WGCNA analysis pipeline for RNA-Seq.
Usage
wgcna_pipeline(
sample_gene,
group_sample,
R_cutofff = 0.85,
max_block = 5000,
min_module = 20,
network_type = "unsigned",
merge_cutoff = 0.15,
cor_type = "pearson",
na_color = "#cdcdcd",
xlab_angle = 45,
text_size = 0.7
)Arguments
- sample_gene
Dataframe: All genes in all samples expression dataframe of RNA-Seq (1st-col: Genes, 2nd-col~: Samples).
- group_sample
Dataframe: Samples and groups for gene expression (1st-col: Samples, 2nd-col: Groups).
- R_cutofff
Numeric: Rsquare cutoff. Default: 0.85, min: 0.00, max: 1.00.
- max_block
Numeric: max block size. Default: 5000.
- min_module
Numeric: min module gene number. Default: 20.
- network_type
Character: network type. Default: "unsigned", options: "unsigned", "signed", "signed hybrid".
- merge_cutoff
Numeric: merge modules cutoff. Default: 0.15.
- cor_type
Character: correlation type. Default: "pearson", options: "pearson", "bicor".
- na_color
Character: NA value color (color name or hex value). Default: "#cdcdcd".
- xlab_angle
Numeric: X axis lable angle. Default: 45, min: 0, max: 360.
- text_size
Numeric: cell text size. Default: 0.7, min: 0, max: NULL.
Examples
# 1. Library TOmicsVis package
library(TOmicsVis)
# 2. Use example dataset
data(gene_expression)
head(gene_expression)
#> Genes CT_1 CT_2 CT_3 LT20_1 LT20_2 LT20_3 LT15_1 LT15_2
#> 1 transcript_0 655.78 631.08 669.89 654.21 402.56 447.09 510.08 442.22
#> 2 transcript_1 92.72 112.26 150.30 88.35 76.35 94.55 120.24 80.89
#> 3 transcript_10 21.74 31.11 22.58 15.09 13.67 13.24 12.48 7.53
#> 4 transcript_100 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00
#> 5 transcript_1000 0.00 14.15 36.01 0.00 0.00 193.59 208.45 0.00
#> 6 transcript_10000 89.18 158.04 86.28 82.97 117.78 102.24 129.61 112.73
#> LT15_3 LT12_1 LT12_2 LT12_3 LT12_6_1 LT12_6_2 LT12_6_3
#> 1 399.82 483.30 437.89 444.06 405.43 416.63 464.75
#> 2 73.94 96.25 82.62 85.48 65.12 61.94 73.44
#> 3 13.35 11.16 11.36 6.96 7.82 4.01 10.02
#> 4 0.00 0.00 0.00 0.00 0.00 0.00 0.00
#> 5 232.40 148.58 0.00 181.61 0.02 12.18 0.00
#> 6 85.70 80.89 124.11 115.25 113.87 107.69 119.83
data(samples_groups)
head(samples_groups)
#> Samples Groups
#> 1 CT_1 CT
#> 2 CT_2 CT
#> 3 CT_3 CT
#> 4 LT20_1 LT20
#> 5 LT20_2 LT20
#> 6 LT20_3 LT20