dStructome {dStruct} | R Documentation |
This function provides a convenient way to call the dStruct or dStructGuided functions for multiple transcripts simultaneously. By default, the transcripts are processed in using multiple parallel processes if available.
dStructome( rl, reps_A, reps_B, batches = FALSE, min_length = 11, check_signal_strength = TRUE, check_nucs = TRUE, check_quality = TRUE, quality = "auto", evidence = 0, signal_strength = 0.1, within_combs = NULL, between_combs = NULL, ind_regions = TRUE, gap = 1, processes = "auto", method = "denovo", proximity_assisted = FALSE, proximity = 10, proximity_defined_length = 30 )
rl |
List of dataframes of reactivities for each sample. |
reps_A |
Number of replicates of group A. |
reps_B |
Number of replicates of group B. |
batches |
Logical suggesting if replicates of group A and B were performed in batches and are labelled accordingly. If TRUE, a heterogeneous/homogeneous subset may not have multiple samples from the same batch. |
min_length |
Minimum length of constructed regions. |
check_signal_strength |
Logical, if TRUE, construction of regions must be based on nucleotides that have a minimum absolute value of reactivity. |
check_nucs |
Logical, if TRUE, constructed regions must have a minimum number of nucleotides participating in Wilcoxon signed rank test. |
check_quality |
Logical, if TRUE, check constructed regions for quality. |
quality |
Worst allowed quality for a region to be tested. |
evidence |
Minimum evidence of increase in variation from within-group comparisons to between-group comparisons for a region to be tested. |
signal_strength |
Threshold for minimum signal strength. |
within_combs |
Data.frame with each column containing groupings of replicates of groups A or B, which will be used to assess within-group variation. |
between_combs |
Dataframe with each column containing groupings of replicates of groups A and B, which will be used to assess between-group variation. |
ind_regions |
Logical, if TRUE, test each region found in the transcript separately. |
gap |
Integer. Join regions if they are separated by these many nucleotides. |
processes |
Number of parallel processes to use. |
method |
Character specifying either guided or de novo discovery approach. |
proximity_assisted |
Logical, if TRUE, proximally located regions are tested together. |
proximity |
Maximum distance between constructed regions for them to be considered proximal. |
proximity_defined_length |
If performing a "proximity-assisted" test, minimum end-to-end length of a region to be tested. |
Constructs regions, reports p-value and median difference of between-group and within-group d-scores for each region, and FDR for them.
Krishna Choudhary
Choudhary, K., Lai, Y. H., Tran, E. J., & Aviran, S. (2019). dStruct: identifying differentially reactive regions from RNA structurome profiling data. Genome biology, 20(1), 1-26.
#Load data from Lai et al., 2019 data(lai2019) #Run dStruct in de novo discovery mode for all the transcripts in this data in one step. dStructome(lai2019, 3, 2, batches= TRUE, min_length = 21, between_combs = data.frame(c("A3", "B1", "B2")), within_combs = data.frame(c("A1", "A2", "A3")), ind_regions = TRUE, processes = 1) #Load data from Wan et al., 2014 data(wan2014) #Run dStruct in guide discovery mode for all the transcript regions in this data in one step. dStructome(wan2014, reps_A = 2, reps_B = 1, method = "guided", processes = 1)