research-reflections

week10.md

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+https://onlinelibrary.wiley.com/doi/full/10.1111/cgf.13610#cgf13610-bib-0143
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+For our final project, and my research project, I am interested in creating an interactive network viasualization. Specifically, I would like to create some sort of visualization for WGCNA analysis. I am still trying to figure out what that will look like so I started researching network visualizations and found this network visualization review paper that expalins what considerations and applications are useful when creating and analyzing a network visualization. 
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+Table 2 provided a very useful comparison of existing network visualization tools and what exactly they did in clear and concise manner. The way that they used smaller visualizations above Table 2 to describe the columns in Table 2 were very helpful in explaining the difference between tasks.

week11.md

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+https://bmcbioinformatics.biomedcentral.com/articles/10.1186/s12859-021-04556-z#:~:text=ChromoMap%20is%20an%20efficient%20and,with%20an%20available%20genome%20assembly.
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+This paper is an R package describing different visualizations for chromomsal engineering and features of interest. My lab uses this to visualize edited genomes with engineered sequences in a clear way. This package provides a lot more functionality than we use and I was intrested in expanding our usage or build a similar tool for a pipeline we are using.

week12.md

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+Paper: GraphOmics: an interactive platform to explore and integrate multi-omics data
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+This paper provides a tool for multi-omics data exploration. It gives researchers the ability to conduct complex bioinformatic analysis in a web-browser. This is a very cool tool with good final visualizations that are often only available through coding that is now being accessbile for any researcher with internet access.
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+However, it does not provide as much customization as I would like, but it is still a very nice tool that can be helpful for a lot of applications.

week13.md

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+Paper: https://bmcbioinformatics.biomedcentral.com/articles/10.1186/1471-2105-9-559
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+Weighted Gene Co-expression Network Analysis is a way to cluster RNA-seq analysis data by trends across conditions providing novel insights into metabolic pathways. This is the dataset we will be using to perform our visualization and I wanted to use their visualizations as comparisons and figure out ways to improve upon them.
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+Most of their figures are difficult to gain any biological insight due to the static and dense nature of them. By adding interactivity to our charts, these large figures can be parsed and searched to gain understanding. 

week14.md

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+Paper: https://support.sas.com/resources/papers/proceedings17/SAS0312-2017.pdf
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+Heatmaps can display numerical and spatial data in one chart. This much data is often hard to clearly show, and is missed in common charts such as a scatter plots or tables. 
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+In big datasets, heatmaps can be easier to interpret patterns and relationships because to shows the frequency of observations in each cell. Heatmaps are really good for correlation data because you can scale the colors [0,1] with a white gradient in between.
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+This paper provided many examples using various tools to make heatmaps with emphasis on different features. I appreciate that this paper provided code along side images to clearly show what was being discussed.

week15.md

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+Paper: https://dl.acm.org/doi/pdf/10.1145/1463160.1463206
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+This paper discusses the problem of visualizing large webpages. They can be difficult to navigate and there is no way to track what movements have already been done. They introduce a tool that helps with these problems. 
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+They basically created an index and sidebar that tracks time spent at each section of the webpage and visualizes that with a heatmap. 
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+I am interested in using this tool to help navigate our heatmaps as it is multiple "pages" long and cannot be viewed all in one screen. While I do not think this is exactly what I wanted, it is helpful to read through their considerations and concerns when building this tool.