Full-DIA, a freely available software for single-cell diaPASEF data analysis that leverages deep learning to improve proteome coverage, quantitative accuracy and analysis speed. Most notably, Full-DIA is the first to automatically generate a missing-value-free protein matrix under global FDR control, which may offer superior biological interpretability and insight into single-cell proteomics data compared to conventional matrices with missing values.
We recommend using Conda to create a Python environment for using Full-DIA, whether on Windows or Linux.
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Create a Python environment with version 3.9.18.
conda create -n full_env python=3.9.18 "numpy<2.0.0" conda activate full_env -
Install the corresponding PyTorch and CuPy packages based on your CUDA version (which can be checked using the
nvidia-smicommand). Full-DIA requires an NVIDIA GPU with more than 10 GB of VRAM, a minimum of 64 GB RAM, and a high-performance Intel CPU.
- CUDA-12
pip install torch==2.3.1 --index-url https://download.pytorch.org/whl/cu121 pip install cupy-cuda12x==13.3 conda install cudatoolkit
- CUDA-11
pip install torch==2.3.1 --index-url https://download.pytorch.org/whl/cu118 pip install cupy-cuda11x==13.3 conda install cudatoolkit
- Install Full-DIA
pip install full_dia
full_dia -lib "Absolute path of the spectral library" -ws "Absolute path of the .d folder or a folder containing multiple .d folders"(Please note that the path needs to be enclosed in quotes if running on a Windows platform.)
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-lib
This parameter is used to specify the absolute path of the spectral library. Full-DIA currently supports the spectral library with the suffix .speclib predicted by DIA-NN (v1.9 and v1.9.1) or .parquet produced by DIA-NN (>= v1.9). It supports oxygen modifications on methionine (M) but does not include modifications such as phosphorylation or acetylation. Refer to this for instructions on how to generate prediction spectral libraries and convert to .parquet format using DIA-NN. Full-DIA will develop its own predictor capable of forecasting the peptide retention time, ion mobility, and fragmentation pattern. It may also be compatible with other formats of spectral libraries based on requests. -
-ws
This parameter specifies the folder that contains multiple .d directories to be analyzed.
Other optional params are list below by entering full_dia -h:
******************
* Full-DIA x.y.z *
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Usage: full_dia -ws WS -lib LIB
optional arguments for users:
-h, --help Show this help message and exit.
-ws WS Specify the folder that is .d or contains .d files.
-lib LIB Specify the absolute path of a .speclib or .parquet spectra library.
-out_name OUT_NAME Specify the folder name of outputs. Default: full_dia.
-gpu_id GPU_ID Specify the GPU-ID (e.g. 0, 1, 2) which will be used. Default: 0.
Full-DIA will generate report.log.txt and report.parquet in output folder.
The report.parquet contains precursor and protein IDs, as well as plenty of associated information.
Most column names are consistent with DIA-NN and are self-explanatory.
- Protein.Group - inferred proteins. Full-DIA uses IDPicker algorithm to infer proteins.
- Protein.Ids - all proteins matched to the precursor in the library.
- Protein.Names names (UniProt names) of the proteins in the Protein.Group.
- PG.Quantity.Raw raw quantity of the Protein.Group.
- PG.Quantity.Deep corrected quantity of the Protein.Group.
- Precursor.Id peptide seq + precursor charge.
- Precursor.Charge the charge of precursor.
- Q.Value run-specific precursor q-value.
- Global.Q.Value global precursor q-value.
- PG.Q.Value run-specific q-value for the protein group.
- Global.PG.Q.Value global q-value for the protein group.
- Proteotypic indicates the peptide is specific to a protein.
- Precursor.Quantity.Raw raw quantity of the precursor.
- Precursor.Quantity.Deep corrected quantity of the precursor.
- RT the retention time of the precursor.
- IM the ion mobility of the precursor.
- Please create a GitHub issue and we will respond as soon as possible.
- Email: [email protected]