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SCMA: Single-cell metabolomics analysis

Installation

install from PyPI

python -m pip install --index-url https://test.pypi.org/simple/ --no-deps scma==0.1.1

install from GitHub

git clone https://github.com/tkisss/SCMA.git
cd SCMA
python setup.py install

Quick Start

Command line

scma.py -f INPUT_FILE -g group_name1 group_name2 group_name3... -o OUTPUT_PATH

INPUT_FILE: file of single-cell metabolomics, each two columns are a group which contains the ratio of nuclear to mass and its signal value. Cell names should be like groupname-xx, for example: A549-1,A549-2, gefitinib-6,gefitinib-8..., name before the first '-' will be considered as the group name, so the groups are A549 and gefitinib. Otherwise, -g should be provided.
OUTPUT_PATH: output path

other parameters

  • ppm_threshold_peak: peak error threshold for peak selection in the same cell. Default:10
  • ppm_threshold_cell: peak error threshold when combining different cells. Default: 20
  • decrease: peak selection. Default:True
  • peak: whether the input file has selected peak. Default:False
  • filter: peaks appearing in less than a% cells will be filtered. Default:0.5
  • knn: use knn for missing value imputation. Default:True
  • n_neighbors: knn algorithm parameter. Default:5
  • method: dimensional reduction methods, including 'PLS','PCA','UMAP','tSNE'. Default:'PLS'
  • p_value: differential analysis parameter. Default:0.05
  • log2fold: differential analysis parameter. Default:0.5

Output

Output will be saved in the output folder including:

  • processed.csv: preprocessed data after peak selection and coordinate alignment
  • filtered.csv: results after peak filtering, peaks appearing in less than a% cells will be filtered (parameter: --filter)
  • knn.csv: results after knn imputation
  • PLS.txt: dimensional reduction result
  • de.csv: differential peaks among different groups (parameter: --p_value and --log2fold)
  • violinplot.png: violin plot correspond to processed.csv, show how many cells each peak involved in
  • embedding.pdf: dimensional reduction embedding
  • heatmap.png: heatmap of differential peaks among different groups

Help

Looking for more usages of scMA

scma.py --help