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run_experiment

get weights for all combinations

Usage​

run_experiment(data_source, gr_source, ctVal_source, tmpFolder = NULL,
  sub_names = NULL, combs_name_mat = NULL,
  sub_samples_for_weights = NULL, data_target = NULL,
  gr_target = NULL, ctVal_target = NULL, k = 2, iter = F,
  keep = 50, retain_iters = F, retain_thr = 10,
  genorm_k_stables = 10, weight_methods = c("geom_sd_hybrid"),
  algors = c("SDCV"), data_source_norm = NULL,
  data_target_norm = NULL, norm_method = "high_exp",
  norm_method_exp_thr = 35, weights_from_raw = F, val_on_source = T,
  val_on_target = T, verbose = T, remove_left_over = T,
  saveRDS = F, mc.cores = 10, cuda_kernel = "InterOptCuda")

Arguments​

data_source a matrix of genes expression. rows are genes and columns are the samples. its rownames should be the names of the genes

gr_source a vector of characters showing the groups of the samples

ctVal_source logical. if TRUE, the elements in data_source are considered as qPCR CT values. if FALSE, they are considered as normalized expression of an RNA-seq experiment (count per million)

tmpFolder a temporary directory to store intermediate files while calculating weights. If not specified an automatic temporary directory is built and used

sub_names a character vector of gene names to consider in combinations. if NULL, all genes are considered

combs_name_mat a matrix of characters. each row shows a combination of genes. if NULL, all combinations of all genes or a subset of them specified by sub_names is used.

sub_samples_for_weights a vector of sample indices to consider for calculating aggregation weights. this argument is used only for benchmarking purposes

data_target a matrix of external data genes expression. This parameter is used when you want to examine the calculated weights on a separate external data. rows are genes and columns are the samples.

gr_target a vector of characters showing the groups of the samples of the external data.

ctVal_target logical. if TRUE, the elements in data_target are considered as qPCR CT values. if FALSE, they are considered as normalized expression of an RNA-seq experiment (count per million)

k integer, the number of genes in each combination.

iter logical, if TRUE, an iterative approach is utilized. instead of calculating all combinations for k>2, in each iteration the top most stable combinations (defined by the 'keep' parameter) are crossed with other genes to make the new combinations. useful for k>3 when the number of combinations is very high

keep integer, the number of genes to keep in each iteration of iterative mode

retain_iters if TRUE, all intermediate iterations results are also reported in the output. (only for iterative mode)

retain_thr integer, the number of iterations to retain in the output result. (only when retain_iters=TRUE)

genorm_k_stables integer, number of top stable genes (in terms of standard deviation) to consider in the modified Genorm stability measure. default is 10

weight_methods a character vector of methods to calculate aggregation weights. available methods are 'arith', 'random','arith_cv','geom','geom_cv', 'geom_cv_exh','geom_sd','geom_sd_soft','geom_sd_hybrid','arith_sd','sd_simple'

algors a character vector of stability measures. available measures are 'SD', 'CV', 'Genorm', 'NormFinder'

data_source_norm a matrix of normalized genes expression. if NULL, the default automatic normalization is used. aggregation weights are calculated based on the normalized data by default unless weight_from_raw=TRUE. Moreover the stability measures are also calculated based on the normalized data

data_target_norm a matrix of normalized external genes expression

norm_method a single character string. 'median_sd' or 'high_exp'. In 'median_sd' method, the average of half of the genes with lower standard deviation is used as a reference gene to normalize the data. In 'high_exp' method only in each sample only the ct values larger than norm_method_exp_thr are considered.

norm_method_exp_thr integer, the CT threshold to use in high_exp normalization method.

weights_from_raw if TRUE, aggregation weights are calculated based on the raw CT values instead of the normalized data.

val_on_source if TRUE, the stability measures are calculated on data_source.

val_on_target if TRUE, the stability measures are calculated on data_target.

verbose logical, print the calculation process in console

remove_left_over logical, if FALSE, the intermediate files used for CUDA calculation are not removed in the tmpFolder. (used for debugging purposes)

saveRDS logical, if TRUE, the final output is also saved as an RDS file in the tmpFolder

mc.cores number of the cpu cores to use for calculation SD and CV stability measures.

cuda_kernel a single character string for the InterOpt cuda kernel executable. defauly is 'InterOptCuda'