Search Results for: EBV transformation

VlnPlot(adp, features = "percent.mt", group.by="orig.ident") + scale_fill_manual(values=cnames) + geom_hline(yintercept=10,color="red") Warning: Default search for "data" layer in " Read More...

A way to add variables to a plot beyond mapping them to an aesthetic is to use facets or subplots. There are two primary functions to add facets, facet_wrap() and facet_grid() . If faceting Read More...

Many graphs, like scatterplots, plot the raw values of your dataset. Other graphs, like bar charts, calculate new values to plot: bar charts, histograms, and frequency polygons bin your data and then plot bin counts, Read More...

Additional Resources Getting started with R Hands on Programming with R R for Data Science (R4DS) R Cheatsheets and references Navigating RStudio cheatsheet R reference card readr / readxl cheatsheet Tidyr (data reshaping) cheatsheet Stringr / Read More...

A way to add variables to a plot beyond mapping them to an aesthetic is to use facets or subplots. There are two primary functions to add facets, facet_wrap() and facet_grid() . If faceting Read More...

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...

What if we want to transform all of our counts spread across multiple columns in acount using scale() , which applies a z-score transformation? In this case we use across() within mutate() , which has replaced the Read More...

09/14/2023 - While there is a positive correlation between cancer and aging, the mechanisms underlying this relationship remain unclear. Clonal hematopoiesis, a benign condition that is both associated with aging and predisposes to increased risk of Read More...

geom_bar() uses stat_count() by default: it counts the number of cases at each x position. --- ggplot2 documentation stat_count() requires mapping for either an x OR a y variable but not both. Read More...

In scatter plots, the raw data is the focus of the plot, but for many other plots, this is not the case. We will discuss statistical transformation more in lesson 4 and how they apply. However, Read More...

one or more datasets, one or more geometric objects that serve as the visual representations of the data, – for instance, points, lines, rectangles, contours, descriptions of how the variables in the data are mapped to Read More...

one or more datasets, one or more geometric objects that serve as the visual representations of the data, – for instance, points, lines, rectangles, contours, descriptions of how the variables in the data are mapped to Read More...

A way to add variables to a plot beyond mapping them to an aesthetic is to use facets or subplots. There are two primary functions to add facets, facet_wrap() and facet_grid() . If faceting Read More...

Many graphs, like scatterplots, plot the raw values of your dataset. Other graphs, like bar charts, calculate new values to plot: bar charts, histograms, and frequency polygons bin your data and then plot bin counts, Read More...

01/07/2021 - Presenter: Gary Patti, Ph.D. Departments of Chemistry, Genetics, and Medicine Washington University in St. Louis It is well established that the metabolism of cancer cells is reprogrammed to support the demands of rapid Read More...

In scatter plots, the raw data is the focus of the plot, but for many other plots, this is not the case. You may wish to overlay a stat on your PCA. For example, ellipses Read More...

The AgingResearchBiobank was officially launched in January 2019 with a mission to provide a state-of-the-art inventory system for the storage, maintenance, and distribution of de-identified biospecimens and associated phenotypic, clinical, and imaging data from numerous NIA-funded Read More...

03/22/2024 - This 3-hour seminar is tailored for biologists, data analysts, and researchers who are eager to dive into the essentials of computational flow cytometry analysis using R. Flow cytometry is a crucial technique in cell Read More...

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 . You can obtain the data outside of class here . The diffexp_ Read More...

Lesson 5 Exercise Questions: Tidyverse 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 . You can obtain the data outside of Read More...

Bray-Curtis dissimilarity quantitative Takes into consideration abundance and presence absence Jaccard - qualitative - presence / absence - percentage of taxa not found in both samples Weighted UniFrac quantitative similar to Bray-Curtis but takes into consideration Read More...

Let's grab some data. library ( tidyverse ) acount_smeta % dplyr :: rename ( "Feature" = "...1" ) acount #differential expression results dexp % filter ( ! Feature %in% dexp $ feature ) ## # A tibble: 48,176 × 9 ## Feature SRR1039508 SRR1039509 SRR1039512 SRR1039513 SRR1039516 SRR1039517 ## ## 1 Read More...

Help Session Lesson 6 Let's grab some data. library ( tidyverse ) acount_smeta % dplyr :: rename ( "Feature" = "...1" ) acount #differential expression results dexp % filter ( ! Feature %in% dexp $ feature ) ## # A tibble: 48,176 × 9 ## Feature SRR1039508 SRR1039509 SRR1039512 Read More...

Let's take a look at a bar plot constructed using the default stat="count" transformation. Below, we plot the number of tooth length measurements taken at each dose. Setting color="black& Read More...

Above, we learned about the number of tooth length measurements taken at each dose and supplement combination using the default stat="count" transformation of geom_bar . But what if we want to specify Read More...

Understanding data distribution can help us decide appropriate downstream steps in analysis such as which statistical test to use. A histogram is a good way to visualize distribution. It divides the data into bins or Read More...

Understanding data distribution can help us decide appropriate downstream steps in analysis such as which statistical test to use. A histogram is a good way to visualize distribution. It divides the data into bins or Read More...

/* Whole document: */ body{ font-family: Times; font-size: 16pt; } Stat Transformations: Bar plots, box plots, and histograms Objectives Review the grammar of graphics template Learn about the statistical transformations inherent to geoms Review data types Create bar Read More...

Stat Transformations: Bar plots, box plots, and histograms Objectives Review the grammar of graphics template Learn about the statistical transformations inherent to geoms Review data types Create bar plots, box & whisker plots, and histograms. Read More...

06/17/2021 - The intrinsic stochasticity of transcription leads to gene expression variation across cells in a clonal cell population. The expression variation can translate into phenotypic variation that can persist through several rounds of cell division. Read More...

What if we want to transform all of our counts spread across multiple columns in acount using scale(), which applies a z-score transformation? In this case we use across() within mutate(), which has replaced the Read More...

To create a publication quality plot, you will need to make several modifications to your basic PCA biplot code. We have already seen how to modify the default coordinate system, how to add additional statistics ( Read More...

Tasks to do at the Analysis Wizard: Provide an input gene list (either copy paste or upload as a text file) Specify the gene identifier type. Gene identifiers can be gene symbol, Ensembl IDs, Entrez Read More...

Principal component analysis (PCA) is an exploratory linear dimension reduction method applied to highly dimensional (multivariate) data. It is an usupervised learning technique that treats all variables equally. The goal of PCA is to reduce Read More...

Let's learn how to further work with vectors, including creating, sub-setting, modifying, and saving. First, we will create a few vectors. Again, the c() vector is necessary for this task. #Some possible RNASeq data Read More...

Let's grab some data to work with. library(tidyverse) acount_smeta<-read_tsv("../data/countsANDmeta.txt") acount_smeta #raw count data acount<-read_csv("../data/airway_rawcount. Read More...

There are many packages and functions available in R programming for performing PCA. Some of the most popular functions are stats::prcomp(), stats::princomp(), FactoMineR::PCA(), and ade4::dudi.pca(). These functions largely differ in Read More...

The Biopharmaceutical Development Program (BDP) provides resources for the development of investigational biological agents. The BDP supports feasibility through development and Phase I/II cGMP manufacturing plus regulatory documentation.The BDP was established in 1993. We Read More...

Learning Objectives This tutorial was designed to demonstrate common secondary analysis steps in a scRNA-Seq workflow. We will start with a merged Seurat Object with multiple data layers representing multiple samples. Throughout this tutorial we Read More...

Data visualization with ggplot2 Objectives To learn how to create publishable figures using the ggplot2 package in R. By the end of this lesson, learners should be able to create simple, pretty, and effective figures. Read More...

Objectives Review the grammar of graphics template. Learn about the statistical transformations inherent to geoms. Learn more about fine tuning figures with labels, legends, scales, and themes. Learn how to save plots with ggsave() . Review Read More...

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...

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...

Introduction to ggplot2 Objectives Learn the ggplot2 syntax. Build a ggplot2 general template. By the end of the course, students should be able to create simple, pretty, and effective figures. Data Visualization in the tidyverse Read More...

Lesson 6 . Learning Objectives Introduce several beta diversity metrics Discover different ordination methods Learn about statistical methods that are applicable Beta diversity Beta diversity is between sample diversity. This is useful for answering the question, how Read More...

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...

dplyr : joining, tranforming, and summarizing data frames Objectives Today we will continue to wrangle data using the tidyverse package, dplyr . We will learn: how to join data frames using dplyr how to transform and create Read More...

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...

Data visualization with ggplot2 Objectives To learn how to create publishable figures using the ggplot2 package in R. By the end of this lesson, learners should be able to create simple, pretty, and effective figures. Read More...

Objectives Review the grammar of graphics template. Learn about the statistical transformations inherent to geoms. Learn more about fine tuning figures with labels, legends, scales, and themes. Learn how to save plots with ggsave() . Review Read More...

Introduction to Data Wrangling with the Tidyverse Objectives Wrangle data using tidyverse functionality (i.e., dplyr ). To this end, you should understand: 1. how to use common dplyr functions (e.g., select() , group_by() , arrange() , mutate() , Read More...