Skip to contents

PMF regression

asap_pmf_regression.Rd

PMF regression

Usage

asap_pmf_regression(
  y_dn,
  log_beta,
  beta_row_names,
  r_log_delta = NULL,
  do_stdize_beta = TRUE,
  do_stdize_r = TRUE,
  do_log1p = FALSE,
  a0 = 1,
  b0 = 1,
  max_iter = 10L,
  verbose = FALSE,
  NUM_THREADS = 0L,
  BLOCK_SIZE = 1000L
)

Arguments

y_dn

sparse data matrix (D x N)

log_beta

D x K log dictionary/design matrix

beta_row_names

row names log_beta (D vector)

r_log_delta

D x B log batch effect matrix

do_stdize_beta

use standardized log_beta (Default: TRUE)

do_stdize_r

standardize correlation matrix R (default: TRUE)

do_log1p

do log(1+y) transformation

a0

gamma(a0, b0) (default: 1)

b0

gamma(a0, b0) (default: 1)

max_iter

maximum iterations (default: 10)

verbose

verbosity

NUM_THREADS

number of threads in data reading

BLOCK_SIZE

disk I/O block size (number of columns)

MAX_ROW_WORD

maximum words per line in row_files[i]

ROW_WORD_SEP

word separation character to replace white space

MAX_COL_WORD

maximum words per line in col_files[i]

COL_WORD_SEP

word separation character to replace white space

Value

a list that contains:

  • beta dictionary matrix (row x factor)

  • delta the dictionary matrix of batch effects (row x batch)

  • corr empirical correlation (column x factor)

  • theta factor loading (column x factor)

  • log.theta log-scaled factor loading (column x factor)

  • colsum the sum of each column (column x 1)

  • rownames row/feature names

  • colnames column/sample names