Description Usage Arguments Value Examples
View source: R/function_crosstable.R
Venn diagram of differential expression genes list
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final_data |
A character string indicating the name of which diferential expression package should be used to get the statistical values in the final list. The default is "EBSeq". |
n_pack |
A numerical value indicating the number of expression analysis to be used in venn diagram. It is expected the number 2 or 3. The default is 3. |
package_names |
A character vector indicating the names of at least two diferential expression packages used in previous steps: "DESeq2", "edgeR", "DESeq2, or "All". |
pair_name |
A character string indicating which condition name should be
used. When there are only two groups the default is |
width |
Graphical parameters. See par for more
details. As default |
height |
Graphical parameters. See par for more
details. As default |
res |
Graphical parameters. See par for more
details. As default |
unit |
Graphical parameters. See par for more
details. As default |
colors |
A character vector indicating the colors to be used in the venn diagram. The default is c('green', 'blue', "red"). |
venn_diagram_imagetype |
A character string indicating the venn_diagram_imagetype (e.g. "tiff", "png" or "svg"). The default is "png". |
work_dir |
A character string specifying the path to work directory. |
fc_cutoff |
Numerical value indicating the maximum value for Fold
Change '(FC). The default is |
fdr_cutoff |
Numerical value indicating the maximum value for FDR
values. The default is |
env |
A character string containing the environment name that should be used. If none has been set yet, the function will create one in global environment following the standard criteria:
|
a list of differentially expressed genes in common between two or three differential expression analysis packages.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 | library(DOAGDC)
# data already downloaded using the 'download_gdc' function
concatenate_expression("gene",
name = "HIF3A",
data_base = "legacy",
tumor = "CHOL",
work_dir = "~/Desktop"
)
# separating gene HIF3A expression data patients in two groups
groups_identification_mclust("gene", 2,
name = "HIF3A",
modelName = "E",
env = CHOL_LEGACY_gene_tumor_data,
tumor = "CHOL"
)
# load not normalized data
concatenate_expression("gene",
normalization = FALSE,
name = "HIF3A",
data_base = "legacy",
tumor = "CHOL",
env = CHOL_LEGACY_gene_tumor_data,
work_dir = "~/Desktop"
)
# start DE analysis
dea_edger(
name = "HIF3A",
group_gen = "mclust",
env = CHOL_LEGACY_gene_tumor_data
)
dea_ebseq(
pair_name = "G2_over_G1",
rounds = 2,
name = "HIF3A",
group_gen = "mclust",
env = CHOL_LEGACY_gene_tumor_data
)
# run the Venn diagram
venn_diagram(
final_data = "edgeR",
n_pack = 2,
package_names = c("EBSeq", "edgeR"),
env = CHOL_LEGACY_gene_tumor_data
)
|
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