Core engine to generate report. Here we perform all the computation related to CrossTalkeR
Usage
analise_LR(
lrpaths,
genes = NULL,
tf_genes = NULL,
out_path,
sep = ",",
threshold = 0,
colors = NULL,
out_file = NULL,
output_fmt = "html_document",
sel_columns = c("source", "target", "gene_A", "gene_B", "type_gene_A", "type_gene_B",
"MeanLR"),
org = "hsa",
comparison = NULL
)
Arguments
- lrpaths
Paths of single condition LR data
- genes
list of genes to be considered in the sankey plots
- out_path
output directory path
- sep
character used on csv
- threshold
percentage of edges to be pruned
- colors
celltypes colorscheme
- out_file
output file names
- output_fmt
rmarkdown render output format parameter
- sel_columns
columns from data
- report
decide if a report is generated or not
Examples
paths <- c('CTR' = system.file("extdata",
"CTR_LR.csv",
package = "CrossTalkeR"),
'EXP' = system.file("extdata",
"EXP_LR.csv",
package = "CrossTalkeR"))
output = system.file("extdata", package = "CrossTalkeR")
genes <- c('TGFB1')
data <- generate_report(lrpaths = paths,
genes = genes,
out_path = paste0(output,'/'),
threshold = 0,
out_file = "report.html")
#> Create a Differential Table
#> Calculating CCI Ranking
#> EXP_x_CTR
#>
#> Calculating GCI Ranking
#> EXP_x_CTR
#> Annotating the top Cell Genes
#> 'select()' returned 1:many mapping between keys and columns
#> 'select()' returned 1:many mapping between keys and columns
#> Reading KEGG annotation online: "https://rest.kegg.jp/link/hsa/pathway"...
#> Reading KEGG annotation online: "https://rest.kegg.jp/list/pathway/hsa"...
#> 'select()' returned 1:many mapping between keys and columns
#> 'select()' returned 1:many mapping between keys and columns
#> 'select()' returned 1:many mapping between keys and columns
#> 'select()' returned 1:many mapping between keys and columns
#> 'select()' returned 1:many mapping between keys and columns
#> 'select()' returned 1:many mapping between keys and columns
#> 'select()' returned 1:many mapping between keys and columns
#> 'select()' returned 1:many mapping between keys and columns
#> 'select()' returned 1:many mapping between keys and columns
#> 'select()' returned 1:many mapping between keys and columns
#> 'select()' returned 1:many mapping between keys and columns
#> 'select()' returned 1:many mapping between keys and columns
#> 'select()' returned 1:many mapping between keys and columns
#> 'select()' returned 1:many mapping between keys and columns
#> 'select()' returned 1:many mapping between keys and columns
#> 'select()' returned 1:many mapping between keys and columns
#> 'select()' returned 1:many mapping between keys and columns
#> 'select()' returned 1:many mapping between keys and columns
#> 'select()' returned 1:many mapping between keys and columns
#> 'select()' returned 1:many mapping between keys and columns
#> 'select()' returned 1:many mapping between keys and columns
#> 'select()' returned 1:many mapping between keys and columns
#> 'select()' returned 1:many mapping between keys and columns
#> 'select()' returned 1:many mapping between keys and columns
#> 'select()' returned 1:many mapping between keys and columns
#> 'select()' returned 1:many mapping between keys and columns
#> 'select()' returned 1:many mapping between keys and columns
#> 'select()' returned 1:many mapping between keys and columns
#> 'select()' returned 1:many mapping between keys and columns
#> 'select()' returned 1:many mapping between keys and columns
#> Adding missing grouping variables: `cellpair`
#> Adding missing grouping variables: `cellpair`
#> Network Analysis Done
#> Defining templates
#> Generating Report
#> Preparing Single Phenotype Report
#> Warning: ggrepel: 34 unlabeled data points (too many overlaps). Consider increasing max.overlaps
#> Warning: ggrepel: 33 unlabeled data points (too many overlaps). Consider increasing max.overlaps
#> Preparing Comparative Phenotype Report
#> Warning: ggrepel: 72 unlabeled data points (too many overlaps). Consider increasing max.overlaps
#> Report Done!
#> Analysis Complete