svmATAC

Enhancement and imputation of peak signal enables accurate identification of cell type in scATAC-seq

svmATAC is fully open source and under MIT license.

Dependencies

• R 3.6
• Python 3.7
• Cicero 3.11

Quick Start

The pipeline of svmATAC is consist of two steps:

• 1.Pre-process:

  • If you are trying to reproduce the results in svmATAC paper from raw sequencing data(Can be found in preprocess/XXX/input folder), you may need this step.
  • If you are trying to reproduce the results in svmATAC paper using our provided data(Can be found in XXX-dataset/XXX/input folder), or your data are already merged to 0-1 matrix (peak-cell) and labelled, you may skip this step.

• 2.Training-Classification:

  • The training scripts and classification scripts are stored in bin/ folder of each experiment, you can try these scripts and reproduce the results in svmATAC paper through following chapters:
    • intra-dataset experiment
      • Corces2016
      • Buenrostro2018
      • 10xPBMCsV1
      • 10xPBMCsNextGem
      • 10xPBMCsV1-labeled
      • 10xPBMCsNextGem-labeled
    • inter-dataset experiment
      • 10xPBMCs-labeled
      • 10xPBMCs-unlabeled
  • For each single experiment:
    • All the scripts are available in the bin/ folder
      • All scripts are numbered and users should execute one by one.
      • The intermediate temporary files are stored in tmp/ folder and you can ignore these files.
    • All the input data required are available in the input/ folder
    • All the output data generated are stored in the output/ folder

Content

Pre-process

This chapter stores the scripts for processing raw data.

• The Corces2016 dataset (folder 'Corces2016')

  • bin
  • input
  • output

• The Buenrostro2018 dataset (folder 'Buenrostro2018')

  • bin
  • input
  • output

• The 10xPBMCsV1 dataset (folder '10xPBMCsV1')

  • bin
  • input
  • output

• The 10xPBMCsNextGem dataset (folder '10xPBMCsNextGem')

  • bin
  • input
  • output

This chapter stores the scripts for assigning labels to 10x PBMCs v1 and nextGem scATAC-seq data from labeled scRNA-seq data using Seurat.

• The scRNA-seq-5k-v3 dataset (folder 'scRNA-seq-5k-v3')

  • bin
  • data
  • output

• The scATAC-seq-5k-v1 dataset (folder 'scATAC-seq-5k-v1')

  • bin
  • input
  • output

• The scATAC-seq-5k-nextgem dataset (folder 'scATAC-seq-5k-nextgem')

  • bin
  • input
  • output

Training-Classification

intra-dataset

This chapter stores the scripts for intra-dataset experiments which are described in manuscript.

• The Corces2016 dataset (folder 'Corces2016')

  • bin
  • input
  • output

• The Buenrostro2018 dataset (folder 'Buenrostro2018')

  • bin
  • input
  • output

• The 10xPBMCsV1 dataset (folder '10xPBMCsV1')

  • bin
  • input
  • output

• The 10xPBMCsNextGem dataset (folder '10xPBMCsNextGem')

  • bin
  • input
  • output

• The 10xPBMCsV1-labeled dataset (folder '10xPBMCsV1_labeled')

  • bin
  • input
  • output

• The 10xPBMCsNextGem-labeled dataset (folder '10xPBMCsNextGem_labeled')

  • bin
  • input
  • output

inter-dataset

This chapter stores the scripts for inter-dataset experiments which are described in manuscript.

• The 10xPBMCs-labeled dataset (folder '10xPBMCs_labeled')

  • bin
  • input
  • output

• The 10xPBMCs-unlabeled dataset (folder '10xPBMCs_unlabeled')

  • bin
  • input
  • output