In malhamdoosh/abseqR: Reporting and data analysis functionalities for Rep-Seq datasets of antibody libraries
# Buidling rmd using loops: https://github.com/rstudio/flexdashboard/issues/80
library(flexdashboard)
library(plotly)
library(png)
library(grid)
library(gridExtra)
.getFile <- function(path, pattern, .stop = FALSE, .callback = NULL) {
flist <- list.files(path = path, pattern = pattern, full.names = TRUE)
n <- length(flist)
if (n != 1 && is.null(.callback)) {
if (.stop) {
stop("Expected to find one file, but found ", n, " from ",
paste(flist, collapse = ", "), " instead.")
} else {
return(NA)
}
} else if (!is.null(.callback)) {
flist <- flist[lapply(flist, .callback) == TRUE]
if (length(flist) != 1) {
if (.stop) {
stop("Expected to find one file, but found ", n, " from ",
paste(flist, collapse = ", "), " instead.")
} else {
return(NA)
}
}
}
return(flist[[1]])
}
.compositionNoGene <- function(x) {
return(length(grep("-", basename(x), fixed = TRUE)) == 0)
}
r if(!params$hasAnnot) {"\\begin{comment}"}
Summary {data-orientation=rows data-icon="fa-list-ul"}
Row
r paste(knitr::knit(text = knitr::knit_expand(text = paste("### Overview of", params$name))))
sampleNames <- unlist(strsplit(params$name, "_vs_"))
# counts
splitRaw <- unlist(strsplit(params$rawReads, ","))
splitAnnot <- unlist(strsplit(params$annotReads, ","))
splitFiltered <- unlist(strsplit(params$filteredReads, ","))
splitProd <- unlist(strsplit(params$productiveReads, ","))
# filtering
splitBit <- unlist(strsplit(params$bitfilters, ","))
splitAl <- unlist(strsplit(params$alignfilters, ","))
splitSStart <- unlist(strsplit(params$sstartfilters, ","))
splitQStart <- unlist(strsplit(params$qstartfilters, ","))
overviewOut <- lapply(seq_along(sampleNames), function(i) {
rawR <- as.numeric(splitRaw[i])
annotR <- as.numeric(splitAnnot[i])
filtR <- as.numeric(splitFiltered[i])
# might be NA if productivity analysis wasn't conducted
prodR <- suppressWarnings(as.numeric(splitProd[i]))
line1 <- knitr::knit_expand(text = sprintf("#### %s\n\n", sampleNames[i]))
line2 <- knitr::knit_expand(text = sprintf("* Number of raw reads %s\n", prettyNum(rawR, big.mark = ",")))
line3 <- knitr::knit_expand(text = sprintf("* Number of annotated reads %s%% (%s/%s)\n", round((annotR/rawR)*100, 2) , prettyNum(annotR, big.mark = ","), prettyNum(rawR, big.mark = ",")))
line4 <- knitr::knit_expand(text = sprintf("* Number of reads after filtering %s%% (%s/%s)\n",
round((filtR/annotR)*100, 2), prettyNum(filtR, big.mark = ","), prettyNum(annotR, big.mark = ",")))
line5 <- knitr::knit_expand(text = sprintf("* Number of productive reads %s%% (%s/%s)\n", round((prodR/filtR)*100, 2), prettyNum(prodR, big.mark = ","), prettyNum(filtR, big.mark = ",")))
line6 <- knitr::knit_expand(text = "\nThe following criteria are applied on the _V gene only_\n")
line7 <- knitr::knit_expand(text = "\n\n|Filtering criterion|Value|\n")
line8 <- knitr::knit_expand(text = "|--------------------|:-----:|\n")
line9 <- knitr::knit_expand(text = sprintf("|Bitscore | %s|\n", splitBit[i]))
line10 <- knitr::knit_expand(text = sprintf("|V gene alignment length | %s|\n", splitAl[i]))
line11 <- knitr::knit_expand(text = sprintf("|Subject start position | %s|\n", splitSStart[i]))
line12 <- knitr::knit_expand(text = sprintf("|Query start position | %s|\n", splitQStart[i]))
line13 <- knitr::knit_expand(text = "\n\nSequences that do not satisfy the above filtering criteria are _excluded_ from the analysis.\n\n")
paste0(line1, line2, line3, line4, line5, line6, line7, line8, line9,
line10, line11, line12, line13)
})
r paste(knitr::knit(text = paste(overviewOut, collapse = "\n")))
Row
Sequence lengths without outlier removal
fname <- .getFile(path = file.path(params$rootDir, "annot"),
pattern = ".*_all_clones_len_dist\\.Rdata")
if (!is.na(fname)) {
load(fname)
if (params$interactive) {
ggplotly(plot)
} else {
plot
}
} else {
ggplot(data.frame()) + xlim(0, 10) + ylim(0, 10) + annotate("text", x = 5, y = 5, label = "Plot unavailable")
}
Row
Sequence lengths with outlier removal
fname <- .getFile(path = file.path(params$rootDir, "annot"),
pattern = ".*_all_clones_len_dist_no_outliers\\.Rdata")
if (!is.na(fname)) {
load(fname)
if (params$interactive) {
ggplotly(plot)
} else {
plot
}
} else {
ggplot(data.frame()) + xlim(0, 10) + ylim(0, 10) + annotate("text", x = 5, y = 5, label = "Plot unavailable")
}
r if(!params$hasAnnot) {"\\end{comment}"}
r if(!params$hasAbun) {"\\begin{comment}"}
Quality {data-orientation=rows data-icon="fa-clipboard-list"}
Row
Indels (Gaps)
fname <- .getFile(path = file.path(params$rootDir, "abundance"),
pattern = ".*_igv_gaps_dist\\.Rdata")
if (!is.na(fname)) {
load(fname)
if (params$interactive) {
ggplotly(plot)
} else {
plot
}
} else {
ggplot(data.frame()) + xlim(0, 10) + ylim(0, 10) + annotate("text", x = 5, y = 5, label = "Plot unavailable")
}
Plot of insertions and deletions found within the V gene
Mismatches
fname <- .getFile(path = file.path(params$rootDir, "abundance"),
pattern = ".*_igv_mismatches_dist\\.Rdata")
if (!is.na(fname)) {
load(fname)
if (params$interactive) {
ggplotly(plot)
} else {
plot
}
} else {
ggplot(data.frame()) + xlim(0, 10) + ylim(0, 10) + annotate("text", x = 5, y = 5, label = "Plot unavailable")
}
Plot of nucleotide base mismatches found within the V gene
r if(!params$hasAbun) {"\\end{comment}"}
r if(!params$hasAbun || !params$single) {"\\begin{comment}"}
Row
.renderHMText <- function(alignQual) {
fileInteractiveness <- if (params$interactive) "" else "_static"
alignmentQuality <- lapply(seq_along(alignQual), function(i) {
line1 <- knitr::knit_expand(text = sprintf("### %s\n", names(alignQual)[i]))
line2 <- knitr::knit_expand(text = sprintf("\n```r", alignQual[[i]]))
line3 <- knitr::knit_expand(text = sprintf('\nfname <- .getFile(path = file.path(params$rootDir, "abundance"), pattern = ".*_igv_align_quality_%s_hm%s\\\\.Rdata")', alignQual[[i]], fileInteractiveness))
line4 <- knitr::knit_expand(text = '\nif(!is.na(fname)) { load(fname); plot } else { ggplot(data.frame()) + xlim(0, 10) + ylim(0, 10) + annotate("text", x = 5, y = 5, label = "404!") }')
line5 <- knitr::knit_expand(text = "\n```\n")
paste(line1, line2, line3, line4, line5, collapse = "\n")
})
return(alignmentQuality)
}
alignQual <- list("Bitscore vs Alignment Length" = "bitscore",
"Identity vs Alignment Length" = "identity")
alignmentQuality <- .renderHMText(alignQual)
r if (params$hasAbun && params$single) { paste(knitr::knit(text = paste(alignmentQuality, collapse = "\n"))) }
Row
alignQual <- list("Gaps vs Alignment Length" = "gaps",
"Mismatches vs Alignment Length" = "mismatches")
alignmentQuality.pt2 <- .renderHMText(alignQual)
r if (params$hasAbun && params$single) { paste(knitr::knit(text = paste(alignmentQuality.pt2, collapse = "\n"))) }
Row
alignQual <- list("Subject Start vs Query Start" = "start")
alignmentQuality.pt3 <- .renderHMText(alignQual)
r if (params$hasAbun && params$single) { paste(knitr::knit(text = paste(alignmentQuality.pt3, collapse = "\n"))) }
Placeholder
ggplot(data.frame()) + xlim(0, 10) + ylim(0, 10) + annotate("text", x = 5, y = 5, label = "Placeholder")
r if(!params$hasAbun || !params$single) {"\\end{comment}"}
r if(!params$hasAbun) {"\\begin{comment}"}
Abundance {data-orientation=rows data-icon="fa-chart-bar"}
Row
V family
fname <- .getFile(path = file.path(params$rootDir, "abundance"),
pattern = ".*igv_dist_family_level\\.Rdata")
if (!is.na(fname)) {
load(fname)
if (params$interactive) {
ggplotly(plot)
} else {
plot
}
} else {
ggplot(data.frame()) + xlim(0, 10) + ylim(0, 10) + annotate("text", x = 5, y = 5, label = "Plot unavailable")
}
V gene
fname <- .getFile(path = file.path(params$rootDir, "abundance"),
pattern = ".*igv_dist_gene_level\\.Rdata")
if (!is.na(fname)) {
load(fname)
# TODO: ggplotly is buggy with horizontal plot in tabsets
if (params$interactive) {
plot
#ggplotly(plot)
} else {
plot
}
} else {
ggplot(data.frame()) + xlim(0, 10) + ylim(0, 10) + annotate("text", x = 5, y = 5, label = "Plot unavailable")
}
Only the top 15 from each sample are shown
r if(!params$hasAbun) {"\\end{comment}"}
r if(!params$inclD || !params$hasAbun) {"\\begin{comment}"}
Row
D family
fname <- .getFile(path = file.path(params$rootDir, "abundance"),
pattern = ".*igd_dist_family_level\\.Rdata")
if (!is.na(fname)) {
load(fname)
if (params$interactive) {
ggplotly(plot)
} else {
plot
}
} else {
ggplot(data.frame()) + xlim(0, 10) + ylim(0, 10) + annotate("text", x = 5, y = 5, label = "Plot unavailable")
}
D gene
fname <- .getFile(path = file.path(params$rootDir, "abundance"),
pattern = ".*igd_dist_gene_level\\.Rdata")
if (!is.na(fname)) {
load(fname)
# TODO: ggplotly is buggy with horizontal plot in tabsets
if (params$interactive) {
plot
#ggplotly(plot)
} else {
plot
}
} else {
ggplot(data.frame()) + xlim(0, 10) + ylim(0, 10) + annotate("text", x = 5, y = 5, label = "Plot unavailable")
}
Only the top 15 from each sample are shown
r if(!params$inclD || !params$hasAbun) {"\\end{comment}"}
r if(!params$hasAbun) {"\\begin{comment}"}
Row
J family
fname <- .getFile(path = file.path(params$rootDir, "abundance"),
pattern = ".*igj_dist_family_level\\.Rdata")
if (!is.na(fname)) {
load(fname)
if (params$interactive) {
ggplotly(plot)
} else {
plot
}
} else {
ggplot(data.frame()) + xlim(0, 10) + ylim(0, 10) + annotate("text", x = 5, y = 5, label = "Plot unavailable")
}
J variant
fname <- .getFile(path = file.path(params$rootDir, "abundance"),
pattern = ".*igj_dist_variant_level\\.Rdata")
if (!is.na(fname)) {
load(fname)
# TODO: ggplotly is buggy with horizontal plot in tabsets
if (params$interactive) {
plot
#ggplotly(plot)
} else {
plot
}
} else {
ggplot(data.frame()) + xlim(0, 10) + ylim(0, 10) + annotate("text", x = 5, y = 5, label = "Plot unavailable")
}
Only the top 15 from each sample are shown
r if(!params$hasAbun) {"\\end{comment}"}
r if(!params$hasAbun || !params$single) {"\\begin{comment}"}
Row
V-J association
fname <- .getFile(path = file.path(params$rootDir, "abundance"), pattern = ".*_vjassoc\\.png")
if (!is.na(fname)) {
img <- readPNG(fname)
grid.raster(img)
} else {
ggplot(data.frame()) + xlim(0, 10) + ylim(0, 10) + annotate("text", x = 5, y = 5, label = "Plot unavailable")
}
Plot associations between V genes and J genes on the family level
r if(!params$hasAbun || !params$single) {"\\end{comment}"}
r if(!params$hasProd) {"\\begin{comment}"}
Productivity {data-orientation=rows data-icon="fa-check"}
Row
Productivity summary
fname <- .getFile(path = file.path(params$rootDir, "productivity"),
pattern = ".*_productivity\\.Rdata")
if (!is.na(fname)) {
load(fname)
if (params$interactive) {
# TODO: buggy facet plot
plot
# ggplotly(plot)
} else {
plot
}
} else {
ggplot(data.frame()) + xlim(0, 10) + ylim(0, 10) + annotate("text", x = 5,
y = 5, label =
"404!")
}
Productivity distribution of sample based on stop codons and frameshifts
Frameshift distribution
fname <- .getFile(path = file.path(params$rootDir, "productivity"),
pattern = ".*_vjframe_dist\\.Rdata")
if (!is.na(fname)) {
load(fname)
if (params$interactive) {
ggplotly(plot)
} else {
plot
}
} else {
ggplot(data.frame()) + xlim(0, 10) + ylim(0, 10) + annotate("text", x = 5,
y = 5, label =
"404!")
}
Distribution of in-frame and out-of-frame sequences
Row {.tabset .tabset-fade}
titles <- c("FR1", "CDR1", "FR2", "CDR2", "FR3", "CDR3")
frcdrMisGaps <- lapply(seq_along(titles), function(i) {
line1 <- knitr::knit_expand(text = sprintf("### %s\n", titles[i]))
line2 <- knitr::knit_expand(text = sprintf("\n```r", i))
line3 <- knitr::knit_expand(text = sprintf('\nfname <- .getFile(file.path(params$rootDir, "productivity"), pattern = ".*_%s_mismatches_dist\\\\.Rdata")', tolower(titles[i])))
line4 <- knitr::knit_expand(text = sprintf('\nif (!is.na(fname)) { load(fname); if (params$interactive) { mismatch <- ggplotly(plot + labs(title = "Mismatches, gaps and gaps in out-of-frame sequences in %s")) } else { mismatch <- plot } } else { df <- data.frame(); mismatch <- ggplot(df) + xlim(0, 10) + ylim(0, 10) + annotate("text", x = 5, y = 5, label = "Plot unavailable") }', titles[i]))
line5 <- knitr::knit_expand(text = sprintf('\nfname <- .getFile(file.path(params$rootDir, "productivity"), pattern = ".*_%s_gaps_dist\\\\.Rdata")', tolower(titles[i])))
line6 <- knitr::knit_expand(text = '\nif (!is.na(fname)) { load(fname); if (params$interactive) { gaps <- ggplotly(plot + labs(title = "")) } else { gaps <- plot } } else { df <- data.frame(); gaps <- ggplot(df) + xlim(0, 10) + ylim(0, 10) + annotate("text", x = 5, y = 5, label = "Plot unavailable")}')
line7 <- knitr::knit_expand(text = sprintf('\nfname <- .getFile(file.path(params$rootDir, "productivity"), pattern = ".*_%s_gaps_dist_out_of_frame\\\\.Rdata")', tolower(titles[i])))
line8 <- knitr::knit_expand(text = '\nif (!is.na(fname)) { load(fname); if (params$interactive) { gapsOut <- ggplotly(plot + labs(title = "")) } else { gapsOut <- plot } } else { df <- data.frame(); gapsOut <- ggplot(df) + xlim(0, 10) + ylim(0, 10) + annotate("text", x = 5, y = 5, label = "Plot unavailable") }')
line9 <- knitr::knit_expand(text = "\nif (params$interactive) { subplot(mismatch, gaps, gapsOut, titleX = TRUE, titleY = TRUE) } else { grid.arrange(mismatch, gaps, gapsOut, nrow = 1)}")
line10 <- knitr::knit_expand(text = "\n```\n")
line11 <- knitr::knit_expand(text = sprintf("\n> Proportion of mismatches in productive sequences, gaps in productive sequences, and gaps in out-of-frame sequences in %s region\n", titles[i]))
paste(line1, line2, line3, line4, line5, line6, line7, line8, line9, line10,
line11, collapse = "\n")
})
if (params$inclD) {
titles <- c("IGV", "IGD", "IGJ")
} else {
titles <- c("IGV", "IGJ")
}
vdjMisGaps <- lapply(seq_along(titles), function(i) {
line1 <- knitr::knit_expand(text = sprintf("### %s\n", titles[i]))
line2 <- knitr::knit_expand(text = sprintf("\n```r", i))
line3 <- knitr::knit_expand(text = sprintf('\nfname <- .getFile(file.path(params$rootDir, "productivity"), pattern = ".*_%s_mismatches_dist\\\\.Rdata")', tolower(titles[i])))
line4 <- knitr::knit_expand(text = sprintf('\nif (!is.na(fname)) { load(fname); if (params$interactive) { mismatch <- ggplotly(plot + labs(title = "Mismatches and gaps in %s")) } else { mismatch <- plot } } else { df <- data.frame(); mismatch <- ggplot(df) + xlim(0, 10) + ylim(0, 10) + annotate("text", x = 5, y = 5, label = "Plot unavailable") }', titles[i]))
line5 <- knitr::knit_expand(text = sprintf('\nfname <- .getFile(file.path(params$rootDir, "productivity"), pattern = ".*_%s_gaps_dist\\\\.Rdata")', tolower(titles[i])))
line6 <- knitr::knit_expand(text = '\nif (!is.na(fname)) { load(fname); if (params$interactive) { gaps <- ggplotly(plot + labs(title = "")) } else { gaps <- plot } } else { df <- data.frame(); gaps <- ggplot(df) + xlim(0, 10) + ylim(0, 10) + annotate("text", x = 5, y = 5, label = "Plot unavailable") }')
line7 <- knitr::knit_expand(text = "\nif (params$interactive) { subplot(mismatch, gaps, titleX = TRUE, titleY = TRUE) } else { grid.arrange(mismatch, gaps, nrow = 1)}")
line8 <- knitr::knit_expand(text = "\n```\n")
line9 <- knitr::knit_expand(text = sprintf("\n> Proportion of mismatches and gaps in productive sequences in %s\n", titles[i]))
paste(line1, line2, line3, line4, line5, line6, line7, line8, line9, collapse = "\n")
})
r paste(knitr::knit(text = paste(frcdrMisGaps, collapse = "\n")))
r paste(knitr::knit(text = paste(vdjMisGaps, collapse = "\n")))
Row {.tabset .tabset-fade}
IGV productive distribution
fname <- .getFile(path = file.path(params$rootDir, "productivity"),
pattern = ".*_igv_dist_productive\\.Rdata")
if (!is.na(fname)) {
load(fname)
if (params$interactive) {
ggplotly(plot)
} else {
plot
}
} else {
ggplot(data.frame()) + xlim(0, 10) + ylim(0, 10) + annotate("text", x = 5, y = 5, label = "Plot unavailable")
}
Distribution of productive sequences from IGV family
IGV inframe, unproductive distribution
fname <- .getFile(path = file.path(params$rootDir, "productivity"),
pattern = ".*_igv_dist_inframe_unproductive\\.Rdata")
if (!is.na(fname)) {
load(fname)
if (params$interactive) {
ggplotly(plot)
} else {
plot
}
} else {
ggplot(data.frame()) + xlim(0, 10) + ylim(0, 10) + annotate("text", x = 5, y = 5, label = "Plot unavailable")
}
Distribution of in-frame but unproductive sequences from IGV family
IGV out-of-frame distribution
fname <- .getFile(path = file.path(params$rootDir, "productivity"),
pattern = ".*_igv_dist_out_of_frame\\.Rdata")
if (!is.na(fname)) {
load(fname)
if (params$interactive) {
ggplotly(plot)
} else {
plot
}
} else {
ggplot(data.frame()) + xlim(0, 10) + ylim(0, 10) + annotate("text", x = 5, y = 5, label = "Plot unavailable")
}
Distribution of out-of-frame sequences from IGV family
Row {.tabset .tabset-fade}
Inframe
fname <- .getFile(path = file.path(params$rootDir, "productivity"),
pattern = ".*_stopcodon_region_inframe\\.Rdata", .stop = FALSE)
if (is.na(fname)) {
ggplot(data.frame()) + xlim(0, 10) + ylim(0, 10) + annotate("text", x = 5, y = 5, label = "Plot unavailable")
} else {
load(fname)
if (params$interactive) {
ggplotly(plot)
} else {
plot
}
}
Distribution of stop codons in CDR/FR regions from in-frame sequences
Out-of-frame
fname <- .getFile(path = file.path(params$rootDir, "productivity"),
pattern = ".*_stopcodon_region_outframe\\.Rdata", .stop = FALSE)
if (is.na(fname)) {
ggplot(data.frame()) + xlim(0, 10) + ylim(0, 10) + annotate("text", x = 5, y = 5, label = "Plot unavailable")
} else {
load(fname)
if (params$interactive) {
ggplotly(plot)
} else {
plot
}
}
Distribution of stop codons in CDR/FR regions from out-of-frame sequences
r if(!params$hasProd) {"\\end{comment}"}
r if(!params$hasDiv) {"\\begin{comment}"}
Diversity {data-orientation=rows data-icon="fa-chart-line"}
Row {.tabset .tabset-fade}
CDR-V
fname <- .getFile(path = file.path(params$rootDir, "diversity"),
pattern = ".*_cdr_v_duplication\\.Rdata")
if (!is.na(fname)) {
load(fname)
dup <- plot + labs(title = "Sequence duplication level, rarefaction, and recapture")
} else {
dup <- ggplot(data.frame()) + xlim(0, 10) + ylim(0, 10) + annotate("text", x = 5, y = 5, label = "Plot unavailable")
}
fname <- .getFile(path = file.path(params$rootDir, "diversity"),
pattern = ".*_cdr_v_rarefaction\\.Rdata")
if (!is.na(fname)) {
load(fname)
rare <- plot + labs(title = "")
} else {
rare <- ggplot(data.frame()) + xlim(0, 10) + ylim(0, 10) + annotate("text", x = 5, y = 5, label = "Plot unavailable")
}
fname <- .getFile(path = file.path(params$rootDir, "diversity"),
pattern = ".*_cdr_v_recapture\\.Rdata")
if (!is.na(fname)) {
load(fname)
recap <- plot + labs(title = "")
} else {
recap <- ggplot(data.frame()) + xlim(0, 10) + ylim(0, 10) + annotate("text", x = 5, y = 5, label = "Plot unavailable")
}
if (params$interactive) {
dup <- hide_legend(dup)
rare <- hide_legend(rare)
p <- subplot(hide_legend(dup), hide_legend(rare), recap,
titleX = TRUE, titleY = TRUE)
.p <- plotly_build(p)
for (i in seq_along(.p$x$data)) {
if (length(grep(",\\d+(,NA)?\\)$", .p$x$data[[i]]$name))) {
.p$x$data[[i]]$showlegend <- FALSE
}
}
.p
} else {
grid.arrange(dup, rare, recap, nrow = 1)
}
Duplication level, rarefaction and recapture plots for CDR (regions) and
(V)ariable domain
CDR
fname <- .getFile(path = file.path(params$rootDir, "diversity"),
pattern = ".*_cdr_duplication\\.Rdata")
if (!is.na(fname)) {
load(fname)
dup <- plot + labs(title = "Sequence duplication level, rarefaction, and recapture")
} else {
dup <- ggplot(data.frame()) + xlim(0, 10) + ylim(0, 10) + annotate("text", x = 5, y = 5, label = "Plot unavailable")
}
fname <- .getFile(path = file.path(params$rootDir, "diversity"),
pattern = ".*_cdr_rarefaction\\.Rdata")
if (!is.na(fname)) {
load(fname)
rare <- plot + labs(title = "")
} else {
rare <- ggplot(data.frame()) + xlim(0, 10) + ylim(0, 10) + annotate("text", x = 5, y = 5, label = "Plot unavailable")
}
fname <- .getFile(path = file.path(params$rootDir, "diversity"),
pattern = ".*_cdr_recapture\\.Rdata")
if (!is.na(fname)) {
load(fname)
recap <- plot + labs(title = "")
} else {
recap <- ggplot(data.frame()) + xlim(0, 10) + ylim(0, 10) + annotate("text", x = 5, y = 5, label = "Plot unavailable")
}
if (params$interactive) {
dup <- hide_legend(dup)
rare <- hide_legend(rare)
p <- subplot(hide_legend(dup), hide_legend(rare), recap,
titleX = TRUE, titleY = TRUE)
.p <- plotly_build(p)
for (i in seq_along(.p$x$data)) {
if (length(grep(",\\d+(,NA)?\\)$", .p$x$data[[i]]$name))) {
.p$x$data[[i]]$showlegend <- FALSE
}
}
.p
} else {
grid.arrange(dup, rare, recap, nrow = 1)
}
Duplication level, rarefaction and recapture plots for CDR (regions)
FR
fname <- .getFile(path = file.path(params$rootDir, "diversity"),
pattern = ".*_fr_duplication\\.Rdata")
if (!is.na(fname)) {
load(fname)
dup <- plot + labs(title = "Sequence duplication level, rarefaction, and recapture")
} else {
dup <- ggplot(data.frame()) + xlim(0, 10) + ylim(0, 10) + annotate("text", x = 5, y = 5, label = "Plot unavailable")
}
fname <- .getFile(path = file.path(params$rootDir, "diversity"),
pattern = ".*_fr_rarefaction\\.Rdata")
if (!is.na(fname)) {
load(fname)
rare <- plot + labs(title = "")
} else {
rare <- ggplot(data.frame()) + xlim(0, 10) + ylim(0, 10) + annotate("text", x = 5, y = 5, label = "Plot unavailable")
}
fname <- .getFile(path = file.path(params$rootDir, "diversity"),
pattern = ".*_fr_recapture\\.Rdata")
if (!is.na(fname)) {
load(fname)
recap <- plot + labs(title = "")
} else {
recap <- ggplot(data.frame()) + xlim(0, 10) + ylim(0, 10) + annotate("text", x = 5, y = 5, label = "Plot unavailable")
}
if (params$interactive) {
dup <- hide_legend(dup)
rare <- hide_legend(rare)
p <- subplot(hide_legend(dup), hide_legend(rare), recap,
titleX = TRUE, titleY = TRUE)
.p <- plotly_build(p)
for (i in seq_along(.p$x$data)) {
if (length(grep(",\\d+(,NA)?\\)$", .p$x$data[[i]]$name))) {
.p$x$data[[i]]$showlegend <- FALSE
}
}
.p
} else {
grid.arrange(dup, rare, recap, nrow = 1)
}
Duplication level, rarefaction and recapture plots for FR (regions)
Row {.tabset .tabset-fade}
regions <-
c(
"FR1" = "_fr1_spectratype",
"CDR1" = "_cdr1_spectratype",
"FR2" = "_fr2_spectratype",
"CDR2" = "_cdr2_spectratype",
"FR3" = "_fr3_spectratype",
"CDR3" = "_cdr3_spectratype",
"CDR3 without outliers" = "_cdr3_spectratype_no_outliers",
"FR4" = "_fr4_spectratype",
"V domain" = "_v_spectratype"
)
specOut <- lapply(seq_along(regions), function(i) {
line1 <- knitr::knit_expand(text = sprintf("### %s\n", names(regions)[[i]]))
line2 <- knitr::knit_expand(text = sprintf("\n```r\n", i))
line3 <- knitr::knit_expand(text = sprintf('\nfname <- .getFile(path = file.path(params$rootDir, "diversity", "spectratypes"), pattern = ".*%s\\\\.Rdata")\n', regions[[i]]))
line4 <- knitr::knit_expand(text = '\nif (!is.na(fname)) { load(fname); if (params$interactive) { ggplotly(plot); } else { plot; } } else { ggplot(data.frame()) + xlim(0, 10) + ylim(0, 10) + annotate("text", x = 5, y = 5, label = "Plot unavailable"); }')
line5 <- knitr::knit_expand(text = "\n```\n")
line6 <- knitr::knit_expand(text = sprintf("\n> Amino acid length distribution for %s region\n", names(regions)[[i]]))
paste(line1, line2, line3, line4, line5, line6, collapse = "\n")
})
r if (params$hasDiv) { paste(knitr::knit(text = paste(specOut, collapse = '\n'))) }
r if(!params$hasDiv) {"\\end{comment}"}
r if(!params$hasDiv || !params$single) {"\\begin{comment}"}
Row {.tabset .tabset-fade}
regions = c("FR1", "CDR1", "FR2", "CDR2", "FR3", "CDR3", "FR4")
compOut <- lapply(seq_along(regions), function(i) {
line1 <- knitr::knit_expand(text = sprintf("### %s\n", regions[i]))
line2 <- knitr::knit_expand(text = sprintf("\n```r\n", i))
line3 <- knitr::knit_expand(text = sprintf('\nfname <- .getFile(file.path(params$rootDir, "diversity", "composition_logos", "%s"), pattern = ".*_cumulative_logo\\\\.Rdata", .callback = .compositionNoGene)', regions[i]))
line4 <- knitr::knit_expand(text = '\nremoveLegend <- FALSE; if (!is.na(fname)) { load(fname); unscaled <- plot; } else { unscaled <- ggplot(data.frame()) + xlim(0, 10) + ylim(0, 10) + annotate("text", x = 5, y = 5, label = "404!"); }')
line5 <- knitr::knit_expand(text = sprintf('\nfname <- .getFile(file.path(params$rootDir, "diversity", "composition_logos", "%s"), pattern = ".*_cumulative_logo_scaled\\\\.Rdata", .callback = .compositionNoGene)', regions[i]))
line6 <- knitr::knit_expand(text = '\nif (!is.na(fname)) { load(fname); scaled <- plot; removeLegend <- TRUE; } else { scaled <- ggplot(data.frame()) + xlim(0, 10) + ylim(0, 10) + annotate("text", x = 5, y = 5, label = "404!"); }')
line7 <- knitr::knit_expand(text = '\nif (params$interactive) { if (removeLegend) { unscaled <- hide_legend(ggplotly(unscaled)) }; p <- subplot(unscaled, scaled); .p <- plotly_build(p); for (i in seq_along(.p$x$data)) { if (length(grep("x\\\\d+", .p$x$data[[i]]$xaxis))) { .p$x$data[[i]]$showlegend <- FALSE } }; .p; } else { grid.arrange(unscaled, scaled, nrow = 1) }')
line8 <- knitr::knit_expand(text = "\n```\n")
if (regions[i] == "FR3") {
line9 <- knitr::knit_expand(text = "\n> Composition logo plot was truncated to the first 30 amino acids.\n")
} else {
line9 <- knitr::knit_expand(text = sprintf("\n> Amino acid composition logos in %s - unscaled (sum to 1 in each individual position) and scaled (to the max number of sequences in a given position).\n", regions[i]))
}
paste(line1, line2, line3, line4, line5, line6, line7, line8, line9, collapse = "\n")
})
r if (params$single && params$hasDiv) { paste(knitr::knit(text = paste(compOut, collapse = '\n'))) }
Row {.tabset .tabset-fade}
regions = c("FR1", "CDR1", "FR2", "CDR2", "FR3", "CDR3", "FR4")
motifOut <- lapply(seq_along(regions), function(i) {
line1 <- knitr::knit_expand(text = sprintf("### %s (aligned, unaligned)\n", regions[i]))
line2 <- knitr::knit_expand(text = sprintf("\n```r", i))
line3 <- knitr::knit_expand(text = sprintf('\nfname <- .getFile(path = file.path(params$rootDir, "diversity", "motifs"), pattern = ".*%s_motif_aligned_logo\\\\.png")', tolower(regions[i])))
line4 <- knitr::knit_expand(text = '\nif (!is.na(fname)) { imgAl <- rasterGrob(as.raster(readPNG(fname)), interpolate = FALSE) } else { imgAl <- ggplot(data.frame()) + xlim(0, 10) + ylim(0, 10) + annotate("text", x = 5, y = 5, label = "Plot unavailable") }')
line5 <- knitr::knit_expand(text = sprintf('\nfname <- .getFile(path = file.path(params$rootDir, "diversity", "motifs"), pattern = ".*%s_motif_logo\\\\.png")', tolower(regions[i])))
line6 <- knitr::knit_expand(text = '\nif (!is.na(fname)) { img <- rasterGrob(as.raster(readPNG(fname)), interpolate = FALSE) } else { img <- ggplot(data.frame()) + xlim(0, 10) + ylim(0, 10) + annotate("text", x = 5, y = 5, label = "Plot unavailable") }')
line7 <- knitr::knit_expand(text = "\ngrid.arrange(imgAl, img, nrow = 1)")
line8 <- knitr::knit_expand(text = "\n```\n")
line9 <- knitr::knit_expand(text = sprintf("\n> (Left) %s aligned using clustal-omega (Right) unaligned motif discovery\n", regions[i]))
paste(line1, line2, line3, line4, line5, line6, line7, line8, line9, collapse = "\n")
})
r if (params$single && params$hasDiv) { paste(knitr::knit(text = paste(motifOut, collapse = '\n'))) }
r if(!params$hasDiv || !params$single) {"\\end{comment}"}
r if(!params$hasDiv || params$single) {"\\begin{comment}"}
Clonotypes {data-orientation=rows data-icon="fa-dna"}
Row {.tabset .tabset-fade}
files <- list.files(path = file.path(params$rootDir, "clonotype_analysis"),
pattern = ".*_clone_scatter\\.png",
full.names = TRUE)
scatterOut <- lapply(seq_along(files), function(i) {
line1 <- knitr::knit_expand(text = sprintf("### %s\n",
gsub("_", " ",
stringr::str_extract(basename(files[[i]]),
"^.*(?=(_clone_scatter))"))))
line2 <- knitr::knit_expand(text = sprintf("\n```r\n", i))
line3 <- knitr::knit_expand(text = sprintf('grid.raster(readPNG("%s"))', files[[i]]))
line4 <- knitr::knit_expand(text = "\n```\n")
line5 <- knitr::knit_expand(text = "\n> Clonotype frequencies from 2 samples. The axis values are scaled to log10 of the clone proportion. The marginal density plots show the density of total(grey), non-overlapping(purple), and overlapping(blue) clonotype abundances.\n")
paste(line1, line2, line3, line4, line5, collapse = "\n")
})
r if(!params$single && params$hasDiv) { paste(knitr::knit(text = paste(scatterOut, collapse = '\n'))) }
Row
Distribution of top 10 clonotypes from each sample
fname <- .getFile(path = file.path(params$rootDir, "clonotype_analysis"), pattern = ".*_top10Clonotypes\\.Rdata")
if (!is.na(fname)) {
load(fname)
if (params$interactive) {
ggplotly(plot)
} else {
plot
}
} else {
ggplot(data.frame()) + xlim(0, 10) + ylim(0, 10) + annotate("text", x = 5, y = 5, label = "Plot unavailable")
}
Clonotype distributions are scaled to top 10
Number of overlapping and non-overlapping clonotypes
fname <- .getFile(path = file.path(params$rootDir, "clonotype_analysis"), pattern = ".*_cdr3_clonotypeIntersection\\.png")
if (!is.na(fname)) {
img <- readPNG(fname)
grid.raster(img)
} else {
ggplot(data.frame()) +
xlim(0, 10) +
ylim(0, 10) +
annotate("text", x = 5, y = 5, label = "Can't find clonotype venn diagram, you probably have > 5 samples")
}
Counts show number of clonotypes
Row
Pearson correlation
fname <- .getFile(path = file.path(params$rootDir, "clonotype_analysis"), pattern = "pearson\\.Rdata")
if (!is.na(fname)) {
load(fname)
if (params$interactive) {
ggplotly(plot)
} else {
plot
}
} else {
ggplot(data.frame()) + xlim(0, 10) + ylim(0, 10) + annotate("text", x = 5, y = 5, label = "Plot unavailable")
}
Spearman correlation
fname <- .getFile(path = file.path(params$rootDir, "clonotype_analysis"), pattern = "spearman\\.Rdata")
if (!is.na(fname)) {
load(fname)
if (params$interactive) {
ggplotly(plot)
} else {
plot
}
} else {
ggplot(data.frame()) + xlim(0, 10) + ylim(0, 10) + annotate("text", x = 5, y = 5, label = "Plot unavailable")
}
Row
Morisita Horn
fname <- .getFile(path = file.path(params$rootDir, "clonotype_analysis"), pattern = "morisita_horn\\.Rdata")
if (!is.na(fname)) {
load(fname)
plot
} else {
ggplot(data.frame()) + xlim(0, 10) + ylim(0, 10) + annotate("text", x = 5, y = 5, label = "Plot unavailable")
}
Bray Curtis
fname <- .getFile(path = file.path(params$rootDir, "clonotype_analysis"), pattern = "bray_curtis\\.Rdata")
if (!is.na(fname)) {
load(fname)
plot
} else {
ggplot(data.frame()) + xlim(0, 10) + ylim(0, 10) + annotate("text", x = 5, y = 5, label = "Plot unavailable")
}
Jaccard
fname <- .getFile(path = file.path(params$rootDir, "clonotype_analysis"), pattern = "jaccard\\.Rdata")
if (!is.na(fname)) {
load(fname)
plot
} else {
ggplot(data.frame()) + xlim(0, 10) + ylim(0, 10) + annotate("text", x = 5, y = 5, label = "Plot unavailable")
}
r if(!params$hasDiv || params$single) {"\\end{comment}"}
malhamdoosh/abseqR documentation built on May 24, 2019, 12:36 a.m.
R Package Documentation
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# Buidling rmd using loops: https://github.com/rstudio/flexdashboard/issues/80 library(flexdashboard) library(plotly) library(png) library(grid) library(gridExtra) .getFile <- function(path, pattern, .stop = FALSE, .callback = NULL) { flist <- list.files(path = path, pattern = pattern, full.names = TRUE) n <- length(flist) if (n != 1 && is.null(.callback)) { if (.stop) { stop("Expected to find one file, but found ", n, " from ", paste(flist, collapse = ", "), " instead.") } else { return(NA) } } else if (!is.null(.callback)) { flist <- flist[lapply(flist, .callback) == TRUE] if (length(flist) != 1) { if (.stop) { stop("Expected to find one file, but found ", n, " from ", paste(flist, collapse = ", "), " instead.") } else { return(NA) } } } return(flist[[1]]) } .compositionNoGene <- function(x) { return(length(grep("-", basename(x), fixed = TRUE)) == 0) }
r if(!params$hasAnnot) {"\\begin{comment}"}
Summary {data-orientation=rows data-icon="fa-list-ul"}
Row
r paste(knitr::knit(text = knitr::knit_expand(text = paste("### Overview of", params$name))))
sampleNames <- unlist(strsplit(params$name, "_vs_")) # counts splitRaw <- unlist(strsplit(params$rawReads, ",")) splitAnnot <- unlist(strsplit(params$annotReads, ",")) splitFiltered <- unlist(strsplit(params$filteredReads, ",")) splitProd <- unlist(strsplit(params$productiveReads, ",")) # filtering splitBit <- unlist(strsplit(params$bitfilters, ",")) splitAl <- unlist(strsplit(params$alignfilters, ",")) splitSStart <- unlist(strsplit(params$sstartfilters, ",")) splitQStart <- unlist(strsplit(params$qstartfilters, ",")) overviewOut <- lapply(seq_along(sampleNames), function(i) { rawR <- as.numeric(splitRaw[i]) annotR <- as.numeric(splitAnnot[i]) filtR <- as.numeric(splitFiltered[i]) # might be NA if productivity analysis wasn't conducted prodR <- suppressWarnings(as.numeric(splitProd[i])) line1 <- knitr::knit_expand(text = sprintf("#### %s\n\n", sampleNames[i])) line2 <- knitr::knit_expand(text = sprintf("* Number of raw reads %s\n", prettyNum(rawR, big.mark = ","))) line3 <- knitr::knit_expand(text = sprintf("* Number of annotated reads %s%% (%s/%s)\n", round((annotR/rawR)*100, 2) , prettyNum(annotR, big.mark = ","), prettyNum(rawR, big.mark = ","))) line4 <- knitr::knit_expand(text = sprintf("* Number of reads after filtering %s%% (%s/%s)\n", round((filtR/annotR)*100, 2), prettyNum(filtR, big.mark = ","), prettyNum(annotR, big.mark = ","))) line5 <- knitr::knit_expand(text = sprintf("* Number of productive reads %s%% (%s/%s)\n", round((prodR/filtR)*100, 2), prettyNum(prodR, big.mark = ","), prettyNum(filtR, big.mark = ","))) line6 <- knitr::knit_expand(text = "\nThe following criteria are applied on the _V gene only_\n") line7 <- knitr::knit_expand(text = "\n\n|Filtering criterion|Value|\n") line8 <- knitr::knit_expand(text = "|--------------------|:-----:|\n") line9 <- knitr::knit_expand(text = sprintf("|Bitscore | %s|\n", splitBit[i])) line10 <- knitr::knit_expand(text = sprintf("|V gene alignment length | %s|\n", splitAl[i])) line11 <- knitr::knit_expand(text = sprintf("|Subject start position | %s|\n", splitSStart[i])) line12 <- knitr::knit_expand(text = sprintf("|Query start position | %s|\n", splitQStart[i])) line13 <- knitr::knit_expand(text = "\n\nSequences that do not satisfy the above filtering criteria are _excluded_ from the analysis.\n\n") paste0(line1, line2, line3, line4, line5, line6, line7, line8, line9, line10, line11, line12, line13) })
r paste(knitr::knit(text = paste(overviewOut, collapse = "\n")))
Row
Sequence lengths without outlier removal
fname <- .getFile(path = file.path(params$rootDir, "annot"), pattern = ".*_all_clones_len_dist\\.Rdata") if (!is.na(fname)) { load(fname) if (params$interactive) { ggplotly(plot) } else { plot } } else { ggplot(data.frame()) + xlim(0, 10) + ylim(0, 10) + annotate("text", x = 5, y = 5, label = "Plot unavailable") }
Row
Sequence lengths with outlier removal
fname <- .getFile(path = file.path(params$rootDir, "annot"), pattern = ".*_all_clones_len_dist_no_outliers\\.Rdata") if (!is.na(fname)) { load(fname) if (params$interactive) { ggplotly(plot) } else { plot } } else { ggplot(data.frame()) + xlim(0, 10) + ylim(0, 10) + annotate("text", x = 5, y = 5, label = "Plot unavailable") }
r if(!params$hasAnnot) {"\\end{comment}"}
r if(!params$hasAbun) {"\\begin{comment}"}
Quality {data-orientation=rows data-icon="fa-clipboard-list"}
Row
Indels (Gaps)
fname <- .getFile(path = file.path(params$rootDir, "abundance"), pattern = ".*_igv_gaps_dist\\.Rdata") if (!is.na(fname)) { load(fname) if (params$interactive) { ggplotly(plot) } else { plot } } else { ggplot(data.frame()) + xlim(0, 10) + ylim(0, 10) + annotate("text", x = 5, y = 5, label = "Plot unavailable") }
Plot of insertions and deletions found within the V gene
Mismatches
fname <- .getFile(path = file.path(params$rootDir, "abundance"), pattern = ".*_igv_mismatches_dist\\.Rdata") if (!is.na(fname)) { load(fname) if (params$interactive) { ggplotly(plot) } else { plot } } else { ggplot(data.frame()) + xlim(0, 10) + ylim(0, 10) + annotate("text", x = 5, y = 5, label = "Plot unavailable") }
Plot of nucleotide base mismatches found within the V gene
r if(!params$hasAbun) {"\\end{comment}"}
r if(!params$hasAbun || !params$single) {"\\begin{comment}"}
Row
.renderHMText <- function(alignQual) { fileInteractiveness <- if (params$interactive) "" else "_static" alignmentQuality <- lapply(seq_along(alignQual), function(i) { line1 <- knitr::knit_expand(text = sprintf("### %s\n", names(alignQual)[i])) line2 <- knitr::knit_expand(text = sprintf("\n```r", alignQual[[i]])) line3 <- knitr::knit_expand(text = sprintf('\nfname <- .getFile(path = file.path(params$rootDir, "abundance"), pattern = ".*_igv_align_quality_%s_hm%s\\\\.Rdata")', alignQual[[i]], fileInteractiveness)) line4 <- knitr::knit_expand(text = '\nif(!is.na(fname)) { load(fname); plot } else { ggplot(data.frame()) + xlim(0, 10) + ylim(0, 10) + annotate("text", x = 5, y = 5, label = "404!") }') line5 <- knitr::knit_expand(text = "\n```\n") paste(line1, line2, line3, line4, line5, collapse = "\n") }) return(alignmentQuality) }
alignQual <- list("Bitscore vs Alignment Length" = "bitscore", "Identity vs Alignment Length" = "identity") alignmentQuality <- .renderHMText(alignQual)
r if (params$hasAbun && params$single) { paste(knitr::knit(text = paste(alignmentQuality, collapse = "\n"))) }
Row
alignQual <- list("Gaps vs Alignment Length" = "gaps", "Mismatches vs Alignment Length" = "mismatches") alignmentQuality.pt2 <- .renderHMText(alignQual)
r if (params$hasAbun && params$single) { paste(knitr::knit(text = paste(alignmentQuality.pt2, collapse = "\n"))) }
Row
alignQual <- list("Subject Start vs Query Start" = "start") alignmentQuality.pt3 <- .renderHMText(alignQual)
r if (params$hasAbun && params$single) { paste(knitr::knit(text = paste(alignmentQuality.pt3, collapse = "\n"))) }
Placeholder
ggplot(data.frame()) + xlim(0, 10) + ylim(0, 10) + annotate("text", x = 5, y = 5, label = "Placeholder")
r if(!params$hasAbun || !params$single) {"\\end{comment}"}
r if(!params$hasAbun) {"\\begin{comment}"}
Abundance {data-orientation=rows data-icon="fa-chart-bar"}
Row
V family
fname <- .getFile(path = file.path(params$rootDir, "abundance"), pattern = ".*igv_dist_family_level\\.Rdata") if (!is.na(fname)) { load(fname) if (params$interactive) { ggplotly(plot) } else { plot } } else { ggplot(data.frame()) + xlim(0, 10) + ylim(0, 10) + annotate("text", x = 5, y = 5, label = "Plot unavailable") }
V gene
fname <- .getFile(path = file.path(params$rootDir, "abundance"), pattern = ".*igv_dist_gene_level\\.Rdata") if (!is.na(fname)) { load(fname) # TODO: ggplotly is buggy with horizontal plot in tabsets if (params$interactive) { plot #ggplotly(plot) } else { plot } } else { ggplot(data.frame()) + xlim(0, 10) + ylim(0, 10) + annotate("text", x = 5, y = 5, label = "Plot unavailable") }
Only the top 15 from each sample are shown
r if(!params$hasAbun) {"\\end{comment}"}
r if(!params$inclD || !params$hasAbun) {"\\begin{comment}"}
Row
D family
fname <- .getFile(path = file.path(params$rootDir, "abundance"), pattern = ".*igd_dist_family_level\\.Rdata") if (!is.na(fname)) { load(fname) if (params$interactive) { ggplotly(plot) } else { plot } } else { ggplot(data.frame()) + xlim(0, 10) + ylim(0, 10) + annotate("text", x = 5, y = 5, label = "Plot unavailable") }
D gene
fname <- .getFile(path = file.path(params$rootDir, "abundance"), pattern = ".*igd_dist_gene_level\\.Rdata") if (!is.na(fname)) { load(fname) # TODO: ggplotly is buggy with horizontal plot in tabsets if (params$interactive) { plot #ggplotly(plot) } else { plot } } else { ggplot(data.frame()) + xlim(0, 10) + ylim(0, 10) + annotate("text", x = 5, y = 5, label = "Plot unavailable") }
Only the top 15 from each sample are shown
r if(!params$inclD || !params$hasAbun) {"\\end{comment}"}
r if(!params$hasAbun) {"\\begin{comment}"}
Row
J family
fname <- .getFile(path = file.path(params$rootDir, "abundance"), pattern = ".*igj_dist_family_level\\.Rdata") if (!is.na(fname)) { load(fname) if (params$interactive) { ggplotly(plot) } else { plot } } else { ggplot(data.frame()) + xlim(0, 10) + ylim(0, 10) + annotate("text", x = 5, y = 5, label = "Plot unavailable") }
J variant
fname <- .getFile(path = file.path(params$rootDir, "abundance"), pattern = ".*igj_dist_variant_level\\.Rdata") if (!is.na(fname)) { load(fname) # TODO: ggplotly is buggy with horizontal plot in tabsets if (params$interactive) { plot #ggplotly(plot) } else { plot } } else { ggplot(data.frame()) + xlim(0, 10) + ylim(0, 10) + annotate("text", x = 5, y = 5, label = "Plot unavailable") }
Only the top 15 from each sample are shown
r if(!params$hasAbun) {"\\end{comment}"}
r if(!params$hasAbun || !params$single) {"\\begin{comment}"}
Row
V-J association
fname <- .getFile(path = file.path(params$rootDir, "abundance"), pattern = ".*_vjassoc\\.png") if (!is.na(fname)) { img <- readPNG(fname) grid.raster(img) } else { ggplot(data.frame()) + xlim(0, 10) + ylim(0, 10) + annotate("text", x = 5, y = 5, label = "Plot unavailable") }
Plot associations between V genes and J genes on the family level
r if(!params$hasAbun || !params$single) {"\\end{comment}"}
r if(!params$hasProd) {"\\begin{comment}"}
Productivity {data-orientation=rows data-icon="fa-check"}
Row
Productivity summary
fname <- .getFile(path = file.path(params$rootDir, "productivity"), pattern = ".*_productivity\\.Rdata") if (!is.na(fname)) { load(fname) if (params$interactive) { # TODO: buggy facet plot plot # ggplotly(plot) } else { plot } } else { ggplot(data.frame()) + xlim(0, 10) + ylim(0, 10) + annotate("text", x = 5, y = 5, label = "404!") }
Productivity distribution of sample based on stop codons and frameshifts
Frameshift distribution
fname <- .getFile(path = file.path(params$rootDir, "productivity"), pattern = ".*_vjframe_dist\\.Rdata") if (!is.na(fname)) { load(fname) if (params$interactive) { ggplotly(plot) } else { plot } } else { ggplot(data.frame()) + xlim(0, 10) + ylim(0, 10) + annotate("text", x = 5, y = 5, label = "404!") }
Distribution of in-frame and out-of-frame sequences
Row {.tabset .tabset-fade}
titles <- c("FR1", "CDR1", "FR2", "CDR2", "FR3", "CDR3") frcdrMisGaps <- lapply(seq_along(titles), function(i) { line1 <- knitr::knit_expand(text = sprintf("### %s\n", titles[i])) line2 <- knitr::knit_expand(text = sprintf("\n```r", i)) line3 <- knitr::knit_expand(text = sprintf('\nfname <- .getFile(file.path(params$rootDir, "productivity"), pattern = ".*_%s_mismatches_dist\\\\.Rdata")', tolower(titles[i]))) line4 <- knitr::knit_expand(text = sprintf('\nif (!is.na(fname)) { load(fname); if (params$interactive) { mismatch <- ggplotly(plot + labs(title = "Mismatches, gaps and gaps in out-of-frame sequences in %s")) } else { mismatch <- plot } } else { df <- data.frame(); mismatch <- ggplot(df) + xlim(0, 10) + ylim(0, 10) + annotate("text", x = 5, y = 5, label = "Plot unavailable") }', titles[i])) line5 <- knitr::knit_expand(text = sprintf('\nfname <- .getFile(file.path(params$rootDir, "productivity"), pattern = ".*_%s_gaps_dist\\\\.Rdata")', tolower(titles[i]))) line6 <- knitr::knit_expand(text = '\nif (!is.na(fname)) { load(fname); if (params$interactive) { gaps <- ggplotly(plot + labs(title = "")) } else { gaps <- plot } } else { df <- data.frame(); gaps <- ggplot(df) + xlim(0, 10) + ylim(0, 10) + annotate("text", x = 5, y = 5, label = "Plot unavailable")}') line7 <- knitr::knit_expand(text = sprintf('\nfname <- .getFile(file.path(params$rootDir, "productivity"), pattern = ".*_%s_gaps_dist_out_of_frame\\\\.Rdata")', tolower(titles[i]))) line8 <- knitr::knit_expand(text = '\nif (!is.na(fname)) { load(fname); if (params$interactive) { gapsOut <- ggplotly(plot + labs(title = "")) } else { gapsOut <- plot } } else { df <- data.frame(); gapsOut <- ggplot(df) + xlim(0, 10) + ylim(0, 10) + annotate("text", x = 5, y = 5, label = "Plot unavailable") }') line9 <- knitr::knit_expand(text = "\nif (params$interactive) { subplot(mismatch, gaps, gapsOut, titleX = TRUE, titleY = TRUE) } else { grid.arrange(mismatch, gaps, gapsOut, nrow = 1)}") line10 <- knitr::knit_expand(text = "\n```\n") line11 <- knitr::knit_expand(text = sprintf("\n> Proportion of mismatches in productive sequences, gaps in productive sequences, and gaps in out-of-frame sequences in %s region\n", titles[i])) paste(line1, line2, line3, line4, line5, line6, line7, line8, line9, line10, line11, collapse = "\n") })
if (params$inclD) { titles <- c("IGV", "IGD", "IGJ") } else { titles <- c("IGV", "IGJ") } vdjMisGaps <- lapply(seq_along(titles), function(i) { line1 <- knitr::knit_expand(text = sprintf("### %s\n", titles[i])) line2 <- knitr::knit_expand(text = sprintf("\n```r", i)) line3 <- knitr::knit_expand(text = sprintf('\nfname <- .getFile(file.path(params$rootDir, "productivity"), pattern = ".*_%s_mismatches_dist\\\\.Rdata")', tolower(titles[i]))) line4 <- knitr::knit_expand(text = sprintf('\nif (!is.na(fname)) { load(fname); if (params$interactive) { mismatch <- ggplotly(plot + labs(title = "Mismatches and gaps in %s")) } else { mismatch <- plot } } else { df <- data.frame(); mismatch <- ggplot(df) + xlim(0, 10) + ylim(0, 10) + annotate("text", x = 5, y = 5, label = "Plot unavailable") }', titles[i])) line5 <- knitr::knit_expand(text = sprintf('\nfname <- .getFile(file.path(params$rootDir, "productivity"), pattern = ".*_%s_gaps_dist\\\\.Rdata")', tolower(titles[i]))) line6 <- knitr::knit_expand(text = '\nif (!is.na(fname)) { load(fname); if (params$interactive) { gaps <- ggplotly(plot + labs(title = "")) } else { gaps <- plot } } else { df <- data.frame(); gaps <- ggplot(df) + xlim(0, 10) + ylim(0, 10) + annotate("text", x = 5, y = 5, label = "Plot unavailable") }') line7 <- knitr::knit_expand(text = "\nif (params$interactive) { subplot(mismatch, gaps, titleX = TRUE, titleY = TRUE) } else { grid.arrange(mismatch, gaps, nrow = 1)}") line8 <- knitr::knit_expand(text = "\n```\n") line9 <- knitr::knit_expand(text = sprintf("\n> Proportion of mismatches and gaps in productive sequences in %s\n", titles[i])) paste(line1, line2, line3, line4, line5, line6, line7, line8, line9, collapse = "\n") })
r paste(knitr::knit(text = paste(frcdrMisGaps, collapse = "\n")))
r paste(knitr::knit(text = paste(vdjMisGaps, collapse = "\n")))
Row {.tabset .tabset-fade}
IGV productive distribution
fname <- .getFile(path = file.path(params$rootDir, "productivity"), pattern = ".*_igv_dist_productive\\.Rdata") if (!is.na(fname)) { load(fname) if (params$interactive) { ggplotly(plot) } else { plot } } else { ggplot(data.frame()) + xlim(0, 10) + ylim(0, 10) + annotate("text", x = 5, y = 5, label = "Plot unavailable") }
Distribution of productive sequences from IGV family
IGV inframe, unproductive distribution
fname <- .getFile(path = file.path(params$rootDir, "productivity"), pattern = ".*_igv_dist_inframe_unproductive\\.Rdata") if (!is.na(fname)) { load(fname) if (params$interactive) { ggplotly(plot) } else { plot } } else { ggplot(data.frame()) + xlim(0, 10) + ylim(0, 10) + annotate("text", x = 5, y = 5, label = "Plot unavailable") }
Distribution of in-frame but unproductive sequences from IGV family
IGV out-of-frame distribution
fname <- .getFile(path = file.path(params$rootDir, "productivity"), pattern = ".*_igv_dist_out_of_frame\\.Rdata") if (!is.na(fname)) { load(fname) if (params$interactive) { ggplotly(plot) } else { plot } } else { ggplot(data.frame()) + xlim(0, 10) + ylim(0, 10) + annotate("text", x = 5, y = 5, label = "Plot unavailable") }
Distribution of out-of-frame sequences from IGV family
Row {.tabset .tabset-fade}
Inframe
fname <- .getFile(path = file.path(params$rootDir, "productivity"), pattern = ".*_stopcodon_region_inframe\\.Rdata", .stop = FALSE) if (is.na(fname)) { ggplot(data.frame()) + xlim(0, 10) + ylim(0, 10) + annotate("text", x = 5, y = 5, label = "Plot unavailable") } else { load(fname) if (params$interactive) { ggplotly(plot) } else { plot } }
Distribution of stop codons in CDR/FR regions from in-frame sequences
Out-of-frame
fname <- .getFile(path = file.path(params$rootDir, "productivity"), pattern = ".*_stopcodon_region_outframe\\.Rdata", .stop = FALSE) if (is.na(fname)) { ggplot(data.frame()) + xlim(0, 10) + ylim(0, 10) + annotate("text", x = 5, y = 5, label = "Plot unavailable") } else { load(fname) if (params$interactive) { ggplotly(plot) } else { plot } }
Distribution of stop codons in CDR/FR regions from out-of-frame sequences
r if(!params$hasProd) {"\\end{comment}"}
r if(!params$hasDiv) {"\\begin{comment}"}
Diversity {data-orientation=rows data-icon="fa-chart-line"}
Row {.tabset .tabset-fade}
CDR-V
fname <- .getFile(path = file.path(params$rootDir, "diversity"), pattern = ".*_cdr_v_duplication\\.Rdata") if (!is.na(fname)) { load(fname) dup <- plot + labs(title = "Sequence duplication level, rarefaction, and recapture") } else { dup <- ggplot(data.frame()) + xlim(0, 10) + ylim(0, 10) + annotate("text", x = 5, y = 5, label = "Plot unavailable") } fname <- .getFile(path = file.path(params$rootDir, "diversity"), pattern = ".*_cdr_v_rarefaction\\.Rdata") if (!is.na(fname)) { load(fname) rare <- plot + labs(title = "") } else { rare <- ggplot(data.frame()) + xlim(0, 10) + ylim(0, 10) + annotate("text", x = 5, y = 5, label = "Plot unavailable") } fname <- .getFile(path = file.path(params$rootDir, "diversity"), pattern = ".*_cdr_v_recapture\\.Rdata") if (!is.na(fname)) { load(fname) recap <- plot + labs(title = "") } else { recap <- ggplot(data.frame()) + xlim(0, 10) + ylim(0, 10) + annotate("text", x = 5, y = 5, label = "Plot unavailable") } if (params$interactive) { dup <- hide_legend(dup) rare <- hide_legend(rare) p <- subplot(hide_legend(dup), hide_legend(rare), recap, titleX = TRUE, titleY = TRUE) .p <- plotly_build(p) for (i in seq_along(.p$x$data)) { if (length(grep(",\\d+(,NA)?\\)$", .p$x$data[[i]]$name))) { .p$x$data[[i]]$showlegend <- FALSE } } .p } else { grid.arrange(dup, rare, recap, nrow = 1) }
Duplication level, rarefaction and recapture plots for CDR (regions) and (V)ariable domain
CDR
fname <- .getFile(path = file.path(params$rootDir, "diversity"), pattern = ".*_cdr_duplication\\.Rdata") if (!is.na(fname)) { load(fname) dup <- plot + labs(title = "Sequence duplication level, rarefaction, and recapture") } else { dup <- ggplot(data.frame()) + xlim(0, 10) + ylim(0, 10) + annotate("text", x = 5, y = 5, label = "Plot unavailable") } fname <- .getFile(path = file.path(params$rootDir, "diversity"), pattern = ".*_cdr_rarefaction\\.Rdata") if (!is.na(fname)) { load(fname) rare <- plot + labs(title = "") } else { rare <- ggplot(data.frame()) + xlim(0, 10) + ylim(0, 10) + annotate("text", x = 5, y = 5, label = "Plot unavailable") } fname <- .getFile(path = file.path(params$rootDir, "diversity"), pattern = ".*_cdr_recapture\\.Rdata") if (!is.na(fname)) { load(fname) recap <- plot + labs(title = "") } else { recap <- ggplot(data.frame()) + xlim(0, 10) + ylim(0, 10) + annotate("text", x = 5, y = 5, label = "Plot unavailable") } if (params$interactive) { dup <- hide_legend(dup) rare <- hide_legend(rare) p <- subplot(hide_legend(dup), hide_legend(rare), recap, titleX = TRUE, titleY = TRUE) .p <- plotly_build(p) for (i in seq_along(.p$x$data)) { if (length(grep(",\\d+(,NA)?\\)$", .p$x$data[[i]]$name))) { .p$x$data[[i]]$showlegend <- FALSE } } .p } else { grid.arrange(dup, rare, recap, nrow = 1) }
Duplication level, rarefaction and recapture plots for CDR (regions)
FR
fname <- .getFile(path = file.path(params$rootDir, "diversity"), pattern = ".*_fr_duplication\\.Rdata") if (!is.na(fname)) { load(fname) dup <- plot + labs(title = "Sequence duplication level, rarefaction, and recapture") } else { dup <- ggplot(data.frame()) + xlim(0, 10) + ylim(0, 10) + annotate("text", x = 5, y = 5, label = "Plot unavailable") } fname <- .getFile(path = file.path(params$rootDir, "diversity"), pattern = ".*_fr_rarefaction\\.Rdata") if (!is.na(fname)) { load(fname) rare <- plot + labs(title = "") } else { rare <- ggplot(data.frame()) + xlim(0, 10) + ylim(0, 10) + annotate("text", x = 5, y = 5, label = "Plot unavailable") } fname <- .getFile(path = file.path(params$rootDir, "diversity"), pattern = ".*_fr_recapture\\.Rdata") if (!is.na(fname)) { load(fname) recap <- plot + labs(title = "") } else { recap <- ggplot(data.frame()) + xlim(0, 10) + ylim(0, 10) + annotate("text", x = 5, y = 5, label = "Plot unavailable") } if (params$interactive) { dup <- hide_legend(dup) rare <- hide_legend(rare) p <- subplot(hide_legend(dup), hide_legend(rare), recap, titleX = TRUE, titleY = TRUE) .p <- plotly_build(p) for (i in seq_along(.p$x$data)) { if (length(grep(",\\d+(,NA)?\\)$", .p$x$data[[i]]$name))) { .p$x$data[[i]]$showlegend <- FALSE } } .p } else { grid.arrange(dup, rare, recap, nrow = 1) }
Duplication level, rarefaction and recapture plots for FR (regions)
Row {.tabset .tabset-fade}
regions <- c( "FR1" = "_fr1_spectratype", "CDR1" = "_cdr1_spectratype", "FR2" = "_fr2_spectratype", "CDR2" = "_cdr2_spectratype", "FR3" = "_fr3_spectratype", "CDR3" = "_cdr3_spectratype", "CDR3 without outliers" = "_cdr3_spectratype_no_outliers", "FR4" = "_fr4_spectratype", "V domain" = "_v_spectratype" ) specOut <- lapply(seq_along(regions), function(i) { line1 <- knitr::knit_expand(text = sprintf("### %s\n", names(regions)[[i]])) line2 <- knitr::knit_expand(text = sprintf("\n```r\n", i)) line3 <- knitr::knit_expand(text = sprintf('\nfname <- .getFile(path = file.path(params$rootDir, "diversity", "spectratypes"), pattern = ".*%s\\\\.Rdata")\n', regions[[i]])) line4 <- knitr::knit_expand(text = '\nif (!is.na(fname)) { load(fname); if (params$interactive) { ggplotly(plot); } else { plot; } } else { ggplot(data.frame()) + xlim(0, 10) + ylim(0, 10) + annotate("text", x = 5, y = 5, label = "Plot unavailable"); }') line5 <- knitr::knit_expand(text = "\n```\n") line6 <- knitr::knit_expand(text = sprintf("\n> Amino acid length distribution for %s region\n", names(regions)[[i]])) paste(line1, line2, line3, line4, line5, line6, collapse = "\n") })
r if (params$hasDiv) { paste(knitr::knit(text = paste(specOut, collapse = '\n'))) }
r if(!params$hasDiv) {"\\end{comment}"}
r if(!params$hasDiv || !params$single) {"\\begin{comment}"}
Row {.tabset .tabset-fade}
regions = c("FR1", "CDR1", "FR2", "CDR2", "FR3", "CDR3", "FR4") compOut <- lapply(seq_along(regions), function(i) { line1 <- knitr::knit_expand(text = sprintf("### %s\n", regions[i])) line2 <- knitr::knit_expand(text = sprintf("\n```r\n", i)) line3 <- knitr::knit_expand(text = sprintf('\nfname <- .getFile(file.path(params$rootDir, "diversity", "composition_logos", "%s"), pattern = ".*_cumulative_logo\\\\.Rdata", .callback = .compositionNoGene)', regions[i])) line4 <- knitr::knit_expand(text = '\nremoveLegend <- FALSE; if (!is.na(fname)) { load(fname); unscaled <- plot; } else { unscaled <- ggplot(data.frame()) + xlim(0, 10) + ylim(0, 10) + annotate("text", x = 5, y = 5, label = "404!"); }') line5 <- knitr::knit_expand(text = sprintf('\nfname <- .getFile(file.path(params$rootDir, "diversity", "composition_logos", "%s"), pattern = ".*_cumulative_logo_scaled\\\\.Rdata", .callback = .compositionNoGene)', regions[i])) line6 <- knitr::knit_expand(text = '\nif (!is.na(fname)) { load(fname); scaled <- plot; removeLegend <- TRUE; } else { scaled <- ggplot(data.frame()) + xlim(0, 10) + ylim(0, 10) + annotate("text", x = 5, y = 5, label = "404!"); }') line7 <- knitr::knit_expand(text = '\nif (params$interactive) { if (removeLegend) { unscaled <- hide_legend(ggplotly(unscaled)) }; p <- subplot(unscaled, scaled); .p <- plotly_build(p); for (i in seq_along(.p$x$data)) { if (length(grep("x\\\\d+", .p$x$data[[i]]$xaxis))) { .p$x$data[[i]]$showlegend <- FALSE } }; .p; } else { grid.arrange(unscaled, scaled, nrow = 1) }') line8 <- knitr::knit_expand(text = "\n```\n") if (regions[i] == "FR3") { line9 <- knitr::knit_expand(text = "\n> Composition logo plot was truncated to the first 30 amino acids.\n") } else { line9 <- knitr::knit_expand(text = sprintf("\n> Amino acid composition logos in %s - unscaled (sum to 1 in each individual position) and scaled (to the max number of sequences in a given position).\n", regions[i])) } paste(line1, line2, line3, line4, line5, line6, line7, line8, line9, collapse = "\n") })
r if (params$single && params$hasDiv) { paste(knitr::knit(text = paste(compOut, collapse = '\n'))) }
Row {.tabset .tabset-fade}
regions = c("FR1", "CDR1", "FR2", "CDR2", "FR3", "CDR3", "FR4") motifOut <- lapply(seq_along(regions), function(i) { line1 <- knitr::knit_expand(text = sprintf("### %s (aligned, unaligned)\n", regions[i])) line2 <- knitr::knit_expand(text = sprintf("\n```r", i)) line3 <- knitr::knit_expand(text = sprintf('\nfname <- .getFile(path = file.path(params$rootDir, "diversity", "motifs"), pattern = ".*%s_motif_aligned_logo\\\\.png")', tolower(regions[i]))) line4 <- knitr::knit_expand(text = '\nif (!is.na(fname)) { imgAl <- rasterGrob(as.raster(readPNG(fname)), interpolate = FALSE) } else { imgAl <- ggplot(data.frame()) + xlim(0, 10) + ylim(0, 10) + annotate("text", x = 5, y = 5, label = "Plot unavailable") }') line5 <- knitr::knit_expand(text = sprintf('\nfname <- .getFile(path = file.path(params$rootDir, "diversity", "motifs"), pattern = ".*%s_motif_logo\\\\.png")', tolower(regions[i]))) line6 <- knitr::knit_expand(text = '\nif (!is.na(fname)) { img <- rasterGrob(as.raster(readPNG(fname)), interpolate = FALSE) } else { img <- ggplot(data.frame()) + xlim(0, 10) + ylim(0, 10) + annotate("text", x = 5, y = 5, label = "Plot unavailable") }') line7 <- knitr::knit_expand(text = "\ngrid.arrange(imgAl, img, nrow = 1)") line8 <- knitr::knit_expand(text = "\n```\n") line9 <- knitr::knit_expand(text = sprintf("\n> (Left) %s aligned using clustal-omega (Right) unaligned motif discovery\n", regions[i])) paste(line1, line2, line3, line4, line5, line6, line7, line8, line9, collapse = "\n") })
r if (params$single && params$hasDiv) { paste(knitr::knit(text = paste(motifOut, collapse = '\n'))) }
r if(!params$hasDiv || !params$single) {"\\end{comment}"}
r if(!params$hasDiv || params$single) {"\\begin{comment}"}
Clonotypes {data-orientation=rows data-icon="fa-dna"}
Row {.tabset .tabset-fade}
files <- list.files(path = file.path(params$rootDir, "clonotype_analysis"), pattern = ".*_clone_scatter\\.png", full.names = TRUE) scatterOut <- lapply(seq_along(files), function(i) { line1 <- knitr::knit_expand(text = sprintf("### %s\n", gsub("_", " ", stringr::str_extract(basename(files[[i]]), "^.*(?=(_clone_scatter))")))) line2 <- knitr::knit_expand(text = sprintf("\n```r\n", i)) line3 <- knitr::knit_expand(text = sprintf('grid.raster(readPNG("%s"))', files[[i]])) line4 <- knitr::knit_expand(text = "\n```\n") line5 <- knitr::knit_expand(text = "\n> Clonotype frequencies from 2 samples. The axis values are scaled to log10 of the clone proportion. The marginal density plots show the density of total(grey), non-overlapping(purple), and overlapping(blue) clonotype abundances.\n") paste(line1, line2, line3, line4, line5, collapse = "\n") })
r if(!params$single && params$hasDiv) { paste(knitr::knit(text = paste(scatterOut, collapse = '\n'))) }
Row
Distribution of top 10 clonotypes from each sample
fname <- .getFile(path = file.path(params$rootDir, "clonotype_analysis"), pattern = ".*_top10Clonotypes\\.Rdata") if (!is.na(fname)) { load(fname) if (params$interactive) { ggplotly(plot) } else { plot } } else { ggplot(data.frame()) + xlim(0, 10) + ylim(0, 10) + annotate("text", x = 5, y = 5, label = "Plot unavailable") }
Clonotype distributions are scaled to top 10
Number of overlapping and non-overlapping clonotypes
fname <- .getFile(path = file.path(params$rootDir, "clonotype_analysis"), pattern = ".*_cdr3_clonotypeIntersection\\.png") if (!is.na(fname)) { img <- readPNG(fname) grid.raster(img) } else { ggplot(data.frame()) + xlim(0, 10) + ylim(0, 10) + annotate("text", x = 5, y = 5, label = "Can't find clonotype venn diagram, you probably have > 5 samples") }
Counts show number of clonotypes
Row
Pearson correlation
fname <- .getFile(path = file.path(params$rootDir, "clonotype_analysis"), pattern = "pearson\\.Rdata") if (!is.na(fname)) { load(fname) if (params$interactive) { ggplotly(plot) } else { plot } } else { ggplot(data.frame()) + xlim(0, 10) + ylim(0, 10) + annotate("text", x = 5, y = 5, label = "Plot unavailable") }
Spearman correlation
fname <- .getFile(path = file.path(params$rootDir, "clonotype_analysis"), pattern = "spearman\\.Rdata") if (!is.na(fname)) { load(fname) if (params$interactive) { ggplotly(plot) } else { plot } } else { ggplot(data.frame()) + xlim(0, 10) + ylim(0, 10) + annotate("text", x = 5, y = 5, label = "Plot unavailable") }
Row
Morisita Horn
fname <- .getFile(path = file.path(params$rootDir, "clonotype_analysis"), pattern = "morisita_horn\\.Rdata") if (!is.na(fname)) { load(fname) plot } else { ggplot(data.frame()) + xlim(0, 10) + ylim(0, 10) + annotate("text", x = 5, y = 5, label = "Plot unavailable") }
Bray Curtis
fname <- .getFile(path = file.path(params$rootDir, "clonotype_analysis"), pattern = "bray_curtis\\.Rdata") if (!is.na(fname)) { load(fname) plot } else { ggplot(data.frame()) + xlim(0, 10) + ylim(0, 10) + annotate("text", x = 5, y = 5, label = "Plot unavailable") }
Jaccard
fname <- .getFile(path = file.path(params$rootDir, "clonotype_analysis"), pattern = "jaccard\\.Rdata") if (!is.na(fname)) { load(fname) plot } else { ggplot(data.frame()) + xlim(0, 10) + ylim(0, 10) + annotate("text", x = 5, y = 5, label = "Plot unavailable") }
r if(!params$hasDiv || params$single) {"\\end{comment}"}
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