View source: R/differential_expression.R
findGiniMarkers | R Documentation |
Identify marker feats for selected clusters based on gini detection and expression scores.
findGiniMarkers(
gobject,
feat_type = NULL,
spat_unit = NULL,
expression_values = c("normalized", "scaled", "custom"),
cluster_column,
subset_clusters = NULL,
group_1 = NULL,
group_1_name = NULL,
group_2 = NULL,
group_2_name = NULL,
min_expr_gini_score = 0.2,
min_det_gini_score = 0.2,
detection_threshold = 0,
rank_score = 1,
min_feats = 5,
min_genes = NULL
)
gobject |
giotto object |
feat_type |
feature type |
spat_unit |
spatial unit |
expression_values |
feat 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_1_name |
custom name for group_1 clusters |
group_2 |
group 2 cluster IDs from cluster_column for pairwise comparison |
group_2_name |
custom name for group_2 clusters |
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 feat expression |
rank_score |
rank scores for both detection and expression to include |
min_feats |
minimum number of top feats to return |
min_genes |
deprecated, use min_feats |
Detection of marker feats using the https://en.wikipedia.org/wiki/Gini_coefficientgini coefficient is based on the following steps/principles per feat:
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 feat create combined score = detection rank x expression rank x expr gini-coefficient x detection gini-coefficient
7. for each feat sort on expression and detection rank and combined score
As a results "top gini" feats are feats 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 custom selected groups of cells you need to specify the cell_ID column to parameter cluster_column and provide the individual cell IDs to the parameters group_1 and group_2
By default group names will be created by pasting the different id names within each selected group. When you have many different ids in a single group it is recommend to provide names for both groups to group_1_name and group_2_name
data.table with marker feats
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