idiogramFISH

Shiny App. Idiograms with Marks and Karyotype Indices

The goal of idiogramFISH functions or shiny-app is to plot karyotypes, plasmids and circular chr. having a set of data.frames for chromosome data and optionally marks’ data (Roa and PC Telles, 2021). Karyotypes can also be plotted in concentric circles. It is possible to calculate also chromosome and karyotype indexes (Romero-Zarco, 1986; Watanabe et al., 1999) and classify chromosome morphology in the categories of Levan (1964), and Guerra (1986). Six styles of marks are available: square (squareLeft), dots, cM (cMLeft), cenStyle, upArrow (downArrow), exProtein (inProtein) (column style in dfMarkColor data.frame); its legend (label) (parameter legend) can be drawn inline or to the right of karyotypes. Three styles of centromere are available: rounded, triangle and inProtein (cenFormat parameter). Chromosome regions (column chrRegion in dfMarkPos data.frame) for monocentrics are p, q, cen, pcen, qcen. The last three cannot accommodate most mark styles, but can be colored. The region w can be used both in monocentrics and holocentrics.

IdiogramFISH was written in R (R Core Team, 2019) and also uses crayon (Csárdi, 2017), tidyr (Wickham and Henry, 2020), plyr (Wickham, 2011) and dplyr packages (Wickham et al., 2019). Documentation was written with R-packages roxygen2 (Wickham et al., 2018), usethis (Wickham and Bryan, 2019), bookdown (Xie, 2016), knitr (Xie, 2015), pkgdown (Wickham and Hesselberth, 2019), Rmarkdown (Xie et al., 2018), rvcheck (Yu, 2019a), badger (Yu, 2019b), kableExtra (Zhu, 2019), rmdformats (Barnier, 2020) and RCurl (Temple Lang and CRAN team, 2019). For some vignette figures, packages rentrez (Winter, 2017), phytools (Revell, 2012), ggtree (Yu et al., 2018), ggplot2 (Wickham, 2016) and ggpubr (Kassambara, 2019) were used.

In addition, the shiny app runBoard() uses shiny (Chang et al., 2021), shinydashboard (Chang and Borges Ribeiro, 2018), rhandsontable (Owen, 2018), gtools (Warnes et al., 2020) and rclipboard (Bihorel, 2021).

Run the Shiny app with docker

• No need to install R
• Install docker on your system
• Make sure you can run a docker example image, i.e. ubuntu, in the console (system terminal)

docker pull fercyto/idiogramfish

# Run the image
docker run -d -p 8080:8080 fercyto/idiogramfish

In your internet browser go to localhost:8080

# Stop the container
docker ps
docker stop {container id}

Installation

You can install idiogramFISH from CRAN with:

install.packages("idiogramFISH")

Windows users: To avoid installation of packages in OneDrive

.libPaths("D:R/lib") # for example
.libPaths()          # set or read libraries

To do that permanently: Search (magnifier) “environment variables” and set R_LIBS_USER to D:\R\lib (example)

Releases

News

CRAN archive

Download history

Need help?

Manual in Bookdown style

Documentation in Pkgdown style

Vignettes

Online:

Launch vignettes from R for the installed version:

library(idiogramFISH)
packageVersion("idiogramFISH")
browseVignettes("idiogramFISH")

Citation

To cite idiogramFISH in publications, please use:

Roa F, Telles M. idiogramFISH: Shiny app. Idiograms with Marks and Karyotype Indices. doi: 10.5281/zenodo.3579417

To write citation to file:

sink("idiogramFISH.bib")
toBibtex(citation("idiogramFISH"))
sink()

Authors

Fernando Roa
Mariana PC Telles

Working online

Each chapter has a jupyter version. A jupyter notebook seems an interactive vignette.

They are hosted in github

They can be accessed with google colab to work online.

Chapters can be accessed locally in your jupyter-lab or jupyter notebook

After installing jupyter, you can install the R kernel with:

install.packages("IRkernel")
IRkernel::installspec()

Shiny App

Attention windows users, might require the last R version to plot correctly.

library(idiogramFISH)
runBoard()

Shiny App in the cloud availability:
shinyapps.io

Basic examples

1 How to plot a karyotype:

Define your plotting window size with something like par(pin=c(10,6)), or with svg(), png(), etc. Add chromosome morphology according to Guerra (1986) or (Levan et al., 1964)

library(idiogramFISH)

data(dfOfChrSize) # chromsome data
data(dfMarkColor) # mark general data
data(dfOfMarks2)  # mark position data (inc. cen.)

dfOfMarks2[which(dfOfMarks2$markName == "5S"), ]$markSize <- 0.8 # modif. of mark size

# column Mbp not for plotting purposes
dfOfChrSize$Mbp <- (dfOfChrSize$shortArmSize + dfOfChrSize$longArmSize) * 100

opar <- par(no.readonly = TRUE)      # make a copy of current settings if you want to restore them later
# par(opar) # recover par

par(mar = rep(0, 4))

plotIdiograms(dfChrSize = dfOfChrSize,    # data.frame of chr. size
  dfMarkColor = dfMarkColor,  # d.f of mark style <- Optional
  dfMarkPos = dfOfMarks2,     # df of mark positions (includes cen. marks)

  karHeight = 5,            # kar. height
  chrWidth = 1.2,           # chr. width
  chrSpacing = 1,           # space among chr.

  morpho = "Guerra",        # chr. morpho. classif. (Guerra, Levan, both, "" ) ver. >= 1.12 only
  chrIndex = "CI",          # cen. pos. (CI, AR, both, "" ) ver. >= 1.12 only
  chrSize = TRUE,           # add chr. sizes under chr.
  chrSizeMbp = TRUE,        # add Mbp sizes under chr. (see above)

  rulerPos = 0,              # position of ruler
  ruler.tck = -0.01,         # size and orientation of ruler ticks
  rulerNumberSize = .8       # font size of rulers
  , xPosRulerTitle = 3       # ruler units (title) pos.

  , legendWidth = 1          # width of legend items
  , fixCenBorder = TRUE      # use chrColor as border color of cen. or cen. marks
  , distTextChr = 1.2        # chr. text separation

  , xlimLeftMod = 2          # xlim left param.
  , ylimBotMod = 0           # modify ylim bottom argument
  , ylimTopMod = 0           # modify ylim top argument
)

# dev.off() # close svg()

Let’s explore the data.frames for monocentrics:

dfOfChrSize

dfMarkColor

p, q and w marks can have empty columns markDistCen and markSize since v. 1.9.1 to plot whole arms (p, q) and whole chr. w.

dfOfMarks2

2 How to plot a karyotype of holocentrics:

library(idiogramFISH)

# column Mbp not for plotting purposes
dfChrSizeHolo$Mbp <- dfChrSizeHolo$chrSize * 100

# svg("testing.svg",width=14,height=8 )
par(mar = c(0, 0, 0, 0), omi = rep(0, 4))

plotIdiograms(dfChrSize  = dfChrSizeHolo, # data.frame of chr. size
  dfMarkColor = dfMarkColor,   # df of mark style
  dfMarkPos  = dfMarkPosHolo,  # df of mark positions

  addOTUName = FALSE,      # do not add OTU names
  distTextChr = 1,         # chr. name distance to chr.
  chrSize = TRUE,          # show chr. size under chr.
  chrSizeMbp = TRUE,       # show chr. size in Mbp under chr. requires Mbp column

  rulerPos = -.4,            # position of ruler
  rulerNumberPos = .9,       # position of numbers of rulers
  xPosRulerTitle = 4.5       # ruler units (title) horizon. pos.

  , xlimLeftMod = 2           # modify xlim left argument of plot
  , ylimBotMod = .2           # modify ylim bottom argument of plot
  , legendHeight = .5         # height of legend labels
  , legendWidth = 1.2         # width of legend labels
  , xModifier = 20            # separ. among chromatids
)

# dev.off() # close svg()

Let’s explore the data.frames for holocentrics:

dfChrSizeHolo

dfMarkColor

dfMarkPosHolo

3. Plotting both mono. and holo.

See vignettes for a circular version.

Merge data.frames with plyr (Wickham, 2011)

# chromsome data, if only 1 species, column OTU is optional
require(plyr)
dfOfChrSize$OTU   <- "Species mono"
dfChrSizeHolo$OTU <- "Species holo"

monoholoCS <- plyr::rbind.fill(dfOfChrSize, dfChrSizeHolo)

dfOfMarks2$OTU     <- "Species mono"
dfMarkPosHolo$OTU <- "Species holo"

monoholoMarks <- plyr::rbind.fill(dfOfMarks2, dfMarkPosHolo)
monoholoMarks[which(monoholoMarks$markName == "5S"), ]$markSize <- .25
monoholoMarks
   chrName markName chrRegion markSize markDistCen          OTU markPos
1        1       5S         p     0.25         0.5 Species mono      NA
2        1      45S         q     1.00         0.5 Species mono      NA
3        X      45S         p       NA          NA Species mono      NA
4        3     DAPI         q     1.00         1.0 Species mono      NA
5        1     DAPI       cen       NA          NA Species mono      NA
6        X      CMA       cen       NA          NA Species mono      NA
7        3       5S      <NA>     0.25          NA Species holo     1.0
8        3     DAPI      <NA>     0.50          NA Species holo     1.5
9        1      45S      <NA>     0.50          NA Species holo     2.0
10       2     DAPI      <NA>     0.50          NA Species holo     2.0
11       4      CMA      <NA>     0.50          NA Species holo     2.0
12       4       5S      <NA>     0.25          NA Species holo     0.5

library(idiogramFISH)

# svg("testing.svg",width=10,height=6 )
par(mar = rep(0, 4))
plotIdiograms(dfChrSize  = monoholoCS,   # data.frame of chr. size
  dfMarkColor = dfMarkColor,  # df of mark style
  dfMarkPos  = monoholoMarks, # df of mark positions, includes cen. marks

  chrSize = TRUE,            # show chr. size under chr.

  squareness = 4,            # vertices squareness
  addOTUName = TRUE,         # add OTU names
  OTUTextSize = .7,          # font size of OTU
  distTextChr = 0.7,         # separ. among chr. and text and among chr. name and indices

  karHeiSpace = 4,           # karyotype height inc. spacing
  karIndexPos = .2,          # move karyotype index

  legendHeight = 1,          # height of legend labels
  legendWidth = 1,           # width of legend labels
  fixCenBorder = TRUE,       # use chrColor as border color of cen. or cen. marks

  rulerPos = 0,              # position of ruler
  ruler.tck = -0.02,         # size and orientation of ruler ticks
  rulerNumberPos = .9,       # position of numbers of rulers
  xPosRulerTitle = 4,        # ruler units (title) pos.

  xlimLeftMod = 1,           # modify xlim left argument of plot
  xlimRightMod = 3,          # modify xlim right argument of plot
  ylimBotMod = .2            # modify ylim bottom argument of plot

  , chromatids = TRUE        # do not show separ. chromatids
  , autoCenSize = FALSE,
  centromereSize = 0.5

  # for Circular Plot, add:

  # ,useOneDot=FALSE           # two dots

  # ,circularPlot = TRUE       # circularPlot
  # ,shrinkFactor = .9         # percentage 1 = 100% of circle with chr.
  # ,circleCenter = 3          # X coordinate of circleCenter (affects legend pos.)
  # ,chrLabelSpacing = .9      # chr. names spacing

  # ,OTUsrt = 0                # angle for OTU name (or number)
  # ,OTUplacing = "number"     # Use number and legend instead of name
  # ,OTULabelSpacerx = -0.6    # modify position of OTU label, when OTUplacing="number" or "simple"
  # ,OTUlegendHeight = 1.5     # space among OTU names when in legend - OTUplacing
  # ,separFactor = 0.75        # alter separ. of kar.
)

# dev.off() # close svg()

References

Guerra M. 1986. Reviewing the chromosome nomenclature of Levan et al. Brazilian Journal of Genetics, 9(4): 741–743

Levan A, Fredga K, Sandberg AA. 1964. Nomenclature for centromeric position on chromosomes Hereditas, 52(2): 201–220. https://doi.org/10.1111/j.1601-5223.1964.tb01953.x. https://doi.org/10.1111/j.1601-5223.1964.tb01953.x

Romero-Zarco C. 1986. A new method for estimating karyotype asymmetry Taxon, 35(3): 526–530. https://doi.org/10.2307/1221906

Watanabe K, Yahara T, Denda T, Kosuge K. 1999. Chromosomal evolution in the genus Brachyscome (Asteraceae, Astereae): statistical tests regarding correlation between changes in karyotype and habit using phylogenetic information Journal of Plant Research, 112: 145–161. https://link.springer.com/article/10.1007/PL00013869

R-packages

Csárdi G. 2017. Crayon: Colored terminal output. R package version 1.3.4. https://CRAN.R-project.org/package=crayon

Kassambara A. 2019. Ggpubr: ’ggplot2’ based publication ready plots. R package version 0.2.3. https://CRAN.R-project.org/package=ggpubr

R Core Team. 2019. R: A language and environment for statistical computing R Foundation for Statistical Computing, Vienna, Austria. https://www.R-project.org/

Revell LJ. 2012. Phytools: An r package for phylogenetic comparative biology (and other things). Methods in Ecology and Evolution, 3: 217–223. https://doi.org/10.1111/j.2041-210X.2011.00169.x

Roa F, PC Telles M. 2021. idiogramFISH: Shiny app. Idiograms with marks and karyotype indices Universidade Federal de Goiás, UFG, Goiânia. R-package. version 2.0.0. https://doi.org/10.5281/zenodo.3579417. https://ferroao.gitlab.io/manualidiogramfish/

Wickham H. 2011. The split-apply-combine strategy for data analysis Journal of Statistical Software, 40(1): 1–29. https://www.jstatsoft.org/article/view/v040i01

Wickham H. 2016. ggplot2: Elegant graphics for data analysis Springer-Verlag New York. https://ggplot2.tidyverse.org

Wickham H, François R, Henry L, Müller K. 2019. Dplyr: A grammar of data manipulation. R package version 0.8.3. https://CRAN.R-project.org/package=dplyr

Wickham H, Henry L. 2020. Tidyr: Tidy messy data. R package version 1.0.2. https://CRAN.R-project.org/package=tidyr

Winter DJ. 2017. rentrez: An r package for the NCBI eUtils API The R Journal, 9: 520–526

Yu G, Lam TT-Y, Zhu H, Guan Y. 2018. Two methods for mapping and visualizing associated data on phylogeny using ggtree. Molecular Biology and Evolution, 35: 3041–3043. https://doi.org/10.1093/molbev/msy194. https://doi.org/10.1093/molbev/msy194

Shiny App

Bihorel S. 2021. Rclipboard: Shiny/r wrapper for clipboard.js. R package version 0.1.3. https://github.com/sbihorel/rclipboard/

Chang W, Borges Ribeiro B. 2018. Shinydashboard: Create dashboards with shiny. R package version 0.7.1. http://rstudio.github.io/shinydashboard/

Chang W, Cheng J, Allaire J, Sievert C, Schloerke B, Xie Y, Allen J, McPherson J, Dipert A, Borges B. 2021. Shiny: Web application framework for r. R package version 1.6.0. https://shiny.posit.co/

Owen J. 2018. Rhandsontable: Interface to the handsontable.js library. R package version 0.3.7. http://jrowen.github.io/rhandsontable/

Warnes GR, Bolker B, Lumley T. 2020. Gtools: Various r programming tools. R package version 3.8.2. https://github.com/r-gregmisc/gtools

Documentation

Barnier J. 2020. Rmdformats: HTML output formats and templates for ’rmarkdown’ documents. R package version 0.3.7. https://CRAN.R-project.org/package=rmdformats

Temple Lang D, CRAN team the. 2019. RCurl: General network (HTTP/FTP/...) Client interface for r. R package version 1.95-4.12. https://CRAN.R-project.org/package=RCurl

Wickham H, Bryan J. 2019. Usethis: Automate package and project setup. R package version 1.5.1. https://CRAN.R-project.org/package=usethis

Wickham H, Danenberg P, Eugster M. 2018. roxygen2: In-line documentation for r. R package version 6.1.1. https://CRAN.R-project.org/package=roxygen2

Wickham H, Hesselberth J. 2019. Pkgdown: Make static HTML documentation for a package. R package version 1.4.1. https://CRAN.R-project.org/package=pkgdown

Xie Y. 2015. Dynamic documents with R and knitr Chapman; Hall/CRC, Boca Raton, Florida. ISBN 978-1498716963. https://yihui.org/knitr/

Xie Y. 2016. Bookdown: Authoring books and technical documents with R markdown Chapman; Hall/CRC, Boca Raton, Florida. ISBN 978-1138700109. https://github.com/rstudio/bookdown

Xie Y, Allaire JJ, Grolemund G. 2018. R markdown: The definitive guide Chapman; Hall/CRC, Boca Raton, Florida. ISBN 9781138359338. https://bookdown.org/yihui/rmarkdown

Yu G. 2019b. Badger: Badge for r package. R package version 0.0.6. https://CRAN.R-project.org/package=badger

Yu G. 2019a. Rvcheck: R/package version check. R package version 0.1.6. https://CRAN.R-project.org/package=rvcheck

Zhu H. 2019. kableExtra: Construct complex table with ’kable’ and pipe syntax. R package version 1.1.0. https://CRAN.R-project.org/package=kableExtra