13

Converting your data.frame to a matrix (and then removing the data.frame) will often free up enough memory that you won't run into this. Note that a matrix is more memory efficient than a data.frame and you're requiring Rtsne() to hold both in memory at the same time (many math-centric functions will end up converting things to a matrix at some point for ...


13

This plot is clearly done using core R functions. There are smoother alternatives how to make a pretty volcano plot (like ggplot with example here), but if you really wish to, here is my attempt to reproduce it : I obviously had to generate data since I do not have the expression data from the figure, but the procedure will be about the same with the real ...


6

You've apparently colored your alignments by read strand. In this case, red indicates "+ (watson) strand" and blue indicates "- (strand) strand". This strand association is determined by the orientation of read #1 in a pair (or just "the read" if you have single-end data). This isn't actually documented in the user manual, but you can find one of the Broad ...


6

I would also recommend two very recent Hi-C visualization frameworks (with some public data available in both): HiGlass and JuiceBox.


6

Found a solution, using D-Genies, worked great. Some examples from their website: Thanks to @user172818.


6

This kind of visualization showing the banding patterns of chromosomes are called "chromosome ideograms". You can use for example IdeoViz in R to generate them.


5

I would strongly discourage you from making discontinuous axis, it's going to be very confusing for a reader. The facet plot you proposed seems like a good solution to me. Alternatively you can use log transformation. To demonstrate I made it on simulated data that look appox like yours : set.seed(940401) data.plot <- data.frame(Methyl_Average = c(0....


5

DNASTAR's software is for purchase, but high quality. GenVision Pro does genomic visualization, including Sashimi plots. Edit: not sure why this answer is being downvoted, unless it's because the software isn't free. OP has tried IGV and SeqMonk, I mentioned an alternative he might not have heard of. Here is a video demonstrating the use of Sashimi plots ...


5

I think in R, Gviz is the best choice. I have used it for human sequences but it will probably also be suitable for Prokaryotes. Another option is MS powerpoint, if you only need the arrows...


5

Could it be that these are reads with their mates found on other chromosomes? That's the part of the IGV documentation which seems to make sense in this context: for paired end reads that are coded by the chromosome on which their mates can be found


5

To get around limitations in using Venn diagrams for set overlaps, when there are more than three sets, back around 2013 or so I created something I called an Eulergrid plot (example at the bottom of the page), which an UpsetR plot appears to recapitulate, today. The Eulergrid code I wrote was in a mix of Perl and R; the UpsetR plot code uses R. There ...


5

Sequenticon is a Python library for generating identicons for DNA sequences. For instance, the sequence ATGGTGCA gets converted into the following icon: A web interface is also provided at EGF CUBA (Collection of Useful Biological Apps): Render Sequenticons Disclaimer: I'm the current maintainer of Sequenticon


4

The best solution I could find was to use the \shaderegion function on all the sequences so it ended up looking something like this: \shaderegion{1}{CG}{Red}{OrangeRed} \shaderegion{2}{CG}{Red}{OrangeRed} ... Then I cast the rest of shading similarity to grey so the CG pairs were more easily distinguishable. \shadingcolors{grays} Two examples one with \...


4

You probably want to include query start (qstart) and query end (qend) in your blast output. Something like this: blastn -query your.fasta -out blast.out.txt -db your.db -outfmt '6 qseqid sseqid qstart qend length evalue' In R you can take the "qstart:qend" from each line for density plot. There are many ways in R to plot the densities of these start and ...


4

There's no equivalent to the wiggle header in bigWig (or bigBed) files, which is why UCSC uses the file name. This is actually the reason for the track line stuff that you linked to, since you can then specify a name and just point to where the bigWig (or other format) file is on the internet. BTW, you can certainly convert your bigWig to wiggle, add the ...


4

You can easily color 3D pca plots in R based on the code given below: library("scatterplot3d") colors <- c("#999999", "#E69F00", "#56B4E9") # Number of color according to the number of groups colors <- colors[as.numeric(iris$Species)] # you can put here the column containing the name of population or sample etc. pca1 <- prcomp(iris[, -5]) s3d <-...


4

Here is a solution using the pheatmap library to cluster and visualise the correlation matrix, then extract the groups from the cluster dendrograms: gen <- read.csv(file = "TF_NORMAL_NEW_40.txt", header = TRUE, row.names = 1, sep = ",") gendat <- t(gen) library(corrplot) library(Hmisc) macolor = colorRampPalette(c("navyblue", "white", "red"))(100) ...


4

An easy way to visualize this is to make a tab-delimited table that contains edge information for both species and both networks, color the edges depending on the species, and make the thickness and transparency dependent on the interaction strength. For example, say you have a list of orthologs for the two species and just assign them a number or a name. ...


4

I would ignore peak calling for this and instead compute enrichment of ChIP/input for the genome (e.g., with deepTools or presumably homer) and then plot it for the genes of interest individually (e.g., using IGV or pyGenomeTracks) or as a group (e.g., with computeMatrix). If the peaks are obvious and you trust your peak calling then sure you can just use ...


3

Below a few lines of code that accompany BC Wang's answer. After using MergeSeurat the sample name will be added to meta data under orig.ident. this can then be used to color the tSNE either using group.by or pt.shape. The former will show colors for each sample, the latter will color each cluster and give sample id another shape. path1 <- file.path("...


3

Without a minimal reproducible example (MWE), I can't reproduce the plot but I would suggest using existing volcanoplot functions such as the limma package on Bioconductor. Here is a typical workflow for a differential expression analysis that produces a violinplot: #install package source("https://bioconductor.org/biocLite.R") biocLite("limma") #load ...


3

IGB gives the intended result without much hassle. It doesn't have the bells and whistles of IGV, but presents a clean and intuitive view of the individual reads. In IGB, each read is presented as a single, contiguous line. If you zoom in you can see the read ID for each read. For a more holistic view, IGV's Sashimi plot can be modified to exclude ...


3

The Sashmi Plot feature built into IGV. It gives a nice summary of the spliced transcripts and the coverage of each exon. For example:


3

If you have more than a dozen or even hundreds of regions that you want to compare you could check a companion tool of HiGlass called HiPiler. HiPiler lets you arrange, cluster, and pile up to thousands of genomic regions. Take a look at this 5min video introduction to see if the tool could help you to answer your questions. Disclaimer: I am the creator of ...


3

gggenes is a ggplot2 based solution and looks very appealing. Plus, it has a nice introduction with examples. library(ggplot2) library(gggenes) ggplot(example_genes, aes(xmin = start, xmax = end, y = molecule, fill = gene)) + geom_gene_arrow() + facet_wrap(~ molecule, scales = "free", ncol = 1) + scale_fill_brewer(palette = "Set3") +...


3

I ended up using Easyfig, which is made for the task and requires little tinkering around, once fed the genbank files. (Granted, being so simple, it's hard to make the image more advanced, but it serves my purposes for now). Here's an example of my image.


3

GFF2PS was developed for exactly this sort of thing. It takes a GFF file as input, and will produce a pretty PS output showing the annotations. It was used to create the poster for Celera's human genome draft [1]. Here's a very simple example of what it can do: And a more complex one from the mosquito genome paper [2] (click here for a full size, 1.5M ...


3

The adjustcolor function from grDevices can be used to generate colours with transparency. What you want is colours based on the pathway and transparency to form a colour gradient for the p-value. adjustcolor( "red", alpha.f = 0.2) For the ggplot example, the aes function has an alpha parameter. ggplot(df2, aes(x = factor(Pathway_names), y = ...


3

If one assumes that repeat elements of a given type (e.g., LINEs) don't overlap each other, then the following will work: Split your BED file by repeat element, such that you have a LINE.bed, SINE.bed, etc. Convert those to bedGraph (e.g., awk 'BEGIN{OFS="\t"}{print $1,$2,$3,"1.0"}' LINE.bed > LINE.bedGraph). Use UCSC tools to convert those bedGraph ...


3

Such a figure is called ideogram, or chromosome ideogram.


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