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Introduction to linux command line/ipython/git/R for bioinformatics:

• https://github.com/biom262/biom262-2016
• https://github.com/ricket-sjtu/bi028
• https://github.com/YeoLab/single-cell-bioinformatics/
• https://github.com/YeoLab/single-cell-bioinformatics/tree/master/notebooks
• https://github.com/ltminty/Bioinformatics-and-R/blob/master/Rbioinformatics.ipynb

RNA-seq:

• https://github.com/Caroldm/RNA-Seq-Tutorial---Jupyter-notebook/blob/master/RNAseq%20tutorial.ipynb

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#!/usr/bin/env python

"""
A simple example of how to use the Accelerator API
to train a vision model on a dummy dataset.

Accelerator enables training on a single or multiple GPUs.

Run once `accelerate config` to set up your configuration file.

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"""
Run the experiments for the quantization speedup and plot results.
"""

import subprocess

import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns

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import numpy as np
import matplotlib.pyplot as plt


# class from here: http://nbviewer.ipython.org/gist/tillahoffmann/f844bce2ec264c1c8cb5
class gaussian_kde(object):
    """Representation of a kernel-density estimate using Gaussian kernels.

    Kernel density estimation is a way to estimate the probability density
    function (PDF) of a random variable in a non-parametric way.

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Introduction to next-generation sequencing (NGS)

General workflow

The current used technology for next generation sequencing is Illumina sequencing - all others cannot compete with its speed, price and output power - they have therefore specialized in niche applications (not discussed here).

Nevertheless, no sequencing technology cannot simply start sequencing one end of a chromosome until the other end.

The approach therefore is:

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"""
Convert a TIFF file generated by IndicaLabs (HALO) to a usable TIFF file.
"""

import typing as tp
from pathlib import Path
from xml.etree import ElementTree

import tifffile
from tifffile.tiffcomment import tiffcomment

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from pathlib import Path
import tifffile
import numpy as np
import matplotlib.pyplot as plt
from matplotlib.backends.backend_pdf import PdfPages
from stardist.models import StarDist2D
from csbdeep.utils import normalize

_dir = Path("HumanLiverH35")
files = list(

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# This annotation of cell type markers is tailored for surface proteins
# normally profilid with multiplexed imaging (IMC, CODEX).
# It is a simple reference for myself and may be more detailed in some parts than others.
# This reference is hierarchical.
# This annotation is generally pan-tissue.
# Author: Andre Rendeiro
# License: CC BY-SA 2.0

cell_types:
    structural:

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import pathlib

import numpy as np

def mask_to_labelme(
    labeled_image: np.ndarray,
    filename: pathlib.Path,
    overwrite: bool = False,
    simplify: bool = True,
    simplification_threshold: float = 5.0,

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import string

import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns

# generate some data
n_groups = 4
n_per_group = 5