1. Heatmaps: Exploring Patterns in High-Dimensional Data

Heatmaps are a go-to visualization for representing high-dimensional datasets, such as gene expression or metabolomics data. They use color gradients to display data intensity, making patterns and clusters easily detectable.

• Applications: Gene expression analysis, pathway enrichment, methylation studies.

• Tools: Seaborn (Python), ComplexHeatmap (R), Morpheus (web-based).

Tip: Add dendrograms to visualize clustering of rows and columns for hierarchical relationships.

2. Volcano Plots: Highlighting Differential Features

Volcano plots are indispensable for identifying significantly differentially expressed genes or proteins. They plot the log2 fold change against –log10(p-value), making it easy to spot statistically significant changes.

• Applications: RNA-seq, proteomics, and metabolomics.

• Tools: ggplot2 (R), EnhancedVolcano (R), Plotly (Python).

Tip: Use color to highlight significant features and label key genes or proteins.

3. PCA Plots: Reducing Complexity with Principal Component Analysis

Principal Component Analysis (PCA) plots are used to reduce dimensionality and uncover trends or clusters in data. They provide insights into sample variability and grouping.

• Applications: Transcriptomics, metabolomics, microbiome studies.

• Tools: scikit-learn + Matplotlib (Python), prcomp (R), ClustVis (web-based).

Tip: Annotate clusters with metadata to enhance interpretability.

4. Manhattan Plots: Genome-Wide Association Studies

Manhattan plots visualize p-values across the genome, making it easy to identify significant associations in genome-wide studies. They resemble city skylines, with the highest peaks indicating loci of interest.

• Applications: GWAS, QTL mapping.

• Tools: qqman (R), Matplotlib (Python).

Tip: Use alternating colors for chromosomes and highlight significant SNPs for clarity.

5. Circular Plots (Circos): Visualizing Genomic Relationships

Circular plots are ideal for visualizing relationships across the genome, such as structural variations, gene duplications, or synteny.

• Applications: Comparative genomics, structural variation studies.

• Tools: Circos (standalone), Rcircos (R), pyCircos (Python).

Tip: Keep the plot clean and avoid overcrowding to maintain readability.

6. Sankey Diagrams: Tracking Data Flows

Sankey diagrams visualize flows or relationships between categories, often used to track changes in gene expression or pathway enrichment across conditions.

• Applications: Pathway analysis, gene set enrichment analysis.

• Tools: Plotly (Python), networkD3 (R).

Tip: Use gradients or distinct colors to highlight key transitions.

7. Network Graphs: Mapping Interactions

Network graphs represent relationships between entities, such as protein-protein interactions or gene regulatory networks. Nodes represent entities, and edges represent relationships.

• Applications: Systems biology, interactomics.

• Tools: Cytoscape (standalone), igraph (R), NetworkX (Python).

Tip: Use edge thickness or node size to represent interaction strength or centrality.

8. Violin Plots: Visualizing Data Distribution

Violin plots combine a boxplot with a density plot, showing the distribution and variability of data.

• Applications: Single-cell RNA-seq, quantitative trait analysis.

• Tools: Seaborn (Python), ggplot2 (R).

Tip: Split violins by groups for side-by-side comparisons.

9. Time-Series Plots: Monitoring Changes Over Time

Time-series plots display changes in variables across time points, useful for tracking gene expression dynamics or metabolic fluxes.

• Applications: Time-course experiments, cell cycle studies.

• Tools: Matplotlib (Python), ggplot2 (R).

Tip: Smooth the data to highlight trends while avoiding overfitting.

10. Genome Tracks: Visualizing Genomic Features

Genome tracks display multiple layers of genomic data, such as gene annotations, sequencing coverage, and epigenetic marks.

• Applications: ChIP-seq, ATAC-seq, whole-genome sequencing.

• Tools: IGV (standalone), pyGenomeTracks (Python).

Tip: Stack related tracks for direct comparisons.

11. UpSet Plots: Visualizing Set Intersections

UpSet plots are a powerful alternative to Venn diagrams for visualizing intersections between multiple datasets.

• Applications: Overlap analysis for gene sets, pathways, or variants.

• Tools: UpSetR (R), ComplexUpset (Python).

Tip: Use bar plots to represent the size of each intersection for added clarity.

12. Ridge Plots: Comparing Distributions

Ridge plots visualize the distributions of multiple datasets, stacked for easy comparison.

• Applications: Transcriptomics, single-cell RNA-seq.

• Tools: ggridges (R), Matplotlib (Python).

Tip: Use transparency and consistent scaling for better readability.

13. Chord Diagrams: Visualizing Connections Between Groups

Chord diagrams illustrate relationships between categories, such as shared genes between pathways or overlaps in regulatory elements.

• Applications: Pathway overlap, synteny, co-expression networks.

• Tools: Circlize (R), Holoviews (Python).

Tip: Use distinct colors for each group to emphasize relationships.

14. Treemaps: Hierarchical Data Representation

Treemaps visualize hierarchical data as nested rectangles, with area proportional to data size.

• Applications: Ontology enrichment, pathway analysis.

• Tools: Treemapify (R), Plotly (Python).

Tip: Use colors to represent additional variables, like significance or enrichment scores.

15. T-SNE/UMAP Plots: Dimensionality Reduction for Clustering

T-SNE and UMAP plots are great for visualizing high-dimensional data in two dimensions while preserving local or global structure.

• Applications: Single-cell transcriptomics, clustering analyses.

• Tools: scikit-learn (Python), Seurat (R).

Tip: Combine with metadata annotations for better cluster interpretation.

Bringing It All Together

The choice of visualization can significantly impact the insights gained from bioinformatics data. By selecting plots tailored to your data type and analysis goals, you can effectively communicate your findings and make your research more impactful. Whether you’re a seasoned bioinformatician or a beginner, mastering these visualizations will elevate your analyses and presentations.

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Then I thaught it migh be a problem of graphical display and I opened the terminal out of curiosity, and I found out that there is no ~/Desktop folder at all. What happened? What do I need to do?

Then I google the problem and found this is a very common problem after updates. To fix this problem, follow these steps:

You need to edit the ~/.config/user-dirs.dirs file, and make sure the contents of the file are like the following:

XDG_DESKTOP_DIR="$HOME/Desktop"
XDG_DOWNLOAD_DIR="$HOME/Downloads"
XDG_TEMPLATES_DIR="$HOME/"
XDG_PUBLICSHARE_DIR="$HOME/Share"
XDG_DOCUMENTS_DIR="$HOME/Documents"
XDG_MUSIC_DIR="$HOME/Music"
XDG_PICTURES_DIR="$HOME/Pictures"
XDG_VIDEOS_DIR="$HOME/Videos"

Then restart nautilus:

killall nautilus

or

nautilus -q

Then, open nautilus via Unity menu (press the Super key) or using the run command (Alt+F2)

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Department of Evolutionary Genetics
Max Planck Institute for Evolutionary Anthropology
Deutscher Platz 6
04103 Leipzig
Germany
Phone: +49 341 3550 500

Job:
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python -V

at the terminal. For purposes of this example we'll assume you have 3.1 installed. You'll next need to execute the following commands:

sudo apt-get install python2.6 idle-python2.6
sudo update-alternatives --install /usr/bin/python python /usr/bin/python3.1 1
sudo update-alternatives --install /usr/bin/python python /usr/bin/python2.6 10
sudo update-alternatives --config python

This last command will allow you to choose which version of python to use by default. If you have done everything above correctly, python2.6 should already be set as the default. If it is not, choose it to be the default. From now on, running python should start version 2.6.

sudo update-alternatives --remove-all python
sudo ln -s python3.1 /usr/bin/python

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The following table shows SLURM commands on the SOE cluster.

For more information, run man on the commands above. See some examples below.

1. Info about the partitions and nodes
List all the partitions available to you and the nodes therein:

sinfo

Nodes in state idle can accept new jobs.

Show a partition configuratuin, for example, SOE_main

scontrol show partition=SOE_main

Show current info about a specific node:

scontrol show node=<nodename>

You can also specify a group of nodes in the command above. For example, if your MPI job is running across soenode05,06,35,36, you can execute the command below to get the info on the nodes you are interested in:

scontrol show node=soenode[05-06,35-36]

An informative parameter in the output to look at would be CPULoad. It allows you to see how your application utilizes the CPUs on the running nodes.

2. Submit scripts
The header in a submit script specifies job name, partition (queue), time limit, memory allocation, number of nodes, number of cores, and files to collect standard output and error at run time, for example

#!/bin/bash

#SBATCH --job-name=OMP_run     # job name, "OMP_run"
#SBATCH --partition=SOE_main   # partition (queue)
#SBATCH -t 0-2:00              # time limit: (D-HH:MM) 
#SBATCH --mem=32000            # memory per node in MB 
#SBATCH --nodes=1              # number of nodes
#SBATCH --ntasks-per-node=16   # number of cores
#SBATCH --output=slurm.out     # file to collect standard output
#SBATCH --error=slurm.err      # file to collect standard errors

If the time limit is not specified in the submit script, SLURM will assign the default run time, 3 days. This means the job will be terminated by SLURM in 72 hrs. The maximum allowed run time is two weeks, 14-0:00.
If the memory limit is not requested, SLURM will assign the default 16 GB. The maximum allowed memory per node is 128 GB. To see how much RAM per node your job is using, you can run commands sacct or sstat to query MaxRSS for the job on the node - see examples below.
Depending on a type of application you need to run, the submit script may contain commands to create a temporary space on a computational node - see the discussion about using the file systems on the cluster.
Then it sets the environment specific to the application and starts the application on one or multiple nodes - see sbatch sample scripts in directory /usr/local/Samples on soemaster1.hpc.rutgers.edu.
You can submit your job to the cluster with sbatch command:

sbatch myscript.sh

3. Query job information
List all currently submitted jobs in running and pending states for a user:

squeue -u <username>

Command squeue can be run with format options to expose specific information, for example, when pending job #706 is scheduled to start running:

squeue -j 706 --format="%S"

START_TIME
2015-04-30T09:54:32

More info can be shown by placing additional format options, for example:

squeue -j 706 --format="%i %P %j %u %T %l %C %S"

JOBID PARTITION   NAME    USER STATE   TIMELIMIT  CPUS START_TIME
706   SOE_main  Par_job_3 mike PENDING 3-00:00:00 64   2015-04-30T09:54:32

To see when all the jobs, pending in the queue, are scheduled to start:

squeue --start 

List all running and completed jobs for a user

sqlog -u <username>

or

sqlog -j <JobID>

The following appreviations are used for the job states:

       CA   CANCELLED      Job was cancelled.

       CD   COMPLETED      Job completed normally.

       CG   COMPLETING     Job is in the process of completing.

       F    FAILED         Job termined abnormally.

       NF   NODE_FAIL      Job terminated due to node failure.

       PD   PENDING        Job is pending allocation.

       R    RUNNING        Job currently has an allocation.

       S    SUSPENDED      Job is suspended.

       TO   TIMEOUT        Job terminated upon reaching its time limit.

You can specify the fields you would like to see in the output of sqlog:

sqlog --format=list

The command below, for example, provides Job ID, user name, exit state, start date-time, and end date-time for job #2831:

sqlog -j 2831 --format=jid,user,state,start,end

List status info for a currently running job:

sstat -j <jobid>

A formatted output can be used to gain only a specific info, for example, the maximum resident RAM usage on a node:

sstat --format="JobID,MaxRSS" -j <jobid>

To get statistics on completed jobs by jobID:

sacct --format="JobID,JobName,MaxRSS,Elapsed" -j <jobid>

To view the same information for all jobs of a user:

sacct --format="JobID,JobName,MaxRSS,Elapsed" -u <username>

To print a list of fields that can be specified with the --format option:

sacct --helpformat

For example, to get Job ID, Job name, Exit state, start date-time, and end date-time for job #2831:

sacct -j 2831 --format="JobID,JobName,State,Start,End"

Another useful command to gain information about a running job is scontrol:

scontrol show job=<jobid>

4. Cancel a job
To cancel one job:

scancel <jobid>

To cancel one job and delete the TMP directory created by the submit script on a node:

sdel <jobid>

To cancel all the jobs for a user:

scancel -u <username>

To cancel one or more jobs by name:

scancel --name <myJobName>