stPlus

stPlus is a reference-based method for the accurate enhancement of spatial transcriptomics. Based on an auto-encoder with a carefully tailored loss function, stPlus performs joint embedding and predicts spatial gene expression via a weighted k-NN. The spatial transcriptomics enhanced by stPlus can provide better characterization of cell heterogeneity.

Find more details on the website of stPlus.

Installation

Anaconda users can first create a new Python environment and activate it via (this is unnecessary if your Python environment is managed in other ways)

conda create python=3.8 -n stPlus
conda activate stPlus

stPlus is implemented based on the Pytorch framework. Running stPlus on CUDA is recommended if available. For the reproduction of results, we install Pytorch via

pip install torch==1.6.0+cu92 torchvision==0.7.0+cu92 -f https://download.pytorch.org/whl/torch_stable.html

You can also access the installation command here for specific operating system, CUDA version, and Pytorch version.

stPlus is available on pypi here and can be installed via

pip install stPlus

You can also install stPlus from GitHub via

git clone git://github.com/xy-chen16/stPlus.git
cd stPlus
python setup.py install

The dependencies will be installed along with stPlus.

Quick Start

Input

• spatial_df: normalized and logarithmized original spatial data
• scrna_df: normalized and logarithmized reference scRNA-seq data
• genes_to_predict: spatial genes to be predicted

Output

• stPlus_res: predicted spatial transcriptomics data

For calling stPlus programmatically

	import pandas as pd
	from stPlus import *
	# Load the normalized and logarithmized spatial and scRNA-seq data, and the genes to predict
	# The data can be accessed via: 
	# 	git clone git://github.com/xy-chen16/stPlus.git
	# 	cd stPlus
	# 	tar -zxvf data.tar.gz
	spatial_df_file = './data/osmFISH_df.csv'
	scrna_df_file   = './data/Zeisel_df.csv'
	genes_file = './data/genes_to_predict.txt'
	spatial_df = pd.read_csv(spatial_df_file) # (cell by gene pandas dataframe)
	scrna_df   = pd.read_csv(scrna_df_file)   # (cell by gene pandas dataframe)
	genes_to_predict = pd.read_csv(genes_file, header=None).iloc[:,0].values # 1D numpy array
	# Run stPlus
	stPlus_res = stPlus(spatial_df, scrna_df, genes_to_predict)

stPlus can also be seamlessly integrated with Scanpy, a widely-used Python library for the analysis of single-cell data.

	import scanpy as sc
	# Load the spatial and scRNA-seq data as Scanpy objects (spatial_sc and scrna_sc)
	# Normalize and logarithmize if the data contains raw counts
	sc.pp.normalize_total(spatial_sc)
	sc.pp.log1p(spatial_sc)
	sc.pp.normalize_total(scrna_sc)
	sc.pp.log1p(scrna_sc)
	# Transfer to DataFrames
	spatial_df = pd.DataFrame(spatial_sc.X, columns=spatial_sc.var.index.values)
	scrna_df   = pd.DataFrame(scrna_sc.X, columns=scrna_sc.var.index.values)
	# Run stPlus
	stPlus_res = stPlus(spatial_df, scrna_df, genes_to_predict)

Documentation notebook

We also provide a quick-start notebook which describes the fundamentals in detail and reproduces the results of stPlus.

For calling stPlus by Bash commands

git clone git://github.com/xy-chen16/stPlus.git
cd stPlus
tar -zxvf data.tar.gz
python stPlus.py --spatial_df_file data/osmFISH_df.csv  --scrna_df_file data/Zeisel_df.csv \
		 --genes_file data/genes_to_predict.txt --output_file stPlus_res.csv

Look for more usage of stPlus via

python stPlus.py --help

usage: stPlus.py [-h] [--spatial_df_file SPATIAL_DF_FILE] [--scrna_df_file SCRNA_DF_FILE]
                 [--genes_file GENES_FILE] [--output_file OUTPUT_FILE] [--log_file LOG_FILE]
                 [--gpu_id GPU_ID] [--top_k TOP_K] [--t_min T_MIN] [--n_neighbors N_NEIGHBORS]
                 [--max_epoch_num MAX_EPOCH_NUM] [--batch_size BATCH_SIZE] [--random_seed RANDOM_SEED]
                 [--save_path_prefix SAVE_PATH_PREFIX] [--data_quality DATA_QUALITY]
                 [--converge_ratio CONVERGE_RATIO] [--learning_rate LEARNING_RATE]
                 [--weight_decay WEIGHT_DECAY] [--verbose VERBOSE]

stPlus: reference-based enhancement of spatial transcriptomics

optional arguments:
  -h, --help            show this help message and exit
  --spatial_df_file SPATIAL_DF_FILE, -s SPATIAL_DF_FILE
                        file path of normalized and logarithmized original spatial data (comma separated CSV
                        file without index)
  --scrna_df_file SCRNA_DF_FILE, -r SCRNA_DF_FILE
                        file path of normalized and logarithmized reference scRNA-seq data (comma separated
                        CSV file without index)
  --genes_file GENES_FILE, -g GENES_FILE
                        file path of spatial genes to be predicted
  --output_file OUTPUT_FILE, -o OUTPUT_FILE
                        file path of predicted spatial data
  --log_file LOG_FILE, -l LOG_FILE
                        file path of running logs
  --gpu_id GPU_ID       ID of GPU to use
  --top_k TOP_K         number of highly variable genes to use
  --t_min T_MIN         number of epochs with minimal loss using to ensemble learning
  --n_neighbors N_NEIGHBORS
                        number of neighbors used to predict
  --max_epoch_num MAX_EPOCH_NUM
                        maximum number of epochs
  --batch_size BATCH_SIZE
                        batch size for model training
  --random_seed RANDOM_SEED
                        random seed in torch
  --save_path_prefix SAVE_PATH_PREFIX, -m SAVE_PATH_PREFIX
                        prefix of path of trained t models with minimal loss
  --data_quality DATA_QUALITY
                        user-specified or 1 minus the sparsity of scRNA-seq data (default)
  --converge_ratio CONVERGE_RATIO
                        loss converge ratio
  --learning_rate LEARNING_RATE
                        learning rate for model training
  --weight_decay WEIGHT_DECAY
                        weight decay for model training
  --verbose VERBOSE     display the running progress or not