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Bioinformatics
Let's create a column in our original differential expression data frame denoting significant transcripts (those with an FDR corrected p-value less than 0.05 and a log fold change greater than or equal to 2). dexp_sigtrnsc Read More...
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Bioinformatics
Let's create a column in our original differential expression data frame denoting significant transcripts (those with an FDR corrected p-value less than 0.05 and a log fold change greater than or equal to 2). ::: {.cell} dexp_ Read More...
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Bioinformatics
#use `|` operator #look at only results with named genes (not NAs) #and those with a log fold change greater than 2 #and either a p-value or an FDR corrected p_value 2, (PValue | FDR
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Functions can be chained together using a pipe (|>, %>%). The pipe improves the readability of the code by minimizing nesting. For example, ex<- -5.679 ex |> round() |& 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 4 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
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 4 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
mutate() adds new variables and preserves existing ones; transmute() adds new variables and drops existing ones. New variables overwrite existing variables of the same name. --- dplyr.tidyverse.org Let's create a column in 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 4 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
These steps can be used to create a publish worthy figure. For example, let's create a volcano plot of our differential expression results. A volcano plot is a type of scatterplot that shows statistical Read More...
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Bioinformatics
mutate() creates new columns that are functions of existing variables. It can also modify (if the name is the same as an existing column) and delete columns (by setting their value to NULL). --- dplyr. Read More...
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The Antibody Engineering Program (AEP) is located at the Laboratory of Molecular Biology, which is part of the Center for Cancer Research (CCR), an intramural program at the National Cancer Institute (NCI). AEP focuses on Read More...
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Bioinformatics
These steps can be used to create a publish worthy figure. For example, let's create a volcano plot of our differential expression results. A volcano plot is a type of scatterplot that shows statistical 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
Now let's filter the rows based on a condition. Let's look at only the treated samples in scaled_counts using the function filter() . filter() requires the df as the first argument followed by Read More...
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Bioinformatics
Now let's filter the rows based on a condition. Let's look at only the treated samples in scaled_counts using the function filter() . filter() requires the df as the first argument followed by Read More...
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Bioinformatics
Other useful data manipulation functions from dplyr include mutate() and transmute() . These functions allow you to create a new variable from existing variables. Perhaps you want to know the ratio of two columns or convert Read More...
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Bioinformatics
08/31/2020 - Alternative splicing (AS) and alternative back-splicing (ABS) are essential to understanding the development of cancer and may play a role as a target of personalized cancer therapeutics. However, the existing reference transcriptome annotation databases Read More...
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Bioinformatics
We can use multiple expressions in a single call to filter(). For example, let's filter dexp to include only named transcripts (i.e.,no NAs), values of |log fold change| is greater than 2, and Read More...
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Bioinformatics
Now that we know how to create a PCA biplot, let's use what we have learned to also make a volcano plot. A volcano plot is a type of scatterplot that shows statistical significance ( Read More...
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Confocal
Our Team Tatiana S. Karpova Ph.D.Core Headkarpovat@nih.govBuilding 41, Room C615240-760-6637 David A. Ball Ph.D.Core Biologistballa@nih.govBuilding 41, Room B114D240-760-6577 Mohamadreza Fazel, Ph.D.Core Biologistmohamadreza. Read More...
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General Usage Policies Principal investigators should read this document and sign it. PI’s/postdocs should attach a short-written summary of the project to the signed doc. If the project changes, Read More...
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2025 Sebastian R, Sun EG, Fedkenheuer M, Fu H, Jung S, Thakur BL, Redon CE, Pegoraro G, Tran AD, Gross JM, Mosavarpour S, Kusi NA, Ray A, Dhall A, Pongor LS, Casellas R, Aladjem MI. Mechanism Read More...
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Stephen Lockett, Ph.D. Director, OMAL locketts@nih.gov 301-846-5515 Valentin Magidson, Ph.D. Scientist magidsonv@mail.nih.gov 301-846-6092 Will Heinz, Ph.D. Scientist heinzwf@nih.gov 301-846-1239 David Read More...
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Training We recommend that users become familiar with the principals of fluorescence labeling and optical microscopy before arranging for training. We recommend the following sites for learning about microscopy: Introductions to Fluorescence Microscopy Fluorescence Labeling Read More...
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Nikon SoRa Spinning Disk Capabilities: Inverted microscope Photo-metrics BSI sCMOS camera Yokogawa SoRa CSU-W1 spinning disk unit Super-resolution, confocal and wide-field imaging modes 4x, 10x, 20x and 60x objective lenses Mad City Labs 500 mm piezo Read More...
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2024 Date: Tuesday, October 15, 2024 Time and Location: 11 am EST, ZOOM (INVITATION BY LMIG LIST SERVER) Speaker: Dr. Diego Presman (U Buenos Aires) Title: “Insights on Glucocorticoid Receptor Activity Through Live Cell Imaging” Summary: Eucaryotic transcription factors ( Read More...
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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
This lesson will introduce data wrangling with R. Attendees will learn to filter data using base R and tidyverse (dplyr) functionality. Learning Objectives Understand the concept of tidy data. Become familiar with the tidyverse packages. Read More...
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Bioinformatics
All solutions should use the pipe. Import the file "./data/filtlowabund_scaledcounts_airways.txt" and save to an object named sc . Create a subset data frame from sc that only includes the columns Read More...
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Help Session Lesson 5 All solutions should use the pipe. Import the file "./data/filtlowabund_scaledcounts_airways.txt" and save to an object named sc . Create a subset data frame from sc that only Read More...
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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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Introduction to dplyr and the %>% Objectives Today we will begin to wrangle data using the tidyverse package, dplyr . To this end, you will learn: how to filter data frames using dplyr how to employ 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...
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Bioinformatics
09/22/2015 - Learn the basics of microarray gene expression analysis using Partek Genomics Suite and Open Source Tools. As we walk though hands-on analysis of a cancer dataset, you will learn the principles of experimental design, Read More...
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Bioinformatics
All solutions use the pipe. Solutions have multiple possibilities. Q1. Import the file "./data/filtlowabund_scaledcounts_airways.txt" and save to an object named sc. Create a data frame from sc that only Read More...