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gathering_data.R
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library(IBRAP)
library(SingleCellExperiment)
library(splatter)
library(SymSim)
library(phytools)
library(Seurat)
library(SeuratData)
### Section 1 ###
IBRAP_objects <- list()
# Download Pancreas Samples
InstallData('panc8')
pancreas <- LoadData('panc8')
for(x in unique(pancreas$dataset)) {
IBRAP_objects[[x]] <- createIBRAPobject(counts = pancreas@assays$RNA@counts[,pancreas$dataset == x], original.project = x)
IBRAP_objects[[x]]@sample_metadata$batch <- x
IBRAP_objects[[x]]@sample_metadata$batch <- pancreas[,pancreas$dataset == x]$celltype
}
# Cell Lines
# Download Cell Lines, the files can be found at: https://github.com/LuyiTian/sc_mixology/tree/master/data
load('sincell_with_class.RData')
load('sincell_with_class_5cl.RData')
IBRAP_objects[['sc_10x']] <- createIBRAPobject(counts = counts(sce_sc_10x_qc), original.project = 'sc_10x')
IBRAP_objects[['sc_10x']]@sample_metadata$batch <- 'sc_10x'
IBRAP_objects[['sc_10x']]@sample_metadata$celltype <- colData(sce_sc_10x_qc)$cell_line
IBRAP_objects[['sc_10x_5cl']] <- createIBRAPobject(counts = counts(sce_sc_10x_5cl_qc), original.project = 'sc_10x_5cl')
IBRAP_objects[['sc_10x_5cl']]@sample_metadata$batch <- 'sc_10x_5cl'
IBRAP_objects[['sc_10x_5cl']]@sample_metadata$celltype <- colData(sce_sc_10x_5cl_qc)$cell_line
IBRAP_objects[['sc_CELseq2']] <- createIBRAPobject(counts = counts(sce_sc_CELseq2_qc), original.project = 'sc_CELseq2')
IBRAP_objects[['sc_CELseq2']]@sample_metadata$batch <- 'sc_CELseq2'
IBRAP_objects[['sc_CELseq2']]@sample_metadata$celltype <- colData(sce_sc_CELseq2_qc)$cell_line
IBRAP_objects[['sc_Dropseq']] <- createIBRAPobject(counts = counts(sce_sc_Dropseq_qc), original.project = 'sc_Dropseq')
IBRAP_objects[['sc_Dropseq']]@sample_metadata$batch <- 'sc_Dropseq'
IBRAP_objects[['sc_Dropseq']]@sample_metadata$celltype <- colData(sce_sc_Dropseq_qc)$cell_line
IBRAP_objects[['Celseq2_5cl_p1']] <- createIBRAPobject(counts = counts(sc_Celseq2_5cl_p1), original.project = 'Celseq2_5cl_p1')
IBRAP_objects[['Celseq2_5cl_p1']]@sample_metadata$batch <- 'Celseq2_5cl_p1'
IBRAP_objects[['Celseq2_5cl_p1']]@sample_metadata$celltype <- colData(sc_Celseq2_5cl_p1)$cell_line
IBRAP_objects[['Celseq2_5cl_p2']] <- createIBRAPobject(counts = counts(sc_Celseq2_5cl_p2), original.project = 'Celseq2_5cl_p2')
IBRAP_objects[['Celseq2_5cl_p2']]@sample_metadata$batch <- 'Celseq2_5cl_p2'
IBRAP_objects[['Celseq2_5cl_p2']]@sample_metadata$celltype <- colData(sc_Celseq2_5cl_p2)$cell_line
IBRAP_objects[['Celseq2_5cl_p3']] <- createIBRAPobject(counts = counts(sc_Celseq2_5cl_p3), original.project = 'sc_CELseq2')
IBRAP_objects[['Celseq2_5cl_p3']]@sample_metadata$batch <- 'Celseq2_5cl_p3'
IBRAP_objects[['Celseq2_5cl_p3']]@sample_metadata$celltype <- colData(sc_Celseq2_5cl_p3)$cell_line
# Splatter simulations
params <- newSplatParams()
params <- setParam(params, "nGenes", 20000)
params <- setParams(params, mean.shape = 0.5, de.prob = 0.2)
params <- newSplatParams(lib.loc = 12, lib.scale = 0.7)
sim.8_unequal <- splatSimulate(params,
batchCells = 1000,
group.prob = c(0.20,0.15,0.1,0.05,0.30,0.02,0.07,0.11),
method = "groups")
sim.4_unequal <- splatSimulate(params,
batchCells = 1000,
group.prob = c(0.3,0.18,0.42,0.1),
method = "groups")
sim.8_equal <- splatSimulate(params,
batchCells = 1000,
group.prob = c(0.125,0.125,0.125,0.125,0.125,0.125,0.125,0.125),
method = "groups")
sim.4_equal <- splatSimulate(params,
batchCells = 1000,
group.prob = c(0.25,0.25,0.25,0.25),
method = "groups")
IBRAP_objects[['sim.8_unequal']] <- createIBRAPobject(counts = counts(sim.8_unequal), original.project = 'sim.8_unequal')
IBRAP_objects[['sim.8_equal']] <- createIBRAPobject(counts = counts(sim.8_equal), original.project = 'sim.8_unequal')
IBRAP_objects[['sim.4_unequal']] <- createIBRAPobject(counts = counts(sim.4_unequal), original.project = 'sim.8_unequal')
IBRAP_objects[['sim.4_equal']] <- createIBRAPobject(counts = counts(sim.4_equal), original.project = 'sim.8_unequal')
# SymSim simulations
ngenes <- 10000
data(gene_len_pool)
gene_len <- sample(gene_len_pool, 10000, replace = FALSE)
counts <- SimulateTrueCounts(ncells_total=3000, min_popsize=50, ngenes=ngenes, nevf=30,
evf_type="discrete", n_de_evf=12, vary="s", Sigma=0.4,
phyla=Phyla5(), randseed=0)
UMI_highdepth_5 <- True2ObservedCounts(true_counts=counts[[1]], meta_cell=counts[[3]],
protocol="UMI", alpha_mean=0.04, alpha_sd=0.2,
gene_len=gene_len, depth_mean=5e7, depth_sd=3e4)
counts <- SimulateTrueCounts(ncells_total=3000, min_popsize=50, ngenes=ngenes, nevf=30,
evf_type="discrete", n_de_evf=12, vary="s", Sigma=0.4,
phyla=Phyla5(), randseed=0)
UMI_lowdepth_5 <- True2ObservedCounts(true_counts=counts[[1]], meta_cell=counts[[3]],
protocol="UMI", alpha_mean=0.04, alpha_sd=0.2,
gene_len=gene_len, depth_mean=5e5, depth_sd=3e3)
counts <- SimulateTrueCounts(ncells_total=3000, min_popsize=50, ngenes=ngenes, nevf=30,
evf_type="discrete", n_de_evf=12, vary="s", Sigma=0.4,
phyla=Phyla3(), randseed=0)
UMI_highdepth_3 <- True2ObservedCounts(true_counts=counts[[1]], meta_cell=counts[[3]],
protocol="UMI", alpha_mean=0.04, alpha_sd=0.2,
gene_len=gene_len, depth_mean=5e7, depth_sd=3e4)
counts <- SimulateTrueCounts(ncells_total=3000, min_popsize=50, ngenes=ngenes, nevf=30,
evf_type="discrete", n_de_evf=12, vary="s", Sigma=0.4,
phyla=Phyla3(), randseed=0)
UMI_lowdepth_3 <- True2ObservedCounts(true_counts=counts[[1]], meta_cell=counts[[3]],
protocol="UMI", alpha_mean=0.04, alpha_sd=0.2,
gene_len=gene_len, depth_mean=5e5, depth_sd=3e3)
counts <- UMI_highdepth_5$counts
rownames(counts) <- paste0('gene-', 1:10000)
colnames(counts) <- UMI_highdepth_5$cell_meta$cellid
IBRAP_objects[['UMI_highdepth_5']] <- createIBRAPobject(counts = counts, original.project = 'UMI_highdepth_5')
IBRAP_objects[['UMI_highdepth_5']]@sample_metadata$celltype <- UMI_highdepth_5$cell_meta$pop
counts <- UMI_lowdepth_5$counts
rownames(counts) <- paste0('gene-', 1:10000)
colnames(counts) <- UMI_lowdepth_5$cell_meta$cellid
IBRAP_objects[['UMI_lowdepth_5']] <- createIBRAPobject(counts = counts, original.project = 'UMI_lowdepth_5')
IBRAP_objects[['UMI_lowdepth_5']]@sample_metadata$celltype <- UMI_lowdepth_5$cell_meta$pop
counts <- UMI_highdepth_3$counts
rownames(counts) <- paste0('gene-', 1:10000)
colnames(counts) <- UMI_highdepth_3$cell_meta$cellid
IBRAP_objects[['UMI_highdepth_3']] <- createIBRAPobject(counts = counts, original.project = 'UMI_highdepth_3')
IBRAP_objects[['UMI_highdepth_3']]@sample_metadata$celltype <- UMI_highdepth_3$cell_meta$pop
counts <- UMI_highdepth_5$counts
rownames(counts) <- paste0('gene-', 1:10000)
colnames(counts) <- UMI_lowdepth_3$cell_meta$cellid
IBRAP_objects[['UMI_lowdepth_3']] <- createIBRAPobject(counts = counts, original.project = 'UMI_lowdepth_3')
IBRAP_objects[['UMI_lowdepth_3']]@sample_metadata$celltype <- UMI_lowdepth_3$cell_meta$pop
### SECTION 2 ###
integrated_samples <- list()
# Pancreas Samples
integrated_samples[['analysis_1']] <- merge(IBRAP_objects[[1]], c(IBRAP_objects[[2]], IBRAP_objects[[3]], IBRAP_objects[[4]],
IBRAP_objects[[5]], IBRAP_objects[[6]], IBRAP_objects[[7]], IBRAP_objects[[8]]))
# Cell lines = sc_CELseq2, sc_Dropseq, and sc_10x
integrated_samples[['analysis_2']] <- merge(IBRAP_objects[[9]], c(IBRAP_objects[[10]], IBRAP_objects[[12]]))
# Cell lines = sc_CELseq2, sc_Dropseq, and sc_10x_5cl
integrated_samples[['analysis_3']] <- merge(IBRAP_objects[[9]], c(IBRAP_objects[[10]], IBRAP_objects[[11]]))
batch_dataset <- SimulateTrueCounts(ncells_total=3000, min_popsize=50, ngenes=ngenes, nevf=30,
evf_type="discrete", n_de_evf=12, vary="s", Sigma=0.4,
phyla=Phyla5(), randseed=0)
batch_dataset_highdepth_5 <- True2ObservedCounts(true_counts=batch_dataset[[1]], meta_cell=batch_dataset[[3]],
protocol="UMI", alpha_mean=0.04, alpha_sd=0.2,
gene_len=gene_len, depth_mean=5e7, depth_sd=3e4)
# Symsim simulations
integrated_samples[['analysis_4']] <- DivideBatches(observed_counts_res = batch_dataset_highdepth_5,
nbatch = 3, batch_effect_size = 1)
integrated_samples[['analysis_5']] <- DivideBatches(observed_counts_res = batch_dataset_highdepth_5,
nbatch = 3, batch_effect_size = 5)
### SECTION 3 ###
supervised_clustering <- list()
reference <- panc[,panc$dataset!='celseq']
query <- panc[,panc$dataset=='celseq']
supervised_clustering[['celseq_reference']] <- createIBRAPobject(counts = round(reference@assays$RNA@counts),
original.project = 'celseq_reference',
meta.data = [email protected][,c('celltype','dataset')])
supervised_clustering[['celseq_query']] <- createIBRAPobject(counts = round(query@assays$RNA@counts),
original.project = 'celseq_query',
meta.data = [email protected][,c('celltype','dataset')])
reference <- pancreas[,panc$dataset!='celseq2']
query <- pancreas[,panc$dataset=='celseq2']
supervised_clustering[['celseq2_reference']] <- createIBRAPobject(counts = round(reference@assays$RNA@counts),
original.project = 'celseq2_reference',
meta.data = [email protected][,c('celltype','dataset')])
supervised_clustering[['celseq2_query']] <- createIBRAPobject(counts = round(query@assays$RNA@counts),
original.project = 'celseq2_query',
meta.data = [email protected][,c('celltype','dataset')])
reference <- pancreas[,pancreas$dataset!='smartseq2']
query <- pancreas[,pancreas$dataset=='smartseq2']
supervised_clustering[['smartseq2_reference']] <- createIBRAPobject(counts = round(reference@assays$RNA@counts),
original.project = 'smartseq2_reference',
meta.data = [email protected][,c('celltype','dataset')])
supervised_clustering[['smartseq2_query']] <- createIBRAPobject(counts = round(query@assays$RNA@counts),
original.project = 'smartseq2_query',
meta.data = [email protected][,c('celltype','dataset')])
reference <- pancreas[,pancreas$dataset!='fluidigmc1']
query <- pancreas[,pancreas$dataset=='fluidigmc1']
supervised_clustering[['fluidigmc1_reference']] <- createIBRAPobject(counts = round(reference@assays$RNA@counts),
original.project = 'fluidigmc1_reference',
meta.data = [email protected][,c('celltype','dataset')])
supervised_clustering[['fluidigmc1_query']] <- createIBRAPobject(counts = round(query@assays$RNA@counts),
original.project = 'fluidigmc1_query',
meta.data = [email protected][,c('celltype','dataset')])
reference <- pancreas[,pancreas$dataset!='indrop1']
query <- pancreas[,pancreas$dataset=='indrop1']
supervised_clustering[['indrop1_reference']] <- createIBRAPobject(counts = round(reference@assays$RNA@counts),
original.project = 'indrop1_reference',
meta.data = [email protected][,c('celltype','dataset')])
supervised_clustering[['indrop1_query']] <- createIBRAPobject(counts = round(query@assays$RNA@counts),
original.project = 'indrop1_query',
meta.data = [email protected][,c('celltype','dataset')])
reference <- pancreas[,pancreas$dataset!='indrop1']
query <- pancreas[,pancreas$dataset=='indrop1']
supervised_clustering[['indrop1_reference']] <- createIBRAPobject(counts = round(reference@assays$RNA@counts),
original.project = 'indrop1_reference',
meta.data = [email protected][,c('celltype','dataset')])
supervised_clustering[['indrop1_query']] <- createIBRAPobject(counts = round(query@assays$RNA@counts),
original.project = 'indrop1_query',
meta.data = [email protected][,c('celltype','dataset')])
reference <- pancreas[,pancreas$dataset!='indrop2']
query <- pancreas[,pancreas$dataset=='indrop2']
supervised_clustering[['indrop2_reference']] <- createIBRAPobject(counts = round(reference@assays$RNA@counts),
original.project = 'indrop2_reference',
meta.data = [email protected][,c('celltype','dataset')])
supervised_clustering[['indrop2_query']] <- createIBRAPobject(counts = round(query@assays$RNA@counts),
original.project = 'indrop2_query',
meta.data = [email protected][,c('celltype','dataset')])
reference <- pancreas[,pancreas$dataset!='indrop3']
query <- pancreas[,pancreas$dataset=='indrop3']
supervised_clustering[['indrop3_reference']] <- createIBRAPobject(counts = round(reference@assays$RNA@counts),
original.project = 'indrop3_reference',
meta.data = [email protected][,c('celltype','dataset')])
supervised_clustering[['indrop3_query']] <- createIBRAPobject(counts = round(query@assays$RNA@counts),
original.project = 'indrop3_query',
meta.data = [email protected][,c('celltype','dataset')])
reference <- pancreas[,pancreas$dataset!='indrop4']
query <- pancreas[,pancreas$dataset=='indrop4']
supervised_clustering[['indrop4_reference']] <- createIBRAPobject(counts = round(reference@assays$RNA@counts),
original.project = 'indrop4_reference',
meta.data = [email protected][,c('celltype','dataset')])
supervised_clustering[['indrop4_query']] <- createIBRAPobject(counts = round(query@assays$RNA@counts),
original.project = 'indrop4_query',
meta.data = [email protected][,c('celltype','dataset')])
# saving data
saveRDS(IBRAP_objects, 'individual_samples.rds')
saveRDS(integrated_samples, 'integrated_samples.rds')
saveRDS(supervised_clustering, 'reference_samples.rds')