Magellan: A Pipeline Manager

set.seed(1)

## ---------------------------------------------------------
## Creating psms as <double> rather than <integer>
## ---------------------------------------------------------
#' importFrom stats rnorm
element1 <- data.frame(A1 = rnorm(1:5),
                       A2 = rnorm(1:5),
                       A3 = rnorm(1:5),
                       B1 = rnorm(1:5),
                       B2 = rnorm(1:5),
                       B3 = rnorm(1:5)
)


element2 <- data.frame(A1 = rnorm(1:5),
                       A2 = rnorm(1:5),
                       A3 = rnorm(1:5),
                       B1 = rnorm(1:5),
                       B2 = rnorm(1:5),
                       B3 = rnorm(1:5)
)

feat1 <- list(assay1 = round(element1, digits=2),
              assay2 = round(element2, digits=2))

save(feat1, file = "../../data/feat1.rda", compress = "xz", compression_level = 9)

print(feat1)

# 
# 
# ## ---------------------------------------------------------
# ## Test data for joining assays
# ## ---------------------------------------------------------
# 
# m1 <- matrix(1:40, ncol = 4) + 0.0
# m2 <- matrix(1:16, ncol = 4) + 0.0
# m3 <- matrix(1:28, ncol = 4) + 0.0
# colnames(m1) <- paste0("S", 1:4)
# colnames(m2) <- paste0("S", 5:8)
# colnames(m3) <- paste0("S", 9:12)
# rownames(m1) <- letters[1:10]
# rownames(m2) <- letters[8:11]
# rownames(m3) <- letters[c(1, 2, 10:14)]
# 
# df1 <- DataFrame(Prot = paste0("P", rownames(m1)),
#                  x = rnorm(1:10),
#                  row.names = rownames(m1))
# cd1 <- DataFrame(Var1 = rnorm(4),
#                  Var2 = LETTERS[1:4],
#                  row.names = colnames(m1))
# 
# 
# df2 <- DataFrame(Prot = paste0("P", rownames(m2)),
#                  x = rnorm(1:4),
#                  y = rnorm(1:4),
#                  row.names = rownames(m2))
# cd2 <- DataFrame(Var1 = rnorm(4),
#                  Var2 = LETTERS[5:8],
#                  row.names = colnames(m2))
# 
# df3 <- DataFrame(Prot = paste0("P", rownames(m3)),
#                  x = rnorm(1:7),
#                  y = rnorm(1:7),
#                  row.names = rownames(m3))
# cd3 <- DataFrame(Var1 = rnorm(4),
#                  row.names = colnames(m3))
# 
# 
# se1 <- SummarizedExperiment(m1, df1, colData = cd1)
# se2 <- SummarizedExperiment(m2, df2, colData = cd2)
# se3 <- SummarizedExperiment(m3, df3, colData = cd3)
# 
# el <- list(assay1 = se1, assay2 = se2, assay3 = se3)
# feat2 <- QFeatures(el)
# 
# save(feat2, file = "../../data/feat2.rda", compress = "xz", compression_level = 9)
# 
# ## ---------------------------------------------------------
# ## Test data for complex AssayLinks
# ## ---------------------------------------------------------
# 
# ## The dataset is a nice example that tests QFeatures as it could 
# ## routinely be used within a pipeline. Note that the 3 special cases 
# ## of AssayLinks are present in this dataset:
# ## * One to one link: link between 1 parent and 1 child with one to 
# ##   one row mapping. E.g. "peptides" to "normpeptides"
# ## * One parent to multiple children: link between 1 parents and 
# ##   multiple children mapping. E.g. "peptides" to "normpeptides" and
# ##   "proteins"
# ## * Multiple parents to one child: link between multiple parents and 
# ##   one child mapping. E.g. "psms1" and "psms2" to "psmsall"
# ##
# ## see the `plot(feat3)` for an overview of the relationships
# 
# ## Simulate a processing workflow of feat1
# ## 1. Split PSM assay in 2 batches
# se1 <- feat1[["psms"]][1:7, ]
# colnames(se1) <- paste0("Sample", 1:2)
# se2 <- feat1[["psms"]][3:10, ]
# colnames(se2) <- paste0("Sample", 3:4)
# feat3 <- QFeatures(SimpleList(psms1 = se1,
#                               psms2 = se2))
# ## 2. Process the PSM data
# feat3 <- joinAssays(feat3, c("psms1", "psms2"), name = "psmsall")
# feat3 <- aggregateFeatures(feat3, i = "psmsall", fcol = "Sequence",
#                            name = "peptides")
# feat3 <- aggregateFeatures(feat3, i = "peptides", fcol = "Protein",
#                            name = "proteins")
# feat3 <- normalize(feat3, i = "peptides", name = "normpeptides", 
#                    method = "sum")
# feat3 <- aggregateFeatures(feat3, i = "normpeptides", fcol = "Protein",
#                            name = "normproteins")
# 
# save(feat3, file = "../../data/feat3.rda", compress = "xz", compression_level = 9)