## ----echo = FALSE-------------------------------------------------------------
knitr::opts_chunk$set(collapse = TRUE, comment = "#>", results = "hold")

## ----bioc_install, eval=FALSE-------------------------------------------------
#  if (!requireNamespace("BiocManager", quietly = TRUE))
#      install.packages("BiocManager")
#  
#  BiocManager::install("APL")

## ----git_install, eval=FALSE--------------------------------------------------
#  library(devtools)
#  install_github("VingronLab/APL")

## ----git_vignette, eval=FALSE-------------------------------------------------
#  install_github("VingronLab/APL", build_vignettes = TRUE, dependencies = TRUE)

## ----reticulate, eval=FALSE---------------------------------------------------
#  library(reticulate)
#  install_miniconda()
#  use_condaenv(condaenv = file.path(miniconda_path(),"envs/r-reticulate"),
#               required=TRUE)
#  
#  conda_install(envname = "r-reticulate", packages = "numpy")
#  conda_install(envname = "r-reticulate", packages = "pytorch")

## ----setup, message=FALSE, warning=FALSE--------------------------------------
library(APL)
library(scRNAseq)
library(SingleCellExperiment)
library(scran)
library(scater)
set.seed(1234)


## ----load_data----------------------------------------------------------------
darmanis <- DarmanisBrainData()
darmanis

## ----preprocess---------------------------------------------------------------

set.seed(100)
clust <- quickCluster(darmanis) 
darmanis <- computeSumFactors(darmanis, cluster=clust, min.mean=0.1)
darmanis <- logNormCounts(darmanis)

dec <- modelGeneVar(darmanis)
top_darmanis <- getTopHVGs(dec, n=5000)
darmanis <- fixedPCA(darmanis, subset.row=top_darmanis) 
darmanis <- runUMAP(darmanis, dimred="PCA")

plotReducedDim(darmanis, dimred="UMAP", colour_by="cell.type")


## ----runAPL-------------------------------------------------------------------
runAPL(darmanis,
       assay = "logcounts",
       top = 5000,
       group = which(darmanis$cell.type == "oligodendrocytes"),
       type = "ggplot",
       python = TRUE)

## ----cacomp-------------------------------------------------------------------
# Computing CA on logcounts
logcounts <- logcounts(darmanis)
ca <- cacomp(obj = logcounts,
             top = 5000,
             python = TRUE)

# The above is equivalent to:
# ca <- cacomp(obj = darmanis,
#              assay = "logcounts",
#              top = 5000,
#              python = TRUE)


## ----print_cacomp-------------------------------------------------------------
ca

## ----std_coords---------------------------------------------------------------
cacomp_slot(ca, "std_coords_cols")[1:5, 1:5]

## ----ca_pbmc------------------------------------------------------------------
darmanis <- cacomp(obj = darmanis,
               assay = "logcounts",
               top = 5000,
               return_input = TRUE,
               python = TRUE)

plotReducedDim(darmanis, dimred="CA",
               ncomponents = c(1,2),
               colour_by="cell.type")
plotReducedDim(darmanis, dimred="CA",
               ncomponents = c(3,4),
               colour_by="cell.type")


## ----convert------------------------------------------------------------------
# Converting the object pbmc to cacomp
ca <- as.cacomp(darmanis)

## ----scree_plot---------------------------------------------------------------
pick_dims(ca, method = "scree_plot") +
  xlim(c(0,20))

## ----pick_dims, results = "hide"----------------------------------------------
pd <- pick_dims(ca,
                mat = logcounts(darmanis),
                method = "elbow_rule",
                reps = 3, 
                python = TRUE)

## ----show_dims, message=FALSE-------------------------------------------------
pd

## ----expl_inert---------------------------------------------------------------
# Compute the amount of inertia explained by each of the dimensions
D <- cacomp_slot(ca, "D")
expl_inertia <- (D^2/sum(D^2))*100

# Compute the amount of intertia explained 
# by the number of dimensions defined by elbow rule
sum(expl_inertia[seq_len(pd)])

## ----subset_dims--------------------------------------------------------------
ca <- subset_dims(ca, dims = pd)

## ----apl_platelets------------------------------------------------------------
# Specifying a cell cluster of interest
endo <- which(darmanis$cell.type == "endothelial")

# Calculate Association Plot coordinates for endothelial cells
ca <- apl_coords(ca, group = endo)

## ----apl_platelets_plot, fig.wide = TRUE--------------------------------------

# endothelial marker genes
marker_genes <- c("APOLD1", "TM4SF1", "SULT1B1", "ESM1", "SELE")

# Plot APL
apl(ca,
    row_labs = TRUE,
    rows_idx = marker_genes, 
    type = "ggplot") # type = "plotly" for an interactive plot


## ----apl_score, results = "hide"----------------------------------------------
# Compute S-alpha score
# For the calculation the input matrix is also required.
ca <- apl_score(ca,
                mat = logcounts(darmanis),
                reps = 5,
                python = TRUE)

## ----apl_plot_platelets, fig.wide = TRUE--------------------------------------
apl(ca,
    show_score = TRUE,
    type = "ggplot") 

## ----print_score--------------------------------------------------------------
head(cacomp_slot(ca, "APL_score"))

## ----seurat_apl, fig.wide = TRUE----------------------------------------------
scores <- cacomp_slot(ca, "APL_score")

plotExpression(darmanis, 
               features = head(scores$Rowname,3),
               x = "cell.type", 
               colour_by = "cell.type")

plotReducedDim(darmanis, 
               dimred="UMAP",
               colour_by= scores$Rowname[2])

## ----biplot, fig.wide = TRUE--------------------------------------------------

# Specifying a cell cluster of interest
endo <- which(darmanis$cell.type == "endothelial")

# Creating a static plot
ca_biplot(ca, col_labels = endo, type = "ggplot")

# Creating an interactive plot
# ca_biplot(ca, type = "plotly", col_labels = platelets)

# 3D plot
# ca_3Dplot(ca, col_labels = platelets)


## ----cluster_three, results="hide"--------------------------------------------
# Get indices of microglia cells
microglia <- which(darmanis$cell.type == "microglia")

# Calculate Association Plot coordinates of the genes and the $S_\alpha$-scores
ca <- apl_coords(ca, group = microglia)

ca <- apl_score(ca,
                mat = logcounts(darmanis),
                reps = 5,
                python = TRUE)

## ----topGO, message=FALSE-----------------------------------------------------
enr <- apl_topGO(ca,
                 ontology = "BP",
                 organism = "hs",
                 score_cutoff = 1)
head(enr)

## ----topGO_plot, message=FALSE------------------------------------------------
plot_enrichment(enr)

## ----sessionInfo, echo=FALSE--------------------------------------------------
sessionInfo()

