AngelaCamille Aguila-Toral

Interested in host-microbiome interaction and Pathobiome! Dreamer - living a life with calculated risk. #WomeninSTEM

[AngelaCamille Aguila-Toral @GELAaace]

Feeling helpless with this bed rest situation. 😭 Feeling guilty not to contribute to work among many other things. Please Lord wag na sana maulit yun. 🥺

[Pathogens MDPI @Pathogens_MDPI]

📢 Call for Reading
👉 Title: Surveillance of #Adenovirus and #Norovirus Contaminants in the Water and #Shellfish of Major #Oyster Breeding Farms and Fishing Ports in Taiwan
🔗 Link: https://t.co/2XCrW2UkYa
🎯 Article Views: 3605; Citations: 6

[Tominaga K. (tomiken) @pacyc1841]

review on comparative analysis of marine and freshwater fish gut microbiomes: insights into environmental impact on gut microbiota | FEMS Microbiology Ecology | Oxford Academic https://t.co/dOxALuXupG

[Joachim Schork @JoachimSchork]

Looking to visualize set intersections in R? ggupset, an extension of ggplot2, provides a powerful and intuitive way to create UpSet plots, making it easy to uncover patterns and relationships across overlapping sets.

✔️ Effortlessly generate clear and insightful UpSet plots.
✔️ Customize layouts, colors, and annotations for clarity and precision.
✔️ Efficiently handle both small and large data sets.
✔️ Seamlessly integrate with ggplot2’s syntax for a consistent workflow.

Whether you're analyzing gene sets, survey responses, or customer segments, ggupset transforms complex intersections into visually compelling and easy-to-interpret plots.

This visualization is sourced from the package website: https://t.co/1smcH6fUYn

Learn more about ggplot2 and its extensions in my online course "Data Visualization in R Using ggplot2 & Friends." More info: https://t.co/ztlEzoEDWv

#DataVisualization #ggplot2 #RStudio #Data #VisualAnalytics #RStats

[Stephen Turner 🦋 @stephenturner.us @strnr]

Review: Pangenome graphs and their applications in biodiversity genomics https://t.co/IDh0o1YAaP (read free: https://t.co/k9t3NG3BQs) 🧬🖥️🧪

[AngelaCamille Aguila-Toral @GELAaace]

@skribble012 Saan kaa mhiee

[AngelaCamille Aguila-Toral @GELAaace]

@dxmendiola HAHAHA

[Applied Epi @appliedepi]

🎄Day 20: Ggplot efficiency: part 2 🎄

Visualizing the same variables in different ways (e.g. sex by time/place)?

💡Tip: Easily standardize colour palettes by defining them once at the start of your code. Then reuse the palettes within scale_fill/color_manual() across plots

[AngelaCamille Aguila-Toral @GELAaace]

@snorcheling Nilibre kasi kami kahapon 😉 ehe UwU

[Robert Aboukhalil @RobAboukhalil]

🎁 New RNA-Seq tutorial! Learn to run differential expression analysis with DESeq2, and create your own bioinformatics projects using public data and the Expression Atlas.

Link in the next tweet because Twitter hates links.

[AngelaCamille Aguila-Toral @GELAaace]

@skribble012 HAHAHAHHA GHORL

[🧬Jacob L Steenwyk @jlsteenwyk]

NEW preprint!🥳 Orthogroups are a prerequisite for comparative genomics and Tree of Life inquiries

Introducing #OrthoHMM, software that improves orthogroup inference, which may refine our understanding of genome evolution and the Tree of Life

🔗 https://t.co/pd3NQFRe5X

[rcsb pdb 💉🧬💻🔬💊🌱🧠🦠 @buildmodels]

Watch the Webinar: Python Scripting for Molecular Docki
This virtual course introduces life scientists to the power and flexibility of python scripting
https://t.co/2ndCAXL0po

[AngelaCamille Aguila-Toral @GELAaace]

@snorcheling Hala kumain kami kahapon di ko napansin. Kklk

[AngelaCamille Aguila-Toral @GELAaace]

@skribble012

[Evolutionary Genomics @evol_genomics]

Phylogenetic Analysis of 590 Species Reveals Distinct Evolutionary Patterns of Intron-Exon Gene Structures Across Eukaryotic Lineages https://t.co/KNoz4uRArU @MolBioEvol

[SmallThingsLab @SmallThingsLab]

🚨 PhD Opportunity Alert! 🚨
Passionate about virus-protist interactions?
Join us to explore Giant Viruses & Polintons ! 🧬 #PhDPosition #Microbiology #Virology #PhDlife
Deadline: 31 March 2025
https://t.co/VfipMMwgLl
@paul_bulzu @SmallThingsLab

[Martin A. Nuñez @Martin_A_Nunez]

One Health Needs Ecology!!
“There needs to be more emphasis on the ecological component of One Health as we consider the problems of biodiversity loss, climate extremes, habitat fragmentation, and emerging disease”
https://t.co/DRgY95kbqO

[AngelaCamille Aguila-Toral @GELAaace]

@snorcheling Girl same feels kami ni Belay HAHAHAH

[Joachim Schork @JoachimSchork]

Curious about how Principal Component Analysis (PCA) can streamline your data before diving into K-means clustering? Let's explore!

🔍 PCA Before K-means Clustering:

1️⃣ Data Simplification: PCA condenses complex data by capturing its essential information into fewer dimensions, making it more manageable for clustering.
2️⃣ Dimension Reduction: It reduces the number of features while retaining most of the variance, making subsequent clustering more efficient.
3️⃣ Enhanced Interpretation: PCA transforms variables into uncorrelated components, aiding in identifying meaningful patterns and relationships between data points.
4️⃣ Optimal Clustering: By reducing noise and redundant information, PCA helps K-means clustering focus on relevant features, leading to more accurate cluster assignments.
5️⃣ Improved Performance: With fewer dimensions, K-means operates faster and more effectively, especially on large datasets.

By leveraging PCA before K-means clustering, you can:

✅ Enhance Clustering Results: Identify clearer and more distinct clusters within your data.
✅ Boost Efficiency: Speed up the clustering process and conserve computational resources.
✅ Facilitate Interpretation: Simplify understanding by visualizing data in reduced dimensions.
✅ Optimize Resource Allocation: Allocate resources more effectively by concentrating on the most influential features.

Would you like to learn more about PCA? Explore our Statistics Globe online course on how to apply PCA using the R programming language.

More info: https://t.co/DUfoAHuxxD

#datastructure #datascienceeducation #database #DataScience