Based on PALMO::variancefeaturePlot() function
Usage
plot_variance(
var_decomp,
rank = c("Group", "Time"),
features = NULL,
top_n = 20,
ylab = TRUE,
fontsize = 8
)Arguments
- var_decomp
A data frame of variance decomposition result from
variance_decomp()function- rank
Rank the plot by "Group" or "Time"
- features
Features to be plotted (default is NULL)
- top_n
Top n features ranked by
rankto be plotted whenfeaturesis not specified (default is 20)- ylab
Whether to show y axis text (default is TRUE)
- fontsize
Font size for the plot (default is 8)
Value
A plot of variance decomposition for each feature, showing the
percentage of variance explained by Group, Time, and Residual. The features
are ranked by the specified rank variable (Group or Time) and only the
top n features are plotted if features is not specified.
Examples
data("example")
example_obj <- normalise_to_start(example_obj)
var_decomp <- decomp_variance(example_obj, assay = 1)
#>
| | 0 % ~calculating
|+ | 1 % ~03s
|+ | 2 % ~01s
|++ | 3 % ~02s
|++ | 4 % ~01s
|+++ | 5 % ~02s
|+++ | 6 % ~01s
|++++ | 7 % ~01s
|++++ | 8 % ~01s
|+++++ | 9 % ~01s
|+++++ | 10% ~01s
|++++++ | 11% ~01s
|++++++ | 12% ~01s
|+++++++ | 13% ~01s
|+++++++ | 14% ~01s
|++++++++ | 15% ~01s
|++++++++ | 16% ~01s
|+++++++++ | 17% ~01s
|+++++++++ | 18% ~01s
|++++++++++ | 19% ~01s
|++++++++++ | 20% ~01s
|+++++++++++ | 21% ~01s
|+++++++++++ | 22% ~01s
|++++++++++++ | 23% ~01s
|++++++++++++ | 24% ~01s
|+++++++++++++ | 25% ~01s
|+++++++++++++ | 26% ~01s
|++++++++++++++ | 27% ~01s
|++++++++++++++ | 28% ~01s
|+++++++++++++++ | 29% ~01s
|+++++++++++++++ | 30% ~01s
|++++++++++++++++ | 31% ~01s
|++++++++++++++++ | 32% ~01s
|+++++++++++++++++ | 33% ~01s
|+++++++++++++++++ | 34% ~01s
|++++++++++++++++++ | 35% ~01s
|++++++++++++++++++ | 36% ~01s
|+++++++++++++++++++ | 37% ~01s
|+++++++++++++++++++ | 38% ~01s
|++++++++++++++++++++ | 39% ~01s
|++++++++++++++++++++ | 40% ~01s
|+++++++++++++++++++++ | 41% ~01s
|+++++++++++++++++++++ | 42% ~01s
|++++++++++++++++++++++ | 43% ~01s
|++++++++++++++++++++++ | 44% ~01s
|+++++++++++++++++++++++ | 45% ~01s
|+++++++++++++++++++++++ | 46% ~01s
|++++++++++++++++++++++++ | 47% ~01s
|++++++++++++++++++++++++ | 48% ~01s
|+++++++++++++++++++++++++ | 49% ~01s
|+++++++++++++++++++++++++ | 50% ~01s
|++++++++++++++++++++++++++ | 51% ~01s
|++++++++++++++++++++++++++ | 52% ~01s
|+++++++++++++++++++++++++++ | 53% ~01s
|+++++++++++++++++++++++++++ | 54% ~01s
|++++++++++++++++++++++++++++ | 55% ~01s
|++++++++++++++++++++++++++++ | 56% ~01s
|+++++++++++++++++++++++++++++ | 57% ~01s
|+++++++++++++++++++++++++++++ | 58% ~01s
|++++++++++++++++++++++++++++++ | 59% ~01s
|++++++++++++++++++++++++++++++ | 60% ~00s
|+++++++++++++++++++++++++++++++ | 61% ~00s
|+++++++++++++++++++++++++++++++ | 62% ~00s
|++++++++++++++++++++++++++++++++ | 63% ~00s
|++++++++++++++++++++++++++++++++ | 64% ~00s
|+++++++++++++++++++++++++++++++++ | 65% ~00s
|+++++++++++++++++++++++++++++++++ | 66% ~00s
|++++++++++++++++++++++++++++++++++ | 67% ~00s
|++++++++++++++++++++++++++++++++++ | 68% ~00s
|+++++++++++++++++++++++++++++++++++ | 69% ~00s
|+++++++++++++++++++++++++++++++++++ | 70% ~00s
|++++++++++++++++++++++++++++++++++++ | 71% ~00s
|++++++++++++++++++++++++++++++++++++ | 72% ~00s
|+++++++++++++++++++++++++++++++++++++ | 73% ~00s
|+++++++++++++++++++++++++++++++++++++ | 74% ~00s
|++++++++++++++++++++++++++++++++++++++ | 75% ~00s
|++++++++++++++++++++++++++++++++++++++ | 76% ~00s
|+++++++++++++++++++++++++++++++++++++++ | 77% ~00s
|+++++++++++++++++++++++++++++++++++++++ | 78% ~00s
|++++++++++++++++++++++++++++++++++++++++ | 79% ~00s
|++++++++++++++++++++++++++++++++++++++++ | 80% ~00s
|+++++++++++++++++++++++++++++++++++++++++ | 81% ~00s
|+++++++++++++++++++++++++++++++++++++++++ | 82% ~00s
|++++++++++++++++++++++++++++++++++++++++++ | 83% ~00s
|++++++++++++++++++++++++++++++++++++++++++ | 84% ~00s
|+++++++++++++++++++++++++++++++++++++++++++ | 85% ~00s
|+++++++++++++++++++++++++++++++++++++++++++ | 86% ~00s
|++++++++++++++++++++++++++++++++++++++++++++ | 87% ~00s
|++++++++++++++++++++++++++++++++++++++++++++ | 88% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++ | 89% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++ | 90% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++ | 91% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++ | 92% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 93% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 94% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 95% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 96% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 97% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 98% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 99% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=01s
plot_variance(var_decomp, rank = "Time", top_n = 20)
#> Features not specified. Plotting top 20 features ranked by Time.
