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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...
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Bioinformatics
Following normalization, the next step is to find variable features. In most scRNA-seq experiments only a small proportion of the genes will be informative and biologically variable. A subset of cells with high cell to Read More...
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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...
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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...
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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...
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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...
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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...
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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...
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...
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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...
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Bioinformatics
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...
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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...
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Bioinformatics
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...
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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...
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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...
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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...
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Bioinformatics
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...
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Bioinformatics
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...
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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...
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Bioinformatics
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...
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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...
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Bioinformatics
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...
Web Page
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...
Web Page
Bioinformatics
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...
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Bioinformatics
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...
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Bioinformatics
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...
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...
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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...
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Bioinformatics
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...
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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...
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Bioinformatics
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...
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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...
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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...
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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...
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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...
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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...
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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...
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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...
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Bioinformatics
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...
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Bioinformatics
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...
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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...
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Bioinformatics
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...
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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...
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Bioinformatics
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...
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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...
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...
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Bioinformatics
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...
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Bioinformatics
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...
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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...
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Bioinformatics
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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Bioinformatics
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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Bioinformatics
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...
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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...
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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...
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Bioinformatics
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...
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Bioinformatics
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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Bioinformatics
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...
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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...
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Bioinformatics
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...