Generate approximate pseudo-bulk data by random projections

Usage

asap_random_bulk_mtx(
  mtx_file,
  row_file,
  col_file,
  idx_file,
  num_factors,
  r_covar_n = NULL,
  r_covar_d = NULL,
  rows_restrict = NULL,
  rseed = 42L,
  verbose = FALSE,
  NUM_THREADS = 0L,
  CELL_NORM = 10000,
  BLOCK_SIZE = 1000L,
  do_log1p = FALSE,
  do_down_sample = FALSE,
  save_aux_data = FALSE,
  weighted_rand_proj = FALSE,
  CELL_PER_SAMPLE = 100L,
  a0 = 1e-08,
  b0 = 1,
  MAX_ROW_WORD = 2L,
  ROW_WORD_SEP = "_",
  MAX_COL_WORD = 100L,
  COL_WORD_SEP = "@"
)

Arguments

mtx_file: matrix-market-formatted data file (bgzip)
row_file: row names (gene/feature names)
col_file: column names (cell/column names)
idx_file: matrix-market colum index file
num_factors: a desired number of random factors
r_covar_n: N x r covariates (default: NULL)
r_covar_d: D x r covariates (default: NULL)
rseed: random seed
verbose: verbosity
NUM_THREADS: number of threads in data reading
CELL_NORM: sample normalization constant (default: 1e4)
BLOCK_SIZE: disk I/O block size (number of columns)
do_log1p: log(x + 1) transformation (default: FALSE)
do_down_sample: down-sampling (default: FALSE)
save_aux_data: save auxiliary data (default: FALSE)
weighted_rand_proj: save random projection (default: FALSE)
CELL_PER_SAMPLE: down-sampling cell per sample (default: 100)
a0: gamma(a0, b0) (default: 1e-8)
b0: gamma(a0, b0) (default: 1)
MAX_ROW_WORD: maximum words per line in row_file
ROW_WORD_SEP: word separation character to replace white space
MAX_COL_WORD: maximum words per line in col_file
COL_WORD_SEP: word separation character to replace white space

Value

a list

PB pseudobulk (average) data (feature x sample)
sum pseudobulk (sum) data (feature x sample)
size size per sample (sample x 1)
positions pseudobulk sample positions (cell x 1)
rand.dict random dictionary (proj factor x feature)
rand.proj random projection results (sample x proj factor)
colnames column (cell) names
rownames feature (gene) names