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Search Results for: EBV transformation

Total Results Found: 59

Total Results Found: 59

Getting Started with scRNA-Seq Seminar Series: SCTransform

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Bioinformatics

Rather than relying on the above steps ( NormalizeData() , FindVariableFeatures() , and ScaleData() ), we are going to proceed with a newer method ( SCtransform ) instead. This method uses Pearson residuals for transformation, which better accounts for the overall Read More...

R Introductory Series: Test your learning

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Bioinformatics

Using mutate apply a base 10 logarithmic transformation to the counts_scaled column of sscaled . Save the resulting data frame to an object called log10counts. Hint: see the function log10() . ::: {.cell} log10counts mutate ( logCounts = Read More...

R Introductory Series: Test your learning

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Bioinformatics

Using mutate apply a base 10 logarithmic transformation to the counts_scaled column of sscaled . Save the resulting data frame to an object called log10counts. Hint: see the function log10() . {{Sdet}} Possible Solution{{Esum}} log10 Read More...

Data Wrangling with R: R Cheatsheets and references

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Bioinformatics

Navigating RStudio cheatsheet R reference card readr / readxl cheatsheet Tidyr (data reshaping) cheatsheet Stringr / regex cheatsheet Data Visualization (ggplot2) cheatsheet Data Transformation (dplyr) cheatsheet Factors with forcats cheatsheet Working with Dates (lubridate) cheatsheet Cheatsheets are Read More...

Data Visualization with R: stat = identity

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Bioinformatics

Above, we learned 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 and plot exactly Read More...

R Introductory Series 2023: Test your learning

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Bioinformatics

Using mutate apply a base-10 logarithmic transformation to the counts_scaled column of sscaled. Save the resulting data frame to an object called log10counts. Hint: see the function log10(). {{Sdet}} Possible Solution{{Esum}} log10 Read More...

BTEP Lessons: Scatter plots

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Bioinformatics

Scatterplots are useful for visualizing treatment–response comparisons, associations between variables, or paired data (e.g., a disease biomarker in several patients before and after treatment). - Holmes and Huber, 2021 Because scatter plots involve mapping Read More...

Clinical Support Laboratory
Frederick, MD

Core Facility

The Clinical Support Laboratory offers processing, tracking, and testing of a broad range of clinical samples. Support can begin at the early stages of clinical trial development to aid in developing a comprehensive strategy for Read More...

October 2021

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CREx News & Updates October 2021 Learn about the NIH Collaborative Research Exchange (CREx), Core Facilities, Webinars, & More NIH Collaborative Research Exchange (CREx) News Site Spotlight FACILITY HIGLIGHTS Learn more about services from the CPTR Read More...

R Introductory Series: Facets

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Bioinformatics

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...

Data Wrangling with R: Additional Resources

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Bioinformatics

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...

Data Wrangling with R: Facets

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Bioinformatics

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...

Microbiome Analysis with QIIME2: Methods in QIIME 2

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Bioinformatics

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...

Data Visualization with R: stat = count

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Bioinformatics

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...

R Introductory Series 2023: Facets

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Bioinformatics

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...

NIH AgingResearchBiobank
Bethesda, Maryland

Core Facility

Repositories

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...

Introduction to Computational Flow Cytometry Using R

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Bioinformatics

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...

R Introductory Series: Practicing the Tidyverse (Part 2)

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Bioinformatics

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...

Data Wrangling with R: Help Session Lesson 6

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Bioinformatics

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...

Data Wrangling with R: Lesson 6: Continuing with Dplyr

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Bioinformatics

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...

Data Visualization with R: stat = count

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Bioinformatics

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...

Data Visualization with R: stat = identity

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Bioinformatics

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...

Data Visualization with R: Histogram

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Bioinformatics

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...

Data Visualization with R: Histogram

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Bioinformatics

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...

Data Visualization with R: Lesson 4

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Bioinformatics

/* 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...

BTEP Lessons: Plot Customization: Using themes

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Bioinformatics

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...

BTEP Lessons: What is PCA?

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Bioinformatics

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...

BTEP Lessons: Perform PCA

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Bioinformatics

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...

Biopharmaceutical Development Program (BDP)
Frederick, MD

Collaborative

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...

Microbiome Analysis with QIIME2: Beta Diversity

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Bioinformatics

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...

Microbiome Analysis with QIIME2: Course Wrap-Up

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Bioinformatics

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...

R Introductory Series 2023: ggplot2 Continued

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Bioinformatics

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...