## ----setup, include = FALSE---------------------------------------------------
#rmarkdown::html_vignette
knitr::opts_knit$set(
self.contained = TRUE)
knitr::opts_chunk$set(
#collapse = TRUE,
dpi = 55,
fig.retina = 1,
comment = "#>"
)
require("genBaRcode")
require("ggplot2")
## ----eval=FALSE, collapse=TRUE------------------------------------------------
#
# if (!requireNamespace("BiocManager", quietly = TRUE)) {
# install.packages("BiocManager")
# }
#
# BiocManager::install(c("Biostrings", "ShortRead", "S4Vectors", "ggtree"))
#
## ----eval=FALSE, collapse=TRUE------------------------------------------------
#
# require("genBaRcode")
#
# bb <- "ACTNNCGANNCTTNNCGANNCTTNNGGANNCTANNACTNNCGANNCTTNNCGANNCTTNNGGANNCTANNACTNNCGANN"
# source_dir <- system.file("extdata", package = "genBaRcode")
#
# BC_data <- processingRawData(file_name = "test_data.fastq.gz",
# source_dir = source_dir,
# results_dir = "/my/results/directory/",
# mismatch = 0,
# label = "test",
# bc_backbone = bb,
# bc_backbone_label = "BC_1",
# min_score = 30,
# min_reads = 2,
# save_it = FALSE,
# seqLogo = FALSE,
# cpus = 1,
# strategy = "sequential",
# full_output = FALSE,
# wobble_extraction = TRUE,
# dist_measure = "hamming")
#
## ----eval=TRUE, collapse=TRUE-------------------------------------------------
getBackboneSelection()
bb <- getBackboneSelection(1)
show(bb)
bb <- getBackboneSelection("BC32-eBFP")
show(bb)
## ----eval=TRUE, collapse=TRUE-------------------------------------------------
bb <- "ACTNNCGANNCTTNNCGANNCTTNNGGANNCTANNACTNNCGANNCTTNNCGANNCTTNNGGANNCTANNACTNNCGANN"
source_dir <- system.file("extdata", package = "genBaRcode")
# if no results_dir is provided the source_dir automatically also becomes the results_dir
BC_data <- processingRawData(file_name = "test_data.fastq.gz",
source_dir = source_dir,
mismatch = 0,
label = "test",
bc_backbone = bb,
bc_backbone_label = "BC_1",
min_score = 30,
min_reads = 2,
save_it = FALSE,
seqLogo = FALSE,
cpus = 1,
strategy = "sequential",
full_output = FALSE,
wobble_extraction = TRUE,
dist_measure = "hamming")
## ----echo = FALSE, eval=TRUE, collapse=TRUE-----------------------------------
methods::slot(BC_data, "results_dir") <- "/my/results/dir/"
## ----eval=TRUE, collapse=TRUE-------------------------------------------------
show(BC_data)
## ----eval=TRUE, collapse=TRUE-------------------------------------------------
# if no results_dir is provided the source_dir automatically also becomes the results_dir
BC_data_multiple <- processingRawData(file_name = "test_data.fastq.gz",
source_dir = source_dir,
mismatch = 0,
label = "test",
bc_backbone = getBackboneSelection(1:2),
bc_backbone_label = c("BC_1", "BC_2"),
min_score = 30,
min_reads = 2,
save_it = FALSE,
seqLogo = FALSE,
cpus = 1,
strategy = "sequential",
full_output = FALSE,
wobble_extraction = FALSE,
dist_measure = "hamming")
## ----echo = FALSE, eval=TRUE, collapse=TRUE-----------------------------------
methods::slot(BC_data_multiple[[1]], "results_dir") <- "/my/results/dir/"
methods::slot(BC_data_multiple[[2]], "results_dir") <- "/my/results/dir/"
## ----eval=TRUE, collapse=TRUE-------------------------------------------------
show(BC_data_multiple)
## ----eval=TRUE, collapse=TRUE-------------------------------------------------
# if no results_dir is provided the source_dir automatically also becomes the results_dir
BC_data_2 <- processingRawData(file_name = "test_data.fastq.gz",
source_dir = source_dir,
mismatch = 4,
label = "test",
bc_backbone = "none",
min_score = 30,
min_reads = 2,
save_it = FALSE,
seqLogo = FALSE,
cpus = 1,
strategy = "sequential",
full_output = FALSE,
wobble_extraction = FALSE,
dist_measure = "hamming")
## ----echo = FALSE, eval=TRUE, collapse=TRUE-----------------------------------
methods::slot(BC_data_2, "results_dir") <- "/my/results/dir/"
## ----eval=TRUE, collapse=TRUE-------------------------------------------------
show(BC_data_2)
## ----eval=TRUE, collapse=TRUE-------------------------------------------------
show(BC_data)
## ----eval=TRUE, collapse=TRUE-------------------------------------------------
head(getReads(BC_data))
show(getResultsDir(BC_data))
show(getBackbone(BC_data))
show(getLabel(BC_data))
## ----eval=FALSE, collapse=TRUE------------------------------------------------
#
# BC_data <- setReads(BC_data, data.frame(read_count = c(1:5), barcode = letters[1:5]))
# BC_data <- setResultsDir(BC_data, "/my/test/folder/")
# BC_data <- setBackbone(BC_data, "AAANNNNGGG")
# BC_data <- setLabel(BC_data, "new label")
#
## ----eval=FALSE, collapse=TRUE------------------------------------------------
#
# BC_data <- readBCdat(path = "/my/test/folder/",
# label = "test",
# BC_backbone = "AAANNNNCCCC",
# file_name = "test.csv",
# s = ";")
#
## ----eval=FALSE, collapse=TRUE------------------------------------------------
#
# BC_data_EC <- errorCorrection(BC_dat = BC_data,
# maxDist = 4,
# save_it = FALSE,
# cpus = 1,
# strategy = "sequential",
# m = "hamming",
# type = "standard",
# only_EC_BCs = TRUE,
# EC_analysis = FALSE,
# start_small = TRUE)
#
## ----eval=TRUE, collapse=TRUE-------------------------------------------------
BC_data_EC <- errorCorrection(BC_dat = BC_data,
maxDist = 4,
save_it = FALSE,
cpus = 1,
strategy = "sequential",
m = "hamming",
type = "standard",
only_EC_BCs = TRUE,
EC_analysis = FALSE,
start_small = TRUE)
## ----eval=TRUE, collapse=TRUE-------------------------------------------------
show(BC_data_EC)
## ----eval=TRUE, collapse=TRUE-------------------------------------------------
BC_data_EC <- errorCorrection(BC_dat = BC_data,
maxDist = 4,
save_it = FALSE,
cpus = 1,
strategy = "sequential",
m = "hamming",
type = "standard",
only_EC_BCs = TRUE,
EC_analysis = FALSE,
start_small = FALSE)
## ----eval=TRUE, collapse=TRUE-------------------------------------------------
show(BC_data_EC)
## ----eval=TRUE, collapse=TRUE-------------------------------------------------
BC_data_EC <- errorCorrection(BC_dat = BC_data,
maxDist = 4,
save_it = FALSE,
cpus = 1,
strategy = "sequential",
m = "hamming",
type = "graph based",
only_EC_BCs = TRUE,
EC_analysis = FALSE,
start_small = FALSE)
## ----eval=TRUE, collapse=TRUE-------------------------------------------------
show(BC_data_EC)
## ----eval=TRUE, collapse=TRUE-------------------------------------------------
BC_data_EC <- errorCorrection(BC_dat = BC_data,
maxDist = 4,
save_it = FALSE,
cpus = 1,
strategy = "sequential",
m = "hamming",
type = "connectivity based",
only_EC_BCs = TRUE,
EC_analysis = FALSE,
start_small = FALSE)
## ----eval=TRUE, collapse=TRUE-------------------------------------------------
show(BC_data_EC)
## ----eval=TRUE, collapse=TRUE-------------------------------------------------
BC_data_EC <- errorCorrection(BC_dat = BC_data,
maxDist = 4,
save_it = FALSE,
cpus = 1,
strategy = "sequential",
m = "hamming",
type = "clustering",
only_EC_BCs = TRUE,
EC_analysis = FALSE,
start_small = FALSE)
## ----eval=TRUE, collapse=TRUE-------------------------------------------------
show(BC_data_EC)
## ----eval=TRUE, fig.width=2.5, fig.height=2, fig.pos = 'H', fig.align='center', fig.show='asis', collapse=TRUE----
s_dir <- system.file("extdata", package = "genBaRcode")
plotNucFrequency(source_dir = s_dir, file_name = "test_data.fastq.gz")
## ----eval=TRUE, fig.height=1.5, fig.width=5, fig.pos = 'H', fig.align='center', fig.show='asis', collapse=TRUE----
plotQualityScoreDis(source_dir = s_dir, file_name = "test_data.fastq.gz", type = "mean")
## ----eval=TRUE, fig.height=1.5, fig.width=5, fig.pos = 'H', fig.align='center', fig.show='asis', collapse=TRUE----
plotQualityScoreDis(source_dir = s_dir, file_name = "test_data.fastq.gz", type = "median")
## ----eval=TRUE, fig.width=6, fig.height=4, fig.pos = 'H', fig.align='center', fig.show='asis', collapse=TRUE----
plotQualityScorePerCycle(source_dir = s_dir, file_name = "test_data.fastq.gz")
## ----eval=TRUE, fig.width=6.5, fig.height=1.5, fig.pos = 'H', fig.align='center', fig.show='asis', collapse=TRUE----
show(BC_data)
plotSeqLogo(BC_dat = BC_data, colrs = NULL)
## ----eval=TRUE, fig.width=6.5, fig.height=1.5, fig.pos = 'H', fig.align='center', fig.show='asis', collapse=TRUE----
# color order correlates to the following nucleotide order A, T, C, G, N
col_vec <- c("#000000",
"#000000",
RColorBrewer::brewer.pal(6, "Paired")[c(5, 6)],
"#000000")
show(col_vec)
plotSeqLogo(BC_dat = BC_data, colrs = col_vec)
## ----eval=TRUE, fig.width=6, fig.height=2, fig.pos = 'H', fig.align='center', fig.show='asis', collapse=TRUE----
show(BC_data)
generateKirchenplot(BC_dat = BC_data)
## ----eval=FALSE, fig.width=6, fig.height=2, fig.pos = 'H', fig.align='center', fig.show='asis', collapse=TRUE----
#
# known_BCs <- c("GGTCGAAGCTTCTTTCGGGCCGCACGGCTGCT",
# "CACGATCCGCTTCTATCGCGTGCACTACATGT",
# "ATTGGGTCCGTCTGAGGGCGTTTCTGCGCCTT")
#
# generateKirchenplot(BC_dat = BC_data, ori_BCs = known_BCs)
#
## ----echo=FALSE, eval=TRUE, fig.width=6, fig.height=2, fig.pos = 'H', fig.align='center', fig.show='asis', collapse=TRUE----
known_BCs <- c("GGTCGAAGCTTCTTTCGGGCCGCACGGCTGCT",
"CACGATCCGCTTCTATCGCGTGCACTACATGT",
"ATTGGGTCCGTCTGAGGGCGTTTCTGCGCCTT")
generateKirchenplot(BC_dat = BC_data, ori_BCs = known_BCs) + ggplot2::theme(legend.text = ggplot2::element_text(size = 6),
legend.key.size = ggplot2::unit(4, "mm"),
legend.title = ggplot2::element_text(size = 7))
## ----eval=TRUE, fig.width=7.2, fig.height=4, fig.pos = 'H', fig.align='center', fig.show='asis', collapse=TRUE----
known_BCs <- c("GGTCGAAGCTTCTTTCGGGCCGCACGGCTGCT",
"CACGATCCGCTTCTATCGCGTGCACTACATGT",
"ATTGGGTCCGTCTGAGGGCGTTTCTGCGCCTT")
contaminations <- c("CACGATCCGCTTCTATCGCGTGCACTACATGC",
"ATTGGGTCCGTCTGAGGGCGTCTCTGCGCCTT",
"CACGATCCGCTTCTATCGCGTGCGCTACATGT",
"TACGATCCGCTTCTATCGCGTGCACTACATGT")
generateKirchenplot(BC_dat = BC_data, ori_BCs = known_BCs, ori_BCs2 = contaminations)
## ----eval=FALSE, fig.width=7.2, fig.height=4, fig.align="center", fig.cap="Figure 5.4: Extracted barcodes and their abundancies.", collapse=TRUE----
#
# known_BCs <- c("GGTCGAAGCTTCTTTCGGGCCGCACGGCTGCT",
# "CACGATCCGCTTCTATCGCGTGCACTACATGT",
# "ATTGGGTCCGTCTGAGGGCGTTTCTGCGCCTT")
# contaminations <- c("CACGATCCGCTTCTATCGCGTGCACTACATGC",
# "ATTGGGTCCGTCTGAGGGCGTCTCTGCGCCTT",
# "CACGATCCGCTTCTATCGCGTGCGCTACATGT",
# "TACGATCCGCTTCTATCGCGTGCACTACATGT")
#
# generateKirchenplot(BC_dat = BC_data,
# ori_BCs = known_BCs, ori_BCs2 = contaminations,
# setLabels = c("known BCs", "stuff", "contaminations"),
# loga = TRUE, col_type = "wild", m = "lv")
#
## ----eval=TRUE, fig.width=2.5, fig.height=2, fig.pos = 'H', fig.align='center', fig.show='asis', collapse=TRUE----
plotReadFrequencies(BC_dat = BC_data)
## ----eval=FALSE, fig.width=2.5, fig.height=2, fig.pos = 'H', fig.align='center', fig.show='hold', collapse=TRUE----
#
# plotReadFrequencies(BC_dat = BC_data, log = TRUE)
# plotReadFrequencies(BC_dat = BC_data, dens = TRUE)
#
## ----eval=FALSE, fig.width=2.5, fig.height=2, fig.pos = 'H', fig.align='center', fig.show='hold', collapse=TRUE----
#
# plotReadFrequencies(BC_dat = BC_data, bw = 30)
# plotReadFrequencies(BC_dat = BC_data, b = 30)
#
## ----eval=FALSE---------------------------------------------------------------
#
# plotDistanceVisNetwork(BC_dat = BC_data, minDist = 1, loga = TRUE, m = "hamming")
# plotDistanceIgraph(BC_dat = BC_data, minDist = 1, loga = TRUE, m = "hamming")
#
## ----eval=TRUE, fig.width=3, fig.height=3, fig.pos = 'H', fig.align='center', fig.show='asis', collapse=TRUE----
ggplotDistanceGraph(BC_dat = BC_data, minDist = 1, loga = TRUE, m = "hamming")
## ----eval=TRUE, fig.width=4.5, fig.height=3, fig.pos = 'H', fig.align='center', fig.show='asis', collapse=TRUE----
known_BCs <- c("GGTCGAAGCTTCTTTCGGGCCGCACGGCTGCT",
"CACGATCCGCTTCTATCGCGTGCACTACATGT",
"ATTGGGTCCGTCTGAGGGCGTTTCTGCGCCTT")
ggplotDistanceGraph(BC_dat = BC_data,
minDist = 1, loga = TRUE, m = "hamming",
ori_BCs = known_BCs, lay = "circle", complete = FALSE,
col_type = "topo.colors", legend_size = 2)
## ----eval=FALSE, collapse=TRUE------------------------------------------------
#
# createGDF(BC_dat = BC_data, minDist = 1, loga = TRUE, m = "hamming")
#
## ----eval=TRUE, fig.width=4, fig.height=4, fig.pos = 'H', fig.align='center', fig.show='hold', collapse=TRUE----
plotClusterTree(BC_dat = BC_data, tree_est = "UPGMA",
type = "fan", tipLabel = FALSE, m = "hamming")
## ----eval=TRUE, fig.width=3, fig.height=3, fig.pos = 'H', fig.align='center', fig.show='hold', collapse=TRUE----
plotClusterGgTree(BC_dat = BC_data, tree_est = "NJ",
type = "rectangular", m = "hamming")
## ----eval=FALSE, collapse=TRUE------------------------------------------------
#
# BC_data_EC <- errorCorrection(BC_dat = BC_data,
# maxDist = 4,
# save_it = FALSE,
# cpus = 1,
# strategy = "sequential",
# m = "hamming",
# type = "standard",
# only_EC_BCs = FALSE,
# EC_analysis = TRUE,
# start_small = FALSE)
#
# error_correction_clustered_HDs(datEC = BC_data_EC, size = 0.75)
#
## ----echo=FALSE, fig.width=2, fig.height=2.5, fig.pos = 'H', fig.align='center', fig.show='hold', collapse=TRUE----
BC_data_EC <- errorCorrection(BC_dat = BC_data,
maxDist = 4,
save_it = FALSE,
cpus = 1,
strategy = "sequential",
m = "hamming",
type = "standard",
only_EC_BCs = FALSE,
EC_analysis = TRUE,
start_small = FALSE)
error_correction_clustered_HDs(datEC = BC_data_EC, size = 0.75) + ggplot2::theme(axis.title = ggplot2::element_text(size = 8))
## ----eval=TRUE, fig.width=4, fig.height=4, fig.pos = 'H', fig.align='center', fig.show='hold', collapse=TRUE----
error_correction_circlePlot(edges = BC_data_EC$edges, vertices = BC_data_EC$vertices)
## ----eval=TRUE, fig.width=3, fig.height=3, fig.pos = 'H', fig.align='center', fig.show='hold', collapse=TRUE----
error_correction_treePlot(edges = BC_data_EC$edges, vertices = BC_data_EC$vertices)
## ----eval=TRUE, fig.width=4, fig.height=4, fig.pos = 'H', fig.align='center', fig.show='hold', collapse=TRUE----
ggplotDistanceGraph_EC(BC_dat = BC_data, BC_dat_EC = BC_data_EC,
minDist = 1, loga = TRUE, m = "hamming")
## ----eval=FALSE, fig.width=3, fig.height=3, fig.pos = 'H', fig.align='center', fig.show='hold', collapse=TRUE----
#
# plotDistanceVisNetwork_EC(BC_dat = BC_data, BC_dat_EC = BC_data_EC,
# minDist = 1, loga = TRUE, m = "hamming")
#
## ----eval=TRUE, fig.width=3, fig.height=3, fig.pos = 'H', fig.align='center', fig.show='hold', collapse=TRUE----
known_BCs <- c("GGTCGAAGCTTCTTTCGGGCCGCACGGCTGCT",
"CACGATCCGCTTCTATCGCGTGCACTACATGT",
"ATTGGGTCCGTCTGAGGGCGTTTCTGCGCCTT")
ggplotDistanceGraph_EC(BC_dat = BC_data, BC_dat_EC = BC_data_EC,
minDist = 1, loga = TRUE, m = "hamming", ori_BCs = known_BCs)
## ----eval=FALSE, collapse=TRUE------------------------------------------------
#
# plotDistanceVisNetwork_EC(BC_dat = BC_data, BC_dat_EC = BC_data_EC,
# minDist = 1, loga = TRUE, m = "hamming", ori_BCs = known_BCs)
#
## ----eval=TRUE, fig.width=3, fig.height=3, fig.pos = 'H', fig.align='center', fig.show='hold', collapse=TRUE----
known_BCs <- c("GGTCGAAGCTTCTTTCGGGCCGCACGGCTGCT",
"CACGATCCGCTTCTATCGCGTGCACTACATGT",
"ATTGGGTCCGTCTGAGGGCGTTTCTGCGCCTT")
ggplotDistanceGraph_EC(BC_dat = BC_data, BC_dat_EC = BC_data_EC,
minDist = 1, loga = TRUE, m = "hamming", BC_threshold = 2)
## ----eval=TRUE, , fig.width=3, fig.height=2.5, fig.pos = 'H', fig.align='center', fig.show='hold', collapse=TRUE----
# path to the package internal data file
source_dir <- system.file("extdata", package = "genBaRcode")
BC_data_tp1 <- processingRawData(file_name = "test_data.fastq.gz",
source_dir,
mismatch = 10,
label = "tp1",
bc_backbone = getBackboneSelection(1),
bc_backbone_label = "BC_1",
min_score = 10,
save_it = FALSE)
BC_data_tp1 <- errorCorrection(BC_data_tp1, maxDist = 2)
BC_data_tp2 <- processingRawData(file_name = "test_data.fastq.gz",
source_dir,
mismatch = 1,
label = "tp2",
bc_backbone = getBackboneSelection(1),
bc_backbone_label = "BC_1",
min_score = 30,
min_reads = 1000,
save_it = FALSE)
BC_data_tp2 <- errorCorrection(BC_data_tp2, maxDist = 4, type = "clustering")
BC_data_tp3 <- processingRawData(file_name = "test_data.fastq.gz",
source_dir,
mismatch = 0,
label = "tp3",
bc_backbone = getBackboneSelection(1),
bc_backbone_label = "BC_1",
min_score = 37,
save_it = FALSE)
BC_data_tp3 <- errorCorrection(BC_data_tp3, maxDist = 8, type = "graph based")
BC_list <- list(BC_data_tp1, BC_data_tp2, BC_data_tp3)
BC_matrix <- generateTimeSeriesData(BC_dat_list = BC_list)
plotTimeSeries(ov_dat = BC_matrix)
plotVennDiagram(BC_dat = BC_list)
## ----eval=FALSE, collapse=TRUE------------------------------------------------
#
# # choose colors
# test_colors <- RColorBrewer::brewer.pal(12, "Set3")
#
# plotTimeSeries(ov_dat = BC_matrix[1:12, ],
# colr = test_colors, tp = c(1,3,4),
# x_label = "test data", y_label = "test freqs")
#
# plotVennDiagram(BC_dat = BC_list, alpha_value = 0.25,
# colrs = c("green", "red", "blue"), border_color = "orange",
# plot_title = "this is the title",
# legend_sort = c("tp2_EC", "tp3_EC", "tp1_EC"),
# annotationSize = 2.5)
#
## ----eval=FALSE---------------------------------------------------------------
#
# # start Shiny app with the package internal test data file
# genBaRcode_app()
#
# # start Shiny app with access to a predefined directory
# genBaRcode_app(dat_dir = "/my/test/directory/")
#
## ----eval=TRUE, out.width = 40, collapse=TRUE---------------------------------
getBackboneSelection()
bb <- getBackboneSelection(1)
show(bb)
bb <- getBackboneSelection("BC32-eBFP")
show(bb)
## ----eval=FALSE, collapse=TRUE------------------------------------------------
#
# BC_data <- readBCdat(path = "/my/test/firectory", label = "test_label", s = ";",
# BC_backbone = "ACTNNGGCNNTGANN", file_name = "test_file.csv")
#
## ----eval=FALSE, collapse=TRUE------------------------------------------------
#
# test_data_frame <- data.frame(read_count = seq(100, 400, 100),
# barcode = c("AAAAAAAA", "GGGGGGGG",
# "TTTTTTTT", "CCCCCCCC"))
#
# BC_data <- asBCdat(dat = test_data_frame,
# label = "test_label",
# BC_backbone = "CCCNNAAANNTTTNNGGGNN",
# resDir = "/my/results/directory/")
#
## ----eval=TRUE, collapse=TRUE-------------------------------------------------
test_data_frame <- data.frame(read_count = seq(100, 400, 100),
barcode = c("AAAAAAAA", "GGGGGGGG",
"TTTTTTTT", "CCCCCCCC"))
## ----eval=TRUE, collapse=TRUE-------------------------------------------------
show(test_data_frame)
## ----eval=TRUE, collapse=TRUE-------------------------------------------------
BC_data_1 <- asBCdat(dat = test_data_frame,
label = "test_label_1",
BC_backbone = "CCCNNAAANNTTTNNGGGNN",
resDir = getwd())
test_data_frame <- data.frame(read_count = c(300, 99, 150, 400),
barcode = c("TTTTTTTT", "AATTTAAA",
"GGGGGGGG", "CCCCCCCC"))
## ----eval=TRUE, collapse=TRUE-------------------------------------------------
show(test_data_frame)
## ----eval=TRUE, collapse=TRUE-------------------------------------------------
BC_data_2 <- asBCdat(dat = test_data_frame,
label = "test_label_2",
BC_backbone = "CCCNNAAANNTTTNNGGGNN",
resDir = getwd())
test <- genBaRcode:::com_pair(BC_dat1 = BC_data_1, BC_dat2 = BC_data_2)
## ----eval=TRUE, collapse=TRUE-------------------------------------------------
show(test)