comparison signature_score.R @ 2:e08419b8ec24 draft

planemo upload for repository https://github.com/ARTbio/tools-artbio/tree/master/tools/gsc_signature_score commit 987e0ceb55e8de1d2f09d0f2ae48ff7cd3e82051

1:01b2c4fcada8

  make_option(
    "--stats",
    default = "./statistics.tab",
    type = 'character',
    help = "statistics path [default : '%default' ]"
  ),
  make_option(
    "--pdf",
    default = "~/output.pdf",
    type = 'character',

logical_genes <- rownames(data.counts) %in% genes

# Retrieve target genes in counts data
signature.counts <- subset(data.counts, logical_genes)


## Descriptive Statistics Function
descriptive_stats = function(InputData) {
  SummaryData = data.frame(
    mean = rowMeans(InputData),

                    order = rank(score, ties.method = "first"),
                    signature = signature_output$rate,
                    stringsAsFactors = F)

pdf(file = opt$pdf)

ggplot(score, aes(x = order, y = score)) +
  geom_line() + 
  geom_segment(x = 0, xend = max(score$order[score$signature == "LOW"]), y = mean(score$score), yend = mean(score$score)) +
  geom_area(aes(fill = signature), alpha = .7) +
  scale_fill_manual(values=c("#ff0000", "#08661e")) +
  geom_text(aes(x = 1, y = mean(score)), label = "Mean", vjust = -0.3, colour = "black") +
  labs(title = "Ordered cell signature scores", x = "Cell index", y = "Score")

density_score <- density(score$score)
ggplot(data.frame(density_score[1:2]), aes(x, y, fill = ifelse(x < mean(score$score), "LOW", "HIGH"))) +
  geom_line() +
  geom_vline(xintercept = mean(score$score)) +
  geom_text(x = mean(score$score), y = max(density_score$y), label = "Mean", hjust = -0.3, colour = "black") +
  geom_area(alpha = .7) +
  scale_fill_manual(values=c("#ff0000", "#08661e")) +
  ylim(0, max(density_score$y)) +
  labs(
    title = "Distribution of Cell signature scores",
    x = paste("N =", density_score$n, "Bandwidth =", density_score$bw),
    y = "Density",
    fill = "Signature"
  )

# Check score independant of low expression
p_gene <- ggplot(signature_output, aes(rate, nGenes)) +
  geom_violin(aes(fill = rate), alpha = .5, trim = F, show.legend = F) +
  scale_fill_manual(values=c("#ff0000", "#08661e")) +
  geom_jitter() + labs(y = "Number of detected genes", x = "Signature")

p_counts <- ggplot(signature_output, aes(rate, total_counts)) +
  geom_violin(aes(fill = rate), alpha = .5, trim = F, show.legend = F) +
  scale_fill_manual(values=c("#ff0000", "#08661e")) +
  geom_jitter() + labs(y = "Total counts", x = "Signature")

grid.arrange(p_gene, p_counts, ncol = 2, top = "Influence of library sequencing depth on cell signature scores")

dev.off()

# Save file
write.table(
  signature_output,

2:e08419b8ec24

  make_option(
    "--stats",
    default = "./statistics.tab",
    type = 'character',
    help = "statistics path [default : '%default' ]"
  ),
  make_option(
    "--correlations",
    default = "./correlations.tab",
    type = 'character',
    help = "Correlations between signature genes  [default : '%default' ]"
  ),
  make_option(
    "--covariances",
    default = "./statistics.tab",
    type = 'character',
    help = "Covariances between signature genes [default : '%default' ]"
  ),
  make_option(
    "--pdf",
    default = "~/output.pdf",
    type = 'character',

logical_genes <- rownames(data.counts) %in% genes

# Retrieve target genes in counts data
signature.counts <- subset(data.counts, logical_genes)

# compute covariance
signature.covariances <- as.data.frame(cov(t(signature.counts)))
signature.covariances <- cbind(gene=rownames(signature.covariances), signature.covariances)
write.table(signature.covariances, file=opt$covariances, quote=F, row.names=F, sep="\t")

# compute signature.correlations
signature.correlations <- as.data.frame(cov(t(signature.counts)))
signature.correlations <- cbind(gene=rownames(signature.correlations), signature.correlations)
write.table(signature.correlations, file=opt$correlations, quote=F, row.names=F, sep="\t")

## Descriptive Statistics Function
descriptive_stats = function(InputData) {
  SummaryData = data.frame(
    mean = rowMeans(InputData),

                    order = rank(score, ties.method = "first"),
                    signature = signature_output$rate,
                    stringsAsFactors = F)

pdf(file = opt$pdf)
myplot <- ggplot(signature_output, aes(x=rate, y=score)) +
                 geom_violin(aes(fill = rate), alpha = .5, trim = F, show.legend = F, cex=0.5) +
                 geom_abline(slope=0, intercept=mean(score$score), lwd=.5, color="red") +
                 scale_fill_manual(values=c("#ff0000", "#08661e")) +
                 geom_jitter(size=0.2) + labs(y = "Score", x = "Rate") +
                 annotate("text", x = 0.55, y = mean(score$score), cex = 3, vjust=1.5,
                           color="black", label = mean(score$score), parse = TRUE) +
                 labs(title = "Violin plots of Cell signature scores")

print(myplot)
dev.off()

# Save file
write.table(
  signature_output,