findGiniMarkers

findGiniMarkers

Description

Identify marker feats for selected clusters based on gini detection and expression scores.

Usage

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
)

Arguments

Argument	Description
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

Details

Detection of marker feats using the https://en.wikipedia.org/wiki/Gini_coefficient gini

coefficient is based on the following steps/principles per feat:

• 1. calculate average expression per cluster

• 1. calculate detection fraction per cluster

• 1. calculate gini-coefficient for av. expression values over all clusters

• 1. calculate gini-coefficient for detection fractions over all clusters

• 1. convert gini-scores to rank scores

• 1. for each feat create combined score = detection rank x expression rank x expr gini-coefficient x detection gini-coefficient

• 1. 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

Value

data.table with marker feats