Package: smartid 1.3.2

Jinjin Chen

smartid: Scoring and Marker Selection Method Based on Modified TF-IDF

This package enables automated selection of group specific signature, especially for rare population. The package is developed for generating specifc lists of signature genes based on Term Frequency-Inverse Document Frequency (TF-IDF) modified methods. It can also be used as a new gene-set scoring method or data transformation method. Multiple visualization functions are implemented in this package.

Authors:Jinjin Chen [aut, cre]

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smartid.pdf |smartid.html✨
smartid/json (API)
NEWS

# Install 'smartid' in R:
install.packages('smartid', repos = c('https://bioc.r-universe.dev', 'https://cloud.r-project.org'))

Peer review:

Bug tracker:https://github.com/davislaboratory/smartid/issues

Pkgdown:https://davislaboratory.github.io

Datasets:
  • sim_sce_test - ScRNA-seq test data of 4 groups simulated by 'splatter'.

On BioConductor:smartid-1.3.2(bioc 3.21)smartid-1.2.0(bioc 3.20)

softwaregeneexpressiontranscriptomics

4.40 score 1 stars 2 scripts 174 downloads 15 exports 94 dependencies

Last updated 1 months agofrom:a93906501d. Checks:OK: 7. Indexed: yes.

TargetResultDate
Doc / VignettesOKDec 13 2024
R-4.5-winOKDec 13 2024
R-4.5-linuxOKDec 13 2024
R-4.4-winOKDec 13 2024
R-4.4-macOKDec 13 2024
R-4.3-winOKSep 24 2024
R-4.3-macOKSep 24 2024

Exports:cal_scoregs_scoregs_score_initidf_iae_methodsmarkers_hdbscanmarkers_mclustmarkers_mixmdlova_score_boxplotscale_mgmscore_barplotsin_score_boxplottop_markerstop_markers_abstop_markers_glmtop_markers_init

Dependencies:abindaskpassbase64encBiobaseBiocGenericsbslibcachemclicolorspacecpp11crayoncrosstalkcurldata.tableDelayedArraydigestdplyrevaluatefansifarverfastmapfontawesomefsgenericsGenomeInfoDbGenomeInfoDbDataGenomicRangesggplot2gluegtablehighrhtmltoolshtmlwidgetshttrIRangesisobandjquerylibjsonlitekernlabknitrlabelinglaterlatticelazyevallifecyclemagrittrMASSMatrixMatrixGenericsmatrixStatsmclustmemoisemgcvmimemixtoolsmunsellnlmeopensslpillarpkgconfigplotlypromisespurrrR6rappdirsRColorBrewerRcpprlangrmarkdownS4ArraysS4VectorssassscalessegmentedSparseArraysparseMatrixStatsstringistringrSummarizedExperimentsurvivalsystibbletidyrtidyselecttinytexUCSC.utilsutf8vctrsviridisLitewithrxfunXVectoryamlzlibbioc

A quick start guide to smartid: Scoring and MARker selection method based on modified Tf-IDf

Jinjin Chen

Rendered fromsmartid_Demo.Rmdusingknitr::rmarkdownon Dec 13 2024.

Last update: 2024-03-29
Started: 2024-01-08

Readme and manuals

Help Manual

Help pageTopics
calculate combined scorecal_score cal_score,AnyMatrix-method cal_score,SummarizedExperiment-method
Calculate score for each feature in each cellcal_score_init
compute overall score based on the given marker listgs_score gs_score,AnyMatrix,ANY-method gs_score,AnyMatrix,list-method gs_score,SummarizedExperiment,ANY-method
Calculate scores of each cell on given featuresgs_score_init
standard inverse average expressioniae
inverse average expression using hdbscan cluster as labeliae_hdb
labeled inverse average expression: IGMiae_igm
inverse average expression: maxiae_m
labeled inverse average expression: probability basediae_prob
labeled inverse average expression: relative frequencyiae_rf
inverse average expression using standard deviation (SD)iae_sd
standard inverse cell frequencyidf
inverse document frequency using hdbscan cluster as labelidf_hdb
Get names of available IDF and IAE methodsidf_iae_methods
labeled inverse cell frequency: IGMidf_igm
inverse cell frequency: maxidf_m
labeled inverse cell frequency: probability basedidf_prob
labeled inverse cell frequency: relative frequencyidf_rf
inverse cell frequency using standard deviation (SD)idf_sd
select markers using HDBSCAN methodmarkers_hdbscan
select markers using mclust EM methodmarkers_mclust
select markers using mixtools EM methodmarkers_mixmdl
boxplot of features overall scoreova_score_boxplot
scale by mean of group mean for imbalanced datascale_mgm
barplot of processed scorescore_barplot
scRNA-seq test data of 4 groups simulated by 'splatter'.sim_sce_test
boxplot of split single feature scoresin_score_boxplot
compute term/feature frequency within each celltf
scale score and return top markerstop_markers top_markers,AnyMatrix-method top_markers,SummarizedExperiment-method
calculate group median, MAD or mean score and order genes based on scorestop_markers_abs
calculate group mean score using glm and order genes based on scores differencetop_markers_glm
compute group summarized score and order genes based on processed scorestop_markers_init