Data Preparation

Download VISp data

In this tutorial we work with a filtered subset of the spot data of the murine primary visual cortex (VISp) profiled using multiplexed smFISH.

To access the full dataset, please refer to this Zenodo record: https://zenodo.org/record/3478502

First, download the data:

curl "https://s3.amazonaws.com/starfish.data.spacetx/spacetx-website/data/smFISH_Allen/s3_spot_table.csv" -o s3_spot_table.csv

Load data into python

Let’s start with loading our python packages:

import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import ssam

Now we can load the mRNA spot table. Each row describes one mRNA spot and the columns contain its coordinates and target gene. We load the required columns into a dataframe:

df = pd.read_csv("s3_spot_table.csv", usecols=['rotated_x', 'rotated_y', 'gene'])

We are only interested in the coordinates of the spots and the gene names, so we filter the dataframe accordingly using the usecols argument.

Now we rename the columns to match the format SSAM expects:

df = df.rename(columns={'rotated_x': 'x', 'rotated_y': 'y'})

SSAM expects the columns to be named x, y, z and gene (z is optional).

If your dataset is organized differently, you will have to reshape it before continuing with the next steps.

Transform Data

As a next step we normalize the coordinates of the spots. We want the origin to be at the top left corner of the tissue, so we subtract the minimum x and y coordinates from all spots:

df.x -= df.x.min()
df.y -= df.y.min()

SSAM expects the coordinates to be in micrometers, so we need to multiply the coordinates by the pixel size of the image.

In this dataset, the coordinates are already in micrometers, so we don’t need to do this. If your data is in a different unit, you will have to convert it into micrometers by multiplying with the conversion factor.

# Below is not needed for this dataset, but to show how it could be done
conversion_factor = 1.0 # change this if your data is in a different unit
df.x *= um_per_pixel
df.y *= um_per_pixel

Create the SSAMDataset and SSAMAnalysis objects

For the SSAM analysis we need to create a SSAMDataset and SSAMAnalysis objects.

The SSAMDataset object will contain all analysis results and intermediate data, while the SSAMAnalysis object will perform the analysis steps.

We create a new SSAMDataset object by providing a name for the dataset. This will initialize a new directory (as a Zarr store) where all the data will be stored.

# Below will create a new directory named "ssam_msmfish" in the current working directory
ds = ssam.SSAMDataset("ssam_msmfish")

Next, we create a SSAMAnalysis object. This object will perform the analysis steps on the dataset.

analysis = ssam.SSAMAnalysis(ds, verbose=True)

The verbose=True argument let us see the progress of the analysis.

It is also possible to specify the number of cores to use for the analysis steps. This is useful for speeding up the analysis on multi-core machines:

analysis = ssam.SSAMAnalysis(ds, ncores=10, verbose=True) # use 10 cores

Now we can start the analysis with the kernel density estimation step.