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R Introductory Series: Test your learning

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Bioinformatics

Which of the following functions is used to print your working directory in R? a. pwd b. Setwd() c. getwd() d. wkdir() {{Sdet}} Solution{{Esum}} C {{Edet}} Which of the following can be used to Read More...

R Introductory Series: Test your learning

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Bioinformatics

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

R Introductory Series: Test your learning

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Bioinformatics

Which of the following will NOT print the "Run" column from scaled_counts? a. scaled_counts$Run b. scaled_counts["Run"] c. scaled_counts[8,] d. scaled_counts[8] {{Sdet}} Solution{{Esum}} C {{ Read More...

R Introductory Series: Test your learning

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Bioinformatics

From the interesting_trnsc data frame select the following columns and save to an object: sample, dex, transcript, counts_scaled. {{Sdet}} Possible Solution{{Esum}} interesting_trnsc_s

R Introductory Series: Test your learning

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Bioinformatics

Filter the interesting_trnsc data frame to only include the following genes: MCL1 and EXT1. {{Sdet}} Possible Solution{{Esum}} interesting_trnsc_f

R Introductory Series: Test your learning

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Bioinformatics

Using what you have learned about select() and filter() , use the pipe ( |> ) to create a subset data frame from scaled_counts that only includes the columns 'sample', 'cell', 'dex', 'transcript', and 'counts_scaled' and 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...

R Introductory Series: Test your learning

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Bioinformatics

Create a data frame summarizing the mean counts_scaled by sample from the scaled_counts data frame. {{Sdet}} Possible Solution{{Esum}} scaled_counts |> group_by(sample) |> summarize(Mean_counts_scaled=mean(counts_scaled)) {{ Read More...

R Introductory Series: Test your learning

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Bioinformatics

Using what you have learned about select() and filter() , use the pipe ( |> ) to create a subset data frame from scaled_counts that only includes the columns 'sample', 'cell', 'dex', 'transcript', and 'counts_scaled' and 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

Create a data frame summarizing the mean counts_scaled by sample from the scaled_counts data frame. ::: {.cell} scaled_counts |> group_by ( sample ) |> summarize ( Mean_counts_scaled = mean ( counts_scaled )) :::

Bioinformatics for Beginners 2022: Terms to know

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Bioinformatics

Normalization - the process of scaling data to account for uncontrolled factors affecting variation. Effect size - "the quantitative measure of the magnitude of a phenomenon" (Biostar Handbook). P-value - "the probability Read More...

Using SAS/STAT

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Bioinformatics

11/13/2024 - This one-hour online training will cover several easy-to-use tools for analytic situations, including PROC FREQ (chi-square tests, Fisher's exact test), PROC TTEST, and PROC NPAR1WAY. This training covers the basic guidelines for Read More...

Data Wrangling with R: Course Expectations

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Bioinformatics

This course will include a series of eight, one hour lessons. Each lesson will be held virtually using the Webex platform on Mondays / Wednesdays at 1 pm. Lessons will immediately be followed by a one-hour help 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...

Turning discovery into health with the spatial biology community

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Bioinformatics

10/03/2023 - Multiplexed antibody-based imaging enables the detailed characterization of molecular and cellular organization in tissues. Significant advances in the field now allow high-parameter data collection (60+ targets); however, considerable expertise and capital are needed to validate Read More...

Bioinformatics for Beginners 2022: Objectives

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Bioinformatics

In this lesson we learned how to align raw sequencing reads to reference and to process alignment results for downstream analysis. Here, we will test our knowledge by continuing with the Golden Snidget dataset.

BD Genomics Rhapsody System

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Total end-to-end system for single-cell research [embed]https://youtu.be/vMzhSzg1rUw[/embed] The BD Rhapsody Single-Cell Analysis system empowers and streamlines your research with a complete system of tools, including reagents and analysis software, Read More...

NCI LASP Genome Modification Core (GMC)
Frederick, MD

Core Facility

NCI LASP Genome Modification Core (GMC) is a CCR-dedicated facility that provides advice, training, and reagents to NCI scientists seeking to utilize CRISPR and other nucleases to generate genome modifications in primary cells, cell lines, Read More...

Comparative Oncology Program (COP)
Bethesda, MD

Collaborative

The COP evaluates novel therapies in pet dogs with cancer to improve outcomes for human patients and established the Comparative Oncology Trial Consortium (COTC), a collaborative effort of NCI and extramural comparative oncology centers at 24 Read More...

Differential Scanning Calorimetry (DSC) – Microcal VP-DSC

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Back Services: Biophysics Facility offers DSC as an open-access instrument. First-time users must complete training before gaining access to the instrument reservation calendar. Location: Building 50, room 3123 Description: The differential scanning calorimeter measures the constant pressure 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...

National Mouse Metabolic Phenotyping Centers (MMPC)
Davis, CA

Trans NIH Facility

The Mouse Metabolic Phenotyping Center (MMPC)Live Program provides standardized, high quality, unique, and hard to find phenotyping services for mouse models of diabetes, obesity, and related metabolic disorders. Emerging as the next iteration of Read More...

NCI Cloud Resources
Rockville, MD

Collaborative

Repositories

The NCI Cloud Resources are components of the NCI Cancer Research Data Commons (CRDC) that bring data and computational power together to enable cancer research and discovery. These cloud-based platforms eliminate the need for researchers Read More...

Statistical Inference: Frequentist Approach, Part 1 of 2

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Bioinformatics

04/25/2025 - In partnership with the NIH Clinical Center's Biostatistics and Clinical Epidemiology Service (BCES), the NIH Library is offering this two-part online training for non-statisticians interested in understanding the basic, intuitive thinking behind the Read More...

R Introductory Series: Exercises: Lesson 2, Part 1

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Bioinformatics

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...

R Introductory Series: Base R and data frames

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Bioinformatics

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...

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...

R Introductory Series: Exercises: Lesson 2 Tidyverse

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Bioinformatics

Lesson 2 Exercise Questions: Part 2 (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 Read More...

R Introductory Series: Practicing the Tidyverse (Part 1)

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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...

Microbiome Analysis with QIIME2: Statistics

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Bioinformatics

Some typical statitstical tests applied to beta diversity metrics include the following: Adonis (PERMANOVA) Similar to a MANOVA, but is permutational and non-parametric. Sensitive to group dispersion, so it is worth running alongside a beta-dispersion Read More...

Bioinformatics for Beginners 2022: Outline

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Bioinformatics

Course setup Intro to DNAnexus and the GOLD learning system Learning Objectives Unix Bootcamp Brief Review of R (for R scripts in later analyses) Introduction to RNA Sequencing Central Dogma of Molecular Biology What is Read More...

Isothermal Titration Calorimetry (ITC) – iTC200

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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...

NCI Genomics and Pharmacology Facility
Bethesda, MD

Core Facility

The Genomics and Pharmacology Facility is part of the NCI's Center for Cancer Research (CCR), within the Developmental Therapeutics Branch. Its mission is to manage and assess molecular interaction data obtained through multiple platforms, increase Read More...

BTEP Video Archive of Past Classes

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Bioinformatics

Listed below are the video recordings of past BTEP events (classes, seminars, workshops). Videos are hosted on various servers and may play slightly differently. Some videos may be downloaded for local viewing. Recorded Videos of Read More...

R Introductory Series: Practicing the Tidyverse

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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...

R Introductory Series: Basics of R Programming

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Bioinformatics

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...

R Introductory Series: Introduction to R and RStudio

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Bioinformatics

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...

Microbiome Analysis with QIIME2: Lesson 5 Practice

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Bioinformatics

This practice lesson is associated with Lesson 5 of the Microbiome Analysis with QIIME 2. In this practice lesson, we will work on choosing a sampling depth to rarefy, running core-metrics, and comparing alpha diveristy between our Read More...

Data Wrangling with R: Course Overview

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Bioinformatics

Course Overview Welcome to the Data Wrangling with R course series The purpose of this course is to introduce you to essential R packages and functions that will make your life easier when it comes 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...

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...