library(SingleR)
library(celldex)
library(SingleCellExperiment)
ref <- HumanPrimaryCellAtlasData(ensembl = F)

mylabels <- SingleR(test = as.SingleCellExperiment(srobj_clus),
                    ref = ref,
                    labels = ref$label.main)
View(data.frame(table(mylabels$labels)))
View(data.frame(mylabels$scores))

srobj_clus$CellTypes <- mylabels$labels
DimPlot(object = srobj_clus,
        reduction = "umap",
        label = T,
        group.by = "CellTypes") +
  NoLegend()

devtools::install_github("chloelulu/scMayoMap")
library(scMayoMap)
scMayoMap.obj <- scMayoMap(data = markers,
                           database = scMayoMapDatabase,
                           tissue = "prostate")
class(scMayoMap.obj)
View(scMayoMap.obj$res)
plt <- scMayoMap.plot(scMayoMap.object = scMayoMap.obj)

BiocManager::install("EasyCellType")
BiocManager::install("org.Mm.eg.db")
library(EasyCellType)
markers_easy <- markers[, c("gene", "cluster", "avg_log2FC")]
colnames(markers_easy) <- c("gene", "cluster", "score")
markers_easy <- markers_easy[order(markers_easy$score, decreasing = TRUE), ]
View(markers_easy)
data(cellmarker_tissue)
View(cellmarker_tissue$Human)
easy_annot <- easyct(data = markers_easy,
                     db = "cellmarker",
                     genetype = "symbol",
                     species = "Human",
                     tissue = "Prostate",
                     p_cut = 0.5,
                     test = "GSEA",
                     scoretype = "pos")
class(easy_annot)

remotes::install_github("bnprks/BPCells/r")
remotes::install_github("satijalab/seurat-wrappers")
devtools::install_github('cole-trapnell-lab/monocle3')
BiocManager::install("monocle")
library(monocle3)

cds <- load_mm_data(mat_path = "matrix.mtx.gz", 
                    feature_anno_path = "features.tsv.gz", 
                    cell_anno_path = "barcodes.tsv.gz")
class(cds)

cds <- preprocess_cds(cds = cds,
                      num_dim = 50,
                      method = "PCA",
                      scaling = TRUE,
                      use_genes = NULL,
                      norm_method = "log")

plot_pc_variance_explained(cds)

cds <- reduce_dimension(cds = cds,
                        reduction_method = "UMAP",
                        preprocess_method = "PCA",
                        umap.metric = "cosine",
                        umap.min_dist = 0.1,
                        umap.n_neighbors = 15,
                        max_components = 2)
cds@reduce_dim_aux$UMAP

cds <- cluster_cells(cds = cds,
                     reduction_method = "UMAP",
                     k = 20,
                     resolution = 1e-5,
                     cluster_method = "leiden")
table(as.numeric(cds@clusters$UMAP$cluster_result$optim_res$membership))

plot_cells(cds = cds,
           reduction_method = "UMAP",
           color_cells_by = "cluster",
           group_label_size = 4)

marker_test_res <- top_markers(cds = cds,
                               group_cells_by = "cluster",
                               reference_cells = 1000,
                               cores = 8)

cds <- learn_graph(cds = cds,
                   use_partition = TRUE,
                   close_loop = FALSE)

plot_cells(cds = cds,
           reduction_method = "UMAP",
           color_cells_by = "cluster",
           group_label_size = 4,
           show_trajectory_graph = TRUE,
           trajectory_graph_color = "darkgreen",
           trajectory_graph_segment_size = 2,
           label_roots = TRUE,
           graph_label_size = 2)

cds <- order_cells(cds = cds,
                   reduction_method = "UMAP")