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test v0.3.0
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@andyjslee
andyjslee committed Jul 23, 2024
1 parent f83d33d commit 590b36d
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398 changes: 199 additions & 199 deletions examples/outputs/hg002_cutesv.tsv
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390 changes: 195 additions & 195 deletions examples/outputs/hg002_sniffles2.tsv
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236 changes: 118 additions & 118 deletions examples/outputs/hg002_svim.tsv
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20 changes: 10 additions & 10 deletions examples/outputs/sample001.svision_pro_v1.8.s3.tsv
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24 changes: 12 additions & 12 deletions examples/outputs/sample001_savana_sv_breakpoints.tsv
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2 changes: 1 addition & 1 deletion pyproject.toml
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Expand Up @@ -21,7 +21,7 @@ build-backend = "maturin"

[project]
name = "vstol"
version = "0.2.9"
version = "0.3.0"
requires-python = ">=3.10"
keywords = [
"somatic variants",
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173 changes: 96 additions & 77 deletions python/vstolib/resources/scripts/visualize.R
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Expand Up @@ -22,6 +22,12 @@ RoundUpToNextZeros <- function(x) {
if (x <= 0) {
stop("Input must be a positive integer")
}
if (x < 10) {
return(10)
}
if (x < 100) {
return(100)
}
magnitude <- 10^(nchar(as.character(x)) - 1)
rounded <- ceiling(x / magnitude) * magnitude
if (rounded == x) {
Expand Down Expand Up @@ -253,94 +259,107 @@ print(plot.variant.size)

# Step 12. Plot a histogram of the reference allele read count
df.variant.calls.temp <- df.variant.calls[df.variant.calls$reference_allele_read_count >= 0,]
p <- ggplot(df.variant.calls.temp, aes(x = reference_allele_read_count)) + geom_histogram()
p.data <- ggplot_build(p)$data[[1]]
max.frequency <- max(p.data$count)
plot.ref.allele.read.count.histogram <- ggplot(df.variant.calls.temp, aes(x = reference_allele_read_count)) +
geom_histogram(color = HISTOGRAM.COLOR, fill = HISTOGRAM.FILL) +
xlab("Reference Allele Read Count") + ylab("Frequency") +
ggtitle("Histogram of reference_allele_read_count") +
scale_y_continuous(expand = c(0,1), limits = c(0, RoundUpToNextZeros(max.frequency))) +
theme_bw() +
theme(plot.title = element_text(size = PLOT.TITLE.SIZE),
panel.grid.minor = element_blank(),
panel.grid.major.x = element_blank(),
axis.title = element_text(size = AXIS.TITLE.SIZE),
axis.text = element_text(size = AXIS.TEXT.SIZE))
print(plot.ref.allele.read.count.histogram)
if (nrow(df.variant.calls.temp) > 0) {
p <- ggplot(df.variant.calls.temp, aes(x = reference_allele_read_count)) + geom_histogram()
p.data <- ggplot_build(p)$data[[1]]
max.frequency <- max(p.data$count)
plot.ref.allele.read.count.histogram <- ggplot(df.variant.calls.temp, aes(x = reference_allele_read_count)) +
geom_histogram(color = HISTOGRAM.COLOR, fill = HISTOGRAM.FILL, boundary = 0) +
xlab("Reference Allele Read Count") + ylab("Frequency") +
ggtitle("Histogram of reference_allele_read_count") +
scale_y_continuous(expand = c(0,1), limits = c(0, RoundUpToNextZeros(max.frequency))) +
theme_bw() +
theme(plot.title = element_text(size = PLOT.TITLE.SIZE),
panel.grid.minor = element_blank(),
panel.grid.major.x = element_blank(),
axis.title = element_text(size = AXIS.TITLE.SIZE),
axis.text = element_text(size = AXIS.TEXT.SIZE))
print(plot.ref.allele.read.count.histogram)
}

# Step 13. Plot a histogram of the alternate allele read count
df.variant.calls.temp <- df.variant.calls[df.variant.calls$alternate_allele_read_count >= 0,]
p <- ggplot(df.variant.calls.temp, aes(x = alternate_allele_read_count)) + geom_histogram()
p.data <- ggplot_build(p)$data[[1]]
max.frequency <- max(p.data$count)
plot.alt.allele.read.count.histogram <- ggplot(df.variant.calls.temp, aes(x = alternate_allele_read_count)) +
geom_histogram(color = HISTOGRAM.COLOR, fill = HISTOGRAM.FILL) +
xlab("Alternate Allele Read Count") + ylab("Frequency") +
ggtitle("Histogram of alternate_allele_read_count") +
scale_y_continuous(expand = c(0,1), limits = c(0, RoundUpToNextZeros(max.frequency))) +
theme_bw() +
theme(plot.title = element_text(size = PLOT.TITLE.SIZE),
panel.grid.minor = element_blank(),
panel.grid.major.x = element_blank(),
axis.title = element_text(size = AXIS.TITLE.SIZE),
axis.text = element_text(size = AXIS.TEXT.SIZE))
print(plot.alt.allele.read.count.histogram)
if (nrow(df.variant.calls.temp) > 0) {
p <- ggplot(df.variant.calls.temp, aes(x = alternate_allele_read_count)) + geom_histogram()
p.data <- ggplot_build(p)$data[[1]]
max.frequency <- max(p.data$count)
plot.alt.allele.read.count.histogram <- ggplot(df.variant.calls.temp, aes(x = alternate_allele_read_count)) +
geom_histogram(color = HISTOGRAM.COLOR, fill = HISTOGRAM.FILL, boundary = 0) +
xlab("Alternate Allele Read Count") + ylab("Frequency") +
ggtitle("Histogram of alternate_allele_read_count") +
scale_y_continuous(expand = c(0,1), limits = c(0, RoundUpToNextZeros(max.frequency))) +
theme_bw() +
theme(plot.title = element_text(size = PLOT.TITLE.SIZE),
panel.grid.minor = element_blank(),
panel.grid.major.x = element_blank(),
axis.title = element_text(size = AXIS.TITLE.SIZE),
axis.text = element_text(size = AXIS.TEXT.SIZE))
print(plot.alt.allele.read.count.histogram)
}

# Step 14. Plot a histogram of the total read count
df.variant.calls.temp <- df.variant.calls[df.variant.calls$total_read_count >= 0,]
p <- ggplot(df.variant.calls.temp, aes(x = total_read_count)) + geom_histogram()
p.data <- ggplot_build(p)$data[[1]]
max.frequency <- max(p.data$count)
plot.total.read.count.histogram <- ggplot(df.variant.calls.temp, aes(x = total_read_count)) +
geom_histogram(color = HISTOGRAM.COLOR, fill = HISTOGRAM.FILL) +
xlab("Total Read Count") + ylab("Frequency") +
ggtitle("Histogram of total_read_count") +
scale_y_continuous(expand = c(0,1), limits = c(0, RoundUpToNextZeros(max.frequency))) +
theme_bw() +
theme(plot.title = element_text(size = PLOT.TITLE.SIZE),
panel.grid.minor = element_blank(),
panel.grid.major.x = element_blank(),
axis.title = element_text(size = AXIS.TITLE.SIZE),
axis.text = element_text(size = AXIS.TEXT.SIZE))
print(plot.total.read.count.histogram)
if (nrow(df.variant.calls.temp) > 0) {
p <- ggplot(df.variant.calls.temp, aes(x = total_read_count)) + geom_histogram()
p.data <- ggplot_build(p)$data[[1]]
max.frequency <- max(p.data$count)
plot.total.read.count.histogram <- ggplot(df.variant.calls.temp, aes(x = total_read_count)) +
geom_histogram(color = HISTOGRAM.COLOR, fill = HISTOGRAM.FILL, boundary = 0) +
xlab("Total Read Count") + ylab("Frequency") +
ggtitle("Histogram of total_read_count") +
scale_y_continuous(expand = c(0,1), limits = c(0, RoundUpToNextZeros(max.frequency))) +
theme_bw() +
theme(plot.title = element_text(size = PLOT.TITLE.SIZE),
panel.grid.minor = element_blank(),
panel.grid.major.x = element_blank(),
axis.title = element_text(size = AXIS.TITLE.SIZE),
axis.text = element_text(size = AXIS.TEXT.SIZE))
print(plot.total.read.count.histogram)
}

# Step 15. Plot a histogram of the alternate allele fraction
df.variant.calls.temp <- df.variant.calls[df.variant.calls$alternate_allele_fraction > 0.0,]
p <- ggplot(df.variant.calls.temp, aes(x = alternate_allele_fraction)) + geom_histogram(binwidth = 0.05)
p.data <- ggplot_build(p)$data[[1]]
max.frequency <- max(p.data$count)
plot.vaf.histogram <- ggplot(df.variant.calls.temp, aes(x = alternate_allele_fraction)) +
geom_histogram(binwidth = 0.05, color = HISTOGRAM.COLOR, fill = HISTOGRAM.FILL) +
xlab("Alternate Allele Fraction") + ylab("Frequency") +
ggtitle("Histogram of alternate_allele_fraction") +
scale_x_continuous(limits = c(0.0,1.0)) +
scale_y_continuous(expand = c(0,1), limits = c(0, RoundUpToNextZeros(max.frequency))) +
theme_bw() +
theme(plot.title = element_text(size = PLOT.TITLE.SIZE),
panel.grid.minor = element_blank(),
panel.grid.major.x = element_blank(),
axis.title = element_text(size = AXIS.TITLE.SIZE),
axis.text = element_text(size = AXIS.TEXT.SIZE))
print(plot.vaf.histogram)
if (nrow(df.variant.calls.temp) > 0) {
p <- ggplot(df.variant.calls.temp, aes(x = alternate_allele_fraction)) + geom_histogram(binwidth = 0.05)
p.data <- ggplot_build(p)$data[[1]]
max.frequency <- max(p.data$count)
plot.vaf.histogram <- ggplot(df.variant.calls.temp, aes(x = alternate_allele_fraction)) +
geom_histogram(binwidth = 0.05, color = HISTOGRAM.COLOR, fill = HISTOGRAM.FILL, boundary = 0) +
xlab("Alternate Allele Fraction") + ylab("Frequency") +
ggtitle("Histogram of alternate_allele_fraction") +
coord_cartesian(xlim = c(0, 1)) +
scale_y_continuous(expand = c(0,1), limits = c(0, RoundUpToNextZeros(max.frequency))) +
theme_bw() +
theme(plot.title = element_text(size = PLOT.TITLE.SIZE),
panel.grid.minor = element_blank(),
panel.grid.major.x = element_blank(),
axis.title = element_text(size = AXIS.TITLE.SIZE),
axis.text = element_text(size = AXIS.TEXT.SIZE))
print(plot.vaf.histogram)
}

# Step 16. Plot a histogram of the quality score
df.variant.calls.temp <- df.variant.calls[df.variant.calls$quality_score >= 0,]
p <- ggplot(df.variant.calls.temp, aes(x = quality_score)) + geom_histogram()
p.data <- ggplot_build(p)$data[[1]]
max.frequency <- max(p.data$count)
plot.quality.score <- ggplot(df.variant.calls.temp, aes(x = quality_score)) +
geom_histogram(color = HISTOGRAM.COLOR, fill = HISTOGRAM.FILL) +
xlab("Quality Score") + ylab("Frequency") +
ggtitle("Histogram of quality_score") +
scale_y_continuous(expand = c(0,1), limits = c(0, RoundUpToNextZeros(max.frequency))) +
theme_bw() +
theme(plot.title = element_text(size = PLOT.TITLE.SIZE),
panel.grid.minor = element_blank(),
panel.grid.major.x = element_blank(),
axis.title = element_text(size = AXIS.TITLE.SIZE),
axis.text = element_text(size = AXIS.TEXT.SIZE))
print(plot.quality.score)
df.variant.calls.temp <- df.variant.calls[
(df.variant.calls$quality_score >= 0) &
(is.na(df.variant.calls$quality_score) == FALSE),
]
if (nrow(df.variant.calls.temp) > 0) {
p <- ggplot(df.variant.calls.temp, aes(x = quality_score)) + geom_histogram()
p.data <- ggplot_build(p)$data[[1]]
max.frequency <- max(p.data$count)
plot.quality.score <- ggplot(df.variant.calls.temp, aes(x = quality_score)) +
geom_histogram(color = HISTOGRAM.COLOR, fill = HISTOGRAM.FILL, boundary = 0) +
xlab("Quality Score") + ylab("Frequency") +
ggtitle("Histogram of quality_score") +
scale_y_continuous(expand = c(0,1), limits = c(0, RoundUpToNextZeros(max.frequency))) +
theme_bw() +
theme(plot.title = element_text(size = PLOT.TITLE.SIZE),
panel.grid.minor = element_blank(),
panel.grid.major.x = element_blank(),
axis.title = element_text(size = AXIS.TITLE.SIZE),
axis.text = element_text(size = AXIS.TEXT.SIZE))
print(plot.quality.score)
}

dev.off()

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