This is a Neoantigen database project, Used for data preprocessing
- /script is a folder with all .R scripts for data preprocessing and data statistic.
- /data is a folder with all data related with your package(attention:This is for your package not for your projects, It could be generated automatically).
- /data-raw is a folder with all datasets you would like to put them in your package +/data_project is a folder with all data related with you project.
- /R is for All function I would like to create a R package.
- /man is a document folder for R function, It could be generated by document() function automatically.
- DESCRIPTION is a file where contain your package meta information.
- Change R function under /R folder.
- Add Documentation comment in front of R code.
- Update DESCRIPTION file if their have a need.
- library(devtools)
- document() #update all document file.
- build() # Build all R function again.
- You can upload your new R package now.
- create a new project use Rstudio version control system. restudio-git-github
- Copy DESCRIPTION and NAMESPACE model files under this folder. make a Rpackage
- mkdir data_project/script folder(This is for your project,all folder or file related with making package could be generate automatically).
4. Add all file you need and update with git button.
- You can also make sample data in your package.(Add data in your Rpackage)
Attention: Please don't forget to git push after you add your commit information.
Contact: If their are any question, please file free to contact with author:[email protected]
1. mhc_ligand_full_select_col.csvstrange_peptide.csv
1.1 For all peptides in Epitope_Description column with "[+]", I only kept the first half of the peptide: For example:changed the peptide from " MLVLLV + FORM(M1)" to "MLVLLV", And I put them into "peptide_change" column; For this kind of situation, There is no need to change classification of peptide but just keeping original classifcation situation.
1.1 For all peptides in Epitope_Description column with "[+]", I only kept the first half of the peptide: For example:changed the peptide from " MLVLLV + FORM(M1)" to "MLVLLV", And I put them into "peptide_change" column; For this kind of situation, There is no need to change classification of peptide but just keeping original classifcation situation.
1.2 For all peptides in Epitope_Description column with "-" and complex form, There is no need to change anything but we need to change all of them into other's classification. For example:
By the way, I also put the changed matrix into a new file, which was named as mhc_ligand_full_select_col.csvstrange_peptide_v2.tsv
2. CancerPPD_CNRD_strange_peptide.csv
2.1 The column of "Peptide_change" is the final peptides form.
2.2 The column of "Peptide_Type" is the classification of change peptides. "Other":means change related peptide to "Other" classification; "no_change":means that we still use the original classification situation.
2.3 Details are showed below:
By the way, I also put the changed matrix into a new file, which was named as ancerPPD_CNRD_strange_peptide_v2.csv
3. tcell_full_v3_select_col.csvstrange_peptide.csv
The description of this file is same as 2; by the way,I also put the changed matrix into a new file, which was named as tcell_full_v3_select_col.csvstrange_peptide_v2.tsv
**4. Neoantigen_CNRD.csvstrange_peptide_v2.tsv **
The description of this file is same as 2;
A position we need to pay attention to is we need to split the original peptide into two peptide, for example: ALWPWLLMA(T) to ALWPWLLMA/ALWPWLLMT, So after preprocessing, we can get ten peptides now.
by the way,I also put the changed matrix into a new file, which was named as Neoantigen_CNRD.csvstrange_peptide_v2.tsv
5. TCGA_TCR_seq_CNRD_strange_peptide.csv We don't need Peptide columns, we Just write it as "CDR3AA", Then if the CDR3AA is wired, Just delete them.
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This is a description of odd disease, we should classify all disease as tissue, detail as follow:
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For file: Neoantigen_CNRD.csv_disease_v2.csv we need to seperate each peptide into different tissue and then classify it.
If you have any question: please feel free to connect me:[email protected].