Identify marker genes for selected clusters based on gini detection and expression scores.
findGiniMarkers( gobject, expression_values = c("normalized", "scaled", "custom"), cluster_column, subset_clusters = NULL, group_1 = NULL, group_2 = NULL, min_expr_gini_score = 0.2, min_det_gini_score = 0.2, detection_threshold = 0, rank_score = 1, min_genes = 5 )
| gobject | giotto object |
|---|---|
| expression_values | gene expression values to use |
| cluster_column | clusters to use |
| subset_clusters | selection of clusters to compare |
| group_1 | group 1 cluster IDs from cluster_column for pairwise comparison |
| group_2 | group 2 cluster IDs from cluster_column for pairwise comparison |
| min_expr_gini_score | filter on minimum gini coefficient for expression |
| min_det_gini_score | filter on minimum gini coefficient for detection |
| detection_threshold | detection threshold for gene expression |
| rank_score | rank scores for both detection and expression to include |
| min_genes | minimum number of top genes to return |
data.table with marker genes
Detection of marker genes using the https://en.wikipedia.org/wiki/Gini_coefficientgini coefficient is based on the following steps/principles per gene:
1. calculate average expression per cluster
2. calculate detection fraction per cluster
3. calculate gini-coefficient for av. expression values over all clusters
4. calculate gini-coefficient for detection fractions over all clusters
5. convert gini-scores to rank scores
6. for each gene create combined score = detection rank x expression rank x expr gini-coefficient x detection gini-coefficient
7. for each gene sort on expression and detection rank and combined score
As a results "top gini" genes are genes that are very selectivily expressed in a specific cluster, however not always expressed in all cells of that cluster. In other words highly specific, but not necessarily sensitive at the single-cell level.
To perform differential expression between cluster groups you need to specificy cluster IDs to the parameters group_1 and group_2.
data(mini_giotto_single_cell) gini_markers = findGiniMarkers(gobject = mini_giotto_single_cell, cluster_column = 'leiden_clus', group_1 = 1, group_2 = 2)