164 lines
3.5 KiB
R
164 lines
3.5 KiB
R
# Total counts per sample
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# Normalized with limma::voom
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source("__ - Preloader.R", verbose=T)
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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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# Total counts per sample
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total.count.per.sample <- expression.data %>%
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tibble::column_to_rownames("Gene") %>%
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colSums()
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data.frame(
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sample = names(total.count.per.sample),
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counts = as.numeric(total.count.per.sample)
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) %>%
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readr::write_csv(file.path(results.dir, "total.counts.per.sample.csv"))
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norm.data <- norm.expr.data %>%
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as.data.frame() %>%
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tibble::rownames_to_column(
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"Gene"
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) %>%
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tidyr::gather(
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key = "sample.id",
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value = "expr.value",
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-Gene
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) %>%
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dplyr::left_join(
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y = master.Table %>%
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dplyr::filter(
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!is.na(GenomeScan_ID)
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) %>%
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dplyr::mutate(
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id = dplyr::case_when(
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stringr::str_trim(gender) == "" ~ paste0("Water ", dplyr::row_number()),
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TRUE ~ sample.id
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),
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gender = dplyr::case_when(
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stringr::str_trim(gender) == "" ~ "water",
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!is.na(gender) ~ as.character(gender)
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)
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) %>%
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dplyr::select(
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GenomeScan_ID,
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gender,
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id
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),
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by = c("sample.id" = "GenomeScan_ID")
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)
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norm.plot <- norm.data %>%
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ggplot2::ggplot(
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mapping = ggplot2::aes(
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x = id,
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y = expr.value,
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fill = gender
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)
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) +
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ggplot2::geom_boxplot() +
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ggplot2::scale_fill_manual(
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values = c(
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"male" = "blue",
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"female" = "red",
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"water" = "green"
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)
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) +
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ggplot2::labs(
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title = "Normalized expression values distribution",
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y = "Normalized expression values (limma::voom)",
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x = "Sample",
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gender = "Gender"
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) +
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ggprism::theme_prism() +
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ggplot2::theme(
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axis.text.x = ggplot2::element_text(angle = 90)
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)
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ggplot2::ggsave(
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filename = file.path(results.dir, "counts.per.sample.normalised.png"),
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plot = norm.plot,
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width = 40,
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height = 20,
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units = "cm"
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)
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expr.data <- expression.data %>%
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tidyr::gather(
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key = "sample.id",
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value = "expr.value",
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-Gene
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) %>%
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dplyr::filter(
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expr.value != 0
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) %>%
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dplyr::left_join(
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y = master.Table %>%
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dplyr::filter(
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!is.na(GenomeScan_ID)
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) %>%
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dplyr::mutate(
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id = dplyr::case_when(
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stringr::str_trim(gender) == "" ~ paste0("Water ", dplyr::row_number()),
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TRUE ~ sample.id
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),
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gender = dplyr::case_when(
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stringr::str_trim(gender) == "" ~ "water",
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!is.na(gender) ~ as.character(gender)
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)
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) %>%
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dplyr::select(
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GenomeScan_ID,
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gender,
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id
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),
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by = c("sample.id" = "GenomeScan_ID")
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)
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expr.plot <- expr.data %>%
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ggplot2::ggplot(
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mapping = ggplot2::aes(
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x = id,
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y = expr.value,
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fill = gender
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)
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) +
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ggplot2::geom_boxplot() +
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ggplot2::scale_fill_manual(
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values = c(
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"male" = "blue",
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"female" = "red",
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"water" = "green"
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)
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) +
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ggplot2::scale_y_continuous(trans='log2') +
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ggplot2::labs(
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title = "Raw expression values distribution, without zero's",
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y = "Expression values",
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x = "Sample",
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gender = "Gender"
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) +
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ggprism::theme_prism() +
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ggplot2::theme(
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axis.text.x = ggplot2::element_text(angle = 90)
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)
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ggplot2::ggsave(
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filename = file.path(results.dir, "counts.per.sample.raw.zeros.removed.png"),
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plot = expr.plot,
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width = 40,
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height = 20,
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units = "cm"
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)
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