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# DeepBlue Epigenomic Data Server - R access library
# Use Case 3
# Author: Markus List
# Obtain genes regions
gene_names = c('CCR1', 'CD164', 'CD1D', 'CD2', 'CD34', 'CD3G', 'CD44')
#select regions of selected genes
q_genes = deepblue_select_genes(gene_names, "gencode v23")
#Filter regions that are protein coding
q_genes_regions = deepblue_filter_regions(
query_id = q_genes,
field = "@GENE_ATTRIBUTE(gene_type)",
operation = "==", value = "protein_coding", type = "string")
#Select all T cell related biosources
related_biosources = deepblue_get_biosource_related("Regulatory T cell")
biosource_names = deepblue_extract_names(related_biosources)
# Obtain the mRNA experiments names
experiments = deepblue_list_experiments(genome = "GRCh38",
type = "signal",
epigenetic_mark = "RNA",
biosource = biosource_names,
project = "BLUEPRINT Epigenome")
experiment_names = deepblue_extract_names(experiments)
#perform aggregation
requests = lapply(experiment_names, function (experiment) {
print(paste("Sending request for aggregating", experiment))
q_exp = deepblue_select_experiments(experiment_name = experiment)
q_agg = deepblue_aggregate(data_id = q_exp,
ranges_id = q_genes_regions,
column='VALUE')
q_filtered = deepblue_filter_regions(query_id = q_agg,
field="@AGG.MEAN",
operation=">",
value="0", type="number")
req = deepblue_get_regions(
query_id = q_filtered,
output_format = "CHROMOSOME,START,END,@GENE_ID(gencode v23),@GENE_NAME(gencode v23),@AGG.MEAN,@AGG.MAX,@AGG.MIN")
})
#download the results and save them to disk
results <- deepblue_batch_export_results(
requests,
target.directory="data/reg_t_cell_aggregation")
#look at the results. from the four experiments listed, two contained the
#information we needed.
results
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