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holstege_deleteome_nps

holstege_deleteome_nps.Rmd

Setup 

library(tidyverse)
library(gprofiler2)
library(rtracklayer)

Raw Data

As of now, the raw data originally published in Kemmeren et al. (2014) is available through the lab’s website. In this example, I am using deleteome_all_mutants_svd_transformed.txt which is the originally published data with an effort made to remove a slow-growth factor of unwanted variance O’Duibhir et al. (2014).

Parsing the kemmeren data 


parse_kemmeren_data = function(kemmeren_svd){

  # read kemmeren_svd 
  kemmeren_data_mutants = read_tsv(kemmeren_svd)
  
  # add gene_id as the column name of the first column
  colnames(kemmeren_data_mutants)[1] = 'gene_id'
  
  # remove any 'matA' samples
  kemmeren_data_mutants = kemmeren_data_mutants %>%
    select(-all_of(
      colnames(kemmeren_data_mutants)[
        str_detect(colnames(kemmeren_data_mutants), "matA")]))

  # create a table with columns deleted_locus, which is extracted from the colnames 
  # of the kemmeren data, and another column with the corresponding column names
  kemmeren_col_data = tibble(
    deleted_locus = str_extract(
      colnames(kemmeren_data_mutants)[3:ncol(kemmeren_data_mutants)], ".+(?=\\.del)"), 
    colnames = colnames(kemmeren_data_mutants)[3:ncol(kemmeren_data_mutants)]
  )

  # use gprofilr2 to get the corresponding scerevisiae systematic IDs from the
  # gene names in the kemmeren column names
  kemmeren_cols_convert = gconvert(kemmeren_col_data$deleted_locus,
                               organism = "scerevisiae",
                               mthreshold = 1,
                               filter_na = FALSE)

  # create a dataframe which will act as the colData of the NetProphetDataSet
  coldata = kemmeren_col_data %>% left_join(
    select(kemmeren_cols_convert, -all_of(c('input_number', 'target_number'))),
    by = c("deleted_locus" = "input")
  ) %>%
    filter(!is.na(target)) %>%
    select(-deleted_locus) %>%
    dplyr::rename(deleted_locus = target, name = name)

  # return a list with slots coldata and expr. The expr matrix is the kemmeren 
  # data, but filtered for those columns in the coldata
  list(
      coldata = coldata,
      expr = kemmeren_data_mutants[,c('gene_id', 'commonName', coldata$colnames)]
  )

}

Read in s. cerevisiae annotations

Yeast Genome files may be found here

YEAST_GFF = "~/Desktop/rnaseq_pipeline/rnaseq_pipeline/genome_files/S288C_R64/S288C_R64.gff"

# list of top level gene features. Note that the kemmeren data does say that it
# is at the transcript level. However, yeast genes have few exons and these seem 
# to be mostly equivalent to genes. That said, I would suggest doing some investigation 
# on your own

GFF_GENE_FEATURES = c('gene', 'pseudogene', 'tRNA_gene', 
                      'rRNA_gene', 'ncRNA_gene', 'snoRNA_gene', 'snRNA_gene')

yeast_gff = import(YEAST_GFF)

yeast_genes = yeast_gff[yeast_gff$type %in% GFF_GENE_FEATURES]

Read in the list of regulators from the NP2 paper

Parse Uniprot data

To get the data I am using, and more, go to the Uniprot website and enter the following into the search:

taxonomy:"Saccharomyces cerevisiae (strain ATCC 204508 / S288c) (Baker's yeast) [559292]" AND reviewed:yes

Note that you can select a different set of columns if you click the ‘column’ button

UNIPROT_DATA = "data/uniprot_yeast_20211102.tab"

# lots of parsing -- provided as an example, but you will likely need to look 
# at the file and extract what you'd like. This will be required if you go to 
# the website and select/download a different set of columns

uniprot = read_tsv(UNIPROT_DATA) %>%
  dplyr::rename(ID = `Gene names  (ordered locus )`,
                dna_binding = `DNA binding`) %>%
  mutate(dna_binding_interval        = str_extract(dna_binding, "\\d+\\.\\.\\d+"),
         dna_binding_domain          = str_extract(dna_binding, '(?<=note=)".+"'),
         dna_binding_domain_evidence = str_extract(dna_binding, '(?<=evidence=)".+"')) %>%
  separate(dna_binding_interval, sep="\\.\\.", into=c("dna_binding_start", "dna_binding_end")) %>%
  mutate(dna_binding_start = as.integer(dna_binding_start),
         dna_binding_end = as.integer(dna_binding_end),
         dna_binding_domain = trimws(str_remove_all(dna_binding_domain, ';|"|/evidence.+')),
         dna_binding_domain_evidence = str_remove_all(dna_binding_domain_evidence, ';|"'),
         dna_binding_domain = str_replace(dna_binding_domain,"A\\.T hook 1 DNA_BIND 1502\\.\\.1513  /note=A.T hook 2 DNA_BIND 1516\\.\\.1526  /note=A\\.T hook 3", "A.T hook"))

colnames(uniprot) = trimws(str_remove_all(colnames(uniprot), "Cross-reference|\\(|\\)|"), which = "both")

Read in the list of regulators used in the NP2 paper 

ROWNAMES_GENE_CONVERT = here("data/gProfiler_scerevisiae_11-1-2021_5-31-10.csv")
regulators_df = read_csv(ROWNAMES_GENE_CONVERT)

create kemmeren granges 

regulators_uniprot = uniprot %>%
  filter(ID %in% regulators_df$initial_alias)

cant_find_id = setdiff(kemmeren_data$expr$gene_id, yeast_gff$ID)

# deleted if the annotation has since been removed or merged from the annotations
replace_wrong_ids        = c("YKL047W", "YLR003C", "YAR061W", "DELETED", "DELETED", "DELETED",   "DELETED", "DELETED")
names(replace_wrong_ids) = c("ANR2",    "CMS1",    "YAR062W", "YDL038C", "YGR272C",  "YIL080W",  "YIL168W", "YIR044C")


kemmeren_data$expr[which(kemmeren_data$expr$gene_id %in% names(replace_wrong_ids)), "gene_id"] = replace_wrong_ids

kemmeren_data$expr = kemmeren_data$expr %>%
  filter(gene_id != "DELETED")

setdiff(kemmeren_data$expr$gene_id, yeast_gff$ID[!is.na(yeast_gff$ID)])

kemmeren_granges = yeast_gff[yeast_gff$ID %in% kemmeren_data$expr$gene_id]

kemmeren_granges = kemmeren_granges[order(match(kemmeren_granges$ID,kemmeren_data$expr$gene_id))]

stopifnot(identical(kemmeren_data$expr$gene_id, kemmeren_granges$ID))

add gene-wise data to the granges object 

kemmeren_granges[which(is.na(kemmeren_granges$gene))]$gene =
  kemmeren_data$expr[which(is.na(kemmeren_granges$gene)), 'commonName']

kemmeren_granges$regulator = ifelse(kemmeren_granges$ID %in% regulators_df$initial_alias,
                                    TRUE,
                                    FALSE)

uniprot_cols_for_granges = as_tibble(kemmeren_granges) %>%
  left_join(uniprot)

elementMetadata(kemmeren_granges)[colnames(uniprot)[3:ncol(uniprot)]] =
  uniprot_cols_for_granges[,colnames(uniprot)[3:ncol(uniprot)]]

kem_tfs = kemmeren_granges[!is.na(kemmeren_granges$dna_binding_domain)]

expr = as.matrix(select(kemmeren_data$expr, -gene_id, -commonName))
rownames(expr) = kemmeren_data$expr$gene_id

del_loci_no_gene = setdiff(kemmeren_data$coldata$deleted_locus,
                           kemmeren_granges[kemmeren_granges$ID %in% kemmeren_data$coldata$deleted_locus]$ID)

fltr_coldata = kemmeren_data$coldata[!kemmeren_data$coldata$deleted_locus %in% del_loci_no_gene, ]

fltr_expr = expr[,fltr_coldata$colnames]

create the regulation matrix 


regulators = read_tsv("data/YEAST_DATA/YEAST/OUTPUT/regulators", col_names = FALSE)$X1

regulators = regulators[regulators %in% fltr_coldata$deleted_locus]

tf_fltr = kemmeren_granges$ID %in% regulators

regulation_matrix = matrix(TRUE, nrow(fltr_expr), nrow(fltr_expr),
                           dimnames = list(rownames(fltr_expr), rownames(fltr_expr)))
regulation_matrix = regulation_matrix[,rownames(fltr_expr) %in% regulators]

self_reg = intersect(rownames(regulation_matrix), colnames(regulation_matrix))

regulation_matrix[cbind(self_reg,self_reg)] = FALSE

create the NetProphetDataSet 

kem_np = NetProphetDataSet(
  expr        = fltr_expr,
  regMatrix   = regulation_matrix,
  rowRanges   = kemmeren_granges,
  colData     = fltr_coldata
)

Result

Kemmeren, P., K. Sameith, L. A. van de Pasch, J. J. Benschop, T. L. Lenstra, T. Margaritis, E. O’Duibhir, et al. 2014. “Large-scale genetic perturbations reveal regulatory networks and an abundance of gene-specific repressors.” Cell 157 (3): 740–52.
O’Duibhir, Eoghan, Philip Lijnzaad, Joris J Benschop, Tineke L Lenstra, Dik van Leenen, Marian JA Groot Koerkamp, Thanasis Margaritis, Mariel O Brok, Patrick Kemmeren, and Frank CP Holstege. 2014. “Cell Cycle Population Effects in Perturbation Studies.” Molecular Systems Biology 10 (6): 732. https://doi.org/https://doi.org/10.15252/msb.20145172.