Bar plot of gene(s) expression for 1-, 2-, or 3-factor experiments

Creates a bar plot of relative gene expression (fold change) values from 1-, 2-, or 3-factor experiments, including error bars and statistical significance annotations.

Usage

plotFactor(
  data,
  x_col,
  y_col,
  Lower.se_col,
  Upper.se_col,
  group_col = NULL,
  facet_col = NULL,
  letters_col = NULL,
  letters_d = 0.2,
  col_width = 0.8,
  err_width = 0.15,
  dodge_width = 0.8,
  fill_colors = NULL,
  color = NA,
  alpha = 1,
  base_size = 12,
  legend_position = "right",
  removeCalibratorCols = FALSE,
  removeCalibratorText = FALSE,
  ...
)

Arguments

data: Data frame containing expression results
x_col: Character. Column name for x-axis
y_col: Character. Column name for bar height
Lower.se_col: Character. Column name for lower SE
Upper.se_col: Character. Column name for upper SE
group_col: Character. Column name for grouping bars (optional)
facet_col: Character. Column name for faceting (optional)
letters_col: Character. Column name for significance letters (optional)
letters_d: Numeric. Vertical offset for letters (default 0.2)
col_width: Numeric. Width of bars (default 0.8)
err_width: Numeric. Width of error bars (default 0.15)
dodge_width: Numeric. Width of dodge for grouped bars (default 0.8)
fill_colors: Optional vector of fill colors to change the default colors
color: Optional color for the bar outline
alpha: Numeric. Transparency of bars (default 1)
base_size: Numeric. Base font size for theme (default 12)
legend_position: Character or numeric vector. Legend position (default right)
removeCalibratorCols: = NULL or remove Calibrator Cols
removeCalibratorText: = NULL or remove Calibrator text
...: Additional ggplot2 layer arguments

Value

ggplot2 plot object

Author

Ghader Mirzaghaderi

Examples

data <- read.csv(system.file("extdata", "data_2factorBlock3ref.csv", package = "rtpcr"))

res <- ANOVA_DDCt(x = data,
  numOfFactors = 2,
  numberOfrefGenes = 3,
  block = "block",
  specs = "Concentration",
  p.adj = "none")
#> 
#> Relative Expression
#>   gene contrast     ddCt      RE     LCL     UCL   log2FC      se Lower.se.RE
#> 1   PO       L1  0.00000 1.00000 0.00000 0.00000  0.00000 0.09187     0.93830
#> 2   PO L2 vs L1 -0.94610 1.92666 1.35851 2.73242  0.94610 0.13465     1.75497
#> 3   PO L3 vs L1 -2.19198 4.56931 3.30647 6.31447  2.19198 0.18224     4.02710
#> 4  NLM       L1  0.00000 1.00000 0.00000 0.00000  0.00000 0.09017     0.93941
#> 5  NLM L2 vs L1  0.86568 0.54879 0.42187 0.71389 -0.86568 0.06252     0.52552
#> 6  NLM L3 vs L1 -0.51619 1.43017 1.08699 1.88170  0.51619 0.18103     1.26152
#>   Upper.se.RE Lower.se.log2FC Upper.se.log2FC p.value sig
#> 1     1.06575        -0.09187         0.09187 1.00000    
#> 2     2.11515         0.81145         1.08076 0.00116  **
#> 3     5.18453         2.00974         2.37421 0.00000 ***
#> 4     1.06450        -0.09017         0.09017 1.00000    
#> 5     0.57309        -0.92819        -0.80316 0.00018 ***
#> 6     1.62137         0.33516         0.69722 0.01382   *
#> 
#> The L1 level was used as calibrator.
#> Note: Using default model for statistical analysis: wDCt ~ block + Concentration * Type 

df <- res$relativeExpression

p1 <- plotFactor(
  data = df,
  x_col = "contrast",
  y_col = "RE",
  group_col = "gene",
  facet_col = "gene",
  Lower.se_col = "Lower.se.RE",
  Upper.se_col = "Upper.se.RE",
  letters_col = "sig",
  letters_d = 0.2,
  alpha = 1,
  col_width = 0.7,
  dodge_width = 0.7,
  base_size = 14, 
  legend_position = "none")

p1



data2 <- read.csv(system.file("extdata", "data_3factor.csv", package = "rtpcr"))
#Perform analysis first
res <- ANOVA_DCt(
  data2,
  numOfFactors = 3,
  numberOfrefGenes = 1,
  block = NULL)
#> 
#> Relative Expression
#> 
#>    gene Type Conc SA      dCt      RE   log2FC     LCL     UCL      se
#> 1    PO    R    L A1  1.81000 0.28519 -1.81000 0.18241 0.44589 0.02082
#> 2    PO    S    L A1  0.33000 0.79554 -0.33000 0.50883 1.24380 0.21284
#> 3    PO    R    M A1  0.67333 0.62706 -0.67333 0.40107 0.98039 0.43880
#> 4    PO    S    M A1  1.27000 0.41466 -1.27000 0.26522 0.64831 0.25403
#> 5    PO    R    H A1  0.01667 0.98851 -0.01667 0.63225 1.54552 0.08413
#> 6    PO    S    H A1 -1.57000 2.96905  1.57000 1.89900 4.64204 0.05508
#> 7    PO    R    L A2  3.96333 0.06411 -3.96333 0.04100 0.10023 0.82277
#> 8    PO    S    L A2 -0.61667 1.53333  0.61667 0.98072 2.39732 0.08647
#> 9    PO    R    M A2  1.66667 0.31498 -1.66667 0.20146 0.49246 0.28898
#> 10   PO    S    M A2  0.33000 0.79554 -0.33000 0.50883 1.24380 0.25710
#> 11   PO    R    H A2  1.20333 0.43427 -1.20333 0.27776 0.67897 0.08373
#> 12   PO    S    H A2 -2.37667 5.19335  2.37667 3.32167 8.11969 0.13094
#>    Lower.se.RE Upper.se.RE Lower.se.log2FC Upper.se.log2FC sig
#> 1      0.28111     0.28934        -1.83082        -1.78918   f
#> 2      0.68642     0.92200        -0.54284        -0.11716  cd
#> 3      0.46261     0.84996        -1.11213        -0.23453 cde
#> 4      0.34771     0.49450        -1.52403        -1.01597  ef
#> 5      0.93252     1.04787        -0.10080         0.06746  bc
#> 6      2.85784     3.08458         1.51492         1.62508   a
#> 7      0.03624     0.11340        -4.78610        -3.14057   g
#> 8      1.44412     1.62805         0.53019         0.70314   b
#> 9      0.25780     0.38484        -1.95565        -1.37768   f
#> 10     0.66568     0.95072        -0.58710        -0.07290  cd
#> 11     0.40978     0.46022        -1.28707        -1.11960 def
#> 12     4.74277     5.68675         2.24573         2.50760   a
#> 
#> Note: Using default model for statistical analysis: wDCt ~ Type * Conc * SA 
  
df <- res$relativeExpression
# Generate three-factor bar plot
 p <- plotFactor(
  df,
  x_col = "SA",       
  y_col = "log2FC",       
  group_col = "Type",   
  facet_col = "Conc",    
  Lower.se_col = "Lower.se.log2FC",
  Upper.se_col = "Upper.se.log2FC",
  letters_col = "sig",
  letters_d = 0.3,
  col_width = 0.7, 
  dodge_width = 0.7,
  #fill_colors = c("blue", "brown"),
  color = "black",
  base_size = 14, 
  alpha = 1,
  legend_position = c(0.1, 0.2))
p