findGiniMarkers#

Date

2022-10-06

https://github.com/drieslab/Giotto/tree/suite/R/differential_expression.R#L329

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