Generated by All in One SEO v4.9.4.1, this is an llms.txt file, used by LLMs to index the site.

# biostatsquid.com

Easy bioinformatics and biostatistics

## Sitemaps

- [XML Sitemap](https://biostatsquid.com/sitemap.xml): Contains all public & indexable URLs for this website.

## Posts

- [Time series analysis: easy explanation with examples](https://biostatsquid.com/time-series-analysis/) - [et_pb_section fb_built=”1″ _builder_version=”4.27.4″ _module_preset=”default” background_color=”#FFFFFF” custom_margin=”35px|||||” custom_padding=”0px||0px|||” border_radii=”on|10px|10px|10px|10px” border_width_all=”7px” border_color_all=”#16C5E0″ global_colors_info=”{}” theme_builder_area=”post_content”][et_pb_row _builder_version=”4.21.0″ _module_preset=”default” custom_margin=”-7px|auto||auto||” custom_padding=”||27px|||” global_colors_info=”{}” theme_builder_area=”post_content”][et_pb_column type=”4_4″ _builder_version=”4.18.1″ _module_preset=”default” global_colors_info=”{}” theme_builder_area=”post_content”][et_pb_text _builder_version=”4.27.4″ _module_preset=”default” text_font_size=”25px” global_colors_info=”{}” theme_builder_area=”post_content”]Introduction to time series forecasting[/et_pb_text][/et_pb_column][/et_pb_row][/et_pb_section][et_pb_section fb_built=”1″ _builder_version=”4.18.1″ _module_preset=”default” custom_padding=”0px||3px|||” global_colors_info=”{}” theme_builder_area=”post_content”][et_pb_row _builder_version=”4.18.1″ _module_preset=”default” custom_padding=”30px|||||” global_colors_info=”{}” theme_builder_area=”post_content”][et_pb_column type=”4_4″ _builder_version=”4.18.1″ _module_preset=”default” global_colors_info=”{}” theme_builder_area=”post_content”][et_pb_text _builder_version=”4.27.4″ _module_preset=”default” text_line_height=”1.8em” width=”98.6%” global_colors_info=”{}” theme_builder_area=”post_content”]From predicting
- [Introduction to tidymodels (logistic regression in R)](https://biostatsquid.com/tidymodels-tutorial-logistic-regression-r/)
- [Diversity metrics simply explained: Shannon, Simpson, Chao1](https://biostatsquid.com/alpha-diversity-metrics/) - [et_pb_section fb_built=”1″ _builder_version=”4.27.4″ _module_preset=”default” background_color=”#FFFFFF” custom_margin=”35px|||||” custom_padding=”0px||0px|||” border_radii=”on|10px|10px|10px|10px” border_width_all=”7px” border_color_all=”#16C5E0″ global_colors_info=”{}”][et_pb_row _builder_version=”4.21.0″ _module_preset=”default” custom_margin=”-7px|auto||auto||” custom_padding=”||27px|||” global_colors_info=”{}”][et_pb_column type=”4_4″ _builder_version=”4.18.1″ _module_preset=”default” global_colors_info=”{}”][et_pb_text _builder_version=”4.27.4″ _module_preset=”default” text_font_size=”25px” global_colors_info=”{}”] Exploring alpha diversity indices and how to interpret them: Shannon, Simpson, Gini, Chao1 and more! [/et_pb_text][/et_pb_column][/et_pb_row][/et_pb_section][et_pb_section fb_built=”1″ _builder_version=”4.18.1″ _module_preset=”default” custom_padding=”0px||3px|||” global_colors_info=”{}”][et_pb_row _builder_version=”4.18.1″ _module_preset=”default” custom_padding=”30px|||||” global_colors_info=”{}”][et_pb_column type=”4_4″ _builder_version=”4.18.1″ _module_preset=”default” global_colors_info=”{}”][et_pb_text _builder_version=”4.27.4″ _module_preset=”default”
- [Kaplan-Meier curve - easily explained!](https://biostatsquid.com/kaplan-meier-curve/) - Simple explanation of survival analysis curves and how to interpret them. We will go through the main concepts of the Kaplan-Meier curve, easily explained!
- [Learning to code as a biologist: top programming resources](https://biostatsquid.com/top-coding-resources-bioinformatics/) - Top coding resources and tools for beginner, intermediate and advanced levels. Webpages, courses and online tutorials to learn how to code in R and Python.
- [Types of Cross-Validation in Machine Learning](https://biostatsquid.com/cross-validation-in-machine-learning/) - [et_pb_section fb_built=”1″ _builder_version=”4.27.4″ _module_preset=”default” background_color=”#FFFFFF” custom_margin=”35px|||||” custom_padding=”0px||0px|||” border_radii=”on|10px|10px|10px|10px” border_width_all=”7px” border_color_all=”#16C5E0″ global_colors_info=”{}”][et_pb_row _builder_version=”4.21.0″ _module_preset=”default” custom_margin=”-7px|auto||auto||” custom_padding=”||27px|||” global_colors_info=”{}”][et_pb_column type=”4_4″ _builder_version=”4.18.1″ _module_preset=”default” global_colors_info=”{}”][et_pb_text _builder_version=”4.27.4″ _module_preset=”default” text_font_size=”25px” global_colors_info=”{}”] From a simple train-test split to stratified nested cross-validation! [/et_pb_text][/et_pb_column][/et_pb_row][/et_pb_section][et_pb_section fb_built=”1″ _builder_version=”4.18.1″ _module_preset=”default” custom_padding=”0px||3px|||” global_colors_info=”{}”][et_pb_row _builder_version=”4.18.1″ _module_preset=”default” custom_padding=”30px|||||” global_colors_info=”{}”][et_pb_column type=”4_4″ _builder_version=”4.18.1″ _module_preset=”default” global_colors_info=”{}”][et_pb_text _builder_version=”4.27.4″ _module_preset=”default” text_line_height=”1.8em” width=”98.6%” global_colors_info=”{}”] Imagine studying for
- [Comparing Gene Expression Across Integrated Single-Cell Datasets: NormalizeData(), SCTransform()](https://biostatsquid.com/comparing-gene-expression-across-integrated-single-cell-datasets-normalizedata-sctransform/) - [et_pb_section fb_built=”1″ _builder_version=”4.18.1″ _module_preset=”default” custom_padding=”0px||3px|||” global_colors_info=”{}”][et_pb_row _builder_version=”4.18.1″ _module_preset=”default” global_colors_info=”{}”][et_pb_column type=”4_4″ _builder_version=”4.18.1″ _module_preset=”default” global_colors_info=”{}”][et_pb_text _builder_version=”4.27.4″ _module_preset=”default” text_line_height=”1.8em” global_colors_info=”{}”]One of the most common questions in single-cell RNA sequencing analysis is deceptively straightforward: how many cells express a given gene, and are the expression levels truly comparable across datasets? If you have ever stared at your results wondering
- [How to interpret MA plots](https://biostatsquid.com/how-to-interpret-ma-plots/)
- [Beta diversity: Jaccard, Bray-Curtis, NMDS, PCoA and PERMANOVA](https://biostatsquid.com/beta-diversity/) - [et_pb_section fb_built=”1″ _builder_version=”4.27.4″ _module_preset=”default” background_color=”#FFFFFF” custom_margin=”35px|||||” custom_padding=”0px||0px|||” border_radii=”on|10px|10px|10px|10px” border_width_all=”7px” border_color_all=”#16C5E0″ global_colors_info=”{}”][et_pb_row _builder_version=”4.21.0″ _module_preset=”default” custom_margin=”-7px|auto||auto||” custom_padding=”||27px|||” global_colors_info=”{}”][et_pb_column type=”4_4″ _builder_version=”4.18.1″ _module_preset=”default” global_colors_info=”{}”][et_pb_text _builder_version=”4.27.4″ _module_preset=”default” text_font_size=”25px” global_colors_info=”{}”] How different are communities from each other? Beta diversity easily explained! [/et_pb_text][/et_pb_column][/et_pb_row][/et_pb_section][et_pb_section fb_built=”1″ _builder_version=”4.18.1″ _module_preset=”default” custom_padding=”0px||3px|||” global_colors_info=”{}”][et_pb_row _builder_version=”4.18.1″ _module_preset=”default” custom_padding=”30px|||||” global_colors_info=”{}”][et_pb_column type=”4_4″ _builder_version=”4.18.1″ _module_preset=”default” global_colors_info=”{}”][et_pb_text _builder_version=”4.27.4″ _module_preset=”default” text_line_height=”1.8em” width=”98.6%” global_colors_info=”{}”] If
- [Hill numbers and diversity profiles simply explained](https://biostatsquid.com/hill-numbers/) - [et_pb_section fb_built=”1″ _builder_version=”4.27.4″ _module_preset=”default” background_color=”#FFFFFF” custom_margin=”35px|||||” custom_padding=”0px||0px|||” border_radii=”on|10px|10px|10px|10px” border_width_all=”7px” border_color_all=”#16C5E0″ global_colors_info=”{}”][et_pb_row _builder_version=”4.21.0″ _module_preset=”default” custom_margin=”-7px|auto||auto||” custom_padding=”||27px|||” global_colors_info=”{}”][et_pb_column type=”4_4″ _builder_version=”4.18.1″ _module_preset=”default” global_colors_info=”{}”][et_pb_text _builder_version=”4.27.4″ _module_preset=”default” text_font_size=”25px” global_colors_info=”{}”] Exploring Hill numbers to compare the diversity across communities [/et_pb_text][/et_pb_column][/et_pb_row][/et_pb_section][et_pb_section fb_built=”1″ _builder_version=”4.18.1″ _module_preset=”default” custom_padding=”0px||3px|||” global_colors_info=”{}”][et_pb_row _builder_version=”4.18.1″ _module_preset=”default” custom_padding=”30px|||||” global_colors_info=”{}”][et_pb_column type=”4_4″ _builder_version=”4.18.1″ _module_preset=”default” global_colors_info=”{}”][et_pb_text _builder_version=”4.27.4″ _module_preset=”default” text_line_height=”1.8em” width=”98.6%” global_colors_info=”{}”] Whether you are
- [Top ML and AI resources for bioinformaticians](https://biostatsquid.com/top-ml-and-ai-bioinformatics/) - Top ML and AI resources for bioinformatics Top ML and AI resources for bioinformatics In this blogpost, I share some of the top ML and AI resources for bioinformatics. If you are keen to learn about machine learning and AI, this is the post for you! So if you are ready… let’s dive in! Here
- [Top resources for biostatistics and ML](https://biostatsquid.com/top-resources-biostatistics-ml/) - In this post you will find some of my top recommended resources to learn statistics. Check them out!
- [Fun, interactive and quirky bioinformatics tools and webpages](https://biostatsquid.com/fun-interactive-and-quirky-bioinformatics-tools-and-webpages/) - Fun, interactive and quirky bioinformatics tools and webpages Fun, interactive and quirky bioinformatics tools and webpages In this blogpost, I share some fun, interactive and quirky bioinformatics tools and webpages I’ve come across through the years. So if you are ready… let’s dive in! Because learning bioinformatics doesn’t have to be boring! Here are some
- [Easy Gene Set Enrichment Analysis in R with fgsea()](https://biostatsquid.com/fgsea-tutorial-gsea/) - In this step by step tutorial, you will learn how to perform easy gene set enrichment analysis in R with fgsea() package.
- [Which is the best scRNAseq integration method?](https://biostatsquid.com/compare-scrnaseq-integration-methods/) - [et_pb_section fb_built=”1″ _builder_version=”4.27.4″ _module_preset=”default” background_color=”#FFFFFF” custom_margin=”35px|||||” custom_padding=”0px||0px|||” border_radii=”on|10px|10px|10px|10px” border_width_all=”7px” border_color_all=”#16C5E0″ global_colors_info=”{}”][et_pb_row _builder_version=”4.21.0″ _module_preset=”default” custom_margin=”-7px|auto||auto||” custom_padding=”||27px|||” global_colors_info=”{}”][et_pb_column type=”4_4″ _builder_version=”4.18.1″ _module_preset=”default” global_colors_info=”{}”][et_pb_text _builder_version=”4.27.4″ _module_preset=”default” text_font_size=”25px” global_colors_info=”{}”]Comparing top integration methods for scRNAseq data[/et_pb_text][/et_pb_column][/et_pb_row][/et_pb_section][et_pb_section fb_built=”1″ _builder_version=”4.18.1″ _module_preset=”default” custom_padding=”0px||3px|||” global_colors_info=”{}”][et_pb_row _builder_version=”4.18.1″ _module_preset=”default” custom_padding=”30px|||||” global_colors_info=”{}”][et_pb_column type=”4_4″ _builder_version=”4.18.1″ _module_preset=”default” global_colors_info=”{}”][et_pb_text _builder_version=”4.27.4″ _module_preset=”default” text_line_height=”1.8em” width=”98.6%” global_colors_info=”{}”]When we want to combine multiple scRNA-seq datasets
- [Comparing multiple groups: Kruskal-Wallis test in R](https://biostatsquid.com/comparing-multiple-groups-kruskal-wallis-test-in-r/) - Easy explanation and R tutorial of the Kruskal-Wallis test to compare medians across three or more independent groups when normality assumptions aren’t met.
- [New top bioinformatic tools and resources](https://biostatsquid.com/new-top-bioinformatic-tools-and-resources/) - [et_pb_section admin_label=”section”] [et_pb_row admin_label=”row”] [et_pb_column type=”4_4″][et_pb_text admin_label=”Text”] Top bioinformatic tools and resources Top bioinformatic tools and resources In this blogpost, I share some of the top bioinformatics tools and resources which help me every day! From webpages like Venny that allow you to create quick Venn diagrams, to tools like AlphaFold, which allow you to
- [Integration methods in scRNAseq: easily explained!](https://biostatsquid.com/single-cell-integration-methods/) - [et_pb_section fb_built=”1″ _builder_version=”4.21.0″ _module_preset=”default” background_color=”#FFFFFF” custom_margin=”35px|||||” custom_padding=”0px||0px|||” border_radii=”on|10px|10px|10px|10px” border_width_all=”7px” border_color_all=”#16C5E0″ global_colors_info=”{}”][et_pb_row _builder_version=”4.21.0″ _module_preset=”default” custom_margin=”-7px|auto||auto||” custom_padding=”||27px|||” global_colors_info=”{}”][et_pb_column type=”4_4″ _builder_version=”4.18.1″ _module_preset=”default” global_colors_info=”{}”][et_pb_text _builder_version=”4.27.4″ _module_preset=”default” text_font_size=”25px” global_colors_info=”{}”] An overview of the most popular integration methods for single-cell data [/et_pb_text][/et_pb_column][/et_pb_row][/et_pb_section][et_pb_section fb_built=”1″ _builder_version=”4.18.1″ _module_preset=”default” custom_padding=”0px||3px|||” global_colors_info=”{}”][et_pb_row _builder_version=”4.18.1″ _module_preset=”default” custom_padding=”30px|||||” global_colors_info=”{}”][et_pb_column type=”4_4″ _builder_version=”4.18.1″ _module_preset=”default” global_colors_info=”{}”][et_pb_text _builder_version=”4.27.4″ _module_preset=”default” text_line_height=”1.8em” width=”98.6%” global_colors_info=”{}”] Single-cell
- [Understanding Seurat objects - simply explained!](https://biostatsquid.com/seurat-objects-explained/) - [et_pb_section fb_built=”1″ _builder_version=”4.21.0″ _module_preset=”default” background_color=”#FFFFFF” custom_margin=”35px|||||” custom_padding=”0px||0px|||” border_radii=”on|10px|10px|10px|10px” border_width_all=”7px” border_color_all=”#16C5E0″ global_colors_info=”{}”][et_pb_row _builder_version=”4.21.0″ _module_preset=”default” custom_margin=”-7px|auto||auto||” custom_padding=”||27px|||” global_colors_info=”{}”][et_pb_column type=”4_4″ _builder_version=”4.18.1″ _module_preset=”default” global_colors_info=”{}”][et_pb_text _builder_version=”4.21.0″ _module_preset=”default” text_font_size=”25px” global_colors_info=”{}”] Understanding the structure of Seurat objects version 5 – step-by-step simple explanation! [/et_pb_text][/et_pb_column][/et_pb_row][/et_pb_section][et_pb_section fb_built=”1″ _builder_version=”4.18.1″ _module_preset=”default” custom_padding=”0px||3px|||” global_colors_info=”{}”][et_pb_row _builder_version=”4.18.1″ _module_preset=”default” custom_padding=”30px|||||” global_colors_info=”{}”][et_pb_column type=”4_4″ _builder_version=”4.18.1″ _module_preset=”default” global_colors_info=”{}”][et_pb_text _builder_version=”4.21.0″ _module_preset=”default” text_line_height=”1.8em” width=”98.6%” global_colors_info=”{}”]
- [Pathway Enrichment Analysis with clusterProfiler](https://biostatsquid.com/pathway-enrichment-analysis-tutorial-clusterprofiler/) - Step by step tutorial to carry out pathway enrichment analysis with R package clusterProfiler. From differentially expressed genes to pathways!
- [Easy gene set enrichment analysis with fgsea (old version)](https://biostatsquid.com/easy-gene-set-enrichment-analysis-with-fgsea-old-version/) - [et_pb_section fb_built=”1″ _builder_version=”4.21.0″ _module_preset=”default” background_color=”#FFFFFF” custom_margin=”35px|||||” custom_padding=”0px||0px|||” border_radii=”on|10px|10px|10px|10px” border_width_all=”7px” border_color_all=”#16C5E0″ saved_tabs=”all” global_colors_info=”{}”][et_pb_row _builder_version=”4.21.0″ _module_preset=”default” custom_margin=”-7px|auto||auto||” custom_padding=”||27px|||” global_colors_info=”{}”][et_pb_column type=”4_4″ _builder_version=”4.18.1″ _module_preset=”default” global_colors_info=”{}”][et_pb_text _builder_version=”4.21.0″ _module_preset=”default” text_font_size=”25px” global_colors_info=”{}”] Hey! You’re looking at an old post. Newer version here: fgsea tutorial in R [/et_pb_text][/et_pb_column][/et_pb_row][/et_pb_section][et_pb_section fb_built=”1″ _builder_version=”4.21.0″ _module_preset=”default” background_color=”#FFFFFF” custom_margin=”35px|||||” custom_padding=”0px||0px|||” border_radii=”on|10px|10px|10px|10px” border_width_all=”7px” border_color_all=”#16C5E0″ saved_tabs=”all” global_colors_info=”{}”][et_pb_row _builder_version=”4.21.0″ _module_preset=”default” custom_margin=”-7px|auto||auto||” custom_padding=”||27px|||”
- [Pathway Enrichment Analysis with clusterProfiler (old version)](https://biostatsquid.com/pathway-enrichment-analysis-with-clusterprofiler-old-version/) - [et_pb_section fb_built=”1″ _builder_version=”4.21.0″ _module_preset=”default” background_color=”#FFFFFF” custom_margin=”35px|||||” custom_padding=”0px||0px|||” border_radii=”on|10px|10px|10px|10px” border_width_all=”7px” border_color_all=”#16C5E0″ global_colors_info=”{}”][et_pb_row _builder_version=”4.21.0″ _module_preset=”default” custom_margin=”-7px|auto||auto||” custom_padding=”||27px|||” global_colors_info=”{}”][et_pb_column type=”4_4″ _builder_version=”4.18.1″ _module_preset=”default” global_colors_info=”{}”][et_pb_text _builder_version=”4.21.0″ _module_preset=”default” text_font_size=”25px” global_colors_info=”{}”] Hey! You’re looking at an old post. Newer version here: clusterProfiler tutorial in R [/et_pb_text][/et_pb_column][/et_pb_row][/et_pb_section][et_pb_section fb_built=”1″ _builder_version=”4.21.0″ _module_preset=”default” background_color=”#16C5E0″ custom_margin=”66px|||||” custom_padding=”0px||0px|||” global_colors_info=”{}”][et_pb_row _builder_version=”4.18.1″ _module_preset=”default” global_colors_info=”{}”][et_pb_column type=”4_4″ _builder_version=”4.18.1″ _module_preset=”default” global_colors_info=”{}”][et_pb_text _builder_version=”4.21.0″ _module_preset=”default”
- [MSigDB gene sets: easy msigdbr in R](https://biostatsquid.com/easy-msigdbr-in-r/) - MSigDB gene sets and how to use msigdbr in R
- [Top resources to learn biostatistics](https://biostatsquid.com/top-resources-to-learn-biostatistics/) - Top (bio)statistics resources Top (bio)statistics resources In this blogpost, I share some of the top (bio)statistics resources which have helped me understand better stats behind research and (bio)data analysis. Statistics can be really fun, and building a strong foundation is essential for biological data analysis! These are some of my favourite books, videos and cool
- [Volcano plots in R: easy step-by-step tutorial](https://biostatsquid.com/volcano-plots-r-tutorial/) - Easy, step-by-step guide to create your own volcano plot in R.
- [MA plots with ggplot: easy R tutorial](https://biostatsquid.com/ma-plots-easy-r-tutorial/)
- [Heatmaps with ComplexHeatmap() R tutorial](https://biostatsquid.com/heatmaps-with-complexheatmap-r-tutorial/)
- [Easy DoubletFinder tutorial in R](https://biostatsquid.com/doubletfinder-tutorial/) - An easy DoubletFinder tutorial in R,with a step-by-step explanation on how to detect doublets in your single-cell RNAseq dataset.
- [ANOVA (analysis of variance) easily explained](https://biostatsquid.com/anova-analysis-of-variance-easily-explained/) - [et_pb_section fb_built=”1″ _builder_version=”4.21.0″ _module_preset=”default” background_color=”#FFFFFF” custom_margin=”35px|||||” custom_padding=”0px||0px|||” border_radii=”on|10px|10px|10px|10px” border_width_all=”7px” border_color_all=”#16C5E0″ global_colors_info=”{}” theme_builder_area=”post_content”][et_pb_row _builder_version=”4.21.0″ _module_preset=”default” custom_margin=”-7px|auto||auto||” custom_padding=”||27px|||” global_colors_info=”{}” theme_builder_area=”post_content”][et_pb_column type=”4_4″ _builder_version=”4.18.1″ _module_preset=”default” global_colors_info=”{}” theme_builder_area=”post_content”][et_pb_text _builder_version=”4.21.0″ _module_preset=”default” text_font_size=”25px” hover_enabled=”0″ global_colors_info=”{}” theme_builder_area=”post_content” sticky_enabled=”0″]How to interpret ANOVA (analysis of variance) easily explained![/et_pb_text][/et_pb_column][/et_pb_row][/et_pb_section][et_pb_section fb_built=”1″ _builder_version=”4.18.1″ _module_preset=”default” custom_padding=”0px||3px|||” global_colors_info=”{}” theme_builder_area=”post_content”][et_pb_row _builder_version=”4.18.1″ _module_preset=”default” custom_padding=”30px|||||” global_colors_info=”{}” theme_builder_area=”post_content”][et_pb_column type=”4_4″ _builder_version=”4.18.1″ _module_preset=”default” global_colors_info=”{}” theme_builder_area=”post_content”][et_pb_text _builder_version=”4.21.0″
- [Easy R tutorial: pathway enrichment analysis plots](https://biostatsquid.com/pathway-enrichment-analysis-plots/) - Follow this step-by-step easy R tutorial to visualise your results with these pathway enrichment analysis plots. From barplots to enrichment maps!
- [How to choose log2FC thresholds for DGE analysis](https://biostatsquid.com/choose-thresholds-for-dge-analysis/) - [et_pb_section fb_built=”1″ _builder_version=”4.18.1″ _module_preset=”default” custom_padding=”0px||3px|||” global_colors_info=”{}”][et_pb_row _builder_version=”4.18.1″ _module_preset=”default” global_colors_info=”{}”][et_pb_column type=”4_4″ _builder_version=”4.18.1″ _module_preset=”default” global_colors_info=”{}”][et_pb_text _builder_version=”4.21.0″ _module_preset=”default” text_line_height=”1.8em” global_colors_info=”{}”] Setting thresholds for differential gene expression (DGE) analysis is crucial and depends on several factors. In essence, for a list of genes, we are trying to define what counts as biologically meaningful versus just statistically significant. The question is…
- [Top gene expression analysis tools you should know as a bioinformatician](https://biostatsquid.com/gene-expression-analysis-tools/) - Top gene expression analysis tools you should know for your analyses, including databases, visualisation tools and more!
- [Top bioinformatic tools and resources](https://biostatsquid.com/bioinformatics-resources/) - Top bioinformatics tools and resources for all levels! Check out my updated list!
- [SCTransform - simple and intuitive explanation](https://biostatsquid.com/sctransform-simple-explanation/) - SCTransform is a normalisation method for scRNAseq data which accounts for technical factors while preserving biological variation.
- [Step-by-step heatmap tutorial with pheatmap()](https://biostatsquid.com/step-by-step-heatmap-tutorial-with-pheatmap/) - Follow this easy, step-by-step heatmap tutorial with pheatmap() to create and customize your own heatmaps in R
- [Introduction to single-cell analysis with Seurat v5](https://biostatsquid.com/single-cell-easy-tutorial-with-seurat-v5/) - Explore the power of single-cell RNA-seq analysis with Seurat v5 in this hands-on tutorial, guiding you through data preprocessing, clustering, and visualization in R.
- [Why do genes with the highest logFC not have the lowest p-value?](https://biostatsquid.com/logfc-vs-pvalue/) - [et_pb_section fb_built=”1″ _builder_version=”4.18.1″ _module_preset=”default” custom_padding=”0px||3px|||” global_colors_info=”{}”][et_pb_row _builder_version=”4.18.1″ _module_preset=”default” global_colors_info=”{}”][et_pb_column type=”4_4″ _builder_version=”4.18.1″ _module_preset=”default” global_colors_info=”{}”][et_pb_text _builder_version=”4.21.0″ _module_preset=”default” text_line_height=”1.8em” global_colors_info=”{}”]That’s a really good and very common question in differential gene expression analysis! It feels intuitive that the larger the difference in expression (log fold change, or logFC), the more significant it should be (i.e., the smaller the p-value),
- [Principal Component Analysis (PCA) simply explained](https://biostatsquid.com/pca-simply-explained/) - A simple and practical explanation of Principal Component Analysis or PCA and how to use it to interpret biological data.
- [Easy scDblFinder tutorial: doublet detection in R](https://biostatsquid.com/scdblfinder-tutorial/) - scDblFinder tutorial - detect and remove doublets with scDblFinder package in R
- [Standard scRNAseq pre-processing workflow with Seurat](https://biostatsquid.com/scrnaseq-preprocessing-workflow-seurat/) - Follow a step-by-step standard pipeline for scRNAseq pre-processing using the R package Seurat, including filtering, normalisation, scaling, PCA and more!
- [PCA vs UMAP vs t-SNE](https://biostatsquid.com/pca-umap-tsne-comparison/) - [et_pb_section fb_built=”1″ _builder_version=”4.21.0″ _module_preset=”default” background_color=”#FFFFFF” custom_margin=”35px|||||” custom_padding=”0px||0px|||” border_radii=”on|10px|10px|10px|10px” border_width_all=”7px” border_color_all=”#16C5E0″ global_colors_info=”{}”][et_pb_row _builder_version=”4.21.0″ _module_preset=”default” custom_margin=”-7px|auto||auto||” custom_padding=”||27px|||” global_colors_info=”{}”][et_pb_column type=”4_4″ _builder_version=”4.18.1″ _module_preset=”default” global_colors_info=”{}”][et_pb_text _builder_version=”4.21.0″ _module_preset=”default” text_font_size=”25px” global_colors_info=”{}”] Understanding similarities and differences between dimensionality reduction algorithms: PCA, t-SNE and UMAP [/et_pb_text][/et_pb_column][/et_pb_row][/et_pb_section][et_pb_section fb_built=”1″ _builder_version=”4.18.1″ _module_preset=”default” custom_padding=”0px||3px|||” global_colors_info=”{}”][et_pb_row _builder_version=”4.18.1″ _module_preset=”default” custom_padding=”30px|||||” global_colors_info=”{}”][et_pb_column type=”4_4″ _builder_version=”4.18.1″ _module_preset=”default” global_colors_info=”{}”][et_pb_text _builder_version=”4.21.0″ _module_preset=”default” text_line_height=”1.8em” width=”98.6%” global_colors_info=”{}”]
- [Easy UMAP - explained with an example](https://biostatsquid.com/umap-simply-explained/) - [et_pb_section fb_built=”1″ _builder_version=”4.21.0″ _module_preset=”default” background_color=”#FFFFFF” custom_margin=”35px|||||” custom_padding=”0px||0px|||” border_radii=”on|10px|10px|10px|10px” border_width_all=”7px” border_color_all=”#16C5E0″ global_colors_info=”{}”][et_pb_row _builder_version=”4.21.0″ _module_preset=”default” custom_margin=”-7px|auto||auto||” custom_padding=”||27px|||” global_colors_info=”{}”][et_pb_column type=”4_4″ _builder_version=”4.18.1″ _module_preset=”default” global_colors_info=”{}”][et_pb_text _builder_version=”4.21.0″ _module_preset=”default” text_font_size=”25px” global_colors_info=”{}”] A short but simple explanation of UMAP- easily explained with an example! [/et_pb_text][/et_pb_column][/et_pb_row][/et_pb_section][et_pb_section fb_built=”1″ _builder_version=”4.18.1″ _module_preset=”default” custom_padding=”0px||3px|||” global_colors_info=”{}”][et_pb_row _builder_version=”4.18.1″ _module_preset=”default” custom_padding=”30px|||||” global_colors_info=”{}”][et_pb_column type=”4_4″ _builder_version=”4.18.1″ _module_preset=”default” global_colors_info=”{}”][et_pb_text _builder_version=”4.21.0″ _module_preset=”default” text_line_height=”1.8em” global_colors_info=”{}”] PCA,
- [Easy t-SNE - explained with an example](https://biostatsquid.com/easy-t-sne-explained-with-an-example/) - [et_pb_section fb_built=”1″ _builder_version=”4.21.0″ _module_preset=”default” background_color=”#FFFFFF” custom_margin=”35px|||||” custom_padding=”0px||0px|||” border_radii=”on|10px|10px|10px|10px” border_width_all=”7px” border_color_all=”#16C5E0″ global_colors_info=”{}”][et_pb_row _builder_version=”4.21.0″ _module_preset=”default” custom_margin=”-7px|auto||auto||” custom_padding=”||27px|||” global_colors_info=”{}”][et_pb_column type=”4_4″ _builder_version=”4.18.1″ _module_preset=”default” global_colors_info=”{}”][et_pb_text _builder_version=”4.21.0″ _module_preset=”default” text_font_size=”25px” global_colors_info=”{}”] A short but simple explanation of t-SNE – easily explained with an example! [/et_pb_text][/et_pb_column][/et_pb_row][/et_pb_section][et_pb_section fb_built=”1″ _builder_version=”4.18.1″ _module_preset=”default” custom_padding=”0px||3px|||” global_colors_info=”{}”][et_pb_row _builder_version=”4.18.1″ _module_preset=”default” custom_padding=”30px|||||” global_colors_info=”{}”][et_pb_column type=”4_4″ _builder_version=”4.18.1″ _module_preset=”default” global_colors_info=”{}”][et_pb_text _builder_version=”4.21.0″ _module_preset=”default” text_line_height=”1.8em” global_colors_info=”{}”]
- [How to interpret a volcano plot](https://biostatsquid.com/volcano-plot/) - How to interpret a volcano plot, simply explained! Learn how to read anduse volcano plots to show your gene expression results.
- [Correlation does not imply causation](https://biostatsquid.com/correlation-does-not-imply-causation/) - What does 'correlation does not imply causation' mean? Correlation vs causation. A simple explanation of what is correlation, how to know if two variables are correlated, positive and negative correlation and the correlation coefficient r.
- [Multiple testing correction methods: FDR, q-values vs p-values](https://biostatsquid.com/multiple-testing-correction-fdr/) - Why is multiple testing a big issue in biostatistics? Find out what is multiple testing and the most common correction methods: Bonferroni correction, Benjamini-Hochberg (BH) and q-values.
- [Pathway enrichment analysis for DGE - simply explained](https://biostatsquid.com/pathway-enrichment-analysis-explained/) - Pathway Enrichment Analysis explained in an easy way! Find out the main idea, how it works and what do you need to summarise your differential gene expression results.
- [Gene Set Enrichment Analysis (GSEA) - simply explained!](https://biostatsquid.com/gene-set-enrichment-analysis/) - An overview of Gene Set Enrichment Analysis and how to use it to summarise your differential gene expression results. The basics of GSEA simply explained!
- [Quality Check, Processing and Alignment of Sequencing Reads R tutorial](https://biostatsquid.com/sequencing-reads-r-tutorial/) - Follow this step-by-step preprocessing for sequencing reads R tutorial: quality control, trimming reads, and mapping them onto a reference genome.
- [Heatmaps for gene expression analysis - simple explanation with an example](https://biostatsquid.com/heatmaps-simply-explained/) - Learn how to interpret a heatmap for differential gene expression analysis. This simple explanation will give you an intuitive way to interpret heatmaps and we will apply the theory to practice by interpreting a real-life example!
- [Easy confidence intervals and p-values](https://biostatsquid.com/confidence-intervals-p-values-explained/) - An easy and intuitive explanation of p-values and confidence intervals (CI) with an example.. or should I say, a squidsample!
- [Top visualisation tricks you should know in R](https://biostatsquid.com/top-visualisation-tricks-r/) - Top visualisation tricks and tips to create pretty and publication-ready plots in R!
- [Survival time analysis: easily explained!](https://biostatsquid.com/survival-time-analysis-easily-explained/) - Easy introduction into time-to-event analysis, Kaplan-Meier curves, Cox regression and more! A brief and simple explanation of survival time analysis.
- [Easy log rank test for survival analysis](https://biostatsquid.com/easy-log-rank-test/) - A simple explanation of the log rank test to evaluate differences between survival curves - easily explained with an exampl
- [How to use italics in plots in R: easy tutorial](https://biostatsquid.com/easy-italics-plots-r/) - In this post, you will learn a few ways to use italics in plots in R, using bquote() and as.expression(). You can also make text and titles bold, italics and more!
- [Easy Cox regression for survival analysis](https://biostatsquid.com/easy-cox-regression-for-survival-analysis/) - Follow this easy Cox regression for survival analysis explanation with an example: how to interpret hazard ratios, coefficients, and more!
- [Easy survival analysis in R](https://biostatsquid.com/easy-survival-analysis-r-tutorial/) - In this easy survival analysis in R tutorial, we'll learn how to plot a Kaplan Meier curve, test for differences in survival between groups with log rank test and Cox regression!
- [SingleR tutorial: easy cell type annotation](https://biostatsquid.com/singler-tutorial/) - In this easy, step-by-step SingleR tutorial you will learn how to do cell type annotation using SingleR, a reference-based cell type annotation tool.
- [Logistic regression - easily explained!](https://biostatsquid.com/easy-logistic-regression/) - An simple explanation of logistic regression - easily explained with an example!
- [How to interpret density plots](https://biostatsquid.com/interpret-density-plots/) - How to interpret density plots in six steps: shape, central tendency, variability, tails, area under the curve and comparison.
- [How to interpret boxplots and violin plots](https://biostatsquid.com/interpret-boxplots-and-violin-plots/) - Main concepts behind boxplots and violin plots and how to interpret them.
- [Easy violin plots tutorial in R with ggplot2](https://biostatsquid.com/easy-violin-plots-tutorial-ggplot2/) - In this tutorial we will learn how to create and customise our own violin plots in R with ggplot2. We will also create our own custom function!
- [How to remove doublets from scRNAseq data](https://biostatsquid.com/remove-doublets-scrnaseq/) - Follow this easy, step-by-step tutorial in R to remove doublets from scRNAseq data.
- [A simple explanation of PCA](https://biostatsquid.com/a-simple-explanation-of-pca/) - [et_pb_section fb_built=”1″ _builder_version=”4.21.0″ _module_preset=”default” background_color=”#FFFFFF” custom_margin=”35px|||||” custom_padding=”0px||0px|||” border_radii=”on|10px|10px|10px|10px” border_width_all=”7px” border_color_all=”#16C5E0″ global_colors_info=”{}”][et_pb_row _builder_version=”4.21.0″ _module_preset=”default” custom_margin=”-7px|auto||auto||” custom_padding=”||27px|||” global_colors_info=”{}”][et_pb_column type=”4_4″ _builder_version=”4.18.1″ _module_preset=”default” global_colors_info=”{}”][et_pb_text _builder_version=”4.21.0″ _module_preset=”default” text_font_size=”25px” global_colors_info=”{}”] A short but simple explanation of PCA – easily explained with an example! [/et_pb_text][/et_pb_column][/et_pb_row][/et_pb_section][et_pb_section fb_built=”1″ _builder_version=”4.18.1″ _module_preset=”default” custom_padding=”0px||3px|||” global_colors_info=”{}”][et_pb_row _builder_version=”4.18.1″ _module_preset=”default” custom_padding=”30px|||||” global_colors_info=”{}”][et_pb_column type=”4_4″ _builder_version=”4.18.1″ _module_preset=”default” global_colors_info=”{}”][et_pb_text _builder_version=”4.21.0″ _module_preset=”default” text_line_height=”1.8em” global_colors_info=”{}”]
- [Cell type annotation for scRNAseq](https://biostatsquid.com/cell-type-annotation-simply-explained/) - In this post, I will discuss some common methods to perform cell type annotations on scRNA-seq data and I will also share some tips and tricks I use!

## Pages

- [BiostatSQUID](https://biostatsquid.com/) - Biostatsquid is a blog for computational biology and biostatistics tutorials and easy explanations on bioinformatic tools and methods.
- [BiostatLEARN](https://biostatsquid.com/learn-biostatistics/) - [et_pb_section fb_built=”1″ _builder_version=”4.18.1″ _module_preset=”default” global_colors_info=”{}”][et_pb_row _builder_version=”4.18.1″ _module_preset=”default” global_colors_info=”{}”][et_pb_column type=”4_4″ _builder_version=”4.18.1″ _module_preset=”default” global_colors_info=”{}”][et_pb_text _builder_version=”4.18.1″ _module_preset=”default” global_colors_info=”{}”] BiostatLEARN [/et_pb_text][et_pb_divider divider_weight=”8px” _builder_version=”4.18.1″ _module_preset=”default” custom_margin=”-25px||||false|false” global_colors_info=”{}”][/et_pb_divider][et_pb_text _builder_version=”4.18.1″ _module_preset=”default” global_colors_info=”{}”] Simple and clear explanations of biostatistics methods, statistical concepts and more! I try to keep them maths-free and straight to the point, with many examples of biological applications. [/et_pb_text][/et_pb_column][/et_pb_row][et_pb_row _builder_version=”4.21.0″
- [FAQ](https://biostatsquid.com/faq/) - [et_pb_section fb_built=”1″ admin_label=”section” _builder_version=”4.16″ global_colors_info=”{}”][et_pb_row _builder_version=”4.21.0″ _module_preset=”default” global_colors_info=”{}”][et_pb_column type=”4_4″ _builder_version=”4.21.0″ _module_preset=”default” global_colors_info=”{}”][et_pb_text _builder_version=”4.21.0″ _module_preset=”default” global_colors_info=”{}”] Frequently asked questions [/et_pb_text][/et_pb_column][/et_pb_row][et_pb_row admin_label=”row” _builder_version=”4.21.0″ background_size=”initial” background_position=”top_left” background_repeat=”repeat” module_alignment=”center” custom_margin=”22px|auto|41px|auto||” custom_padding=”10px|20px|10px|20px|false|false” border_radii=”on|10px|10px|10px|10px” border_width_all=”3px” border_color_all=”#16C5E0″ global_colors_info=”{}”][et_pb_column type=”4_4″ _builder_version=”4.16″ custom_padding=”|||” global_colors_info=”{}” custom_padding__hover=”|||”][et_pb_text _builder_version=”4.21.0″ _module_preset=”default” custom_margin=”10px||||false|false” global_colors_info=”{}”] I am new to R and coding but would like to learn how to do
- [BiostatCODE](https://biostatsquid.com/coding-tutorials/) - [et_pb_section fb_built=”1″ admin_label=”section” _builder_version=”4.21.0″ global_colors_info=”{}”][et_pb_row _builder_version=”4.21.0″ _module_preset=”default” global_colors_info=”{}”][et_pb_column type=”4_4″ _builder_version=”4.18.1″ _module_preset=”default” global_colors_info=”{}”][et_pb_text _builder_version=”4.18.1″ _module_preset=”default” global_colors_info=”{}”] BiostatCODE [/et_pb_text][et_pb_divider divider_weight=”8px” _builder_version=”4.18.1″ _module_preset=”default” custom_margin=”-25px||||false|false” global_colors_info=”{}”][/et_pb_divider][et_pb_text _builder_version=”4.18.1″ _module_preset=”default” custom_margin=”||13px|||” global_colors_info=”{}”] Hands-on coding tutorials with biological data [/et_pb_text][et_pb_text _builder_version=”4.18.1″ _module_preset=”default” global_colors_info=”{}”] If you would like to… learn data wrangling and visualisation tools for biological data start learning code with easy
- [BiostatTOOLS](https://biostatsquid.com/biostattools/) - [et_pb_section fb_built=”1″ _builder_version=”4.18.1″ _module_preset=”default” global_colors_info=”{}”][et_pb_row _builder_version=”4.18.1″ _module_preset=”default” global_colors_info=”{}”][et_pb_column type=”4_4″ _builder_version=”4.18.1″ _module_preset=”default” global_colors_info=”{}”][et_pb_text _builder_version=”4.18.1″ _module_preset=”default” global_colors_info=”{}”] BiostatTOOLS [/et_pb_text][et_pb_divider divider_weight=”8px” _builder_version=”4.18.1″ _module_preset=”default” custom_margin=”-25px||||false|false” global_colors_info=”{}”][/et_pb_divider][et_pb_text _builder_version=”4.18.1″ _module_preset=”default” global_colors_info=”{}”] Here you can find useful websites and bioinformatic tools for your analysis, including amazing tutorials, databases and other resources! [/et_pb_text][/et_pb_column][/et_pb_row][et_pb_row make_equal=”on” _builder_version=”4.18.1″ _module_preset=”default” global_colors_info=”{}”][et_pb_column type=”4_4″ _builder_version=”4.18.1″ _module_preset=”default” global_colors_info=”{}”][et_pb_blog fullwidth=”off” include_categories=”37″
- [Contact](https://biostatsquid.com/contact/) - [et_pb_section fb_built=”1″ admin_label=”section” _builder_version=”4.16″ global_colors_info=”{}”][et_pb_row _builder_version=”4.21.0″ _module_preset=”default”][et_pb_column _builder_version=”4.21.0″ _module_preset=”default” type=”4_4″][et_pb_text _builder_version=”4.21.0″ _module_preset=”default” hover_enabled=”0″ sticky_enabled=”0″ width=”51%” max_width=”52%” module_alignment=”center”] Need help or advice? Would you like to learn more about a particular bioinformatics tool? Got any recommendations or suggestions? Or perhaps just a little success story to share? Just drop me an email! [/et_pb_text][/et_pb_column][/et_pb_row][et_pb_row admin_label=”row” _builder_version=”4.21.0″ background_size=”initial”
- [About me](https://biostatsquid.com/about/) - [et_pb_section fb_built=”1″ _builder_version=”4.18.1″ _module_preset=”default” global_colors_info=”{}”][et_pb_row _builder_version=”4.21.0″ _module_preset=”default” column_structure=”1_2,1_2″][et_pb_column _builder_version=”4.21.0″ _module_preset=”default” type=”1_2″][et_pb_image src=”https://biostatsquid.com/wp-content/uploads/2023/09/me_photo_transparent.png” _builder_version=”4.21.0″ _module_preset=”default” title_text=”me_photo_transparent” hover_enabled=”0″ sticky_enabled=”0″ filter_brightness=”90%” filter_sepia=”4%” filter_opacity=”93%” filter_saturate=”77%” background_color=”RGBA(255,255,255,0)” custom_padding=”0px|||||” max_width=”100%” width=”100%”][/et_pb_image][/et_pb_column][et_pb_column _builder_version=”4.21.0″ _module_preset=”default” type=”1_2″][et_pb_text _builder_version=”4.21.0″ _module_preset=”default” text_text_color=”#000000″ custom_margin=”|||-2px||” custom_padding=”0px||0px|||” hover_enabled=”0″ global_colors_info=”{}” sticky_enabled=”0″] Hi there! I am happy you found Biostatsquid😊 My name is Laura, I am a medical biotechnologist specialised in
- [Cookie Policy (EU)](https://biostatsquid.com/cookie-policy-eu/)
- [Courses](https://biostatsquid.com/courses/)
- [Aviso legal y politica de privacidad](https://biostatsquid.com/aviso-legal-y-politica-de-privacidad/) - [et_pb_section fb_built=”1″ _builder_version=”4.18.1″ _module_preset=”default” global_colors_info=”{}”][et_pb_row _builder_version=”4.18.1″ _module_preset=”default” global_colors_info=”{}”][et_pb_column type=”4_4″ _builder_version=”4.18.1″ _module_preset=”default” global_colors_info=”{}”][et_pb_text _builder_version=”4.18.1″ _module_preset=”default” global_colors_info=”{}”] Política de privacidad y aviso legal TITULAR DE LA WEB Nombre: Laura Twomey Díaz NIF: 53991742R Domicilio: Avda. Infante D Luis 21 3D, Boadilla del Monte (28660) Madrid (España) Correo electrónico: biostatsquid@gmail.com Actividad del sitio web (el sitio web, de

## Categories

- [Uncategorized](https://biostatsquid.com/category/uncategorized/)
- [Tutorials](https://biostatsquid.com/category/tutorials/) - Tutorials and step by step guides in R and Python for basic and advanced biostatistics and computational biology analysis.
- [Learning](https://biostatsquid.com/category/learn/) - Simple and complete explanations of (bio)statistic terms, machine learning for biological data and pipelines for transcriptomics (RNAseq), proteomics and metabolomics analysis.
- [Statistics](https://biostatsquid.com/category/learn/simple-and-complete-explanations-of-biostatistic-terms/)
- [Machine learning](https://biostatsquid.com/category/learn/explanations-and-hands-on-tutorials-of-machine-learning-applied-to-biological-data/)
- [RNAseq](https://biostatsquid.com/category/learn/rnaseq-pipelines-preprocessing-and-downstream-analysis-for-gene-expression-analysis/)
- [BiostatTOOLS](https://biostatsquid.com/category/biostattools/)
- [scRNAseq](https://biostatsquid.com/category/learn/scrnaseq/)
- [Visualisation](https://biostatsquid.com/category/tutorials/visualisation/)
- [scRNAseq](https://biostatsquid.com/category/tutorials/scrnaseq-tutorials/)
- [Statistics in R](https://biostatsquid.com/category/tutorials/statistics-in-r/)
- [Machine Learning](https://biostatsquid.com/category/tutorials/machine-learning/)