umiAnalyzer report"

In umiAnalyzer: Tools for Analyzing Sequencing Data with Unique Molecular Identifiers

Import data

library(umiAnalyzer, quietly = TRUE)
library(DT, quietly = TRUE)

samples <- params$samples
assays <- params$assays
data <- params$data
theme<- params$theme
option<- params$colors
direction<- params$direction
y_min<- params$y_min
y_max<- params$y_max
plot.text<- params$plot_mutation
plot.ref<- params$plot_reference
classic.plot<- params$classic

UMI-based sequencing data processed with umi-error-correct was analysed in the umiVisualiser shiny app. Data for the following samples and assays will be shown:

samples
assays

Data visualizations {.tabset}

Amplicons

simsen <- umiAnalyzer::generateAmpliconPlots(
  object = data,
  do.plot = TRUE,
  amplicons = assays,
  samples = samples,
  cut.off = 5,
  theme = theme,
  option = option,
  direction = direction,
  y_min = y_min,
  y_max = y_max,
  plot.text = plot_mutation,
  plot.ref = plot_reference,
  classic.plot = classic
)

UMI counts

simsen <- umiAnalyzer::plotUmiCounts(
  object = data
)

Heatmap

simsen <- umiAnalyzer::amplicon_heatmap(
  object = data, 
  amplicons = assays, 
  samples = samples,
  filter.name = 'user_filter'
)

Data table

filter <- umiAnalyzer::getFilteredData(
  object = data,
  name = 'user_filter'
)

filter %>%
  dplyr::filter(Name %in% assays) %>%
  dplyr::filter(`Sample Name` %in% samples)

DT::datatable(filter)

System information

sessionInfo()

umiAnalyzer documentation built on Nov. 25, 2021, 9:07 a.m.