40 lines
1.2 KiB
R
40 lines
1.2 KiB
R
# Total counts per sample
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# Normalized with limma::voom
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library(limma)
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library(tidyverse)
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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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# We calculate the number of mapped reads 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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# Next thing to do:
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# - Check the number of reads per sample in total.counts.per.sample.csv
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# - Plot the reads distribution (all reads) per sample in a boxplot.
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# - (Optional) Calculate the number of unmapped, multimapped, unique mapped to
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# feature and unique mapped to no feature and plot these in a stacked bar graph.
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