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This function is used to perform the differential expression analysis on sparse eset object. It supports there methods: "limma", "wilcoxon", and "t.test".

Usage

getDE(
  input_eset,
  group_by = "clusterID",
  g1 = NULL,
  g0 = NULL,
  use_method = "limmma"
)

Arguments

input_eset

The expression set object that filtered, normalized and log-transformed

group_by

Character, name of the column for grouping, usually the column of cell types or clusters. Default: "clusterID".

g1

A vector of character defining the fore-ground group or NULL. Default: NULL.

g0

A vector of character defining the back-ground group or NULL. Default: NULL.

use_method

Character, method used for differential analysis: "limma" (the default), "wilcoxon", and "t.test".

Value

A data frame. Rows are genes/drivers, and columns are 11 statistics of differential analysis.

Examples

data(pbmc14k_expression.eset)

## 1. To perform differential expression analysis in a 1-vs-rest manner for all groups in "clusterID" column
de_res <- getDE(input_eset = pbmc14k_expression.eset,
                group_by = "clusterID",
                use_method = "limma")
#> 7 groups were found in group_by column [ clusterID ].
#> Since no group was specified, the differential analysis will be conducted among all groups in the group_by column [ clusterID ] in the 1-vs-rest manner.
#> 	 1 / 7 : group 1 ( 1 ) vs the rest...
#> 	 634 cells were found for g1.
#> 	 2866 cells were found for g0.
#> 	 2 / 7 : group 1 ( 2 ) vs the rest...
#> 	 514 cells were found for g1.
#> 	 2986 cells were found for g0.
#> 	 3 / 7 : group 1 ( 3 ) vs the rest...
#> 	 508 cells were found for g1.
#> 	 2992 cells were found for g0.
#> 	 4 / 7 : group 1 ( 4 ) vs the rest...
#> 	 492 cells were found for g1.
#> 	 3008 cells were found for g0.
#> 	 5 / 7 : group 1 ( 5 ) vs the rest...
#> 	 501 cells were found for g1.
#> 	 2999 cells were found for g0.
#> 	 6 / 7 : group 1 ( 6 ) vs the rest...
#> 	 486 cells were found for g1.
#> 	 3014 cells were found for g0.
#> 	 7 / 7 : group 1 ( 7 ) vs the rest...
#> 	 365 cells were found for g1.
#> 	 3135 cells were found for g0.

## 2. To perform differential expression analysis in a 1-vs-rest manner for one specific group in "clusterID" column
de_res <- getDE(input_eset = pbmc14k_expression.eset,
                group_by = "clusterID",
                g1 = c("1"),
                use_method = "limma")
#> 7 groups were found in group_by column [ clusterID ].
#> Since g1 was specified but g0 was not, all the other cells except those of g1 will be defined as g0.
#> 	 1 / 1 : group 1 ( 1 ) vs group 0 ( 2, 3, 4, 5, 6, 7 ) ...
#> 	 634 cells were found for g1.
#> 	 2866 cells were found for g0.

## 3. To perform differential expression analysis in a rest-vs-1 manner for one specific group in "clusterID" column
de_res <- getDE(input_eset = pbmc14k_expression.eset,
                group_by = "clusterID",
                g0 = c("1"),
                use_method = "limma")
#> 7 groups were found in group_by column [ clusterID ].
#> Since g0 was specified but g1 was not, all the other cells except those of g0 will be defined as g1.
#> 	 1 / 1 : group 1 ( 2, 3, 4, 5, 6, 7 ) vs group 0 ( 1 ) ...
#> 	 2866 cells were found for g1.
#> 	 634 cells were found for g0.

## 4. To perform differential expression analysis in a 1-vs-1 manner for groups in "clusterID" column
de_res <- getDE(input_eset = pbmc14k_expression.eset,
                group_by = "clusterID",
                g1 = c("1"),
                g0 = c("3"),
                use_method = "limma")
#> 7 groups were found in group_by column [ clusterID ].
#> 	 1 / 1 : group 1 ( 1 ) vs group 0 ( 3 ) ...
#> 	 634 cells were found for g1.
#> 	 508 cells were found for g0.
#> Warning: Zero sample variances detected, have been offset away from zero