Compute p value for DAR test between two cell types
Source:R/Insertion_rates_and_DAR_test.R
DAR_by_LRT.RdCompute p value for DAR test between two cell types
Usage
DAR_by_LRT(
pic_mat,
capturing_rates,
cell_type_labels,
n_cores = 1L,
plen = NULL,
min_frag_length = 25,
max_frag_length = 600,
estimation_approach = "MLE",
artifact_threshold = 10L
)Arguments
- pic_mat
The observed peak by cell PIC count matrix
- capturing_rates
A vector of estimated capturing rates for each cell
- cell_type_labels
A vector specifying cell type labels
- n_cores
A positive integer specifying the number of cores. On Windows, values greater than one fall back to serial evaluation. Default = 1.
- plen
A vector of peak length
- min_frag_length
The value for the s1 hyperparameter in the ssPoisson distribution, this stands for the minimum fragment length requirement such that the fragment can be amplifiable and mappable to genome. Default = 25
- max_frag_length
The value for the s2 hyperparameter in the ssPoisson distribution, this stands for the max fragment length requirement such that the fragment can be amplifiable. Default = 600
- estimation_approach
The approach for parameter estimation, either 'MLE' for condition 1 or 'ME' for condition 1+2. The 'MLE' approach is more accurate and usually it has a higher power, but it ignores the size filtering step in snATAC-seq data generation. Default is 'MLE'
- artifact_threshold
A heuristic upper threshold for unreliable counts. Counts strictly greater than this value may reflect mapping errors or unusual fragment structures and are replaced by zero before the remaining counts are capped at 5 for the likelihood model. The default is 10; use
Infto disable artifact replacement. Because this cutoff is assay- and pipeline-dependent, sensitivity analyses with alternative values are recommended.