norm_rank: Ranks NanoString normalizations using cluster validity...

In MarthaCooper/NanoStringClustR: NanoString data normalization and differential gene expression analysis

Description

ranks NanoString normalisation by 1) clustering of groups and; 2) variation in relative log expression. Clustering is assessed by a Generalised Dunn Index using the average linkage and average diamter for inter and intracluster distances, respectively. Overall variation is assessed by Relative Log Expression.

Usage

norm_rank(count_set = count_set)

Arguments

count_set

a normalised, count_set summarized experiment generated by count_set.Default = NULL

Value

ranked normalizations

References

Lukasz Nieweglowski (2013). clv: Cluster Validation Techniques. R package version 0.3-2.1. https://CRAN.R-project.org/package=clv

Examples

# biological groups
rnf5_group <- c(rep("WT", 5), rep("KO", 5))

# sample ids
rnf5_sampleid <- c("GSM3638131", "GSM3638132", "GSM3638133", "GSM3638134",
                  "GSM3638135", "GSM3638136", "GSM3638137", "GSM3638138",
                  "GSM3638139", "GSM3638140")

# build count_set
rnf5_count_set <- count_set(count_data = Rnf5,
                            group = rnf5_group,
                            samp_id = rnf5_sampleid)
# normalize
rnf5_count_set_norm <- multi_norm(count_set = rnf5_count_set,
                                  positive_control_scaling = TRUE,
                                  background_correct = "mean2sd")
                                  #plot_dir = "~/Dropbox/git/NanoStringClustR/plot_test/")
# rank normalizations
rnf5_eval <- norm_rank(rnf5_count_set_norm)

MarthaCooper/NanoStringClustR documentation built on June 25, 2021, 9:41 p.m.