yoannpa / biocompr Goto Github PK

Good afternoon,

To the cross.biplot could you maybe add an option to indicate a 'fill' option, as for the PCAWG palette it works better if you use shape 21 (and up), but that requires "fill" and not "color".

Gr
Miranda

Hi Yoann,
I tried out the ggcoverage function in R 3.6.2.
When I set the log.scaled argument to T, the bars appeared to be placed on top of each other rather than next to each other.
Here is a minimal example:

library(ggplot2)
library(data.table)

code of ggcoverage function copied from GitHub

data_for_plotting <- as.data.table(data.frame(col1 = c("C", "B", "A", "D", "E"), col2 = 100:104, col3 = 35:39))
plot <- ggcoverage(data_for_plotting, log.scaled = T)
print(plot)

I hope this helps with improving the package :).
ggcoverage_trial_plot.pdf

Best wishes
Niklas

Executing first part of the help returns:

Error in ggcraviola(data = df.minimal, lines.col = "black") : 
  Column 1 in data is not of type 'factor'.

library(BiocompR)
fancy.hist(rnorm(100, 5, 1), xmax = 10)

results in

Error in mclapply(seq(length(xbreaks) - 1), mc.cores = ncores, function(i) { :
could not find function "mclapply"

I guess the import of "parallel" package is missing when executing library(BiocompR)

executing library(parallel) prior to running above code solved the problem.

Would it be possible to add a function that can create a heatmap along chromosomes? Thank you :)

Add an option in fancy.hist() to hide/show "cut-off" and "median" annotations.

Hi Yoann,

Description
I'm trying to color the points with a gradient color scale and not by groups.
Below, I used the mtcars data set to replicate my issue. After performing the PCA, I wanted to create a biplot with the points colored by weight on a gradient scale. Instead, the separate values are treated as individual groups and the colors do not represent the gradient.

I think it would be beneficial to add an option to color the points on a gradient scale.

To replicate
mtcars.pca <- prcomp(mtcars[,c(1:5,7,10,11)], center = TRUE, scale. = TRUE)
ggbipca(prcomp.res = mtcars.pca, data = mtcars, color.data = "wt")

Thank you!

When attempting to install BiocompR package (using R 4.0.2) following instructions in the Readme
i get the following error.

devtools::install()
Skipping 1 packages not available: IRanges
✓ checking for file ‘/home/david/Downloads/BiocompR-master/DESCRIPTION’ ...
─ preparing ‘BiocompR’:
✓ checking DESCRIPTION meta-information ...
─ checking for LF line-endings in source and make files and shell scripts
─ checking for empty or unneeded directories
─ building ‘BiocompR_0.0.68.tar.gz’
Running /usr/lib/R/bin/R CMD INSTALL /tmp/Rtmpk6eJ9o/BiocompR_0.0.68.tar.gz --install-tests
installing to library ‘/home/david/R/x86_64-pc-linux-gnu-library/4.0’
installing source package ‘BiocompR’ ...
using staged installation
R
byte-compile and prepare package for lazy loading
Error in FUN(X[[i]], ...) : there is no package called ‘Hmisc’
Error: unable to load R code in package ‘BiocompR’
Execution halted
ERROR: lazy loading failed for package ‘BiocompR’
removing ‘/home/david/R/x86_64-pc-linux-gnu-library/4.0/BiocompR’
Error in (function (command = NULL, args = character(), error_on_status = TRUE, :
System command 'R' failed, exit status: 1, stdout & stderr were printed
Type .Last.error.trace to see where the error occured
.Last.error.trace
Stack trace:
devtools::install()
pkgbuild::with_build_tools(required = FALSE, callr::rcmd("INSTALL", ...
callr::rcmd("INSTALL", c(install_path, opts), echo = !quiet, ...
callr:::run_r(options)
base:::with(options, with_envvar(env, do.call(processx::run, ...
base:::with.default(options, with_envvar(env, do.call(processx::run, ...
base:::eval(substitute(expr), data, enclos = parent.frame())
base:::eval(substitute(expr), data, enclos = parent.frame())
callr:::with_envvar(env, do.call(processx::run, c(list(bin, args = real_cmdargs, ...
base:::force(code)
base:::do.call(processx::run, c(list(bin, args = real_cmdargs, ...
(function (command = NULL, args = character(), error_on_status = TRUE, ...
throw(new_process_error(res, call = sys.call(), echo = echo, ...
x System command 'R' failed, exit status: 1, stdout & stderr were printed

Installing package "Hmisc" using install.packages("Hmisc") solved the problem.

Currently show.annot is a logical that can be either TRUE or FALSE.
A potential improvement would be to make it support different values, such as : "cutoff", "mean", "both", "none".

Hi Yoann,

Description
just a little feedback on the ggvolcano.free() function. Currently I am trying to display 34 genes within the volcano plot using the force.label=c() argument. As those labels seem to be to close together and ggrepel does not tolerate the overlap, non of the labels are returned.

Warning messages:
1: ggrepel: 17 unlabeled data points (too many overlaps). Consider increasing max.overlaps
2: ggrepel: 17 unlabeled data points (too many overlaps). Consider increasing max.overlaps
3: ggrepel: 17 unlabeled data points (too many overlaps). Consider increasing max.overlaps
4: ggrepel: 17 unlabeled data points (too many overlaps). Consider increasing max.overlaps
5: ggrepel: 17 unlabeled data points (too many overlaps). Consider increasing max.overlaps
6: ggrepel: 17 unlabeled data points (too many overlaps). Consider increasing max.overlaps
7: ggrepel: 17 unlabeled data points (too many overlaps). Consider increasing max.overlaps
8: ggrepel: 17 unlabeled data points (too many overlaps). Consider increasing max.overlaps
9: ggrepel: 11 unlabeled data points (too many overlaps). Consider increasing max.overlaps
.....

To reproduce
volcano.plot<-ggvolcano.free(data=input.data, x.cutoff = 0.01, p.cutoff=0.05, force.label =c('REXO5', 'C18orf54','CCT6P1','CEP72','POLE2','MCM4','HELLS','CCT6P1','ANKRD20A11P','EHMT2','LIN9','PAXIP1' ))

You can find the complete script with input data under the following DFKZ path: /.../.../.../data/rathgeber/scripts/neuroblastoma/Methylation_Analysis/Make_gene_methylation_TelNet_genes_BiocompR_volcano_plot.R

Suggestion how to fix
You could tune the ggrepel argument "force" to increase the labels' distance

Session information
sessionInfo()
R version 4.0.0 (2020-04-24)
Platform: x86_64-pc-linux-gnu (64-bit)
Running under: CentOS Linux 7 (Core)

Matrix products: default
BLAS: /usr/lib64/libblas.so.3.4.2
LAPACK: /usr/lib64/liblapack.so.3.4.2

Random number generation:
RNG: Mersenne-Twister
Normal: Inversion
Sample: Rounding

locale:
[1] LC_CTYPE=en_US.UTF-8 LC_NUMERIC=C LC_TIME=en_US.UTF-8
[4] LC_COLLATE=en_US.UTF-8 LC_MONETARY=en_US.UTF-8 LC_MESSAGES=en_US.UTF-8
[7] LC_PAPER=en_US.UTF-8 LC_NAME=C LC_ADDRESS=C
[10] LC_TELEPHONE=C LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C

attached base packages:
[1] stats graphics grDevices utils datasets methods base

other attached packages:
[1] dplyr_1.0.7 BiocompR_0.0.149 ggplot2_3.3.5 data.table_1.14.0

loaded via a namespace (and not attached):
[1] Rcpp_1.0.7 rstudioapi_0.13 magrittr_2.0.1 tidyselect_1.1.1 munsell_0.5.0
[6] colorspace_2.0-2 R6_2.5.0 rlang_0.4.11 fansi_0.5.0 tools_4.0.0
[11] grid_4.0.0 gtable_0.3.0 utf8_1.2.2 cli_3.0.1 DBI_1.1.1
[16] withr_2.4.2 ellipsis_0.3.2 digest_0.6.27 assertthat_0.2.1 tibble_3.1.3
[21] lifecycle_1.0.0 crayon_1.4.1 farver_2.1.0 purrr_0.3.4 vctrs_0.3.8
[26] ggrepel_0.9.1 glue_1.4.2 labeling_0.4.2 compiler_4.0.0 pillar_1.6.2
[31] generics_0.1.0 scales_1.1.1 pkgconfig_2.0.3
There were 12 warnings (use warnings() to see them)

warnings()
Warning messages:
1: ggrepel: 11 unlabeled data points (too many overlaps). Consider increasing max.overlaps
2: ggrepel: 11 unlabeled data points (too many overlaps). Consider increasing max.overlaps
3: ggrepel: 11 unlabeled data points (too many overlaps). Consider increasing max.overlaps
4: ggrepel: 11 unlabeled data points (too many overlaps). Consider increasing max.overlaps
5: ggrepel: 11 unlabeled data points (too many overlaps). Consider increasing max.overlaps
6: ggrepel: 11 unlabeled data points (too many overlaps). Consider increasing max.overlaps
7: ggrepel: 11 unlabeled data points (too many overlaps). Consider increasing max.overlaps
8: ggrepel: 11 unlabeled data points (too many overlaps). Consider increasing max.overlaps
9: ggrepel: 11 unlabeled data points (too many overlaps). Consider increasing max.overlaps
10: ggrepel: 11 unlabeled data points (too many overlaps). Consider increasing max.overlaps
11: ggrepel: 11 unlabeled data points (too many overlaps). Consider increasing max.overlaps
12: ggrepel: 11 unlabeled data points (too many overlaps). Consider increasing max.overlaps