Correlation Heatmap for samples/groups based on Pearson algorithm.

Usage

corr_heatmap(
  data,
  corr_method = "pearson",
  cell_shape = "square",
  fill_type = "full",
  lable_size = 3,
  axis_angle = 45,
  axis_size = 12,
  lable_digits = 3,
  color_low = "blue",
  color_mid = "white",
  color_high = "red",
  outline_color = "white",
  ggTheme = "theme_light"
)

Arguments

data: Dataframe: All genes in all samples expression dataframe of RNA-Seq (1st-col: Genes, 2nd-col~: Samples).
corr_method: Character: correlation method. Default: "pearson", options: "pearson", "spearman", "kendall".
cell_shape: Character: heatmap cell shape. Default: "square", options: "circle", "square".
fill_type: Character: heatmap fill type. Default: "full", options: "upper", "low", "full".
lable_size: Numeric: heatmap label size. Default: 3, min: 0.
axis_angle: Numeric: axis rotate angle. Default: 45, min: 0, max: 360.
axis_size: Numberic: axis font size. Default: 12, min: 0.
lable_digits: Numeric: heatmap label digits. Default: 3, min: 0, max: 3.
color_low: Character: low value color name or hex value. Default: "blue".
color_mid: Character: middle value color name or hex value. Default: "white".
color_high: Character: high value color name or hex value. Default: "red".
outline_color: Character: outline color name or hex value. Default: "white".
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: heatmap plot filled with Pearson correlation values and P values.

Author

benben-miao

Examples

# 1. Library TOmicsVis package
library(TOmicsVis)

# 2. Use example dataset gene_exp
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

# 3. Default parameters
corr_heatmap(gene_expression)
#> Scale for fill is already present.
#> Adding another scale for fill, which will replace the existing scale.


# 4. Set color_low = "#008800"
corr_heatmap(gene_expression, color_low = "#008800")
#> Scale for fill is already present.
#> Adding another scale for fill, which will replace the existing scale.


# 5. Set cell_shape = "circle"
corr_heatmap(gene_expression, cell_shape = "circle")
#> Scale for fill is already present.
#> Adding another scale for fill, which will replace the existing scale.