Search Results for: chemical names

Services: Biophysics Facility offers CD as an open-access instrument.  First-time users must complete a short training session before gaining access to the instrument reservation calendar. Location: Building 50, room 3123 Description: CD spectroscopy measures the difference Read More...

The Chemistry and Synthesis Center (CSC) of the National Heart, Lung, and Blood Institute (NHLBI) provides IRP scientists with targeted imaging probes and chemical tools that help accelerate cell-based assays, in vivo imaging studies, and Read More...

Back Services: Biophysics Facility offers CD as an open-access instrument.  First-time users must complete a short training session before gaining access to the instrument reservation calendar. Location: Building 50, room 3123 Description: CD spectroscopy measures the Read More...

The Biological Products Core provides the AIDS research community with high-quality purified preparations of various strains of Human Immunodeficiency Virus (HIV) and Simian Immunodeficiency Virus (SIV), economically prepared by leveraging the economy of scale. Materials Read More...

NCI LASP Animal Research Technology Support (ARTS) provides customized technical support for basic and translational animal-based research to the scientific community. We offer a wide array of services ranging from expert colony management to the Read More...

The centrally funded Statistics team within the Advanced Biomedical Computational Science group at the Frederick National Lab provides statistical consultation and data analysis support for NCI laboratories. We have broad-range expertise in biomedically relevant areas Read More...

The PPS encompasses all scientific analyses related to pharmacology, once the specimen has been collected and stored. There is a multi-step process to evaluate how the drug is being handled by the body after administration. Read More...

The Laboratory Animal Sciences Program (LASP) of the Frederick National Laboratory operates a Gnotobiotics Facility (GF) to support research focused on the role of microbiota in cancer inflammation, pathogenesis, and treatment response. The GF can Read More...

Back Services: Biophysics Facility offers ITC calorimeters as open-access instruments.  First-time users must complete a short training session before gaining access to the instrument reservation calendar.  Training includes performing a test experiment and Read More...

In order to meet increasing demands from both NIH intramural and extramural communities for access to a small angle X-ray scattering (SAXS) resource, the Center for Cancer Research (CCR) under the leadership of Drs. Jeffrey Read More...

Home About the Biophysics Core Biophysics Core Services [tabby title="Instrumentation"] NHLBI Biophysics Core The Biophysics Core Facility: Overview Core Facilities provide scientific resources, cutting-edge technologies and novel approaches to support DIR scientists. Availability of Read More...

rownames_to_column() - Creates a column in your data frame from existing row names. column_to_rownames() - Creates row names from a column in your data frame.

There should not be spaces in Unix file names or directories. Here is a good method to use: Use the underscore (_) where a space would go, like this, to name a directory containing RNA-Seq data. Read More...

Notice that the column names each begin with a number (e.g., 1_MB231_RNA1). Place an "S" at the beginning of each sample name using rename_with() , which is similar to rename() , but Read More...

Note that we now have differential expression by transcripts and our first column contains the transcript IDs. But what genes do these transcripts map to? We will need to do some data wrangling to find Read More...

{{Sdet}} Solution{{Esum}} #multiple ways to find color blind friendly palettes. #using color brewer scales RColorBrewer :: display.brewer.all ( colorblindFriendly = TRUE ) ggplot ( iris ) + geom_point ( aes ( Petal.Length , Petal.Width , color = Species )) + coord_fixed ( ratio = 1 , Read More...

09/12/2024 - Dr. Nussinov will highlight some scientific questions bordering on physics and biology, where fundamental physics and chemistry can help biology. They span the behavior of a bird, protein activation, signaling in cancer, and neurodevelopmental Read More...

To load a single file, we use W10

Size: nrow() - number of rows ncol() - number of columns Content: head() - returns first 6 rows by default tail() - returns last 6 rows by default Names: colnames() - returns column names rownames() - returns Read More...

There are some immediate problems with the data. The column names begin with numbers, which are not syntactic with R. The gene names are hybrids of Ensembl ID and gene symbols and will match neither Read More...

Learn about the structure of FASTQ files Create a text file contains the base names of the HBR and UHR FASTQ files so that we can use those in the future (base names are file Read More...

Anything that you want assigned to memory must be assigned to an R object.

The Seurat v5 object doesn’t require all assays have the same cells. In this case, Cells() can be used to return the cell names of the default assay while colnames() can be used to Read More...

There are rules regarding the naming of objects. 1. Avoid spaces or special characters EXCEPT '_' and '.' 2. No numbers or underscores at the beginning of an object name. For example: 1a:1:2: unexpected symbol ## 1: 1a ## ^ Note It is Read More...

We can convert to wide format using pivot_wider() , which takes three main arguments: 1. the data we are reshaping 2. the column that includes the new column names - names_from 3. the column that includes the Read More...

We can convert back to long / tidy format using pivot_longer() . pivot_longer() takes four main arguments: 1. the data we want to transform 2. the columns we want to pivot longer 3. the column we want to Read More...

We can convert to wide format using pivot_wider() , which takes three main arguments: 1. the data we are reshaping 2. the column that includes the new column names - names_from 3. the column that includes the Read More...

We can convert back to long / tidy format using pivot_longer() . pivot_longer() takes four main arguments: 1. the data we want to transform 2. the columns we want to pivot longer 3. the column we want to Read More...

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...

list() - create a list names() - create named elements (Also useful for vectors) lapply() , sapply() - for looping over elements of the list

They are typically much faster (~10x) than their base equivalents. Long running jobs have a progress bar, so you can see what’s happening. If you’re looking for raw speed, try data.table::fread(). Read More...

For any next generation sequencing experiment, you will need sample information (sample metadata) to make sense of your data. The key to a good study is to collect good metadata. You should minimally have all Read More...

First, notice that you can easily access columns from the sample metadata ( colData() ) using $ . Using brackets to subset: se$SampleName ## [1] GSM1275862 GSM1275863 GSM1275866 GSM1275867 GSM1275870 GSM1275871 GSM1275874 ## [8] GSM1275875 ## 8 Levels: GSM1275862 GSM1275863 GSM1275866 GSM1275867 ... GSM1275875 se$ 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...

We can use the airway package to see how this container works, including how to access and subset the data. What is the airway package? There are data sets available in R to practice with Read More...

pivot_wider() and pivot_longer() have replaced the functions gather() and spread() . pivot_wider() converts long format data to wide, while pivot_longer() converts wide format data to long. If you haven't guessed already, Read More...

Let's load in a count matrix from airway to work with and reshape. aircount

Now that we have downloaded the Golden Snidget reference files let's take a moment to get to know the references. First, change into the refs folder. How do we do this from the ~/biostar_ Read More...

Most jobs on Biowulf should be run as batch jobs using the "sbatch" command. $ sbatch yourscript.sh Where yourscript.sh is a shell script containing the job commands including input, output, cpus-per-task, and Read More...

Most jobs on Biowulf should be run as batch jobs using the "sbatch" command. $ sbatch yourscript.sh Where "yourscript.sh" contains the job commands including input, output, cpus-per-task, and other steps. Read More...

Most jobs on Biowulf should be run as batch jobs using the "sbatch" command. $ sbatch yourscript.sh Where "yourscript.sh" contains the job commands including input, output, cpus-per-task, and other steps. Read More...

Next, we need to generate the counts (ie. number of reads that map to a transcript). But first, change back into the ~/biostar_class/snidget folder and then take a moment to think about how Read More...

03/11/2026 - This one-hour online training, the second session of the two-part series,  focuses on reshaping and enriching the cleaned patient dataset to prepare it for analysis and reporting. Attendees will practice splitting and Read More...

09/30/2024 - In this webinar, you'll gain insights into the Electronic Medical Record Search Engine (EMERSE). EMERSE is a simple, powerful tool to help researchers like you identify key data within free text clinical notes Read More...

Once we have read in the matrices, the next step is to create a Seurat object. The Seurat object will be used to store the raw count matrices, sample information, and processed data (normalized counts, 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...

The Seurat Object is a data container for single cell RNA-Seq and related data. It is an S4 object, which is a type of data structure that stores complex information (e.g., scRNA-Seq count matrix, Read More...

Some tools have been described in the previous session (see here ). Today, we will be focusing on the SingleR tool, which also requires the celldex package . In short, SingleR operates by comparing your current dataset 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) . adp

To access the NIH Partek Flow server, go to https://partekflow.cit.nih.gov/flow and enter the user's NIH username and then password. Note User may have selected a password different than that Read More...

If we want to export our df ( scaled_counts ) to use with another program, we can write out to a file. write.table(scaled_counts, file = "scaled_counts_mod.txt", quote=FALSE,row. 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 . We are going to use the filtlowabund_scaledcounts_airways.txt file Read More...

Lesson 2 Exercise Questions: Part 1 (BaseR subsetting and Factors) 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 . We are going 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 . We are going to use the filtlowabund_scaledcounts_airways.txt file Read More...

Lesson 3 Exercise Questions: BaseR dataframe manipulation and factors 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 . We are going 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

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...

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...

#Create a list My_exp

The object class used by the DESeq2 package to store the read counts and the intermediate estimated quantities during statistical analysis is the DESeqDataSet. --- Analyzing RNA-seq data with DESeq2 Constructing this object from a Read More...

We can access a column of our data frame using [] , [[]] , or using the $ . We can use colnames() and rownames() to access the column names and row names of a data frame. For example: df[[" Read More...

In tab delimited files, data columns are separated by tabs. To import tab-delimited files there are several options. There are base R functions such as read.delim() and read.table() as well as the readr Read More...

R doesn't care about spaces in your code. However, it can vastly improve readability if you include them. For example, "thisissohardtoread" but "this is fine". You can use tab completion Read More...

When loading tabular data with readr , the default object created will be a tibble . Tibbles are like data frames with some small but apparent modifications. For example, they can have numbers for column names, and Read More...

Let's take a look at the metadata associated with QIIME 2 Cancer Microbiome Intervention tutorial. qiime metadata tabulate \ --m-input-file /data/sample-metadata.tsv \ --o-visualization metadata-summary.qzv This command allows us to interactively explore the metadata. If Read More...

Import data from the sheet "iris_data_long" from the excel workbook (file_path = "./data/iris_data.xlsx"). Make sure the column names are unique and do not contain spaces. Save Read More...

There are rules regarding the naming of objects. Avoid spaces or special characters EXCEPT '_' and '.' No numbers or symbols at the beginning of an object name. For example: 1a:1:2: unexpected symbol ## 1: 1a ## ^ In contrast: a

Reshape iris_long to a wide format. Your new column names will contain names from Measurement.location . Your wide data should look as follows: ## # A tibble: 150 × 6 ## Iris.ID Species Sepal.Length Sepal.Width Petal.Length Read More...

Use pivot_longer to reshape countB. Your reshaped data should look the same as the data below. {{Sdet}} Solution } library ( tidyverse ) countB % rownames_to_column ( "Feature" ) countB_l ## 1 Tspan6 1 703 71 ## 2 Tspan6 2 567 970 ## 3 Tspan6 3 867 242 ## 4 TNMD 1 490 342 ## 5 TNMD 2 482 935 ## 6 Read More...

In lesson 3, we learned how to read and save excel spreadsheet data to a R object using the tidyverse package readxl . Today we will use some example data from an excel spreadsheet to learn the Read More...

In order to create a ggplot2 bar plot we will need to reshape the data. The sample names should be in a single column named Sample and the gene counts in a single column named Read More...

In our example data, the sequences are paired-end demultiplexed data . Raw fastq files are currently in a directory named /data/data_to_import . QIIME2 has specific functions for importing specific types of raw sequencing data. Read More...

Help Session Lesson 3 Loading data Import data from the sheet "iris_data_long" from the excel workbook (file_path = "./data/iris_data.xlsx"). Make sure the column names are unique and Read More...

The computational chemistry and protein modeling team in the Advanced Biomedical Computational Science (ABCS) group provides novel solutions in structural modeling and computational chemistry. Computational scientists in the group collaborate with NCI researchers by using 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...

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...

One particular critique of differential expression in single cell RNASeq analysis is p-value "inflation," where the p-values get so small that there are far too many genes exist with p-values below 0.05, even after Read More...

The FindAllMarkers function is particularly useful in identifying the differentially expressed genes that distinguish several groups, such as seen here in the clusters. What makes this unique is that none of the identities are initially 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...

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...

Accessing Partek Flow at NIH and tips for data transfer Learning objectives Instructions for accessing Partek Flow NCI researchers can find instructions for accessing Partek Flow at https://bioinformatics.ccr.cancer.gov/btep/partek-flow-bulk-and-single-cell-rna-seq-data-analysis/ . But 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...

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...

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...

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...

Lesson 6 Exercise Questions: ggplot2 Putting what we have learned to the test: The following questions synthesize several of the skills you have learned thus far. It may not be immediately apparent how you would go Read More...

The object that we imported, scaled_counts , is a data frame. Let's learn a bit more about our data frame. First, we can learn more about the structure of our data using str() . We 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...

To explore tidyverse functionality, let's read in some data and take a look. #let's use our differential expression results dexp "ENSG00000000003", "ENSG00000000419", "ENSG00000000457", "E… $ albut untrt, Read More...

“Tidy datasets are all alike, but every messy dataset is messy in its own way.” –– Hadley Wickham. Messy data sets tend to share five common problems: Column headers are values, not variable names. Multiple variables 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...

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...

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...

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...

Load in the comma separated file "./data/countB.csv" and save to an object named gcounts . {{Sdet}} Solution } gcounts `...1` colnames ( gcounts )[ 1 ] ## 1 Tspan6 703 567 867 71 970 242 ## 2 TNMD 490 482 18 342 935 469 ## 3 DPM1 921 797 622 661 8 500 ## 4 SCYL3 335 216 222 774 979 793 ## 5 FGR 574 574 515 584 941 344 ## 6 CFH 577 792 672 104 192 936 ## 7 FUCA2 798 766 995 27 756 546 ## 8 GCLC 822 874 923 705 667 522 ## 9 NFYA 622 793 918 868 334 64 {{Edet}} Plot the 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...

Objectives To explore Bioconductor, a repository for R packages related to biological data analysis. To better understand S4 objects as they relate to the Bioconductor core infrastructure. To learn more about a popular Bioconductor S4 Read More...

Lesson 2: Getting Started with QIIME2 Lesson Objectives Obtain sequence data and sample metadata Import data and metadata Discuss other useful QIIME2 features including view QIIME2, provenance tracking, and the QIIME2 forum. DNAnexus DNAnexus provides a 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...

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...

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...

Let's load in some data to work with. In this lesson, we will continue to use sample metadata, raw count data, and differential expression results from the airway RNA-Seq project. Load the data: #sample Read More...

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...

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...

As you can see from the image, there are several accessor functions to access the data from the object: assays() - access matrix-like experimental data (e.g., count data). Rows are genomic features (e.g., Read More...

Help Session Lesson 4 Plotting with ggplot2 For the following plots, let's use the diamonds data ( ?diamonds ). The diamonds dataset comes in ggplot2 and contains information about ~54,000 diamonds, including the price, carat, color, clarity, and 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...

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...