jmuhlich

Instructions for recording a PathViewer user session

Chrome

1. Maximize your browser window.
2. Load up an image in PathViewer.
3. Press F12 to open the DevTools pane.
4. Click on the "Network" tab.
5. Undock DevTools into a separate window if it isn't already. Click the three vertical dots button on the right of the DevTools menu bar, then under "Dock side" click the button depicting two overlapping windows. This will give PathViewer the maximum screen area possible, matching typical real-world usage.
6. Make sure the circle icon in the upper left of the DevTools network tab is red, indicating recording mode is active, or click it so it is.

jmuhlich

import skimage.measure, skimage.filters.rank
import numpy as np

def select_random_crop_centers(mask, n, w, b):
    """
    mask: binary image indicating which pixels are "good"
    n: number of crops to generate
    w: size (width and height) of crops
    b: block reduction size (larger numbers trade off speed for accuracy)

jmuhlich

from __future__ import print_function, division
import warnings
import sys
import os
import re
import io
import struct
import itertools
import uuid
import multiprocessing

jmuhlich

You will perform all of the steps below from the Unix system transfer.rc.hms.harvard.edu, which you access via SSH using your eCommons username and password. The examples below use the username abc123, so in the typed commands after the $ you must substitute your own username (or the username of whomever you're working on behalf of).

1. Download the primary sequence data to ImStor

abc123@transfer:~$ cd /n/files/ImStor/sorger/data/rnaseq
abc123@transfer:/n/files/ImStor/sorger/data/rnaseq$ mkdir abc123

jmuhlich

import sys
import itertools
import pathlib
import json
import numpy as np
import pytiff
from PIL import Image


def composite_channel(target, image, color, range_min, range_max):

jmuhlich

from alembic import op
from alembic.operations.ops import CreatePrimaryKeyOp, CreateForeignKeyOp
import sqlalchemy as sa

def upgrade():

    conn = op.get_bind()
    ctx = op.get_context()
    existing_metadata = sa.schema.MetaData()
    target_metadata = ctx.opts['target_metadata']

jmuhlich

# @File(label="Select a slide to process") filename
# @File(label="Select the output location", style="directory") output_dir
# @String(label="Experiment name (base name for output files)") experiment_name
# @Float(label="Flat field smoothing parameter (0 for automatic)", value=0.1) lambda_flat
# @Float(label="Dark field smoothing parameter (0 for automatic)", value=0.01) lambda_dark

# Takes a slide (or other multi-series BioFormats-compatible file set) and
# generates flat- and dark-field correction profile images with BaSiC. The
# output format is two multi-series TIFF files (one for flat and one for dark)
# which is the input format used by Ashlar.

jmuhlich

Keybase proof

I hereby claim:

• I am jmuhlich on github.
• I am jmuhlich (https://keybase.io/jmuhlich) on keybase.
• I have a public key ASAPI3Ke6iUmVgtBfNHUd52I_CwSP-gO-GUdPRHpnjljpAo

To claim this, I am signing this object:

jmuhlich

Ashlar: Stitching and registration

Ashlar is the tool for turning individual tile images from all of your imaging cycles into a single large multi-channel image:

mkdir my_project
cd my_project
ashlar /path/to/cycle1_image /path/to/cycle2_image ...

This will leave you with a series of full-size TIFF files named cycle_A_B.tif where A is the cycle number and B is the channel number (both starting with 0). The input image files can be any multi-series imaging file with stage positions that BioFormats can read. RareCyte .rcnpnl and InCell .xdce files have both been verified to work.

jmuhlich

import itertools
import pandas as pd

# Create lists with the different factors.
rows = ['B', 'C', 'D', 'E', 'F', 'G']
columns = range(2, 10 + 1)
fields = range(1, 12 + 1)
compartments = ['cyto', 'nuc']
channels = ['DAPI', 'Cy3', 'Cy5', 'FITC']
cycles = [1, 2]