exploreRob: exploreRob: function to explore the robustness

In samExploreR: samExploreR package: high-performance read summarisation to count vectors with avaliability of sequencing depth reduction simulation

Description

This function explores the robustness of analysis with sequencing depth altering

Usage

exploreRob(df_d, lbl, f_vect)

Arguments

df_d	a dataframe containing the dataset to explore with 3 columns : label, f ratio, value to compare (e.g. number of differentially expressed genes)
lbl	a character string specifing the label for which the analysis should be run
f_vect	A numeric vector containing the values of f for which the analysis should be run

Details

exploreRob function to explore the robustness of the analysis with altering of sequencing depth. It runs ANOVA test for values to compare (e.g. number of differentially expressed genes) corresponding to different f ratio values (i.e. values of sequencing depth)

This function takes as input a dataframe containing the dataset to explore.

Here is the example of the dataframe

...
AnnotA	0.1	13
AnnotB	0.1	101	
AnnotC	0.1	36
AnnotA	0.1	13
AnnotB	0.1	101	
AnnotC	0.1	36
AnnotA	0.4	40
AnnotB	0.4	153	
AnnotC	0.4	62
AnnotA	0.8	71
AnnotB	0.8	203
AnnotC	0.8	160
...

exploreRob function subsets the dataset to consider only valyes for one type of annotation and runs ANOVA test for groups corresponding to f values of interest.

Value

An output of aov function

Author(s)

Alexey Stupnikov and Shailesh Tripathi

Examples

#library(samExploreR)
data("df_sole")
#run ANOVA for annotation type labeled 'New, Gene' and f values 0.9, 0.95
exploreRob(df_sole, lbl = 'New, Gene', f_vect = c(0.9, 0.95))


#run ANOVA for annotation type labeled 'Old' and f values 0.5, 0.95
exploreRob(df_sole, lbl = 'Old, Gene', f_vect = c(0.5, 0.95))

samExploreR documentation built on Nov. 8, 2020, 5:45 p.m.