install.packages("devtools")
devtools::install_github("Simon-Leonard/FlexDotPlot")
library(FlexDotPlot)
data(PBMC3K_example_data)
head(PBMC3K_example_data)
#> genes.plot id pct.exp pct.exp2 pct.exp100 avg.exp
#> 1 LDHB CD4+ T cells 0.9244136 0.92 92 13.7873904
#> 2 CD3D CD4+ T cells 0.8696785 0.87 87 10.9083613
#> 3 CD3E CD4+ T cells 0.7688966 0.77 77 7.5569240
#> 4 IL7R CD4+ T cells 0.6620330 0.66 66 6.7337967
#> 5 MAL CD4+ T cells 0.2719374 0.27 27 1.7643244
#> 6 S100A9 CD4+ T cells 0.1589922 0.16 16 0.7389524
#> avg.exp.scale abs_avg_exp_scale avg_logFC avg_logFC2 p_val_adj
#> 1 2.2133094 2.2133094 1.1488046 1.15 2.014606e-230
#> 2 1.3521395 1.3521395 1.0446491 1.04 1.319434e-183
#> 3 1.5524263 1.5524263 0.9718669 0.97 2.800347e-139
#> 4 1.8077169 1.8077169 1.0676981 1.07 4.243063e-125
#> 5 2.4562299 2.4562299 0.9090779 0.91 7.404012e-72
#> 6 -0.4489301 0.4489301 -3.2925866 -3.29 4.984636e-99
#> p_val_adj2 pval_symb
#> 1 2,00E-230 <1e-100
#> 2 1.3e-183 <1e-100
#> 3 2.8e-139 <1e-100
#> 4 4.2e-125 <1e-100
#> 5 7.4e-72 <1e-50
#> 6 5,00E-99 <1e-50
summary(PBMC3K_example_data)
#> genes.plot id pct.exp pct.exp2
#> Length:312 Length:312 Min. :0.0000 Min. :0.0000
#> Class :character Class :character 1st Qu.:0.0328 1st Qu.:0.0300
#> Mode :character Mode :character Median :0.1120 Median :0.1100
#> Mean :0.2910 Mean :0.2909
#> 3rd Qu.:0.4773 3rd Qu.:0.4800
#> Max. :1.0000 Max. :1.0000
#>
#> pct.exp100 avg.exp avg.exp.scale abs_avg_exp_scale
#> Min. : 0.00 Min. : 0.0000 Min. :-0.8368 Min. :0.0207
#> 1st Qu.: 3.00 1st Qu.: 0.1398 1st Qu.:-0.5065 1st Qu.:0.3559
#> Median : 11.00 Median : 0.5717 Median :-0.3759 Median :0.4567
#> Mean : 29.09 Mean : 10.0988 Mean : 0.0000 Mean :0.6930
#> 3rd Qu.: 48.00 3rd Qu.: 4.6576 3rd Qu.:-0.1742 3rd Qu.:0.6398
#> Max. :100.00 Max. :442.2696 Max. : 2.4749 Max. :2.4749
#>
#> avg_logFC avg_logFC2 p_val_adj p_val_adj2
#> Min. :-3.2926 Min. :-3.2900 Min. :0.000000 Length:312
#> 1st Qu.:-1.3714 1st Qu.:-1.3125 1st Qu.:0.000000 Class :character
#> Median :-0.8549 Median : 0.7650 Median :0.000006 Mode :character
#> Mean :-0.4376 Mean : 0.5294 Mean :0.391716
#> 3rd Qu.:-0.1265 3rd Qu.: 1.9550 3rd Qu.:1.000000
#> Max. : 5.9443 Max. : 5.9400 Max. :1.000000
#> NA's :198
#> pval_symb
#> Length:312
#> Class :character
#> Mode :character
#>
#>
#>
#>
Size can be set with a column name using the size_var argument. Size scale can be adjusted with the shape.scale argument
dot_plot(data.to.plot = PBMC3K_example_data, size_var = "pct.exp", shape.scale = 8)
#> Using :
#> - pct.exp values to set shape size
#> - Nothing to set shape color
#> - Nothing to add text on shapes
A column index or a vector can also be provided
# Same results
dot_plot(data.to.plot = PBMC3K_example_data, size_var = 3, shape.scale = 8) # Third column = pct.exp
dot_plot(data.to.plot = PBMC3K_example_data, size_var = PBMC3K_example_data$pct.exp, shape.scale = 8)
Comment : The variable controlling the shape size has to be numeric
class(PBMC3K_example_data$pval_symb)
dot_plot(data.to.plot = PBMC3K_example_data, size_var ="pval_symb") # Error ; size_var column has to be numeric
Scale the size by size or radius with the scale.by argument (default = “radius”) Set lower and upper limits for scaling with scale.min and scale.max arguments, use NA for default values.
Color can be set with a column name using the col_var argument.
dot_plot(data.to.plot = PBMC3K_example_data, col_var = "pct.exp", shape.scale = 20)
#> Using :
#> - Nothing to set shape size
#> - pct.exp values to set shape color
#> - Nothing to add text on shapes
A column index or a vector can also be provided
# Same results
dot_plot(data.to.plot = PBMC3K_example_data, col_var = 3, shape.scale = 20) # Third column = pct.exp
dot_plot(data.to.plot = PBMC3K_example_data, col_var = PBMC3K_example_data$pct.exp, shape.scale = 20)
Comment : A discrete variable can also be used to set the shape color
class(PBMC3K_example_data$pval_symb)
#> [1] "character"
dot_plot(data.to.plot = PBMC3K_example_data, col_var ="pval_symb", shape.scale=20)
#> Using :
#> - Nothing to set shape size
#> - pval_symb values to set shape color
#> - Nothing to add text on shapes
Custom colors can be used with the cols.use parameter
dot_plot(data.to.plot = PBMC3K_example_data, col_var = "pct.exp", cols.use = c("yellow","blue"), shape.scale = 20)
#> Using :
#> - Nothing to set shape size
#> - pct.exp values to set shape color
#> - Nothing to add text on shapes
Variable shape can be set with a column name using the shape_var argument.
dot_plot(data.to.plot = PBMC3K_example_data, shape_var = "pct.exp", shape.scale = 8)
#> Using :
#> - Nothing to set shape size
#> - Nothing to set shape color
#> - Nothing to add text on shapes
#> - pct.exp values to determine shapes (204 shapes detected)
A vector can also be provided
# Same results
dot_plot(data.to.plot = PBMC3K_example_data, shape_var = PBMC3K_example_data$pct.exp, shape.scale = 8)
Comment : A discrete variable can also be used to set the shape
class(PBMC3K_example_data$pval_symb)
#> [1] "character"
dot_plot(data.to.plot = PBMC3K_example_data, shape_var ="pval_symb", shape.scale=20)
#> Using :
#> - Nothing to set shape size
#> - Nothing to set shape color
#> - Nothing to add text on shapes
#> - pval_symb values to determine shapes (5 shapes detected)
You can choose the shape types with the shape_use argument and unicode characters
dot_plot(data.to.plot = PBMC3K_example_data, shape_var ="pval_symb", shape_use=c("\u2605","\u2736","\u25CF","\u2737","\u2726"),
shape.scale=20)
#> Using :
#> - Nothing to set shape size
#> - Nothing to set shape color
#> - Nothing to add text on shapes
#> - pval_symb values to determine shapes (5 shapes detected)
Text can be set with a column name using the text_var argument. Text size can be adjusted with the text.size argument
dot_plot(data.to.plot = PBMC3K_example_data, text_var = "pct.exp2", text.size = 3, shape.scale = 20)
#> Using :
#> - Nothing to set shape size
#> - Nothing to set shape color
#> - pct.exp2 values to add text on shapes
A column index or a vector can also be provided
# Same results
dot_plot(data.to.plot = PBMC3K_example_data, text_var = 4, , text.size = 3, shape.scale = 20)
dot_plot(data.to.plot = PBMC3K_example_data, text_var = PBMC3K_example_data$pct.exp2, , text.size = 3, shape.scale = 20)
Shape, size, colors and text can be provided simultaneously and independently
dot_plot(data.to.plot = PBMC3K_example_data,
size_var = "pct.exp", shape.scale = 8,
shape_var= "pct.exp",
col_var = "avg_logFC"
)
#> Using :
#> - pct.exp values to set shape size
#> - avg_logFC values to set shape color
#> - Nothing to add text on shapes
#> - pct.exp values to determine shapes (204 shapes detected)
dot_plot(data.to.plot = PBMC3K_example_data,
size_var = "pct.exp", shape.scale = 8,
shape_var= ifelse(PBMC3K_example_data$avg_logFC>0, "Up","Down"),
col_var = "avg_logFC", cols.use = c("yellow","blue")
)
#> Using :
#> - pct.exp values to set shape size
#> - avg_logFC values to set shape color
#> - Nothing to add text on shapes
Variable used to compute x and y dendrogramms can be provided within the dend_x_var and dend_y_var respectively. dist_method and hclust_methods arguments control distance method (default = euclidean) and hclust method (default = ward.D).
x dendrogramm only :
dot_plot(data.to.plot = PBMC3K_example_data,
size_var = "pct.exp", shape.scale = 8,
shape_var= "pct.exp",
col_var = "avg_logFC",
dend_x_var = c("pct.exp","avg_logFC"),
dist_method="euclidean", hclust_method="ward.D"
)
#> Using :
#> - pct.exp values to set shape size
#> - avg_logFC values to set shape color
#> - Nothing to add text on shapes
#> - pct.exp values to determine shapes (204 shapes detected)[1] "In FAMD_x_var : Adding y index"
#> [1] "In FAMD_x_var : Adding x index"
x and y dendrogramms :
dot_plot(data.to.plot = PBMC3K_example_data,
size_var = "pct.exp", shape.scale = 8,
shape_var= "pct.exp",
col_var = "avg_logFC",
dend_x_var = c("pct.exp","avg_logFC"),
dend_y_var = c("pct.exp","avg_logFC"),
dist_method="euclidean", hclust_method="ward.D"
)
#> Using :
#> - pct.exp values to set shape size
#> - avg_logFC values to set shape color
#> - Nothing to add text on shapes
#> - pct.exp values to determine shapes (204 shapes detected)[1] "In FAMD_x_var : Adding y index"
#> [1] "In FAMD_x_var : Adding x index"
#> [1] "In FAMD_y_var : Adding y index"
#> [1] "In FAMD_y_var : Adding x index"
Legend title can be modified with the size_legend, col_legend and shape_legend arguments.
dot_plot(data.to.plot = PBMC3K_example_data,
size_var = "pct.exp", shape.scale = 8, size_legend="My size legend",
shape_var= "pval_symb", shape_legend="My shape legend",
col_var = "avg_logFC", col_legend="My col legend"
)
#> Using :
#> - pct.exp values to set shape size
#> - avg_logFC values to set shape color
#> - Nothing to add text on shapes
#> - pval_symb values to determine shapes (5 shapes detected)
If you don't want to plot the legend, set the plot.legend argument to FALSE.
dot_plot(data.to.plot = PBMC3K_example_data,
size_var = "pct.exp", shape.scale = 8,
shape_var= "pval_symb",
col_var = "avg_logFC",
plot.legend=F
)
#> Using :
#> - pct.exp values to set shape size
#> - avg_logFC values to set shape color
#> - Nothing to add text on shapes
#> - pval_symb values to determine shapes (5 shapes detected)
size.breaks.number argument define the number of shapes with different size to display in the legend
dot_plot(data.to.plot = PBMC3K_example_data, size_var = "pct.exp", shape.scale = 8, size.breaks.number = 3)
#> Using :
#> - pct.exp values to set shape size
#> - Nothing to set shape color
#> - Nothing to add text on shapes
You can also directly specify the values to get in the legend with the size.breaks.values argument
dot_plot(data.to.plot = PBMC3K_example_data, size_var = "pct.exp", shape.scale = 8, size.breaks.values = c(0,0.25,0.5,0.75,1))
#> Using :
#> - pct.exp values to set shape size
#> - Nothing to set shape color
#> - Nothing to add text on shapesPutting size.breaks.values in size legend
color.breaks.number argument define the number of labels for the color gradient legend.
dot_plot(data.to.plot = PBMC3K_example_data, col_var = "pct.exp", shape.scale = 20, color.breaks.number = 3)
#> Using :
#> - Nothing to set shape size
#> - pct.exp values to set shape color
#> - Nothing to add text on shapes
You can also directly specify the values to get in the legend with the color.breaks.values argument
dot_plot(data.to.plot = PBMC3K_example_data, col_var = "pct.exp", shape.scale = 20, color.breaks.values = c(0,0.3,0.6,0.9))
#> Using :
#> - Nothing to set shape size
#> - pct.exp values to set shape color
#> - Nothing to add text on shapes
#> Putting color.breaks.values in color legend
Axis label positions can be controled with the x.lab.pos and y.lab.pos arguments. x.lab.pos must be one of “bottom”,“top”,“both” or “none”. y.lab.pos must be one of “left”,“right”,“both"or "none”.
dot_plot(data.to.plot = PBMC3K_example_data,
size_var = "pct.exp", shape.scale = 8,
shape_var= "pct.exp",
col_var = "avg_logFC",
x.lab.pos="bottom",
y.lab.pos="left"
)
#> Using :
#> - pct.exp values to set shape size
#> - avg_logFC values to set shape color
#> - Nothing to add text on shapes
#> - pct.exp values to determine shapes (204 shapes detected)
You can rotate x axis labels with the x.lab.rot argument (default is TRUE). Set it to FALSE if you don't want to rotate x axis labels (It's not recommended here)
Plot can be transposed by setting the transpose argument to TRUE
dot_plot(data.to.plot = PBMC3K_example_data,
size_var = "pct.exp", shape.scale = 8,
shape_var= "pct.exp",
col_var = "avg_logFC",
transpose=TRUE
)
#> Using :
#> - pct.exp values to set shape size
#> - avg_logFC values to set shape color
#> - Nothing to add text on shapes
#> - pct.exp values to determine shapes (204 shapes detected)
Vertical shades can be added with the vertical_coloring argument
dot_plot(data.to.plot = PBMC3K_example_data,
size_var = "pct.exp", shape.scale = 8,
shape_var= "pct.exp",
col_var = "avg_logFC",
vertical_coloring=c(NA, "gray80")
)
#> Using :
#> - pct.exp values to set shape size
#> - avg_logFC values to set shape color
#> - Nothing to add text on shapes
#> - pct.exp values to determine shapes (204 shapes detected)
Horizontal shades can be added with the horizontal_coloring argument
dot_plot(data.to.plot = PBMC3K_example_data,
size_var = "pct.exp", shape.scale = 8,
shape_var= "pct.exp",
col_var = "avg_logFC",
horizontal_coloring=c(NA, "gray80")
)
#> Using :
#> - pct.exp values to set shape size
#> - avg_logFC values to set shape color
#> - Nothing to add text on shapes
#> - pct.exp values to determine shapes (204 shapes detected)
Set do.return to TRUE to get the corresponding ggplot object
ggplot_object <- dot_plot(data.to.plot = PBMC3K_example_data,
size_var = "pct.exp", shape.scale = 8,
shape_var= "pct.exp",
col_var = "avg_logFC",
do.return = TRUE
)
Shiny_dot_plot()
Full script to generate dotplot input can be found at in the FlexDotPlot_CBMC_dataset vignette
### Import data + create the exp.type column
data(CBMC8K_example_data)
### Dotplot
dotplot = dot_plot(
data.to.plot=CBMC8K_example_data,
size_var="RNA.avg.exp.scaled",col_var="ADT.avg.exp.scaled", text_var="ADT.pct.exp.sup.cutoff",
shape_var="canonical_marker", shape_use = c("\u25CF","\u2737"),
x.lab.pos="bottom",y.lab.pos="left",
cols.use=c("lightgrey","orange","red", "darkred"),size_legend="RNA", col_legend="ADT", shape_legend="Canonical marker ?",
shape.scale =12, text.size=3,
plot.legend = TRUE,x.lab.rot = TRUE,
size.breaks.number=4, color.breaks.number=4, shape.breaks.number=5
, dend_x_var=c("RNA.avg.exp.scaled","ADT.avg.exp.scaled"), dend_y_var=c("RNA.avg.exp.scaled","ADT.avg.exp.scaled"), dist_method="euclidean",
hclust_method="ward.D", do.return = TRUE)
#> Using :
#> - RNA.avg.exp.scaled values to set shape size
#> - ADT.avg.exp.scaled values to set shape color
#> - ADT.pct.exp.sup.cutoff values to add text on shapes
#> - canonical_marker values to determine shapes (2 shapes detected)[1] "In FAMD_x_var : Adding y index"
#> [1] "In FAMD_x_var : Adding x index"
#> [1] "In FAMD_y_var : Adding y index"
#> [1] "In FAMD_y_var : Adding x index"
#> R version 4.0.5 (2021-03-31)
#> Platform: x86_64-w64-mingw32/x64 (64-bit)
#> Running under: Windows 10 x64 (build 18363)
#>
#> Matrix products: default
#>
#> locale:
#> [1] LC_COLLATE=C LC_CTYPE=French_France.1252
#> [3] LC_MONETARY=French_France.1252 LC_NUMERIC=C
#> [5] LC_TIME=French_France.1252
#>
#> attached base packages:
#> [1] stats graphics grDevices utils datasets methods base
#>
#> other attached packages:
#> [1] FlexDotPlot_0.2.1
#>
#> loaded via a namespace (and not attached):
#> [1] ggdendro_0.1-20 viridis_0.5.1 jsonlite_1.7.0
#> [4] viridisLite_0.3.0 splines_4.0.5 R.utils_2.9.2
#> [7] shiny_1.7.1 assertthat_0.2.1 highr_0.8
#> [10] blob_1.2.1 ggrepel_0.9.1 pillar_1.6.4
#> [13] lattice_0.20-41 glue_1.6.0 digest_0.6.27
#> [16] promises_1.1.1 polyclip_1.10-0 colorspace_1.4-1
#> [19] R.oo_1.23.0 R.matlab_3.6.2 Matrix_1.4-0
#> [22] htmltools_0.5.2 httpuv_1.5.4 plyr_1.8.6
#> [25] FactoMineR_2.3 XML_3.99-0.3 pkgconfig_2.0.3
#> [28] grImport2_0.2-0 purrr_0.3.4 xtable_1.8-4
#> [31] scales_1.1.1 tweenr_1.0.1 jpeg_0.1-8.1
#> [34] later_1.2.0 ggforce_0.3.2 tibble_3.1.0
#> [37] mgcv_1.8-37 generics_0.0.2 farver_2.0.3
#> [40] ggplot2_3.3.2 ellipsis_0.3.2 DT_0.14
#> [43] sisal_0.48 magrittr_1.5 crayon_1.3.4
#> [46] mime_0.9 evaluate_0.14 R.methodsS3_1.8.0
#> [49] fansi_0.4.1 nlme_3.1-152 MASS_7.3-53.1
#> [52] shinydashboard_0.7.1 colourpicker_1.1.0 tools_4.0.5
#> [55] lifecycle_1.0.1 stringr_1.4.0 munsell_0.5.0
#> [58] cluster_2.1.1 flashClust_1.01-2 compiler_4.0.5
#> [61] rlang_0.4.10 grid_4.0.5 htmlwidgets_1.5.1
#> [64] leaps_3.1 miniUI_0.1.1.1 labeling_0.3
#> [67] base64enc_0.1-3 shinyWidgets_0.5.4 boot_1.3-27
#> [70] gtable_0.3.0 DBI_1.1.0 reshape2_1.4.4
#> [73] R6_2.4.1 gridExtra_2.3 lubridate_1.7.9
#> [76] knitr_1.37 dplyr_1.0.7 fastmap_1.1.0
#> [79] utf8_1.1.4 bsplus_0.1.2 dendextend_1.14.0
#> [82] stringi_1.4.6 Rcpp_1.0.7 vctrs_0.3.8
#> [85] png_0.1-7 scatterplot3d_0.3-41 tidyselect_1.1.0
#> [88] xfun_0.29