# Scoring and Marker Selection Method Based on Modified TF-IDF

## Documentation for package ‘smartid’ version 1.4.0

- DESCRIPTION file.
- User guides, package vignettes and other documentation.
- Package NEWS.

## Help Pages

cal_score | calculate combined score
cal_score-method | calculate combined score
cal_score_init | Calculate score for each feature in each cell
gs_score | compute overall score based on the given marker list
gs_score-method | compute overall score based on the given marker list
gs_score_init | Calculate scores of each cell on given features
iae | standard inverse average expression
iae_hdb | inverse average expression using hdbscan cluster as label
iae_igm | labeled inverse average expression: IGM
iae_m | inverse average expression: max
iae_prob | labeled inverse average expression: probability based
iae_rf | labeled inverse average expression: relative frequency
iae_sd | inverse average expression using standard deviation (SD)
idf | standard inverse cell frequency
idf_hdb | inverse document frequency using hdbscan cluster as label
idf_iae_methods | Get names of available IDF and IAE methods
idf_igm | labeled inverse cell frequency: IGM
idf_m | inverse cell frequency: max
idf_prob | labeled inverse cell frequency: probability based
idf_rf | labeled inverse cell frequency: relative frequency
idf_sd | inverse cell frequency using standard deviation (SD)
markers_hdbscan | select markers using HDBSCAN method
markers_mclust | select markers using mclust EM method
markers_mixmdl | select markers using mixtools EM method
ova_score_boxplot | boxplot of features overall score
scale_mgm | scale by mean of group mean for imbalanced data
score_barplot | barplot of processed score
sim_sce_test | scRNA-seq test data of 4 groups simulated by 'splatter'.
sin_score_boxplot | boxplot of split single feature score
tf | compute term/feature frequency within each cell
top_markers | scale score and return top markers
top_markers-method | scale score and return top markers
top_markers_abs | calculate group median, MAD or mean score and order genes based on scores
top_markers_glm | calculate group mean score using glm and order genes based on scores difference
top_markers_init | compute group summarized score and order genes based on processed scores