# IBRAP marrow script
library(IBRAP)
celseq2 <- createIBRAPobject(counts = celseq2,
meta.data = metadata_celseq2,
original.project = 'celseq2',
method.name = 'RAW',
min.cells = 3,
min.features = 200)
celseq <- createIBRAPobject(counts = celseq,
meta.data = metadata_celseq,
original.project = 'celseq',
method.name = 'RAW',
min.cells = 3,
min.features = 200)
pancreas <- merge(x = celseq2, y = celseq)
pancreas <- perform.singleR.annotation(object = pancreas, ref = smartseq2, ref.labels = metadata_smartseq2$celltype)
rm(metadata_celseq, metadata_celseq2, metadata, pancreas.data, celseq, celseq2)
rm(smartseq2, metadata_smartseq2)
fluidigmc1 <- find_percentage_genes(object = fluidigmc1, pattern = '^MT-',
assay = 'RAW', slot = 'counts',
column.name = 'RAW_percent.mt')
pancreas <- find_percentage_genes(object = pancreas, pattern = 'RPL',
assay = 'RAW', slot = 'counts',
column.name = 'RAW_percent.rp')
plot.QC.vln(object = fluidigmc1,
metadata.columns = c('RAW_total.features',
'RAW_total.counts',
'RAW_percent.mt'))
ggpubr::annotate_figure(p = p, top = ggpubr::text_grob(label = 'Pancreas', size = 14, family = 'Arial'))
plot.QC.scatter(object = fluidigmc1,
x = 'RAW_total.counts',
y = 'RAW_total.features',
split.by = 'original.project')
plot.QC.scatter(object = fluidigmc1,
y = 'RAW_total.counts',
x = 'RAW_percent.mt',
split.by = 'original.project')
plot.QC.scatter(object = pancreas,
y = 'RAW_total.features',
x = 'RAW_percent.rp',
split.by = 'original.project')
sd.value <- sd(fluidigmc1$RAW_total.features)
med.value <- median(fluidigmc1$RAW_total.features)
max.features <- (sd.value*3)+med.value
fluidigmc1 <- filter_IBRAP(object = fluidigmc1,
RAW_total.features < max.features & RAW_total.counts > 200 & RAW_percent.mt < 25)
pancreas <- add.cell.cycle(object = pancreas,
assay = 'RAW',
slot = 'counts',
transform = TRUE)
pancreas <- add.feature.score(object = pancreas,
assay = 'RAW',
slot = 'counts',
transform = TRUE,
features = c('BAG3', 'BLOC1S5-TXNDC5', 'CALU', 'DNAJB1', 'DUSP1', 'EGR1',
'FOS', 'FOSB', 'HIF1A', 'HSP90AA1', 'HSP90AB1', 'HSP90AB2P',
'HSP90AB3P', 'HSP90B1', 'HSPA1A', 'HSPA1B', 'HSPA6', 'HSPB1',
'HSPH1', 'IER2', 'JUN', 'JUNB', 'NFKBIA', 'NFKBIZ', 'RGS2',
'SLC2A3', 'SOCS3', 'UBC', 'ZFAND2A', 'ZFP36', 'ZFP36L1'),
column.name = 'StressScore')
fluidigmc1 <- perform.sct(object = fluidigmc1,
assay = 'RAW',
slot = 'counts')
pancreas <- perform.scran(object = pancreas,
assay = 'RAW',
slot = 'counts',
vars.to.regress = 'RAW_total.counts', do.scale = T)
pancreas <- perform.scanpy(object = pancreas,
vars.to.regress = 'RAW_total.counts', do.scale = T)
# pancreas <- perform.tpm.normalisation(object = pancreas,
# vars.to.regress = 'RAW_total.counts', do.scale = T)
pancreas <- perform.pca(object = pancreas,
assay = c('SCT', 'SCRAN', 'SCANPY'),
n.pcs = 50, reduction.save = 'pca')
pancreas <- perform.bbknn(object = pancreas,
assay = c('SCT', 'SCANPY', 'SCRAN'),
reduction = c('pca'),
batch = 'tech', generate.diffmap = T)
pancreas <- perform.harmony(object = pancreas,
assay = c('SCRAN', 'SCT', 'SCANPY'),
vars.use = 'original.project',
reduction = c('pca'),
max.iter.harmony = 100,
dims.use = list(NULL))
pancreas <- perform.scanorama(object = pancreas,
assay = c('SCT', 'SCRAN', 'SCANPY'),
slot = 'norm.scaled',
split.by = 'original.project',
n.dims = 50)
pancreas <- perform.nn.v1(object = pancreas, assay = c('SCT', 'SCRAN', 'SCANPY'),
reduction = c('pca_harmony','scanorama')
, dims = list(0,0), generate.diffmap = T)
pancreas <- perform.nn.v1(object = pancreas, assay = c('SCT', 'SCRAN', 'SCANPY'),
reduction = c('pca_bbknn_bbknn:diffmap','pca_harmony_nn.v1:diffmap', 'scanorama_nn.v1:diffmap'),
dims = list(0,0,0))
pancreas <- perform.nn.v2(object = pancreas, assay = c('SCT', 'SCRAN', 'SCANPY'),
reduction = c('pca_harmony','scanorama','pca_bbknn_bbknn:diffmap',
'pca_harmony_nn.v1:diffmap', 'scanorama_nn.v1:diffmap'),
dims = list(0,0,0,0,0))
pancreas <- perform.umap(object = pancreas,
assay = c('SCT', 'SCRAN', 'SCANPY'),
reduction = c('pca_harmony', 'scanorama', 'pca_bbknn_bbknn:diffmap', 'pca_harmony_nn.v1:diffmap', 'scanorama_nn.v1:diffmap'),
n_components = 3,
n.dims = list(1:50, 1:50, NULL, NULL, NULL))
pancreas <- perform.umap(object = pancreas, assay = c('SCT', 'SCRAN', 'SCANPY'), graph = 'pca_bbknn_bbknn')
pancreas <- perform.seurat.cluster(object = pancreas, assay = c('SCT', 'SCRAN', 'SCANPY'),
neighbours = c("pca_bbknn","pca_harmony_seurat","scanorama_seurat","pca_harmony_scanpy","scanorama_scanpy",
"pca_bbknn_diffmap_scanpy","pca_harmony_diffmap_scanpy","scanorama_diffmap_scanpy"),
algorithm = 1, cluster.df.name = c("pca_bbknn_louvain","pca_harmony_seurat_louvain","scanorama_seurat_louvain",
"pca_harmony_scanpy_louvain","scanorama_scanpy_louvain",
"pca_bbknn_diffmap_scanpy_louvain","pca_harmony_diffmap_scanpy_louvain",
"scanorama_diffmap_scanpy_louvain"))
pancreas_bench <- benchmark.clustering(object = pancreas, assay = c('SCT', 'SCRAN', 'SCANPY'),
clustering = c("pca_harmony_seurat_louvain",
"scanorama_seurat_louvain",
"pca_harmony_scanpy_louvain",
"scanorama_scanpy_louvain",
"pca_bbknn_diffmap_scanpy_louvain",
"pca_harmony_diffmap_scanpy_louvain",
"scanorama_diffmap_scanpy_louvain"),
reduction = c('pca_harmony_umap',
'scanorama_umap',
'pca_harmony_umap',
'scanorama_umap',
'pca_bbknn_diffmap_umap',
'pca_harmony_diffmap_umap',
'scanorama_diffmap_umap'
),
n.dims = 1:2, ground.truth = pancreas@sample_metadata$celltype)
pancreas_bench <- benchmark.clustering(object = pancreas_bench, assay = c('SCT', 'SCRAN', 'SCANPY'),
clustering = c("pca_bbknn_louvain"),
reduction = c('pca_bbknn_umap'
),
n.dims = 1:2, ground.truth = pancreas@sample_metadata$celltype)
SCT_DE <- perform.seurat.diffexp.all(object = pancreas, assay = 'SCT', test = 'MAST', identity = pancreas@sample_metadata$celltype, latent.vars = 'original.project')
SCRAN_DE <- perform.seurat.diffexp.all(object = pancreas, assay = 'SCT', test = 'MAST', identity = pancreas@sample_metadata$celltype, latent.vars = 'original.project')
SCANPY_DE <- perform.seurat.diffexp.all(object = pancreas, assay = 'SCT', test = 'MAST', identity = pancreas@sample_metadata$celltype, latent.vars = 'original.project')
SCT_DE_GO <- perform.GO.enrichment(result = SCT_DE)
SCRAN_DE_GO <- perform.GO.enrichment(result = SCRAN_DE)
SCANPY_DE_GO <- perform.GO.enrichment(result = SCANPY_DE)
plot.GO.output(result = SCT_DE_GO) + ggplot2::ggtitle(label = 'SCT')
plot.GO.output(result = SCRAN_DE_GO) + ggplot2::ggtitle(label = 'SCRAN')
plot.GO.output(result = SCANPY_DE_GO) + ggplot2::ggtitle(label = 'SCANPY')
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