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There are also special use, null-able values that you should be aware of. Read more to learn about NULL, NA, NaN, and Inf.
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Frederick, MD
Core Facility
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The Mouse Modeling Core assists NIH investigators by generating and preserving genetically-engineered mouse strains. Services include scientific consultation, gene-targeting in mouse embryonic stem cells, micro-injection of nucleic acids, proteins, or ES cells into mouse embryos, Read More...
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Let's draw an image on here, just to show that we can add one. There are two options. We can add an image as if it was another figure or we can simply draw Read More...
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Pandas data frames have a function shape that informs of the number of rows and number of columns in a data frame (in other words the dimensions of a tabular dataset). To get the dimensions Read More...
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Another useful data manipulation function from dplyr is mutate() . mutate() allows you to create a new variable from existing variables. Perhaps you want to know the ratio of two columns or convert the units of Read More...
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Another useful data manipulation function from dplyr is mutate() . mutate() allows you to create a new variable from existing variables. Perhaps you want to know the ratio of two columns or convert the units of Read More...
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The idea behind cluster analysis is to calculate some sort of distance between objects in order to identify the ones that are closer together. When two objects have a small distance, we can conclude they Read More...
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This exercise will use the differential gene expression analysis table from the hbr and uhr study. hbr_uhr_deg_chr22=pandas.read_csv("./hbr_uhr_deg_chr22.csv") The info() attribute will retrieve Read More...
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For this exercise, stay in the /data/username/pies_2023 folder, which should be the present working directory (use pwd to check). If not in the /data/username/pies_2023 folder, change into it. Copy the hbr_ Read More...
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Indices in R start with 1. Incorrect usage of indexing in data structures such as vectors or data frames will not necessarily result in an error, but will often lead to unexpected results. A general subscript Read More...
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Indices in R start with 1. Incorrect usage of indexing in data structures such as vectors or data frames will not necessarily result in an error, but will often lead to unexpected results. A general subscript Read More...
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The data type of an R object affects how that object can be used or will behave. Examples of base R data types include numeric, integer, complex, character, and logical. R objects can also have Read More...
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Now we know a bit about using functions, but what if I had no idea what the function round() was used for or what arguments to provide? Getting help in R is fairly easy. In Read More...
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learn how to apply different types of filtering to your ASV table and representative sequence data. classify your ASVs. Generate a phylogenetic tree. Now that we have imported and denoised, let's move on to Read More...
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ANCOM (Analysis of Composition of Microbiomes) additive log ratio approach assumes that less than 25 % of features change between groups q2-composition plugin Need to filter rare taxa w-statistic - the number of null hypotheses rejected Read More...
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R objects have certain attributes, and these attributes will be important for how they can interact with certain methods / functions. Understanding the mode (storage type) or the class of an object will be important for Read More...
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Now we know a bit about using functions, but what if I had no idea what the function round() was used for or what arguments to provide? Getting help in R is fairly easy. In Read More...
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These are the same results exhibited differently. Notice the seamless integration with tidyverse functionality. clusterProfiler extends ggplot2 functionality to accept enrichment results directly. s_ego %>% filter(p.adjust < 0.03) %>% ggplot( Read More...
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Now we know a bit about using functions, but what if I had no idea what the function setwd() was used for or what arguments to provide? Getting help in R is fairly easy. In Read More...
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We can use clusterProfiler extensions of dplyr verbs (mutate(),filter(),select(),summarize(),slice(),group_by(),arrange()) for easy manipulation of enrichResult, gseaResult, and compareClusterResult objects. mutate() can be particularly powerful and allow us to use Read More...
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The filtlowabund_scaledcounts_airways.txt includes normalized and non-normalized transcript count data from an RNAseq experiment. You can read more about the experiment here . We are going to use the filtlowabund_scaledcounts_airways.txt file Read More...
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Lesson 3 Exercise Questions: BaseR dataframe manipulation and factors The filtlowabund_scaledcounts_airways.txt includes normalized and non-normalized transcript count data from an RNAseq experiment. You can read more about the experiment here . We are going Read More...
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In order to create a ggplot2 bar plot we will need to reshape the data. The sample names should be in a single column named Sample and the gene counts in a single column named Read More...
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We can use the function prcomp() to run PCA on the first four columns of the iris data. The function takes numeric data. colnames(iris)[1:4] ## [1] "Sepal.Length" "Sepal.Width" "Petal. Read More...
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Multi-figure panel Objectives Combine multiple plots into a single figure Learn how to use patchwork and cowplot The primary purpose of this lesson is to learn how to combine multiple figures into a single multi-panel Read More...
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Objectives Combine multiple plots into a single figure Learn how to use aspects of cowplot and patchwork The primary purpose of this lesson is to learn how to combine multiple figures into a single multi-panel Read More...
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Below, we assign the heatmap to the R object hm_ph. hm_ph {"x":{"data":[{"x":[4.875,2.875,null,2.875,2.875,null,2.875,1.75,null,1.75,1.75,null,1.75,1,null,1,1,null,1.75,2.5,null,2.5,2.5,null,2.5,2,null,2,2,null,2.5,3,null,3,3,null,2.875,4,null,4,4, Read More...
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Below, we assign the heatmap to the R object hm_ph and we can import this back to R in the future. hm_ph {"x":{"data":[{"x":[4.875,2.875,null,2.875,2.875,null,2.875,1.75, Read More...
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Visualizing clusters with heatmaps Objectives Introduce the heatmap and dendrogram as tools for visualizing clusters in data. Learn to construct cluster heatmap using the package pheatmap . Learn how to save a non-ggplot2 plot. Introduce ggplotify Read More...
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The filtlowabund_scaledcounts_airways.txt includes normalized and non-normalized transcript count data from an RNAseq experiment. You can read more about the experiment here . We are going to use the filtlowabund_scaledcounts_airways.txt file Read More...
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Lesson 3 Exercise Questions: BaseR dataframe manipulation and factors The filtlowabund_scaledcounts_airways.txt includes normalized and non-normalized transcript count data from an RNAseq experiment. You can read more about the experiment here . We are going Read More...
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The main function in ggpubr to compare means between two or more groups is compare_means(). This function includes methods for each of the tests listed in the above table. Let's see how this Read More...
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The plugin has several configuration options: boardmarkerWidth: an integer, the drawing width of the boardmarker; larger values draw thicker lines. chalkWidth: an integer, the drawing width of the chalk; larger values draw thicker lines. chalkEffect: Read More...
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For this exercise we will use filtlowabund_scaledcounts_airways.txt, which includes normalized and non-normalized transcript count data from an RNAseq experiment. You can read more about the experiment here. To obtain this file, click Read More...
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Objectives To understand some of the most basic features of the R language including: Creating R objects and understanding object types Using mathematical operations Using comparison operators Creating, subsetting, and modifying vectors By the end Read More...
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Learning Objectives To understand: 1. the difference between R and RStudioIDE. 2. how to work within the RStudio environment including: creating an Rproject and Rscript navigating between directories using functions obtaining help how R can enhance data Read More...
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In this lesson, attendees will learn how to transform, summarize, and reshape data using functions from the tidyverse. Learning Objectives Continue to wrangle data using tidyverse functionality. To this end, you should understand: how to Read More...
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In this lesson, attendees will learn how to transform, summarize, and reshape data using functions from the tidyverse. Learning Objectives Continue to wrangle data using tidyverse functionality. To this end, you should understand: how to Read More...
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Lesson 7: Course Wrap-Up Learning Objectives Introduce the QIIME2 microbiome workflow for Biowulf Review key concepts Showcase additional plugins QIIME 2 on Biowulf As mentioned previously, QIIME 2 is installed on Biowulf. To see available versions use module Read More...
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Lesson 4: Feature table filtering, taxonomic classification, and phylogeny Learning objectives learn how to apply different types of filtering to your ASV table and representative sequence data. classify your ASVs. Generate a phylogenetic tree. Now that Read More...
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Objectives Review important data wrangling functions Put our wrangling skills to use on a realistic RNA-Seq data set Data Wrangling Review Important functions by topic Importing / Exporting Data Importing and exporting data into the R Read More...
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Visualizing clusters with heatmaps Objectives Introduce the heatmap and dendrogram as tools for visualizing clusters in data. Learn how to work with the package pheatmap . Learn how to save a non-ggplot2 plot. Introduce ggplotify to Read More...
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Scatter plots and plot customization Objectives Learn to customize your ggplot with labels, axes, text annotations, and themes. Learn how to make and modify scatter plots to make fairly different overall plot representations. Load a Read More...
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R basics Objectives : To understand some of the most basic features of the R language including: Creating R objects and understanding object types Using mathematical operations Using comparison operators Creating, subsetting, and modifying vectors By Read More...
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Introduction to R and RStudio IDE Objectives To understand: 1. the difference between R and RStudioIDE. 2. how to work within the RStudio environment including: creating an Rproject and Rscript navigating between directories using functions obtaining help 3. Read More...