Appendix: Data acquisition

Here, you can find the code used to obtain the benchmark data.

se_cotton.rda

This data set was obtained from (Dong et al. 2022), and it comprises RNA-seq data on cotton (Gossypium) species of different ploidy levels (i.e., allopolyploids and their diploid progenitors) under salt stress. The SummarizedExperiment object was created with the code below:

library(SummarizedExperiment)
library(tidyverse)

# Get count matrix
counts <- read.table(
    file.path(
        "https://raw.githubusercontent.com/Wendellab",
        "SaltStressTranscriptome/master/salt.counts"
    ),
    header = TRUE, sep = "\t", row.names = 1
)[, -c(1:5)]
names(counts) <- gsub("sort.bam", "sort.T.bam", names(counts))
names(counts) <- gsub(".sort|.bam|_5.22|_5.23|6.11", "", names(counts))

# Get subset of data for each species
A2count <- counts[, grep("A2", names(counts))]
A2count <- cbind(
    A2count[, 1:5] + A2count[, 6:10], # Control 1
    A2count[, grep("CK2", names(A2count))], # Control 2
    A2count[, 16:20] + A2count[, 21:25], # Control 3
    A2count[, grep("Salt1", names(A2count))], # Salt 1
    A2count[, 31:35] + A2count[, 36:40], # Salt 2
    A2count[, 41:45] + A2count[, 46:50] # Salt 3
)

# Combine count matrices
c_counts <- cbind(A2count, D5count, TM1count, AD4count)

# Get total counts
diploid_total <- c_counts[, grep("A2.*T$|D5.*T$", names(c_counts))]
TM1_total <- c_counts[, grep("TM1.*A$|TM1.*D$", names(c_counts))]
TM1_total <- TM1_total[, seq(1, 11, by = 2)] + TM1_total[, seq(2, 12, by = 2)]
AD4_total <- c_counts[, grep("AD4.*A$|AD4.*D$", names(c_counts))]
AD4_total <- AD4_total[, seq(1, 11, by = 2)] + AD4_total[, seq(2, 12, by = 2)]
counts_total <- cbind(diploid_total, TM1_total, AD4_total)

# Polish column names
names(counts_total) <- gsub("\\.T|\\.A|_\\.T|_\\.A", "", names(counts_total))
names(counts_total) <- gsub("TM1", "AD1", names(counts_total))

# Create colData
coldata <- data.frame(
    row.names = names(counts_total),
    species = rep(c("A2", "D5", "AD1", "AD4"), each = 6),
    condition = rep(rep(c("Control", "Salt"), each = 3), 4),
    rep = rep(1:3, 8)
) |>
    mutate(
        sample = paste(species, condition, sep = "_"),
        species_name = rep(
            c("Garboreum", "Graimondii", "Ghirsutum_TM1", "Gmustelinum"), 
            each = 6
        ),
        ploidy = rep(c("di", "allo"), each = 12)
    ) |>
    select(species_name, species, ploidy, condition, sample, rep)


# Creating the SummarizedExperiment object
se_cotton <- SummarizedExperiment(
    assays = list(counts = as.matrix(counts_total)),
    colData = coldata
)

# Save object to file
save(
    se_cotton, compress = "xz",
    file = here::here("data", "se_cotton.rda")
)

cotton_functions.rda

This object contains a list of data frames with GO, InterPro, and MapMan annotations for genes in the G. raimondii genome. Data were obtained from PLAZA Dicots 5.0 (Van Bel et al. 2022).

# Get data frames
go_df <- readr::read_tsv(
    "https://ftp.psb.ugent.be/pub/plaza/plaza_public_dicots_05/GO/go.gra.csv.gz",
    skip = 8
) |>
    dplyr::select(gene = `#gene_id`, description)

interpro_df <- readr::read_tsv(
    "https://ftp.psb.ugent.be/pub/plaza/plaza_public_dicots_05/InterPro/interpro.gra.csv.gz",
    skip = 8
) |>
    dplyr::select(gene = `#gene_id`, description)

mapman_df <- readr::read_tsv(
    "https://ftp.psb.ugent.be/pub/plaza/plaza_public_dicots_05/MapMan/mapman.gra.csv.gz",
    skip = 8
) |>
    dplyr::select(gene = gene_id, description = desc)

# Create list
cotton_functions <- list(
    GO = go_df,
    InterPro = interpro_df,
    MapMan = mapman_df
)

# Save object to .rda file
save(
    cotton_functions, compress = "xz", 
    file = here("data", "cotton_functions.rda")
)

se_rice.rda

This file contains a SummarizedExperiment object with data from Zhai et al. (2013), obtained from GEO under accession number GSE41797.

# Read data set from GEO
rice <- readr::read_tsv(
    "https://ftp.ncbi.nlm.nih.gov/geo/series/GSE41nnn/GSE41797/suppl/GSE41797%5Frice%5Fall%5Fresults.txt.gz"
)

# Get count matrix
rice_exp <- rice |>
    dplyr::select(
        Gene_id, R1, R2, R3, R4, X1, X2, X3, X4, F1, F2, F3, F4
    ) |>
    tibble::column_to_rownames("Gene_id") |>
    as.matrix()

# Get sample metadata
rice_coldata <- data.frame(
    row.names = colnames(rice_exp),
    Line = c(
        rep("R9308", 4), rep("Xieqingzao B", 4), rep("Xieyou 9308", 4)
    ),
    Stage = rep(c("Tillering", "Tillering", "Heading", "Heading"), 3),
    Generation = c(
        rep("P1", 4), rep("P2", 4), rep("F1", 4)
    )
)

# Create SummarizedExperiment object
se_rice <- SummarizedExperiment::SummarizedExperiment(
    assays = list(counts = rice_exp),
    colData = rice_coldata
)

# Save object to file
save(
    se_rice, compress = "xz",
    file = here::here("data", "se_rice.rda")
)

rice_functions.rda

This object contains a list of 2-column data frames with functional annotation for rice (Oryza sativa ssp. japonica). List names are GO, InterPro, and MapMan, and each table has columns named gene (gene id as in the count matrix in se_rice), and description (term description).

# Get a table of tx-to-gene mapping
tx2gene <- readr::read_tsv(
    "https://ftp.psb.ugent.be/pub/plaza/plaza_public_monocots_05/IdConversion/id_conversion.osa.csv.gz",
    skip = 8, show_col_types = FALSE
) |>
    dplyr::filter(id_type == "tid") |>
    dplyr::select(tx = id, gene = `#gene_id`)

# Get functional annotation
## GO
go_df <- readr::read_tsv(
    "https://ftp.psb.ugent.be/pub/plaza/plaza_public_monocots_05/GO/go.osa.csv.gz",
    skip = 8, show_col_types = FALSE
) |>
    dplyr::select(gene = `#gene_id`, description) |>
    inner_join(tx2gene) |>
    dplyr::select(gene = tx, description)

## InterPro
interpro_df <- readr::read_tsv(
    "https://ftp.psb.ugent.be/pub/plaza/plaza_public_monocots_05/InterPro/interpro.osa.csv.gz",
    skip = 8, show_col_types = FALSE
) |>
    dplyr::select(gene = `#gene_id`, description) |>
    inner_join(tx2gene) |>
    dplyr::select(gene = tx, description)

## MapMan
mapman_df <- readr::read_tsv(
    "https://ftp.psb.ugent.be/pub/plaza/plaza_public_monocots_05/MapMan/mapman.osa.csv.gz",
    skip = 8, show_col_types = FALSE
) |>
    dplyr::select(gene = gene_id, description = desc) |>
    inner_join(tx2gene) |>
    dplyr::select(gene = tx, description)

# Create list
rice_functions <- list(
    GO = go_df,
    InterPro = interpro_df,
    MapMan = mapman_df
)

# Save object to file
save(
    rice_functions, compress = "xz",
    file = here::here("data", "rice_functions.rda")
)

Session info

This document was created under the following conditions:

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 language (EN)
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─ Packages ───────────────────────────────────────────────────────────────────
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 [1] /home/faalm/R/x86_64-pc-linux-gnu-library/4.3
 [2] /usr/local/lib/R/site-library
 [3] /usr/lib/R/site-library
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References

Dong, Yating, Guanjing Hu, Corrinne E Grover, Emma R Miller, Shuijin Zhu, and Jonathan F Wendel. 2022. “Parental Legacy Versus Regulatory Innovation in Salt Stress Responsiveness of Allopolyploid Cotton (Gossypium) Species.” The Plant Journal 111 (3): 872–87.

Van Bel, Michiel, Francesca Silvestri, Eric M Weitz, Lukasz Kreft, Alexander Botzki, Frederik Coppens, and Klaas Vandepoele. 2022. “PLAZA 5.0: Extending the Scope and Power of Comparative and Functional Genomics in Plants.” Nucleic Acids Research 50 (D1): D1468–74.

Zhai, Rongrong, Yue Feng, Huimin Wang, Xiaodeng Zhan, Xihong Shen, Weiming Wu, Yingxin Zhang, et al. 2013. “Transcriptome Analysis of Rice Root Heterosis by RNA-Seq.” BMC Genomics 14 (1): 1–14.