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PCA dimensional reduction visualization for RNA-Seq.

Source: R/pca_plot.R
pca_plot.Rd

PCA dimensional reduction visualization for RNA-Seq.

Usage

pca_plot(
  sample_gene,
  group_sample,
  xPC = 1,
  yPC = 2,
  multi_shape = TRUE,
  point_size = 5,
  point_alpha = 0.8,
  text_size = 5,
  fill_alpha = 0.05,
  border_alpha = 0,
  sci_fill_color = "Sci_AAAS",
  legend_pos = "right",
  legend_dir = "vertical",
  ggTheme = "theme_light"
)

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).

xPC

Numeric: PC index at x axis. Default: 1, options: 1, 2, 3, ...

yPC

Numeric: PC index at y axis. Default: 2, options: 2, 3, 4, ...

multi_shape

Logical: groups as shapes. Default: TRUE, options: TRUE, FALSE.

point_size

Numeric: PCA plot point size. Default: 5, min: 0.

point_alpha

Numeric: point color alpha. Default: 0.80, min: 0.00, max: 1.00.

text_size

Numeric: PCA plot annotation size. Default: 5, min: 0.

fill_alpha

Numeric: ellipse fill color alpha. Default: 0.10, min: 0.00, max: 1.00.

border_alpha

Numeric: ellipse border color alpha. Default: 0.10, min: 0.00, max: 1.00.

sci_fill_color

Character: ggsci color pallet. Default: "Sci_AAAS", options: "Sci_AAAS", "Sci_NPG", "Sci_Simpsons", "Sci_JAMA", "Sci_GSEA", "Sci_Lancet", "Sci_Futurama", "Sci_JCO", "Sci_NEJM", "Sci_IGV", "Sci_UCSC", "Sci_D3", "Sci_Material".

legend_pos

Character: legend position. Default: "right", options: "none", "left", "right", "bottom", "top".

legend_dir

Character: legend director. Default: "vertical", options: "horizontal", "vertical".

ggTheme

Character: ggplot2 theme. Default: "theme_light", options: "theme_default", "theme_bw", "theme_gray", "theme_light", "theme_linedraw", "theme_dark", "theme_minimal", "theme_classic", "theme_void".

Value

Plot: PCA dimensional reduction visualization for RNA-Seq.

Author

benben-miao

Examples

# 1. Library package TOmicsVis
library(TOmicsVis)

# 2. Load example datasets
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

# 3. Default parameters
pca_plot(gene_expression, samples_groups)


# 4. Set multi_shape = FALSE
pca_plot(gene_expression, samples_groups, multi_shape = FALSE)


# 5. Set sci_fill_color = "Sci_NPG", fill_alpha = 0.10
pca_plot(gene_expression, samples_groups, sci_fill_color = "Sci_NPG", fill_alpha = 0.10)