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Merge pull request #45 from RATs/development to RATs/master
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0.6.1
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@fruce-ki
fruce-ki authored Feb 15, 2018
2 parents b8c6135 + e1b618a commit fe87aca
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4 changes: 2 additions & 2 deletions DESCRIPTION
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@@ -1,7 +1,7 @@
Type: Package
Package: rats
Version: 0.6.0
Date: 2017-09-20
Version: 0.6.1
Date: 2018-02-15
Title: Relative Abundance of Transcripts
Encoding: UTF-8
Authors: c(person("Kimon Froussios", role=c("aut"), email="[email protected]"),
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1 change: 1 addition & 0 deletions NAMESPACE
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Expand Up @@ -14,6 +14,7 @@ export(get_plurality_ids)
export(get_switch_ids)
export(group_samples)
export(maxabs)
export(plot_diagnostics)
export(plot_gene)
export(plot_overview)
export(plot_shiny_volcano)
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58 changes: 37 additions & 21 deletions R/data_simulators.R
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Expand Up @@ -107,51 +107,67 @@ sim_sleuth_data <- function(varname="condition", COUNTS_COL="est_counts", TARGET
#==============================================================================
#' Generate an artificial dataset of bootstrapped abundance estimates, for code-testing or examples.
#'
#' @param varname Name for the variables by which to compare.
#' @param PARENT_COL Name for the bootdtraps column containing counts.
#' @param TARGET_COL Name for annotation column containing transcript identification.
#' @param cnames A vector of (two) name values for the comparison variable.
#' @param PARENT_COL Name for the bootdtraps column containing counts.
#' @param errannot_inconsistent Logical. Introduces an inconsistency in the transcript IDs, for testing of sanity checks. (FALSE)
#' @return A list with 3 elements. First a data.frame with the corresponding annotation.
#' Then, 2 lists of data.tables. One list per condition. Each data table represents a sample and contains the estimates from the bootstrap iterations.
#'
#' @import data.table
#'
#' @export
#'
sim_boot_data <- function(varname="condition", cnames=c("A", "B"), errannot_inconsistent=FALSE, PARENT_COL="parent_id", TARGET_COL="target_id") {
sim <- sim_sleuth_data(cnames=cnames, varname=varname, errannot_inconsistent=errannot_inconsistent, PARENT_COL=PARENT_COL, TARGET_COL=TARGET_COL)
# Emulate non-sleuth bootstrap data.
data_A <- denest_sleuth_boots(sim$slo, sim$annot, c(1,3), COUNTS_COL="est_counts", BS_TARGET_COL="target_id", PARENT_COL=PARENT_COL, TARGET_COL=TARGET_COL)
data_B <- denest_sleuth_boots(sim$slo, sim$annot, c(2,4), COUNTS_COL="est_counts", BS_TARGET_COL="target_id", PARENT_COL=PARENT_COL, TARGET_COL=TARGET_COL)
sim_boot_data <- function(errannot_inconsistent=FALSE, PARENT_COL="parent_id", TARGET_COL="target_id") {
tx <- data.frame(target_id= c("NIB.1", "1A1N-2", "1D1C:one", "1D1C:two", "1B1C.1", "1B1C.2", "CC_a", "CC_b", "1NN", "2NN", "MIX6.c1", "MIX6.c2", "MIX6.c3", "MIX6.c4", "MIX6.nc", "MIX6.d", "LC1", "LC2", "ALLA1", "ALLB1", "ALLB2"),
parent_id= c("NIB", "1A1N", "1D1C", "1D1C", "1B1C", "1B1C", "CC", "CC", "NN", "NN", "MIX6", "MIX6", "MIX6", "MIX6", "MIX6", "MIX6", "LC", "LC", "ALLA", "ALLB", "ALLB"),
stringsAsFactors=FALSE)
names(tx) <- c(TARGET_COL, PARENT_COL)

a <- list()
a[[1]] <- data.table("target"= c("LC1", "NIA1", "NIA2", "1A1N-1", "1A1N-2", "1D1C:one", "1D1C:two", "1B1C.2", "CC_a", "CC_b", "MIX6.c1", "MIX6.c2", "MIX6.c3", "MIX6.c4", "MIX6.nc", "MIX6.d", "1NN", "2NN", "LC2", "ALLA1", "ALLB1", "ALLB2"),
"V1"= c( 3, 333, 666, 10, 20, 0, 76, 52, 20, 50, 103, 321, 0, 100, 90, 0, 10, 30, 4, 50, 0, 0),
"V2"= c( 2, 310, 680, 11, 21, 0, 80, 55, 22, 52, 165, 320, 0, 130, 80, 0, 11, 29, 5, 40, 0, 0),
"V3"= c( 0, 340, 610, 7, 18, 0, 72, 50, 21, 49, 150, 325, 0, 120, 70, 0, 9, 28, 4, 60, 0, 0),
stringsAsFactors=FALSE)

a[[2]] <- data.table("target"= c("NIA1", "1A1N-2", "LC1", "NIA2", "1D1C:one", "1D1C:two", "1B1C.2", "MIX6.c1", "1A1N-1", "MIX6.c2", "MIX6.c3", "MIX6.c4", "MIX6.d", "MIX6.nc", "CC_a", "CC_b", "1NN", "2NN", "LC2", "ALLA1", "ALLB1", "ALLB2"),
"V1"= c( 333, 20, 3, 666, 0, 76, 52, 100, 10, 360, 0, 100, 0, 180, 25, 60, 7, 27, 13, 35, 0, 0),
"V2"= c( 330, 11, 4, 560, 0, 80, 55, 90, 11, 380, 0, 80, 0, 240, 23, 55, 10, 31, 2, 55, 0, 0),
stringsAsFactors=FALSE)

b <- list()
b[[1]] <- data.table("target"= c("NIA1", "LC1", "NIA2", "1A1N-1", "1A1N-2", "1D1C:one", "1D1C:two", "1B1C.2", "CC_a", "CC_b", "MIX6.c1", "MIX6.c2", "MIX6.c3", "MIX6.c4", "MIX6.nc", "MIX6.d", "1NN", "2NN", "LC2", "ALLA1", "ALLB1", "ALLB2"),
"V1"= c( 333, 6, 666, 12, 23, 0, 25, 150, 15, 80, 325, 105, 40, 0, 200, 0, 15, 45, 12, 0, 80, 200),
"V2"= c( 323, 1, 606, 15, 22, 0, 23, 190, 20, 90, 270, 115, 30, 0, 150, 0, 20, 60, 15, 0, 120, 250),
stringsAsFactors=FALSE)

b[[2]] <- data.table("target"= c("NIA2", "1A1N-1", "NIA1", "1A1N-2", "1D1C:one", "1D1C:two", "LC1", "1B1C.2", "MIX6.c2", "MIX6.c3", "MIX6.c1", "MIX6.c4", "MIX6.nc", "MIX6.d", "CC_b", "CC_a", "1NN", "2NN", "LC2", "ALLA1", "ALLB1", "ALLB2"),
"V1"= c( 666, 12, 333, 23, 0, 25, 0, 150, 155, 40, 300, 0, 33, 0, 93, 22, 22, 61, 11, 0, 110, 210),
"V2"= c( 656, 15, 323, 22, 0, 23, 2, 160, 120, 35, 280, 0, 95, 0, 119, 18, 19, 58, 7, 0, 150, 220),
"V3"= c( 676, 13, 343, 21, 0, 20, 3, 145, 133, 30, 270, 0, 55, 0, 80, 23, 17, 50, 14, 0, 130, 200),
stringsAsFactors=FALSE)

if (errannot_inconsistent)
data_A[[2]][14, 1 := "Unexpected-name"]
a[[2]][14, 1] <- "Unexpected-name"

return(list('annot'= sim$annot, 'boots_A'= data_A, 'boots_B'= data_B))
return(list('annot'= tx, 'boots_A'= a, 'boots_B'= b))
}

#==============================================================================
#' Generate an artificial dataset of abundance estimates, for code-testing or examples.
#'
#' @param varname Name for the variables by which to compare.
#' @param PARENT_COL Name for the bootdtraps column containing counts.
#' @param TARGET_COL Name for annotation column containing transcript identification.
#' @param cnames A vector of (two) name values for the comparison variable.
#' @param errannot_inconsistent Logical. Introduces an inconsistency in the transcript IDs, for testing of sanity checks. (FALSE)
#' @return A list with 3 elements. First, a data.frame with the corresponding annotation table.
#' Then, 2 data.tables, one per condition. Each data table contains the abundance estimates for all the samples for the respective condition.
#'
#' @export
#'
sim_count_data <- function(varname="condition", cnames=c("A", "B"), errannot_inconsistent=FALSE, PARENT_COL="parent_id", TARGET_COL="target_id") {
sim <- sim_sleuth_data(cnames=cnames, varname=varname, errannot_inconsistent=errannot_inconsistent, PARENT_COL=PARENT_COL, TARGET_COL=TARGET_COL)
# Emulate non-sleuth bootstrap data.
data_A <- denest_sleuth_boots(sim$slo, sim$annot, c(1,3), COUNTS_COL="est_counts", BS_TARGET_COL="target_id", PARENT_COL=PARENT_COL, TARGET_COL=TARGET_COL)
data_B <- denest_sleuth_boots(sim$slo, sim$annot, c(2,4), COUNTS_COL="est_counts", BS_TARGET_COL="target_id", PARENT_COL=PARENT_COL, TARGET_COL=TARGET_COL)
# Emulate non-bootstrap data.
counts_A <- data_A[[1]]
counts_B <- data_B[[1]]
sim_count_data <- function(errannot_inconsistent=FALSE, PARENT_COL="parent_id", TARGET_COL="target_id") {
sim <- sim_boot_data(errannot_inconsistent=errannot_inconsistent, PARENT_COL=PARENT_COL, TARGET_COL=TARGET_COL)

return(list(sim$annot, 'counts_A'= counts_A, 'counts_B'= counts_B))
return(list('annot'=sim$annot, 'counts_A'= sim$boots_A[[1]], 'counts_B'= sim$boots_B[[1]]))
}


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