Search Results for: lymphoma subtyping test

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

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...

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...

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...

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...

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...

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 )) :::

10/10/2023 - In this talk we will discuss what is a p-value and examples of p-value hacking. We will also review the basics of several statistical tests and when to use them. This session will be Read More...

toy dataset HBR/UHR with spike-in GEO

The primary means of running differential expression in Seurat is through the FindMarkers function. The main usage for this function is as follows: FindMarkers(object,ident.1= ..., ident.2=..., test.use="wilcox", min.pct = 0.01, logfc. Read More...

Helpful search engine for R: rseek Test your regular expressions Troubleshooting Errors

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...

The Clinical Flow Cytometry Laboratory provides extensive support for NCI clinical protocols by providing diagnostic testing for leukemia and lymphoma in patients either on NCI clinical protocols or undergoing testing to determine eligibility for NCI Read More...

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...

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...

Differential expression analysis is the process of identifying genes that have a significant difference in expression between two or more groups. For many sequencing experiments, regardless of methodology, differential analysis lays the foundation of the Read More...

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 about answering these questions. Read More...

Let's use our practice data set again, and see if we can predict group membership (old vs young) by microbial composition. We will use the sample-classifier pipeline. This pipeline splits our data into training Read More...

We are going to begin by working in our console. In general, the console is used to run R code. If we want to run code quickly or test code, the console is the place Read More...

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...

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...

12/08/2023 - This presentation will discuss strategies and policies for effective sharing and reuse of large multidimensional datasets. Dr. Espinosa will discuss his experiences as a data generator, data analyst, collaborator, teacher, and mentor through the Read More...

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...

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.

Fisher Exact Test DAVID performs over representation analysis (ORA) at its core, which aims to find enriched molecular functions, pathways, or other annotations represented by the input gene list. In other words, many genes in Read More...

Fisher Exact Test DAVID performs over representation analysis (ORA) at its core, which aims to find enriched molecular functions, pathways, or other annotations represented by the input gene list. In other words, many genes in Read More...

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) 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...

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...

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...

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...

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...

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...

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...

03/19/2025 - The rising popularity of spatial transcriptomics (ST) has prompted the development of numerous analysis methods, each varying in robustness and user accessibility. These diverse approaches could help provide a better understanding of the tumor Read More...

10/22/2024 - Popular structure prediction program AlphaFold3 and its competitor Chai-1 recently added capabilities to predict 3D RNA structures straight from sequence input. In this talk, we will discuss some test cases for these programs and Read More...

06/18/2024 - The rising popularity of spatial transcriptomics (ST) has prompted the development of numerous analysis methods, each varying in robustness and user accessibility. These diverse approaches could help provide a better understanding of the tumor Read More...

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 have already been filtered Read More...

Many experiments look to compare to distinct populations, and scRNASeq is no exception. The two populations being compared can vary wildly from experiment to experiment; some look to draw comparisons based on the experimental condition, Read More...

Now, that we have clusters, we can use differential expression analysis to uncover markers that define our clusters. These markers can be used to assign cell types to our clusters. First, because we are working 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...

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 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...

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...

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...

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...

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...

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...

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...

To create an R object, you need a name, a value, and an assignment operator (e.g.,

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...

Let's remember back to the design of the study we are examining ( Reconstitution of the gut microbiota of antibiotic-treated patients by autologous fecal microbiota transplant ). This study included a randomized controlled longitudinal trial involving 25 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...

09/12/2023 - Animal models are used to study the development and progression of diseases and to test new treatments. Model organisms are a subset of research organisms that serve as a proxy for understanding human biology. Read More...

Two Statistical Components:(Remember all statistical methods rely on various assumptions regarding the characteristics of the data...if they are not true all bets are off). Normalization of counts - the process of ensuring that Read More...

Here's a good Unix trick to know - tab complete. Start typing the name of the file or directory you want, and hit the tab key. The system will auto-complete the name of the Read More...

Let's say you've got a very large FASTA or FASTQ file, and you want to run an analysis on it. Before working on the whole file, it can be useful to set up Read More...

The Integrative Genome Viewer (IGV) is an open-source genome browser created and maintained by the Broad Institute. We will be using this to visualize genomic data. To obtain IGV, please submit a ticket through service. Read More...

DAVID compares the overlap of user provided gene list to an annotation to the overlap of a background gene list to the same annotation. Thus, DAVID is really using the Fisher exact test to determine Read More...

DAVID compares the overlap of user provided gene list to an annotation to the overlap of a background gene list to the same annotation. Thus, DAVID is using the Fisher exact test to determine if Read More...

Instructions for installing IGV The Integrative Genome Viewer (IGV) is an open-source genome browser created and maintained by the Broad Institute. We will be using this to visualize genomic data. To obtain IGV, please submit Read More...

We will build a database out of all features of the 2014 Ebola genome under accession number KM233118. This data will go into a new directory named "db_2014". mkdir -p db_2014 # Get the 2014 Ebola Read More...

This page uses content directly from the Biostar Handbook by Istvan Albert. Learn * What are sequence adapters? * Do we need to trim them before alignment? * How can I trim with a new adapter sequence? Be Read More...

This page uses content directly from the Biostar Handbook by Istvan Albert. Learn * What are sequence adapters? * Do we need to trim them before alignment? * How can I trim with a new adapter sequence? Be Read More...

Database for Annotation, Visualization and Integrated Discovery (DAVID) - an overview Before getting started, remember to be signed on to the DNAnexus GOLD environment. Lesson 17 review In the previous class, we got an overview of Read More...

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...

>> Want to put the output from cat , head , or tail into a new file? head -n 20 /data/seq1.fasta > smaller.fasta Or we could put the last 20 lines into a file with Read More...

This page uses content directly from the Biostar Handbook by Istvan Albert. Start by activating the bioinfo environment. conda activate bioinfo Create a new directory for the multiqc data. mkdir multi cd multi Retrieve the Read More...

This page uses content directly from the Biostar Handbook by Istvan Albert. Start by activating the bioinfo environment. conda activate bioinfo Create a new directory for the multiqc data. mkdir multi cd multi Retrieve the Read More...

We are going to download some bulk RNA-Seq test data and learn how to decompress it. First we will create a place to store the data. Go to the directory you've created for working Read More...

This page contains content taken directly from the Biostars Handbook by Istvan Albert. Remember to activate the bioinformatics environment. conda activate bioinfo Install the statistical packages we will need for the analysis, curl http://data. Read More...

The instructions that follow were designed to test the skills you learned in Lesson 2. Thus, the primary focus will be navigating directories and manipulating files. Let's navigate our files using the command line. Begin 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...

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...

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...

10/24/2024 - NIH Text Mining and Natural Language Processing SIG is pleased to welcome you to this special event featuring two extraordinary speakers focused on the applications of Deep Learning in Computational Biology.  & 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...

When using SingleR, the 3 primary parameters are the experimental dataset, the reference dataset, and the labels being used. Continuing with the main labels of the MouseRNASeq dataset on the full dataset looks like this: annot = 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...

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...

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...

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...

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...

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...

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...

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...

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...

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...

Lesson 5 Practice 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 Read More...

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...

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...

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...

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...

Lesson 5: Microbial diversity, alpha rarefaction, alpha diversity Learning Objectives Understand the difference between alpha and beta diversity Introduce several alpha diversity metrics Understand what rarefaction is and why it is important Introduce the debate regarding Read More...