Science Data Visualization Methods

1/ Bioinformatics isn't just code. It’s intuition. You run the stats, but you feel when something’s wrong. That feeling is a clue. 2/ One outlier can break your whole analysis. Like this plot: the trend looks strong— Until you see 1 point pulling the line. 3/ That’s why I say: Look at your data. Print the rows. Plot the points. Stare at it like it’s hiding something. Because it often is. 4/ Correlation? An outlier can fake a perfect r = 0.9. Remove it—and your story disappears. Plot before you publish. 5/ Exploratory Data Analysis (EDA) isn’t extra. It’s survival. Boxplots. Histograms. PCA. Use them all. 6/ For genomics: Never trust variant calls blindly. Fire up IGV. Zoom into those BAM files. What looks like a somatic mutation may be a mapping mess. 7/ In ChIP-seq, visualize peaks on a genome browser. Off-target antibodies? Duplicate artifacts? Black-listed regions? They all look different in IGV. 8/ We’ve seen this before: The datasaurus. 12 datasets, same summary stats. But one’s a T-rex. https://lnkd.in/eetYBNai 9/ Or the paper where researchers missed a gorilla in the data. https://lnkd.in/eY-5DgeG They did not see it because they did not plot it 10/ Plotting isn’t just for pretty figures. It’s how you find the story. Or find the mistake before your reviewer does. 11/ Look at: distribution of read counts % mito reads fragment length batch effects in PCA sample swaps in clustering 12/ My rule: If something surprises you, don’t move on. Go back. Plot it again. Chances are, it’s not a surprise—it’s a clue. 13/ EDA saves time. It saves embarrassment. And it makes you a better scientist, not just a better coder. 14/ Key takeaways: Trust your gut when something feels off Visualize before and after every major step Use IGV, PCA, boxplots, histograms Don’t assume—check 15/ Bioinformatics isn’t clean. It’s messy, human, flawed. But when you see the data, you see the truth. That’s where real insight begins. I hope you've found this post helpful. Follow me for more. Subscribe to my FREE newsletter chatomics to learn bioinformatics https://lnkd.in/erw83Svn

#Free RNA-Seq #Tutorial: Why Your DESeq2 Results Are Wrong (And How to Fix It in 30 Minutes) — Here's an uncomfortable truth: most published RNA-seq studies skip the one step that catches their biggest errors. That step is Exploratory Data Analysis (EDA). And every reproducibility crisis I've seen in transcriptomics traces back to it. 🧬 Session 06: Exploratory Data Analysis Before DESeq2 — Now Live (Free) If you've ever: 🔹 Found a beautiful DE gene list that wouldn't replicate 🔹 Discovered a sample swap after publication 🔹 Wondered why your batch effect "disappeared" with normalization 🔹 Wasted weeks chasing artifacts that EDA would have caught in 30 minutes This tutorial is for you. What you'll learn (free, no signup, no paywall): ✅ VST vs rlog transformations — when to use each (and why neither goes into DESeq2) ✅ PCA interpretation that actually makes sense — what PC1 and PC2 really tell you, with real brain RNA-seq examples ✅ Detecting batch effects before they ruin your analysis — the visual signatures every bioinformatician should recognize ✅ Sample distance matrices and hierarchical clustering — catch mislabelled samples and swaps in 5 minutes ✅ The pre-DESeq2 checklist — 10 verification steps before running a single contrast ✅ Brain-specific EDA pitfalls — why sex often dominates PC1, how cell-type shifts mimic batch effects, and when an "outlier" is actually a rare diagnosis (C9orf72, Lewy body co-pathology) The 30-minute rule: Every unexpected finding in your final paper should have been visible in your EDA. If it wasn't, your EDA was incomplete. Real examples covered: ▸ How a sex effect on PC1 looks vs a disease effect ▸ Why PsychENCODE samples cluster by brain bank before disease ▸ The PCA pattern that means "you have a sample swap" ▸ When to exclude a sample vs when to include it as a covariate 🔗 Free access: https://lnkd.in/eyYf3hFU 👇 What's the worst EDA mistake you've ever caught (or missed)? Drop it in the comments — the best stories will be featured in Session 07. Save this post for your next RNA-seq analysis. 📌 #RNASeq #DESeq2 #Bioinformatics #ExploratoryDataAnalysis #PCA #ComputationalBiology #Transcriptomics #DataScience #Genomics #BatchEffects #ReproducibleResearch #OpenScience #FreeTutorial #LearnBioinformatics #PhDLife #PostdocLife #Neurogenomics #PrecisionMedicine #MolecularBiology #SystemsBiology #Multiomics #MultiomeAcademy #Rstats #PythonForBiology #AcademicLinkedIn #DataAnalysis #Biostatistics #ScienceCommunication #LifeSciences #Biotech

Behind every complex dataset lies structure we can’t see directly. People differ in patterns, not just averages. Behaviors co-vary for reasons that aren’t obvious. Latent modeling helps uncover these hidden structures. Principal Component Analysis (PCA) takes many correlated variables and transforms them into fewer uncorrelated components that retain most of the original variance. Each component is a linear combination of the initial variables, capturing how they vary together. PCA simplifies data, reduces noise, and helps visualize multidimensional relationships. It relies on eigenvalues and eigenvectors of the correlation matrix and is data-driven; it describes structure without inferring causes. Factor Analysis (FA) goes further by assuming correlations among variables stem from hidden factors such as traits or abilities. Each observed measure reflects both common factors and unique variance. Exploratory FA searches for these latent dimensions, while Confirmatory FA tests whether a proposed model fits new data. FA accounts for measurement error and aims to reveal theoretical constructs rather than just summarize data. Estimation involves solving for factor loadings and variances through maximum likelihood or least squares and assessing how well the structure explains observed relationships. Latent Class Analysis (LCA) shifts focus from variables to people. It applies to categorical data such as survey responses or ratings and assumes the population contains unobserved subgroups defined by similar response patterns. Each person’s answers are explained by their membership in a latent class, and the model estimates both class sizes and membership probabilities. LCA reveals population heterogeneity, showing that similar averages can hide very different subgroups. Latent Profile Analysis (LPA) extends this idea to continuous data. It assumes individuals belong to profiles characterized by distinct response patterns; one group may show high scores, another moderate, another low. These profiles can be interpreted as types within a population. Like LCA, LPA is a finite mixture model estimated using algorithms such as Expectation - Maximization. Criteria like AIC, BIC, and entropy guide how many profiles best fit the data. LPA exposes structured diversity without forcing arbitrary cutoffs. Latent Dirichlet Allocation (LDA) applies the same principle to text. It models each document as a mixture of topics and each topic as a distribution of words. A document might contain several topics in varying proportions, revealing recurring themes across a corpus. LDA uses Bayesian inference through variational methods or Gibbs sampling to estimate these distributions. It supports large-scale qualitative analysis, identifying emergent ideas and linguistic patterns without manual coding. Topics are probabilistic, adapting as new data appear.

🚀 Exploratory Data Analysis with R Cheat Sheet Most people learn R syntax. Very few learn how to do proper Exploratory Data Analysis with R. This visual cheat sheet focuses on how data scientists actually explore, clean, analyze and visualize data using R and the Tidyverse. 👉 What this cheat sheet covers - Setting up Tidyverse for real projects - Understanding tidy data structure - Inspecting data using glimpse, summary and skim - Handling missing values correctly - Core dplyr verbs like filter, select, mutate, arrange and summarise - Group by and aggregation patterns - Data visualization with ggplot2 - Univariate and bivariate analysis - Formatting plots and using facets - Advanced cleaning like renaming, recoding and reshaping - Correlation checks and outlier detection - Working with dates using lubridate - String operations with stringr If you are learning Data Science with R, preparing for analytics roles, or working on real datasets, this will help you think like a data scientist. I share daily beginner friendly cheat sheets and visual explanations on Data Science, Machine Learning, R, Python, LLMs and Agentic AI. Follow me if you want to learn step by step without confusion. #EDA #ExploratoryDataAnalysis #RStats #Tidyverse #DataScience #DataAnalysis #Analytics #MachineLearning #AI #DataAnalyst #DataEngineer #TechLearning

There's this assumption in bioinformatics that good EDA means exhaustive analysis. But here's the thing: the best exploratory data analysis isn't about doing more. It's about explaining less. The 3-slide test changes everything. Frame it as a 3-slide talk to a non-bioinformatician: Slide 1: What's in the dataset (samples, variables, source, structure) Slide 2: What patterns you see (clusters, gaps, batch effects, outliers) Slide 3: What actions to take (next steps, hypotheses, design flaws) For instance, when analyzing multi-omics data: Slide 1: "80 ovarian cancer samples, metastatic vs non-metastatic, with RNA-seq and DNA methylation data" (not technical pipeline details) Slide 2: "Found 1,200 differentially expressed genes, but only 180 overlap with methylation changes" (not exhaustive gene lists) Slide 3: "Focus on those 180 overlapping genes for biomarker validation" (not complex integration methods) This constraint forces you to simplify, clarify, and prioritize......fast. It cuts through analysis paralysis and gets straight to what matters. Because if you can't explain what you're seeing, you probably don't understand it yet. Try this on your next dataset. What story emerges when you strip away the complexity? #Bioinformatics #DataExploration #ScientificThinking #EDA #DataVisualization #CommunicationInScience #Omics #DataStorytelling #PrecisionMedicine #ComputationalBiology #ResearchTools

🔍 Exploratory Data Analysis (EDA): The Foundation of Every Data Project Understanding the story behind the numbers is crucial when preparing models or crafting impactful dashboards. Here's a breakdown of my typical EDA workflow: 1️⃣ Understand the Data – Load datasets, check types, shapes, and basic stats. 2️⃣ Clean the Data – Address missing values, eliminate duplicates, and rectify data types. 3️⃣ Univariate Analysis – Examine individual variables using histograms and box plots. 4️⃣ Bivariate/Multivariate Analysis – Explore relationships through scatter plots and correlation matrices. 5️⃣ Outlier Detection – Identify anomalies using visual and statistical techniques. 6️⃣ Feature Engineering – Introduce new variables to reveal hidden patterns. 7️⃣ Documentation – Summarize insights and prepare data for modeling or reporting. I often leverage Python (pandas, seaborn, matplotlib) or Power BI for visualization and exploration. Remember, rushing through EDA can lead to missed discoveries—take your time with this phase! Interested to learn about different approaches to EDA. What tools or techniques do you find most effective in your data projects? #EDA #DataAnalytics #Python #PowerBI #SQL #DataScience #ExploratoryDataAnalysis #DataVisualization #OpenToWork