Search Results for: Anaerobic Clostridia Spirogenes Vector

Vectors are probably the most used commonly used object type in R. A vector is a collection of values that are all of the same type (numbers, characters, etc.). The columns that make up a Read More...

Let's learn how to further work with vectors, including creating, sub-setting, modifying, and saving. #Some possible RNASeq data cell_line

There may be a time you want to know whether there are specific values in your vector. To do this, we can use the %in% operator (?match()). This operator returns TRUE for any value that 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...

As we can see above, the glimpse command shows the metadata that can be used to classify the cells. Within Seurat, the metadata is used to define the "identity" of the dataset. This Read More...

Notice that "sample" was treated as numeric, rather than as a character vector. If we intend to work with this column, we will need to convert it or coerce it to a character Read More...

Other functions you may find useful when working with NAs inclue na.omit() and complete.cases() . na.omit() removes the NAs from a vector. na.omit(transcript_counts) ## [1] 679 467 260 60 ## attr(,"na.action") ## [1] 2 6 ## attr(," Read More...

It is also possible to subset in R using logical evaluation or numerical comparison. To do this, we use comparison operators (See table below). Comparison Operator Description > greater than >= greater than or equal Read More...

Given the following R code: fruit 678] c. Total_subjects(Total_subjects < 678) d. Total_subjects[Total_subjects < 678] {{Sdet}} Solution{{Esum}} D {{Edet}}

colnames() will return a vector of column names from our data frame. We can use this vector and [] subsetting to easily modify column names. For example, let's rename the column "Sample" to & Read More...

Given the following R code: numbers

Used to match elements of a vector. %in% returns a logical vector indicating if there is a match or not for its left operand. --- match R Documentation. The returned logical vector will be the Read More...

There are several functions that you will see repeatedly as you use R more and more. One of those is c() , which is used to combine its arguments to form a vector. Vectors are probably Read More...

Notice that "sample" was treated as numeric, rather than as a character vector. If we intend to work with this column, we will need to convert it or coerce it to a character Read More...

Other functions you may find useful when working with NAs inclue na.omit() and complete.cases() . na.omit() removes the NAs from a vector. na.omit(transcript_counts) ## [1] 679 467 260 60 ## attr(,"na.action") ## [1] 2 6 ## attr(," Read More...

It is also possible to subset in R using logical evaluation or numerical comparison. To do this, we use comparison operators (See table below). Comparison Operator Description > greater than >= greater than or equal Read More...

Given the following R code: fruit 678] c. Total_subjects(Total_subjects < 678) d. Total_subjects[Total_subjects < 678] {{Sdet}} Solution{{Esum}} D {{Edet}}

Given the following R code: numbers

CREx Monthly Newsletter Learn about the NIH Collaborative Research Exchange (CREx), Core Facilities, Webinars, & More New Resources on CREx NCI CLIA Molecular Diagnostics Laboratory (CMDL) The NCI CMDL is available to assist all Read More...

A function in R (or any computing language) is a short program that takes some input and returns some output. An R function has three key properties: Functions have a name (e.g. dir, getwd); Read More...

Data structures are objects that store data. Previously, we learned that vectors are collections of values of the same type . A vector is also one of the most basic data structures. Other common data structures Read More...

Before diving into subsetting with dplyr , let's take a step back and learn to subset with base R. Subsetting a data frame is similar to subsetting a vector; we can use bracket notation [] . However, Read More...

Factors are an important data structure in statistical computing. They are specialized vectors (ordered or unordered) for the storage of categorical data. While they appear to be character vectors, data in factors are stored as Read More...

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 of Read More...

We discussed saving the R workspace (.RData), but what if we simply want to save a single object. In such a case, we can use saveRDS() . Let's save our transcript_counts vector to our Read More...

Typically provide two values separated by commas: data.frame[row, column] In cases where you are taking a continuous range of numbers use a colon between the numbers (start:stop, inclusive) For a non continuous Read More...

factor() - to create a factor and reorder levels as.factor() - to coerce to a factor levels() - view the levels of a factor nlevels() - return the number of levels For example: sex

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...

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...

Which of the following will throw an error and why? 4 _ chr :1:2: unexpected input ## 1: 4_ ## ^ . 4 chr :1:3: unexpected symbol ## 1: .4chr ## ^ {{Edet}} Create the following objects; give each object an appropriate name (your best guess at what name to Read More...

This is our first coding help session. We have designed some practice problems to get you acquainted with using R before beginning to wrangle in our next lesson. Practice problems Which of the following will Read More...

select() - Filters data by column. Check out associated helper functions: Select specific columns: everything() last_col() Select columns by matching some aspect of the column name: starts_with() ends_with() contains() matches() num_range() Read More...

R: Data types: integer, numeric, character, and logical Data structures: vectors, lists, data frames, matrices. x

R: Data types: integer, numeric, character, and logical Data structures: vectors, lists, data frames, matrices. ::: {.cell} x ::: :::

Subsetting a data frame is similar to subsetting a vector; we will use bracket notation [] . However, a data frame is two dimensional with both rows and columns, so we can specify either one argument or Read More...

A function in R (or any computing language) is a short program that takes some input and returns some output. An R function has three key properties: Functions have a name (e.g. dir, getwd); Read More...

{{Sdet}} Bonus content{{Esum}} There are infinite uses for find and replace functionality, and like most topics in R, there are multiple ways to search for and replace values in a data frame. You could Read More...

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...

We discussed saving the R workspace (.RData), but what if we simply want to save a single object. In such a case, we can use saveRDS() . Let's save our transcript_counts vector to our Read More...

Mass Spectrometry Section of the Collaborative Protein Technology Resource (Bldg. 37) Core Capabilities: Identification of proteins in complexes, organelles, subcellular fractions, or fluids. Global relative protein quantitation. Quantitation by isotopic labeling of cells in culture (SILAC) Read More...

Protein Expression Laboratory The Protein Expression Laboratory develops, improves, and delivers protein-centric services. Our goal is to help client investigators achieve their research goals with the lowest possible cost in the shortest time. All PEL Read More...

03/20/2025 - Please note: Registration is required to get the Meeting Link for this event. Please pre-register. BTEP and the Single Cell and Spatial Transcriptomics Interest Group jointly present: Quantifying spatiotemporal dynamics during embryogenesis is crucial Read More...

Much like pseudobulk differential expression, the RNA expression can be collapsed into pre-defined components, such as the clusters, if it is believed that cell-to-cell variation is inducing too much confusion in the labeling. This collapsing Read More...

Data frames hold tabular data comprised of rows and columns; they can be created using data.frame() . To understand more about the structure of an object and data frame, consider the following functions: str() displays Read More...

From the paper where this data was obtained, the following (incomplete) list of gene markers was obtained: Mmp3: preadipocytes Mki67: proliferating cells Fabp4: differentiating beige adipocytes and differentiated beige adipocytes Scd1: differentiated beige adipocytes Ucp1: Read More...

Here, we will start with the data stored in a Seurat object. For instructions on data import and creating the object, see an Introduction to scRNA-Seq with R (Seurat) and Getting Started with Seurat: QC Read More...

Let's use some functions. a. Use sum() to add the numbers from 1 to 10. {{Sdet}} Solution{{Esum}} sum ( 1 : 10 ) {{Edet}} b. Compute the base 10 logarithm of the elements in the following vector and save to an Read More...

Lesson 2 Exercise Questions: Base R syntax, objects, and data types Let's use some functions. a. Use sum() to add the numbers from 1 to 10. {{Sdet}} Solution{{Esum}} sum ( 1 : 10 ) {{Edet}} b. Compute the base 10 logarithm of Read More...

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...

#Create a list My_exp

Another important data structure in R is the data matrix. Data frames and data matrices are similar in that both are tabular in nature and are defined by dimensions (i.e., rows (m) and columns ( Read More...

Learning Objectives Learn about data structures including factors, lists, data frames, and matrices. Load, explore, and access data in a tabular format (data frames) Learn to write out (export) data from the R environment Data Read More...

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...

Data frames hold tabular data comprised of rows and columns; they can be created using data.frame() . To understand more about the structure of an object and data frame, consider the following functions: str() displays Read More...

R scripts can be run from the command line with command line arguments. Here is a great resource from software carpentry explaining command line arguments. To use command line arguments with an R script, we Read More...

07/13/2023 - The Comparative Genome Viewer (CGV) is a visualization tool that helps you quickly compare two genomes based on assembly-assembly alignments provided by NCBI. CGV includes eukaryotic (animal, plant and fungal) assemblies, and many cross-species Read More...

There are quite a few parameters to adjust figure labels. To re-position labels, see label_x , label_y , hjust , and vjust . These each take either a single value to move all labels or a vector Read More...

Let's start combining plots using the R package cowplot . cowplot is available on CRAN and can be installed using install.packages("cowplot") . The main function to combine figures using cowplot is plot_ Read More...

When you have a lot of colors and you want to keep these colors consistent, you can use the following convenient functions to set a name attribute for a vector of colors. Let's do Read More...

Let's use some functions. a. Use sum() to add the numbers from 1 to 10. {{Sdet}} Solution{{Esum}} sum ( 1 : 10 ) {{Edet}} b. Compute the base 10 logarithm of the elements in the following vector and save to an Read More...

Lesson 2 Exercise Questions: Base R syntax, objects, and data types Let's use some functions. a. Use sum() to add the numbers from 1 to 10. {{Sdet}} Solution{{Esum}} sum ( 1 : 10 ) {{Edet}} b. Compute the base 10 logarithm of Read More...

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...

At this point, you have seen the term "factor" pop up a few times. Factors can be thought of as vectors which are specialized for categorical data. Given R’s specialization for statistics, Read More...

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...

Another important data structure in R is the data matrix. Data frames and data matrices are similar in that both are tabular in nature and are defined by dimensions (ie. rows (m) and columns (n), Read More...

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...

This lesson provides an introduction to R in the context of single cell RNA-Seq analysis with Seurat. Learning Objectives Learn about options for analyzing your scRNA-Seq data. Learn about resources for learning R programming. Learn Read More...

1. Introduction and Learning Objectives This tutorial has been 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 that Read More...

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...

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...

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...

R Crash Course: A few things to know before diving into wrangling Learning the Basics Objectives 1. Learn about R objects 3. Learn how to recognize and use R functions 4. Learn about data types and accessors Console Read More...

Excel files are the primary means by which many people save spreadsheet data. .xls or .xlsx files store workbooks composed of one or more spreadsheets. Importing excel files requires the R package readxl . While this Read More...

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...

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...

Data import and reshape Objectives 1. Learn to import multiple data types 2. Data reshape with tidyr : pivot_longer() , pivot_wider() , separate() , and unite() Installing and loading packages So far we have only worked with objects that Read More...

Lesson 1: Introduction to Biowulf, Unix, and R Learning Objectives Learn about why you may want to use R on Biowulf. Refresh Unix and R skills. This lesson will not be hands on. Why use R Read More...

Lesson 4: Submitting R Scripts via command line Learning Objectives Learn how to use R with less interaction Learn how to deploy sbatch R jobs, and learn about alternatives such as swarm . Learn about R job Read More...

Learning Objectives Learn about popular programming languagues in bioinformatics Compare advantages and disadvantages of Python and R Discuss what you will need to learn to use these languages Discuss learning resources Choosing a programming language Read More...

Learning Objectives Learn about popular programming languagues in bioinformatics Compare advantages and disadvantages of Python and R Discuss what you will need to learn to use these languages Discuss learning resources Choosing a programming language Read More...

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...

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...

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 frames Objectives To be able to load, explore, and access data in a tabular format. To this end, students should understand the following: 1. how to import and export data 2. how to create, summarize, and Read More...

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...

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...

06/19/2017 - Harvesting the Wealth of TCGA Data The Cancer Genome Atlas (TCGA) is a large-scale study that has catalogued genomic data accumulated from more than 20 different types of cancer including mutations, copy number variation, mRNA Read More...