fastqc: simple comment. trimming: reverted back. overall qc: simplified the scripts and made sure to add instructions.
This commit is contained in:
Jos van Nijnatten
@@ -1,16 +1,28 @@
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# Gender QC
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# Normalized with limma::voom
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library(GSVA)
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library(limma)
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library(edgeR)
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library(tidyverse)
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source("__ - Preloader.R", verbose=T)
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# expression.data. Has columns:
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# - Gene (gene identifier)
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# - [Sample identifiers]
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expression.data <- "mrna_counts_table.csv"
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# master.Table. Has columns:
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# - GenomeScan_ID
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# - gender, levels = c("male", "female")
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# - age
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# - factor(smoking.status, levels = c("Ex-smoker", "Current smoker"))
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master.Table <- "patient_table.csv"
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# results.dir. The directory of where to put the resulting tables (and later your plots.)
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results.dir <- "results"
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# The analysis
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norm.expr.data <- expression.data %>%
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tibble::column_to_rownames("Gene")
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norm.expr.data <- norm.expr.data[rowSums(norm.expr.data) >= 10,] %>%
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limma::voom() %>%
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as.matrix()
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# We first do a differential expression analysis on gender using EdgeR.
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# Afterwards you should plot these results.
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x.genes <- gene.data %>%
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dplyr::filter(chromosome_name == "X") %>%
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dplyr::pull(ensembl_gene_id)
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@@ -87,248 +99,12 @@ gender.qc.genes.to.plot <- gender.qc.results %>%
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chromosome_name %in% c("X", "Y") &
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FDR < 0.05 &
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dplyr::row_number() <= 5
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) |
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hgnc_symbol %in% c(
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"XIST",
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"TSIX",
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"KDM6A",
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"ZFX",
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"KDM5C",
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"ZFY-AS1",
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"ARSDP1",
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"GYG2P1",
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"RBMY2JP",
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"ARSLP1"
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)
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)
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gender.qc.data <- as.data.frame(norm.expr.data) %>%
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rownames_to_column("ensembl.id") %>%
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tidyr::gather(
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key = "rna.seq.sample.id",
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value = "expr.value",
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-ensembl.id
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) %>%
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dplyr::filter(
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ensembl.id %in% gender.qc.genes.to.plot$ensembl.id
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) %>%
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dplyr::left_join(
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y = gender.qc.patients,
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by = c("rna.seq.sample.id" = "GenomeScan_ID")
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) %>%
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#dplyr::filter(
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# !is.na(gender)
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#) %>%
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readr::write_csv(
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file.path(results.dir.gender.plot, "plot.data.voom.csv")
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)
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for (chr in gender.qc.genes.to.plot$chromosome_name) {
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current.gender.qc.genes.to.plot <- gender.qc.genes.to.plot %>%
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dplyr::filter(chromosome_name == chr)
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chromosome_name <- chr
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i <- 0
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for (current.ensembl.id in current.gender.qc.genes.to.plot$ensembl.id) {
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i <- i + 1
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hgnc_symbol <- gene.data %>%
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dplyr::filter(
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ensembl_gene_id == current.ensembl.id
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) %>%
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dplyr::pull(hgnc_symbol)
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# calculate outliers, kinda
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plot.data <- gender.qc.data %>%
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dplyr::filter(
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ensembl.id == current.ensembl.id
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) %>%
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dplyr::mutate(
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gender = dplyr::case_when(
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is.na(gender) | (stringr::str_trim(gender) == "") ~ "other",
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TRUE ~ gender
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)
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)
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if (nrow(plot.data) <= 0) {
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next
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}
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outliers <- boxplot(
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formula = expr.value ~ gender,
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data = plot.data,
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plot = FALSE
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)$out
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result.to.annotate <- plot.data %>%
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dplyr::filter(
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expr.value %in% outliers
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)
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# Visual: plot range (for t-test p-value)
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plot.y.range <- c(
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"min" = as.integer(min(plot.data$expr.value) - 1) ,
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"max" = as.integer(max(plot.data$expr.value) + 1)
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)
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plot.margin <- ((plot.y.range["max"] + (plot.y.range["min"] * -1)) * 0.05)
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plot.y.range["min"] <- plot.y.range["min"] - plot.margin
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plot.y.range["max"] <- plot.y.range["max"] + plot.margin
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# Plot the damn thing as if it is Graphpad Prism
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stat.table <- rstatix::t_test(plot.data, expr.value ~ gender)
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plt <- plot.data %>%
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ggplot2::ggplot(
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mapping = ggplot2::aes(
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x = gender,
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y = expr.value
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)
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) +
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ggplot2::geom_jitter(
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mapping = ggplot2::aes(
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colour = gender,
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shape = gender
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),
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width = 0.1
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) +
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ggrepel::geom_text_repel(
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data = result.to.annotate,
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mapping = ggplot2::aes(
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label = sample.id
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),
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size = 2,
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box.padding = unit(0.35, "lines"),
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point.padding = unit(0.3, "lines")
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) +
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ggplot2::stat_summary(
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fun = "mean",
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geom = "crossbar",
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width = 0.3,
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size = 0.3
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) +
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ggplot2::scale_y_continuous(
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limits = plot.y.range,
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guide = "prism_offset"
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) +
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#ggprism::add_pvalue(
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# stat.table,
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# y.position = plot.y.range["max"]
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#) +
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ggprism::theme_prism() +
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ggprism::scale_colour_prism() +
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ggprism::scale_shape_prism() +
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ggplot2::theme(
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legend.position = "none"
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) +
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ggplot2::labs(
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subtitle = paste0("Gender Check: ", hgnc_symbol, " (chr. ", chromosome_name, ")"),
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x = "Gender",
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y = "Normalised Expression Values"
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)
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ggplot2::ggsave(
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filename = file.path(results.dir.gender.plot, paste0(chromosome_name, ".", i, ".", hgnc_symbol, ".png")),
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plot = plt,
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width = 12.5,
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height = 12.5,
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unit = "cm"
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)
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}
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}
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# Let's try a GSVA
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gsva.groups <- list(
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X = gender.qc.genes.to.plot %>%
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dplyr::filter(chromosome_name == "X") %>%
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dplyr::pull(ensembl.id),
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Y = gender.qc.genes.to.plot %>%
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dplyr::filter(chromosome_name == "Y") %>%
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dplyr::pull(ensembl.id)
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)
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gsva_res = GSVA::gsva(
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norm.expr.data,
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gsva.groups,
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mx.diff = TRUE,
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verbose = FALSE,
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parallel.sz = 1
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)
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gender.qc.gsva.data <- as.data.frame(gsva_res) %>%
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rownames_to_column("gsva.group") %>%
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tidyr::gather(
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key = "rna.seq.sample.id",
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value = "gsva.value",
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-gsva.group
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) %>%
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dplyr::left_join(
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y = gender.qc.patients %>%
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dplyr::select(
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GenomeScan_ID,
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sample.id,
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gender
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),
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by = c("rna.seq.sample.id" = "GenomeScan_ID")
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) %>%
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readr::write_csv(
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file.path(results.dir.gender.plot, "plot.data.gsva.csv")
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)
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for (c.gender in unique(gender.qc.gsva.data$gender)) {
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if (is.na(c.gender)) {
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next
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}
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c.plot.data <- gender.qc.gsva.data %>%
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dplyr::filter(
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gender == c.gender
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)
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outliers <- boxplot(
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formula = gsva.value ~ gsva.group,
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data = c.plot.data,
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plot = FALSE
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)$out
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result.to.annotate <- c.plot.data %>%
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dplyr::filter(
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gsva.value %in% outliers
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)
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plt <- c.plot.data %>%
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ggplot2::ggplot(
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mapping = ggplot2::aes(
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x = gsva.group,
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y = gsva.value
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)
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) +
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ggplot2::geom_boxplot() +
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ggrepel::geom_text_repel(
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data = result.to.annotate,
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mapping = ggplot2::aes(
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label = sample.id
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),
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size = 2,
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box.padding = unit(0.35, "lines"),
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point.padding = unit(0.3, "lines")
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) +
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ggprism::theme_prism() +
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ggprism::scale_colour_prism() +
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ggprism::scale_shape_prism() +
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ggplot2::theme(
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legend.position = "none"
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) +
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ggplot2::labs(
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subtitle = paste0("", toupper(c.gender)),
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x = "Chromosome",
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y = "GSVA Values"
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)
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ggplot2::ggsave(
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filename = file.path(results.dir.gender.plot, paste0("gsva.", c.gender, ".png")),
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plot = plt,
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width = 12.5,
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height = 12.5,
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unit = "cm"
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)
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}
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# Next thing to do:
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# - Plot the normalized expressino values for the genes in gender.qc.genes.to.plot in a boxplot, split and colored by gender.
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# - (Optional) Do a GSVA with as genesets the genes found in gender.qc.genes.to.plot. Plot the boxplots as per the previous point.
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# - (Optional) Plot the number of Y-chromosome reads devided by the number of X chromosome reads in a boxplot as per the first point.
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# - (Optional) Plot the number of Y-chromosome SNPs devided by the number of X chromosome SNPs in a boxplot as per the first point.
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