Package: iimi 1.2.1
iimi: Identifying Infection with Machine Intelligence
A novel machine learning method for plant viruses diagnostic using genome sequencing data. This package includes three different machine learning models, random forest, XGBoost, and elastic net, to train and predict mapped genome samples. Mappability profile and unreliable regions are introduced to the algorithm, and users can build a mappability profile from scratch with functions included in the package. Plotting mapped sample coverage information is provided.
Authors:
iimi_1.2.1.tar.gz
iimi_1.2.1.zip(r-4.5)iimi_1.2.1.zip(r-4.4)iimi_1.2.1.zip(r-4.3)
iimi_1.2.1.tgz(r-4.4-any)iimi_1.1.1.tgz(r-4.3-any)
iimi_1.2.1.tar.gz(r-4.5-noble)iimi_1.2.1.tar.gz(r-4.4-noble)
iimi_1.2.1.tgz(r-4.4-emscripten)iimi_1.2.1.tgz(r-4.3-emscripten)
iimi.pdf |iimi.html✨
iimi/json (API)
| # Install 'iimi' in R: |
| install.packages('iimi', repos = c('https://ubcxzhang.r-universe.dev', 'https://cloud.r-project.org')) |
- example_cov - Coverage profiles of three plant samples.
- example_diag - Known diagnostics result of virus segments
- nucleotide_info - Nucleotide information of virus segments
- trained_en - A trained model using the default Elastic Net settings
- trained_rf - A trained model using the default Random Forest settings
- trained_xgb - A trained model using the default XGBoost settings
- unreliable_regions - The unreliable regions of the virus segments
This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.
Last updated 22 days agofrom:5faed8fe98. Checks:OK: 6 WARNING: 1. Indexed: yes.
| Target | Result | Date |
|---|---|---|
| Doc / Vignettes | OK | Nov 02 2024 |
| R-4.5-win | OK | Nov 02 2024 |
| R-4.5-linux | WARNING | Nov 02 2024 |
| R-4.4-win | OK | Nov 02 2024 |
| R-4.4-mac | OK | Nov 02 2024 |
| R-4.3-win | OK | Nov 02 2024 |
| R-4.3-mac | OK | Oct 25 2024 |
Exports:convert_bam_to_rleconvert_rle_to_dfcreate_high_nucleotide_contentcreate_mappability_profileplot_covpredict_iimitrain_iimi
Dependencies:abindaskpassBHBiobaseBiocGenericsBiocParallelBiostringsbitopscaretclasscliclockcodetoolscolorspacecpp11crayoncurldata.tableDelayedArraydiagramdigestdplyre1071fansifarverforeachformatRfutile.loggerfutile.optionsfuturefuture.applygenericsGenomeInfoDbGenomeInfoDbDataGenomicAlignmentsGenomicRangesggplot2glmnetglobalsgluegowergtablehardhathttripredIRangesisobanditeratorsjsonliteKernSmoothlabelinglambda.rlatticelavalifecyclelistenvlubridatemagrittrMASSMatrixMatrixGenericsmatrixStatsmgcvmimemltoolsModelMetricsMTPSmunsellnlmennetnumDerivopensslparallellypillarpkgconfigplyrpROCprodlimprogressrproxypurrrR.methodsS3R.ooR.utilsR6randomForestrbibutilsRColorBrewerRcppRcppEigenRdpackrecipesreshape2RhtslibrlangrpartRsamtoolsS4ArraysS4VectorsscalesshapesnowSparseArraySQUAREMstringistringrSummarizedExperimentsurvivalsystibbletidyrtidyselecttimechangetimeDatetzdbUCSC.utilsutf8vctrsviridisLitewithrxgboostXVectorzlibbioc
Readme and manuals
Help Manual
| Help page | Topics |
|---|---|
| convert_bam_to_rle | convert_bam_to_rle |
| Convert run-length encodings (RLEs) to a data frame. | convert_rle_to_df |
| create_high_nucleotide_content | create_high_nucleotide_content |
| create_mappability_profile | create_mappability_profile |
| Coverage profiles of three plant samples. | example_cov |
| Known diagnostics result of virus segments | example_diag |
| Nucleotide information of virus segments | nucleotide_info |
| plot_cov() | plot_cov |
| predict_iimi() | predict_iimi |
| train_iimi() | train_iimi |
| A trained model using the default Elastic Net settings | trained_en |
| A trained model using the default Random Forest settings | trained_rf |
| A trained model using the default XGBoost settings | trained_xgb |
| The unreliable regions of the virus segments | unreliable_regions |
