Package: iPRISM 0.1.1
iPRISM: Intelligent Predicting Response to Cancer Immunotherapy Through Systematic Modeling
Immunotherapy has revolutionized cancer treatment, but predicting patient response remains challenging. Here, we presented Intelligent Predicting Response to cancer Immunotherapy through Systematic Modeling (iPRISM), a novel network-based model that integrates multiple data types to predict immunotherapy outcomes. It incorporates gene expression, biological functional network, tumor microenvironment characteristics, immune-related pathways, and clinical data to provide a comprehensive view of factors influencing immunotherapy efficacy. By identifying key genetic and immunological factors, it provides an insight for more personalized treatment strategies and combination therapies to overcome resistance mechanisms.
Authors:
iPRISM_0.1.1.tar.gz
iPRISM_0.1.1.zip(r-4.5)iPRISM_0.1.1.zip(r-4.4)iPRISM_0.1.1.zip(r-4.3)
iPRISM_0.1.1.tgz(r-4.4-any)iPRISM_0.1.1.tgz(r-4.3-any)
iPRISM_0.1.1.tar.gz(r-4.5-noble)iPRISM_0.1.1.tar.gz(r-4.4-noble)
iPRISM_0.1.1.tgz(r-4.4-emscripten)iPRISM_0.1.1.tgz(r-4.3-emscripten)
iPRISM.pdf |iPRISM.html✨
iPRISM/json (API)
| # Install 'iPRISM' in R: |
| install.packages('iPRISM', repos = c('https://hanjunwei-lab.r-universe.dev', 'https://cloud.r-project.org')) |
- Seeds - Seed Node Names
- data.cell - Data.cell
- data.path - Data.path
- data_sig - Data_sig
- genelist_cp - TME gene list after random walks
- genelist_hla - HLA gene list after random walks
- genelist_imm - ICI gene list after random walks
- path_list - Path_list
- ppi - A protein-protein physical interaction network
- pred_value - Original Class Labels for Samples
This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.
Last updated 2 months agofrom:4c4a68f55e. Checks:OK: 1 NOTE: 6. Indexed: yes.
| Target | Result | Date |
|---|---|---|
| Doc / Vignettes | OK | Sep 13 2024 |
| R-4.5-win | NOTE | Sep 13 2024 |
| R-4.5-linux | NOTE | Sep 13 2024 |
| R-4.4-win | NOTE | Sep 13 2024 |
| R-4.4-mac | NOTE | Sep 13 2024 |
| R-4.3-win | NOTE | Sep 13 2024 |
| R-4.3-mac | NOTE | Sep 13 2024 |
Exports:cor_plotget_gsea_pathget_logiModelgseafun
Dependencies:backportsbase64encbslibcachemcheckmatecliclustercolorspacecpp11data.tabledigestdplyrevaluatefansifarverfastmapfontawesomeforeignFormulafsgenericsggplot2gluegridExtragtablehighrHmischtmlTablehtmltoolshtmlwidgetsigraphisobandjquerylibjsonliteknitrlabelinglatticelifecyclemagrittrMASSMatrixmemoisemgcvmimemunsellnlmennetpbapplypillarpkgconfigpurrrR6rappdirsRColorBrewerrlangrmarkdownrpartrstudioapisassscalesstringistringrtibbletidyrtidyselecttinytexutf8vctrsviridisviridisLitewithrxfunyaml
Readme and manuals
Help Manual
| Help page | Topics |
|---|---|
| Intelligent Predicting Response to Cancer Immunotherapy Through Systematic Modeling | iPRISM-package iPRISM |
| Correlation Plot with Significance Points | cor_plot |
| data_sig | data_sig |
| data.cell | data.cell |
| data.path | data.path |
| Enrichment Score Calculation | ESscore |
| Weighted Enrichment Score Calculation | ESscore_weighted |
| TME gene list after random walks | genelist_cp |
| HLA gene list after random walks | genelist_hla |
| ICI gene list after random walks | genelist_imm |
| Gene Set Enrichment Analysis (GSEA) using Multiplex Networks | get_gsea_path |
| Fit Logistic Regression Model | get_logiModel |
| Gene Set Enrichment Analysis (GSEA) Function | gseafun |
| path_list | path_list |
| A protein-protein physical interaction network (PPI network) | ppi |
| Original Class Labels for Samples | pred_value |
| Seed Node Names | Seeds |
