SPERS

SPatialomics Enhanced Research Suite

This package is intended to house a collection of pipelines for spatialomics analysis. It currently only includes our Ficture implementation.

Install

# create a conda env for the install
conda create -n spers python=3.12
conda activate spers

# clone this repo
git clone https://github.com/beardymcjohnface/SPERS.git
cd SPERS/

# install
pip install -e .

# check
spers --help
spers ficture --help

# to update - just use git
git pull

Ficture wrapper

Wrapper for the ficture package, including file conversion.

# for xenium
spers ficture --input transcripts.csv.gz --platform xenium

# for cosmx
spers ficture --input transcripts.csv.gz --platform cosmx

Run the test dataset

spers ficture-test

Current steps covered:

Convert CosMX or Xenium CSV to TSV
Hex binning
Minibatch binning for model training
Model generation
Trained model plot
Transcript rescoring (with overlapping hex bins)
Final plot

Get bins of clustered transcripts

This will let you analyse with single cell tools. TODO: add this as default output.

import pandas as pd
from spers.ficture.workflow.scripts.hex_bin import transcript_to_hex_bins

transcripts_df = pd.read_csv(
    "spers.out/ficture/results/transcripts.tsv.gz",
    sep="\t",
    compression="gzip",
    index_col="transcript_id")
scored_df = pd.read_csv(
    "spers.out/ficture/results/scored_transcripts/transcripts.rescored.tsv.gz",
    sep="\t",
    compression="gzip",
    index_col="transcript_id")

transcripts_df = pd.concat((transcripts_df, scored_df), axis=1, join="inner")
transcripts_df = transcript_to_hex_bins(transcripts_df, hex_width=16) # I used 24 but 16 um is probably better IDK
transcripts_df = transcripts_df.groupby(["hex_id", "topK", "gene"]).size().reset_index()
transcripts_df["bin"] = transcripts_df.groupby(["hex_id", "topK"]).ngroup()
transcripts_df = transcripts_df.loc[:, ["bin", "gene", 0]]
transcripts_df = pd.pivot_table(transcripts_df, index="bin", values=0, columns="gene", fill_value=0)
transcripts_df.to_csv("spers.out/ficture/results/transcripts.scored.binned.csv")

You will probably want to filter low count bins when analysing.

spers = read.csv(
  "spers.out/ficture/results/transcripts.scored.binned.csv", 
  row.names = 1)
spers = t(as.matrix(spers))
spers = CreateSeuratObject(
  counts=spers,
  min.cells = 3,
  min.features = 16
)
# ...

Have a look at our preliminary comparison HERE