subdivide_data | R Documentation |
Preprocessing function for SPMA, divides transcript sequences into n bins.
subdivide_data(sorted_transcript_sequences, n_bins = 40)
sorted_transcript_sequences |
character vector of named sequences (names are usually RefSeq identifiers and sequence region labels, e.g., "NM_1_DUMMY|3UTR"). It is important that the sequences are already sorted by fold change, signal-to-noise ratio or any other meaningful measure. |
n_bins |
specifies the number of bins in which the sequences will be divided, valid values are between 7 and 100 |
An array of n_bins
length, containing the binned sequences
Other SPMA functions:
classify_spectrum()
,
run_kmer_spma()
,
run_matrix_spma()
,
score_spectrum()
# toy example
toy_seqs <- c(
"CAACAGCCUUAAUU", "CAGUCAAGACUCC", "CUUUGGGGAAU", "UCAUUUUAUUAAA",
"AAUUGGUGUCUGGAUACUUCCCUGUACAU", "AUCAAAUUA", "AGAU", "GACACUUAAAGAUCCU",
"UAGCAUUAACUUAAUG", "AUGGA", "GAAGAGUGCUCA", "AUAGAC", "AGUUC", "CCAGUAA"
)
# names are used as keys in the hash table (cached version only)
# ideally sequence identifiers (e.g., RefSeq ids) and
# sequence region labels (e.g., 3UTR for 3'-UTR)
names(toy_seqs) <- c(
"NM_1_DUMMY|3UTR", "NM_2_DUMMY|3UTR", "NM_3_DUMMY|3UTR",
"NM_4_DUMMY|3UTR", "NM_5_DUMMY|3UTR", "NM_6_DUMMY|3UTR",
"NM_7_DUMMY|3UTR",
"NM_8_DUMMY|3UTR", "NM_9_DUMMY|3UTR", "NM_10_DUMMY|3UTR",
"NM_11_DUMMY|3UTR",
"NM_12_DUMMY|3UTR", "NM_13_DUMMY|3UTR", "NM_14_DUMMY|3UTR"
)
foreground_sets <- subdivide_data(toy_seqs, n_bins = 7)
# example data set
background_df <- transite:::ge$background_df
# sort sequences by signal-to-noise ratio
background_df <- dplyr::arrange(background_df, value)
# character vector of named sequences
background_seqs <- background_df$seq
names(background_seqs) <- paste0(background_df$refseq, "|",
background_df$seq_type)
foreground_sets <- subdivide_data(background_seqs)
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