Za... |
bc32f8da3242ff738c888280f31116fad6c4688b9adfc1672fe97f366b8cab62 | Jupyter | 4,122 | 101 | # %% [markdown]
# # Monty Hall Problem
# %% [markdown]
# ### Problem Description:
# The Monty Hall Problem is a very famous problem in Probability Theory. The question goes like:
#
#
# Suppose you're on a game show, and you're given the choice of three doors: Behind one door is a car; behind the others, goats. You p... |
74cec748492e00d0dbc935c0f80c091f36daea0a42ae408c34c4fce262f161a0 | Jupyter | 4,129 | 140 | # %%
library(fasthplus)
library(SpatialExperiment)
library(here)
library("sessioninfo")
library(spatialLIBD)
library(Seurat)
# %%
seuobj = readRDS(paste0("/data/Bin200.merge.seuobj.rds"))
# %%
###read all BayesSpace outcome
meta= readRDS(paste0("/data/work/00.human.brain.atlas/06.Layer.identify.method/00.data/06.Baye... |
1914913452636ce3486b1dd274d9bc209ca3456e8d5615fd7149355dd4b70ffc | Jupyter | 4,135 | 125 | # %% [markdown]
# # GPN demo
#
# Load the published Brassicales GPN with explicit Transformers AutoClass
# registration, inspect its embeddings, and score a masked start-codon nucleotide.
# The committed outputs are rendered statically in the documentation.
# %% [markdown]
# ## Setup
# %%
!pip install --quiet "gpn[i... |
50f8cb8d13542f82df8532af7588f24894731c634dea21c78650c4a37de67e57 | Jupyter | 4,138 | 113 | # %%
cd ..
# %%
import torch
import json
import os
from importlib.machinery import SourceFileLoader
from matplotlib import pyplot as plt
from utils.utils import *
# %%
import skimage
def visualize(tensor, cmap='hsv', vmin=None, vmax=None, figsize=None, axis=True, title=None, psnr=None, ssim=None, l1=None):
plt.f... |
61439da2bc9855b07b56fbf5fba4936a358a235d85ea17f3825ac053cbb842e6 | Jupyter | 4,151 | 112 | # %%
import numpy as np
import pickle
import matplotlib.pyplot as plt
from statsmodels import robust
def plot_curve(bundled_data, title, curve, statistics, save_path=None):
plt.figure(figsize=(6, 6))
lw = 2
handles = []
for auc_mean, auc_sd, mean_curve_x, all_curve_y, wts, sample, color in bundled_dat... |
07676d8bf4e88d8b456a81faf6913aef7acf0180393c9fd25d974ceb7b408503 | Jupyter | 4,171 | 129 | # %% [markdown]
# # Modularity
#
# This analysis answers the question: **"Does modularity differ between task conditions?"**. Individual modularity values are imported for the threshold range of interest. Modularity values for excluded subjects are discarded. Values are subjected into repeated measures ANOVA with two ... |
78c321f4445b017cd2e5c0c02cad3c420d159d7dd3b62c78326084fd3d6b4265 | Jupyter | 4,203 | 153 | # %% [markdown]
# # Creating Discrete Bayesian Networks
# %% [markdown]
# Defining a Discrete Bayesian Network (BN) involves specifying the network structure and its parameterization in terms of Tabular Conditional Probability Distributions(CPDs), also known as Conditional Probability Tables (CPTs).
#
# There are few... |
a4cd605c408b6b25ba988919f82694f06022d0a4688031cce77c6a97d1c0f91a | Jupyter | 4,213 | 134 | # %% [markdown]
# # Deep Learning Segmentation (pixel wise classification)
#
# First Percentile Ratioepare the training dataset.
# For all the categories except white matter, we do some spatial smoothing.
# For white matter we don't since it's very thin.
# %%
import numpy as np
import cv2
import torch
from PIL import... |
94a9f6a26662fef82ecf6fac86a68d3bdfefcdfca5157522d667f1fe3267ee04 | Jupyter | 4,230 | 142 | # %% [markdown]
# # [Numba](https://numba.pydata.org/numba-doc/latest/user/5minguide.html)
#
# Numba is a just-in-time compiler for Python that works best on code that uses NumPy arrays and functions, and loops.
#
# This jupyter notebook gives a short tutorial on how to use Numba to speed up a function. For instance,... |
fa9cdf440719340cfdf4fc5db7c37a6a34e1544c9d2dcaa78a6851484cc90fd5 | Jupyter | 4,259 | 187 | # %% [markdown]
# # Import Libraries
# %%
#torch.cuda.is_available()
# %% [markdown]
# # Import Libraries
# %%
data_dir = "D:/Shahid Sir/Gastrointestinal Tract Kvasir Dataset"# Datasets path
data_dir = pathlib.Path(data_dir)
data_dir
# %%
path = "D:/Shahid Sir/Gastrointestinal Tract Kvasir Dataset"
dir_list = [os.p... |
2f05c54cba4ac42e70e455872e4167f100fd76841406299f7bf3b6af058c1fb2 | Jupyter | 4,266 | 158 | # %%
##### Fig. 2 - Workflow of time-resolved representational similarity analysis (RSA)
#
# written by S-C. Baek
# updated date: 16.12.2024
#
# %%
# import packages
import os
import pickle
import numpy as np
import pandas as pd
from scipy import stats as stats
from scipy.spatial.distance import squareform
import m... |
71e444e8510e998704c6c594fa026749c7f56952827cc09165283f5f0fe3aaf4 | Jupyter | 4,295 | 125 | # %%
### Visualize the Bayesian results and analyze each cluster.
# %%
library(dplyr)
library(Matrix)
library(data.table)
library(Seurat)
library(ggplot2)
library(RColorBrewer)
library(cowplot)
library(future)
library(viridis)
library(SeuratObject)
library(tidyr)
plan("multicore", workers = 16)
options(future.globals.... |
2d63c3fa219b16e9ad0d19326da9a6325de12b69fc3b701210ad1703225fb6cf | Jupyter | 4,317 | 141 | # %% [markdown]
# ### Tutorial of gene imputation on slice #151673 of DLPFC using CPS
# %%
import os,sys
sys.path.append(os.path.realpath(os.path.join(os.getcwd(), '..')))
import warnings
warnings.filterwarnings("ignore")
# %%
import scanpy as sc
import pandas as pd
import numpy as np
import CPS
# %%
opt = CPS.confi... |
d8acf461c9fb1fb80faaa58ad7516642d19c96645f75f6e33d7391c9d92beacf | Jupyter | 4,333 | 143 | # %% [markdown]
# # Basic Operations on Bayesian Networks
# %% [markdown]
# This notebook shows examples of some basic operations that can be performed on a Bayesian Network. We use the Protein Signalling network from the bnlearn repository as the example model: https://www.bnlearn.com/bnrepository/discrete-medium.htm... |
b27af3c076a5c70b9a75ddf8c757d4127e703e0fabf413364b63f66305d6986a | Jupyter | 4,358 | 155 | # %% [markdown]
# # Compare analytical method with simulation (SI, fig. 14):
#
# Here we show that our analytical method to compute the STDDs (see section 4.2.1 and the `stddc`-package) is consistent with the results of numerical simulations (computed by the `neuralsampling`-package based on eq. 8ff)
# %%
import os
... |
8cbdd33a591dfd6a909517d82db7ef44e2134989a02c4180efa2cbef6d1102de | Jupyter | 4,440 | 147 | # %% [markdown]
#
#
# # Tutorial 4: Layouts and Views
#
# This tutorial covers the layout and view options provided by ``surfplot``.
#
# For variety, let's import the left and right Conte69 midthickness surface
# directly using :func:`~brainspace.datasets.load_conte69`. Then, we'll make a
# blank surface plot usin... |
cc7cb7dd7d1292449ce188a52f83a2d3135528061aa7774b5699104686559f8f | Jupyter | 4,449 | 182 | # %% [markdown]
# ## Inputs
# ---
# %%
# reading data and working with arrays
import h5py, nrrd
import numpy as np
# data paths
data_path = '/groups/scicompsoft/home/fleishmang/rnr-exm-work/data/c_elegan_pair0.h5'
hdf5_file = h5py.File(data_path, 'r')
fix = hdf5_file['/fixed'][...]
mov = hdf5_file['/move'][...]
# as... |
9edb25b754aa954c8f25dc8b4ec8c28ba7360be1b57f794e6343dd5a9a220869 | Jupyter | 4,453 | 159 | # %% [markdown]
# # Permuation based Mutual Exclusive and Co-occurrence for group of items
# %%
import numpy as np
import pandas as pd
import scipy.sparse as sparse
import random
# %% [markdown]
# This is the python implementation of the MutEx algorithm described in the PhD Thesis "Analysis and visualization of multi... |
641fbda6cc788634838a930cf7922fed45530ac4f214bbe896952e5d9394e4f9 | Jupyter | 4,517 | 183 | # %% [markdown]
# ## Inputs
# ---
# %%
# reading data and working with arrays
import h5py, nrrd
import numpy as np
# data paths
data_path = '/groups/scicompsoft/home/fleishmang/rnr-exm-work/data/c_elegan_pair1.h5'
hdf5_file = h5py.File(data_path, 'r')
fix = hdf5_file['/fixed'][...]
mov = hdf5_file['/move'][...]
# as... |
f99e19bcef0d78bfab58861c2777b1d7e8470b067a51a517136d05d299eb84fb | Jupyter | 4,522 | 138 | # %% [markdown]
# # Transferring Labels Using UCE
#
# This notebook walks through the example from Figure 4d,4e of transferring labels from mouse kidney norn cells to a human lung disease dataset.
#
# To transfer labels, we use a basic default implementation of sklearn's logistic classifier.
# %%
## Imports
import s... |
f74a06ecb4988378cf105731dff4bb2b99efe0f4ba029a9eca514f6facb2363b | Jupyter | 4,528 | 207 | # %% [markdown]
# # Create the plots used in the stability proof (section 4.2.2, fig. 12)
# %%
from pathlib import Path
import numpy as np
import matplotlib as mpl
import matplotlib.pyplot as plt
from scipy.integrate import trapezoid
from stddc import STDDMaker, alpha_PSP, rect_PSP, exp_PSP
# %%
# define the style ... |
e1c1d22ab11bcd71d8510027a296293800e8b1dcf26d7cc45f3efde5a917c7e9 | Jupyter | 4,544 | 200 | # %% [markdown]
# # Import packages
# %%
import polars as pl
import polars.selectors as cs
import numpy as np
import src.plotting_functions as plotting
from importlib import reload
from src.utils import read_gtf, collapse_isoforms_to_proteoforms, read_SJ, gtf_to_SJ
from src.ryp import to_r, r
from pathlib import Path
... |
ee7dad118e7703571c66ca4c1cbd070eb514f36ccca227c792530d579f722e59 | Jupyter | 4,545 | 122 | # %%
import matplotlib.pyplot as plt
from math import sqrt
from collections import namedtuple
# %%
Trait = namedtuple("Trait", ["name","n_genes","n_intergenic","s_genes","s_intergenic","h2_genes","h2_intergenic"])
n_genes_total = 135207
n_intergenic_total = 9410173
# %%
intelligence = Trait(name = "Intelligence",
... |
8b9d015e15f771400786ddd5fb017270d1709094bafa12307d3e098fa4bb74df | Jupyter | 4,586 | 113 | # %%
import uniport as up
import scanpy as sc
import torch
import scanpy as sc
import pandas as pd
import numpy as np
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
import scanpy as sc
key = 'subclass.v4'
# %%
T_names = [ "T996", "T928", "T906", "T758", "T996", "T756", "T993", "T989"]
# 单细胞数据文件... |
1c6f5f9bc72de44f0e2b33b3a771eeb86c8b5852f1b48f413d5b9919e6f82308 | Jupyter | 4,588 | 131 | # %% [markdown]
# ## Parameter estimation
# %%
import matplotlib.pyplot as plt
import seaborn as sns
import numpy as np
import scipy.io
import os
from sklearn.neighbors import KernelDensity
path_root = os.environ.get('DECIDENET_PATH')
path_code = os.path.join(path_root, 'code')
if path_code not in sys.path:
sys.... |
d24629b87978c52bfe78563b9dc35b7a5e750769df17885ac2d3d1ba86a5551f | Jupyter | 4,610 | 177 | # %%
# Copyright (C) 2025 ETH Zurich, Moritz Thürlemann, and other AMP contributors
from rdkit import Chem
from rdkit.Chem import Draw
import json
methyl_capped_side_chains = [
"CC",
"CCS",
"CCC(O)=O",
"CCCC(O)=O",
"CCC1=CC=CC=C1",
"C",
"CCC1=CNC=N1",
"CC(C)CC",
"CCCCCN",
"CSCC... |
2f050d969c34ffb6635720fe640185c3182a4c13f296ccb0101b569d94f2ee1f | Jupyter | 4,618 | 174 | # %% [markdown]
# ## Inputs
# ---
# %%
# reading data and working with arrays
import h5py, nrrd
import numpy as np
# data paths
data_path = '/groups/scicompsoft/home/fleishmang/rnr-exm-work/validation_data/mouse_pair4.h5'
hdf5_file = h5py.File(data_path, 'r')
fix = hdf5_file['/fixed'][...]
mov = hdf5_file['/move'][..... |
ef8844dcab8d1e9dbe90924fe374c2ba87355ff810d7817ab586d53f2641b752 | Jupyter | 4,620 | 167 | # %% [markdown]
# # Dependencies
# %%
suppressPackageStartupMessages({
library(tidyverse)
library(ggbeeswarm)
})
source("./utils.R")
# %%
limits_ari <- c(-0.05, 1)
# %% [markdown]
# # Fig1D
# %%
ari_tech <- read_tsv("./data/ari.tsv", show_col_types = FALSE, col_select = 2:5)
head(ari_tech)
# %%
options(r... |
69207ce84d45aa7043903e8c0fd3ab98dcfcbb402bbd2ab3987c215ffb731788 | Jupyter | 4,622 | 100 | # %% [markdown]
# # EASI-FISH pipeline tutorial
# ---
#
# This tutorial shows the basic requirements for running the pre-packaged easi-fish registration pipeline. Although we will only be running a few functions in this tutorial, a lot of complex things are happening behind the scenes. At the end of the tutorial I wil... |
7c7383d49bfb10469df596624f07791e5b0c260ae4640a21b0ace99988e19b37 | Jupyter | 4,635 | 152 | # %%
# %%
import sys
import os
from os.path import dirname, realpath, join
base_dir = dirname(dirname(os.getcwd()))
import itertools
import pandas as pd
from os.path import join
base_dir
sys.path.insert(0, base_dir)
from config_path import PROSTATE_DATA_PATH, PLOTS_PATH, GENE_PATH, PROSTATE_LOG_PATH
from data.data_a... |
66ff1af386d34b4d106b51531761dafbe1224d9474f2adf2554979147d7a519a | Jupyter | 4,645 | 168 | # %% [markdown]
# # Batch effect correction (multiple batches)
# %% [markdown]
# Here we will use human pancreas datasets (scRNA-seq data) of different studies as an example to illustrate how Garfield performs scRNA-seq batch correction for multiple batches.
# %%
import os
import pandas as pd
import numpy as np
os.c... |
4d77462ac4ee28d95ec508e213dda795607b22328302dfa0ca3e50bbb4ede968 | Jupyter | 4,652 | 234 | # %%
# Script that analyses area log2 fold changes in transcribing and non-transcribing NCs shown in Figure 5
# Imports
import pandas as pd
import numpy as np
import seaborn as sns
import matplotlib.pyplot as plt
from scipy.stats import ttest_1samp
# Load dataset
df = pd.read_csv('AreasInWindowsNaNMean.csv')
# Ide... |
611e253e197974820e17ecb80d7afc6cddcd61b4fbe4ac847c4884cb51147990 | Jupyter | 4,652 | 159 | # %% [markdown]
# # Hypothesis testing on LSNs agreemnent values
#
# > **Analysis type**: Single γ (results)
# %%
import json
from os.path import join
import numpy as np
import pandas as pd
from dn_utils.networks import fdrcorrection_matrix
from dn_utils.path import path
from dn_utils.plotting import plot_matrix
%m... |
5278ce51f310ed2313e1d31d4f3007e8416a9537242c9ad5736c9b87eb4d294d | Jupyter | 4,659 | 184 | # %% [markdown]
# ## Inputs
# ---
# %%
# reading data and working with arrays
import h5py, nrrd
import numpy as np
from bigstream.transform import apply_transform
# data paths
data_path = '/groups/scicompsoft/home/fleishmang/rnr-exm-work/data/mouse_pair1.h5'
hdf5_file = h5py.File(data_path, 'r')
fix = hdf5_file['/fix... |
675e78297c45a2606c8ee0db69f4ba020c7c156d5a42007c9e6cafeb124c6202 | Jupyter | 4,676 | 187 | # %%
import sys
import os
from os.path import dirname, realpath, join
base_dir = dirname(dirname(dirname(os.getcwd())))
import itertools
import pandas as pd
from os.path import join
base_dir
# %%
sys.path.insert(0, base_dir)
from config_path import PROSTATE_DATA_PATH, PLOTS_PATH
from data.data_access import Data
# %%... |
6f3e55d04726f99af2fc432af04ceae50519e8133c2fc6fb438ef5a73a5e137e | Jupyter | 4,678 | 184 | # %% [markdown]
# ## Inputs
# ---
# %%
# reading data and working with arrays
import h5py, nrrd
import numpy as np
from bigstream.transform import apply_transform
# data paths
data_path = '/groups/scicompsoft/home/fleishmang/rnr-exm-work/data/mouse_pair2.h5'
hdf5_file = h5py.File(data_path, 'r')
fix = hdf5_file['/fix... |
bfb79da983475c91181112b5617bdeeeaac6bae65d89c82dbabeab506a649bff | Jupyter | 4,678 | 184 | # %% [markdown]
# ## Inputs
# ---
# %%
# reading data and working with arrays
import h5py, nrrd
import numpy as np
from bigstream.transform import apply_transform
# data paths
data_path = '/groups/scicompsoft/home/fleishmang/rnr-exm-work/data/mouse_pair0.h5'
hdf5_file = h5py.File(data_path, 'r')
fix = hdf5_file['/fix... |
96e7db49e88cde5ced274e154a8e2e70e83f01ebe2e81df7e970d418c4de597c | Jupyter | 4,691 | 111 | # %% [markdown]
# # Documentation by example for `shap.dependence_plot`
#
# This notebook is designed to demonstrate (and so document) how to use the `shap.dependence_plot` function. It uses an XGBoost model trained on the classic UCI adult income dataset (which is classification task to predict if people made over 50... |
4d29d38611017ca5278c205a23fcc7a7aeffb6733ba681d1b5f5ad0d357e7669 | Jupyter | 4,693 | 190 | # %%
%pylab inline
import scanpy as sc
import pandas as pd
import pickle
# %%
seg_label = np.load('./data/Segerstolpe-test-label.npy')
seg_name = np.load('./data/Segerstolpe-str_label.npy')
# %%
emb_path="./data/seg-emb.pkl"
emb=None
label=None
f=open(emb_path, 'rb')
while 1:
try:
sub_pkl = pickle.load(f)... |
55833befe060e474efe07637b1200581499e6b4e479f75dd8e742b1e4bd7540a | Jupyter | 4,695 | 152 | # %% [markdown]
# # F01_A: NSD ROI Mega-Matrix MDS
#
# Minimal reproduction notebook for manuscript Fig. 1A. This starts from the saved whole-brain `mega_matrix` pickle copied into `streamlined/data/processed/mega_mega_matrix.data`, runs MDS on `1 - mega_matrix`, and exports the plotted coordinates.
# (see https://git... |
5236b63f72a5077cc36d9e21e89506e5b23d0f7242e23cd31da7523e41349b4c | Jupyter | 4,700 | 126 | # %% [markdown]
# A script which creates a nested dictionary in which the final value at every nested key is a vector containing the processed timeseries for a specific trial, for channel, chromophore and participant, using 'fNIRSData.storeChannelData' <br>
#
# Stored in the current working directory.
#
# Inputs:<br... |
0e20dd8343b7718b8363cb1f9e6d508a3853e3809c6a897c48c6f5670fdd4356 | Jupyter | 4,703 | 178 | # %% [markdown]
# ## Inputs
# ---
# %%
# reading data and working with arrays
import h5py, nrrd
import numpy as np
# data paths
data_path = '/groups/scicompsoft/home/fleishmang/rnr-exm-work/validation_data/c_elegan_pair4.h5'
hdf5_file = h5py.File(data_path, 'r')
fix = hdf5_file['/fixed'][...]
mov = hdf5_file['/move']... |
c1ce0984d332d4e6030ede113d9928e8b20e9d14b8e601071256a599a2500722 | Jupyter | 4,713 | 154 | # %% [markdown]
#
#
# # Tutorial 3: Types of Input Data
#
# This tutorial covers what types of data can be passed to the `data` parameter
# of the :func:`~brainspace.plotting.Plot.add_layer` method of
# :class:`~brainspace.plotting.Plot`.
#
# `data` accepts four different types of data:
#
# 1. A numpy array of ve... |
9221808572a5ebe0cc33ae1972493cd37e6c058074529b5cf134e976d86fd8fa | Jupyter | 4,714 | 184 | # %% [markdown]
# ## Inputs
# ---
# %%
# reading data and working with arrays
import h5py, nrrd
import numpy as np
from bigstream.transform import apply_transform
# data paths
data_path = '/groups/scicompsoft/home/fleishmang/rnr-exm-work/data/mouse_pair3.h5'
hdf5_file = h5py.File(data_path, 'r')
fix = hdf5_file['/fix... |
2e7f484ddc69193ac7ccff470be6a4ffcdd9bccb983e39565e21d5ec06593a34 | Jupyter | 4,742 | 124 | # %%
from tasks.scfoundation import load
import scipy.sparse as sp
import anndata as ad
import hdf5plugin
import pandas as pd
import numpy as np
import scanpy as sc
from scipy.spatial.distance import cdist
import matplotlib.pyplot as plt
# %%
def prepare_data(adata, need_normalization=True):
gene_list_df = pd.read... |
1ca4b2aec1c87b33a256d5703777e13af4855983b831e3bab4e028b04be126ca | Jupyter | 4,746 | 131 | # %% [markdown]
# ## Data Quality Assurance (QA)
#
# This script identify bad quality data (e.g. with motion artifacts) and returns vectors specifying which subject can be included in second-level analysis. Script features:
# - identifies high motion subjects
# - returns DataFrame specifying final sample with high qua... |
4472f70c403fa0f296d757422bccb09962ba2133eb40606ba16d4bee7a597d7f | Jupyter | 4,762 | 132 | # %% [markdown]
# # Iris classification with scikit-learn
#
# Here we use the well-known Iris species dataset to illustrate how SHAP can explain the output of many different model types, from k-nearest neighbors, to neural networks. This dataset is very small, with only a 150 samples. We use a random set of 130 for tr... |
7c6f52c4c84b017c13682d961953b849559acde7379d3c8b3e320be295ba5627 | Jupyter | 4,764 | 188 | # %% [markdown]
# # Thigmotaxis
#
# Thigmotaxis (wall-following) analysis and figures.
# %%
import os
import dataframe_image as dfi
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import pingouin as pg
from cichlid_omr.config import data_dir, load_config, output_dir
# %matplotlib widget
# pd.... |
ef4a80619629ce1f0f19e81460cb8a04cde40859a2df5533367dabbb8b323290 | Jupyter | 4,779 | 149 | # %% [markdown]
# ### In this tutorial we will see the faster way to loop over a pandas dataframe
# %%
import pandas as pd
import numpy as np
import time
# %% [markdown]
# ### Generate sample data for the example
#
# Lets create a dataframe with two columns: 'pmid' and 'embedding'. The 'pmid' column will contain the... |
f63ac1de1e2d831e169613be5ff148fc3736ef8ecfbca1c410b46325a07c330b | Jupyter | 4,799 | 146 | # %% [markdown]
# # Large Scale Embedding benchmarks
#
# This notebook includes an example showing how to run large scale embedding benchmarks using scIB [(single-cell integration benchmark)](https://www.nature.com/articles/s41592-021-01336-8)
#
# We use the GPU accelerated version implemented here: https://github.co... |
53fb956dad82fa263d1f5ade8b47081de28fc1ed5383fc55c66911727c87ea5a | Jupyter | 4,805 | 148 | # %% [markdown]
# # Using FISSA with CNMF from MATLAB
#
# CNMF is blind source separation toolbox for cell detection and signal extraction.
#
# Here we illustrate how one can use the ROIs detected by CNMF, and use FISSA to extract and decontaminate the traces.
#
# In this tutorial, we assume the user is using the [... |
4ce49c8166edc205cde28689d627c4d41915bdc86ef3464ff33f395ddbdb81df | Jupyter | 4,816 | 188 | # %% [markdown]
# ## Inputs
# ---
# %%
# reading data and working with arrays
import h5py, nrrd
import numpy as np
# data paths
data_path = '/groups/scicompsoft/home/fleishmang/rnr-exm-work/test_data/c_elegan_pair6.h5'
hdf5_file = h5py.File(data_path, 'r')
fix = hdf5_file['/fixed'][...]
mov = hdf5_file['/move'][...]
... |
ba11fb6de49555f768dc61b7a057a6f77b1f899e751b4c0876388d02c00d8d55 | Jupyter | 4,820 | 125 | # %% [markdown]
#
#
# # Tutorial 5: Colors and colorbars
#
# This tutorial demonstrates how to configure the colorbar(s) with ``surfplot``.
#
# ## Layer color maps and colorbars
#
# The color map can be specified for each added plotting layer using the `cmap`
# parameter of :func:`~surfplot.plotting.Plot.add_lay... |
228b0394cc87061eff0b83a951e3bce5a190b54d7663a4753013f7e083a3d617 | Jupyter | 4,829 | 191 | # %%
%load_ext blackcellmagic
# %% [markdown]
# #### Imports, setup and helper functions
# %%
import numpy as np
import pandas as pd
import deepdish as dd
import h5py
import nibabel.freesurfer.mghformat as mgh
import matplotlib.pyplot as plt
import seaborn as sns
from scipy.stats import ttest_rel
# %%
from spacestr... |
79074d2aba15e07b733e807a95bcd49f6ef5cd68dc8ddfd093db52a46aa6bd33 | Jupyter | 4,837 | 127 | # %% [markdown]
# # 1_SynAnalyzer_ConvertImarisStatsFile.ipynb
# This notebook reads in Imaris statistics files and generates Python-friendly CSV files
# with the relevent surface statistics and image metadata added. The exact parameters used for the
# conversion process were determined empirically and may need adjust... |
c00b47b2aa6b236962122c1d7e4f425f05bd7872943325d1aedebf7b7b318b3d | Jupyter | 4,844 | 187 | # %% [markdown]
# ## Inputs
# ---
# %%
# reading data and working with arrays
import h5py, nrrd
import numpy as np
# data paths
data_path = '/groups/scicompsoft/home/fleishmang/rnr-exm-work/test_data/c_elegan_pair5.h5'
hdf5_file = h5py.File(data_path, 'r')
fix = hdf5_file['/fixed'][...]
mov = hdf5_file['/move'][...]
... |
91e9a11eea263bac55f9749a7f90b7a1e6f6faf3cb7b8b723f5cfb1ab775a657 | Jupyter | 4,856 | 180 | # %% [markdown]
# ### Geneating data needed fo contour plots
#
# Fig. 2a, Fig. S1
# %%
import NeuRosetta as nr
import numpy as np
# %%
fpath = '/home/ndrummond@neuro.mpg.de/Datasets/Paper_data/nr/Flywire/Reduced_dendrites/'
forest = nr.load(fpath, parallel = True, show_progress = True, max_workers = 10, set_units = ... |
3fe38496cbd74536048531958fddbf0092b700d1722363c0a36da10d9197d9e5 | Jupyter | 4,865 | 179 | # %% [markdown]
# # Sequence Generation and Alignment Analysis with Evo2
# This notebook demonstrates how to generate biological sequences using the Evo2 model and analyze them using Biopython alignments.
#
# ## Setup and Dependencies
#
# First, let's import our required libraries and set up our environment. Note you... |
f47326564803561766adcec3d97f7805f0df12a1fef3a83b4477ec790d44e20c | Jupyter | 4,869 | 121 | # %% [markdown]
# # Structure Learning in Bayesian Networks
# %% [markdown]
# In this notebook, we show a few examples of Causal Discovery or Structure Learning in pgmpy. pgmpy currently has the following algorithm for causal discovery:
#
# 1. **PC**: Has $3$ variants original, stable, and parallel. PC is a constrain... |
f2d8efa98d9888358b6e7711cba87ad6d21811f443920da61562156e2b216e3c | Jupyter | 4,873 | 185 | # %% [markdown]
# ## Logging Configuration
# %%
from bigstream.configure_logging import configure_logging
configure_logging(None, True)
# %% [markdown]
# ## Inputs
# ---
# %%
# reading data and working with arrays
import os
import zarr, nrrd
import numpy as np
import os
# data paths
base_path = '/Users/goinac/Work... |
4952c68d224a830694ae5b18e7e54f74dba480c183d2dfc3fb84a9a54877cdb9 | Jupyter | 4,881 | 147 | # %%
%matplotlib inline
import matplotlib.pyplot as plt
from mapalign.embed import DiffusionMapEmbedding
import numpy as np
from sklearn.manifold import SpectralEmbedding, TSNE
from sklearn.cluster import KMeans
# %%
def compare_methods(X, title, cluster=None):
f, axarr = plt.subplots(2, 7, sharex=True, sharey=Tru... |
8b4e797f981c6d27b3fa484b423220aa819e56a056f40eb22227010521991c10 | Jupyter | 4,893 | 153 | # %% [markdown]
# ## AESTETIK with simulated spatial transcritpomics data
#
# In this notebook we apply AESTETIK on simulated spatial transcriptomics data.
#
# You can find the code for generating spatial transcriptomics data [here](https://github.com/ratschlab/simulate_spatial_transcriptomics_tool). https://github.c... |
68334b15908d05d2d49669220034432d39e3f06b70b1b890e745ef2dd7b7721a | Jupyter | 4,894 | 202 | # %% [markdown]
# # Speed and size comparision
#
#
# > This tutorial aims to find out the efficient way to save pandas dataframe in terms of read/write speeds and size of the file.
# %%
import os
import time
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
# %% [markdown]
# Lets create a samp... |
d969aaba396a40aa9b0c597a747446a56bba9a65b7fdb8ed026d0eb9616bdb20 | Jupyter | 4,912 | 190 | # %% [markdown]
# ## Inputs
# ---
# %%
# reading data and working with arrays
import h5py, nrrd
import numpy as np
# data paths
data_path = '/groups/scicompsoft/home/fleishmang/rnr-exm-work/test_data/c_elegan_pair7.h5'
hdf5_file = h5py.File(data_path, 'r')
fix = hdf5_file['/fixed'][...]
mov = hdf5_file['/move'][...]
... |
07b84726d53871872ede182e139afcf89051f86cda8654e27226973b6648fee5 | Jupyter | 4,965 | 114 | # %% [markdown]
# # Zero-shot Multi-modal Integration with Pre-trained scGPT-spatial
# %% [markdown]
# #### Dataset: Multi-modal Developing Fetal Lung (Visium/Xenium) ([Quach et al.](https://www.nature.com/articles/s41467-024-50281-5))
# %% [markdown]
# In this demo, we will explore the integration of two different ... |
eebef9e637127d31cbf24f9a8a5edd7ccbb3c0e14123bb11e8a2d99918d29fb8 | Jupyter | 4,965 | 172 | # %% [markdown]
# # Paired single-cell multi-omics analysis
# %% [markdown]
# Here we will use 10X PBMCs multiome dataset from scanpy as an example to illustrate how Garfield performs multi-omics integration analysis
# %%
import os
os.chdir('/data2/zhouwg_data/project/Garfield')
os.getcwd()
# %%
# load packages
impo... |
6f013a9b8590179e802b1c512b00ca3fe5f1ee372645ef6430e876e7641c40db | Jupyter | 4,998 | 88 | # %%
import matplotlib.pyplot as plt
import numpy as np
# %%
p_coding_005 = [5.79E-03, 5.86E-03, 5.66E-03, 5.91E-03, 5.47E-03, 7.18E-03, 6.68E-03, 6.61E-03, 6.28E-03, 6.97E-03]
p_noncoding_005 = [3.19E-04, 3.67E-04, 4.07E-04, 3.81E-04, 3.56E-04, 3.88E-04, 3.59E-04, 4.51E-04, 4.54E-04, 5.47E-04]
s_coding_005 = [6.12E-0... |
6d3e3a6e7ef521234c044aeaf19f308e52af294ded80d61df29a5a21f260ca44 | Jupyter | 5,016 | 179 | # %% [markdown]
# # Extract Embeddings from Pre-trained Nicheformer Model
#
# This notebook extracts embeddings from a pre-trained Nicheformer model and stores them in an AnnData object.
# %%
import os
import numpy as np
import pytorch_lightning as pl
import torch
from torch.utils.data import DataLoader
import anndat... |
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