Search Results for: Partial Differential Equation

CREx Monthly Newsletter Learn about the NIH Collaborative Research Exchange (CREx), Core Facilities, Webinars, & More New NIH Resource Resources Advance your research with the NIH Mouse Imaging Facility (MIF) The NIH Mouse Imaging 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...

Back Services: Biophysics Facility offers MST as an open-access instrument.  First-time users must complete a short training session before gaining access to the instrument reservation calendar.  Training includes the KD determination of a Read More...

After obtaining the expression counts, we can proceed to differential expression analysis. What tool did we use for this in this course and what are alternatives. {{Sdet}} Answer{{Esum}} We used DESeq2 to obtain differential Read More...

05/22/2024 - This seminar provides an overview of differential expression testing with Seurat. Topics to be covered include preparing data for differential gene expression, d ifferential gene analysis between specific groups, d ifferential gene analysis for Read More...

Retrieve R "helper" scripts developed for Biostars environment. curl -O http://data.biostarhandbook.com/rnaseq/code/deseq1.r curl -O http://data.biostarhandbook.com/rnaseq/code/deseq2.r curl -O http://data.biostarhandbook. Read More...

Differential Expression Differential expression involves the comparison of normalized expression counts of different samples and the application of statistical measures to identify quantitative changes in gene expression between the different samples. Normalization and Statistical Significance Read More...

Next, how do we generate the differential expression results? {{Sdet}} Solution{{Esum}} Rscript $CODE/deseq2.r {{Edet}}

Retrieve R "helper" scripts developed for Biostars environment. curl -O http://data.biostarhandbook.com/rnaseq/code/deseq1.r curl -O http://data.biostarhandbook.com/rnaseq/code/deseq2.r curl -O http://data.biostarhandbook. Read More...

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...

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...

Differential abundance testing examines which taxa are significantly different in abundance between conditions. However, challenges such as sparsity, compositionality, and library size differences make this challenging to determine.

09/20/2023 - This session of the BTEP Coding Club will focus on the tool rMATS for differential alternative splicing event detection from RNA-Seq data. This 1-hour demo will provide a detailed overview of rMATS including why Read More...

03/11/2025 - In this class, participants will get hands-on experience with generating and interpreting differential gene expression analysis results from bulk RNA sequencing. In addition, participants will learn to generate common plots used to visualize bulk Read More...

03/06/2025 - This class introduces participants to the first steps to differential expression analysis in bulk RNA sequencing which involves filtering out noise from (ie. genes without expression across samples) and performing QC on the gene 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...

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...

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...

06/13/2024 - The schedule for this week consists of one presentation: Lorenz Adlung, UMC Hamburg-Eppendorf, will discuss: scMod: Marrying machine learning and deterministic modelling of longitudinal single-cell data Single-cell-based methods such as flow cytometry or single-cell Read More...

In Seurat (since version 4), differential analysis requires a preprocessing step to appropriately scale the normalized SCTransform assay across samples: adp = PrepSCTFindMarkers(adp) Found 8 SCT models. Recorrecting SCT counts using minimum median counts: 8146 As covered earlier, Read More...

01/22/2025 - This three hour online training covers QIAGEN’s CLC Genomics Workbench enables researchers to analyze NGS data without the use of command line and is a powerful tool for determining differential expression. Read More...

Single cell RNASeq is a remarkably powerful tool for analyzing populations of cells that can be recovered from various experiments. Clustering and cell type annotation can be used to distinguish different populations with a level Read More...

library(tidyverse) # dplyr and ggplot2; CRAN library(Seurat) # Seurat toolkit; CRAN library(hdf5r) # for data import; CRAN library(patchwork) # for plotting; CRAN library(presto) # for differential expression; Github library(glmGamPoi) # for sctransform; Bioconductor library( 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...

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...

12/02/2026 - Partek Flow is a point-and-click platform for building analysis workflows for Next Generation Sequences (NGS), including DNA, bulk and single-cell RNA, spatial transcriptomics, ATAC, and ChIP, helping scientists avoid the steep learning curve of Read More...

08/19/2026 - Partek Flow is a point-and-click platform for building analysis workflows for Next Generation Sequences (NGS), including DNA, bulk and single-cell RNA, spatial transcriptomics, ATAC, and ChIP, helping scientists avoid the steep learning curve of Read More...

04/16/2026 - The ability to measure gene expression levels for individual cells (vs. pools of cells) and with spatial resolution is crucial to address many important biological and medical questions, such as the study of stem Read More...

02/18/2026 - Partek Flow is a point-and-click platform for building analysis workflows for Next Generation Sequences (NGS), including DNA, bulk and single-cell RNA, spatial transcriptomics, ATAC, and ChIP, helping scientists avoid the steep learning curve of Read More...

12/10/2025 - Partek Flow enables scientists to construct analysis workflows for multi-omics sequencing data including DNA, bulk and single cell RNA, spatial transcriptomics, ATAC and ChIP. It is a point-and-click software suitable for those who wish Read More...

11/06/2025 - Qlucore Omics Explorer is a point-and-click software that enables analysis of RNA sequencing (bulk and single cell), proteomics and metabolomics data. It’s machine learning capabilities allow for cell type classification. This Read More...

10/30/2025 - Qiagen Ingenuity Pathway Analysis (IPA) is a point-and-click software that enables scientists to discern how genomic, transcriptomic, proteomic, and metabolomic changes influence molecular biology pathways and networks. This software is available to NCI investigators. Read More...

10/23/2025 - Qiagen CLC Genomics Workbench is a point-and-click software that runs on a personal computer and enables bulk RNA sequencing, ChIP sequencing, long reads, and variant analysis that is available to NCI scientists. Submit a Read More...

06/18/2025 - Gene et enrichment analy i (G EA) i a popular tool for evaluating functional enrichment, e pecially following differential expre ion analy i . Thi e ion of the BTEP Coding Club will how attendee Read More...

05/21/2025 - The programming language R i ideal for biomedical re earcher a it ha package that facilitate Next Generation equencing (NG ) data analy i . For example, bulk RNA equencing differential expre ion analy i can Read More...

04/08/2025 - Reactome is a free, open-source, curated and peer-reviewed pathway database that includes bioinformatics tools for the visualization, interpretation and analysis of pathway knowledge. This lesson will showcase the capabilities of Reactome in garnering biological 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...

03/13/2025 - UCSC Xena showcases seminal cancer genomics datasets from The Cancer Genome Atlas (TCGA) and the Pan-Cancer Atlas, as well as the Genomic Data Commons, Pan-cancer Atlas of Whole Genomes, and the International Cancer Genome Read More...

12/12/2024 - This class will introduce bulk RNA sequencing analysis using Qiagen software. Participants will learn how to process FASTQ files and obtain differential expression using CLC Genomics Workbench as well as extract biological insight using Read More...

What is bioinformatics? Bioinformatics integrates biology, statistics, and computer science to develop and apply theory, methods, and tools for the collection, storage, and analysis of biological and related data. Bioinformatics plays a critical role in Read More...

11/08/2024 - Learn microbiome analysis basics in R with phyloseq. This workshop will cover different types of analysis frequently used in microbiome studies, including sample diversity, community composition, and differential taxa. The techniques we learn will Read More...

10/09/2024 - This session will give an overview of the NCI Cancer Research Data Commons and the variety of available cancer data such as RNA expression and protein abundance. These data are hosted in openly accessible Read More...

library(tidyverse) # dplyr and ggplot2 library(Seurat) # Seurat toolkit library(hdf5r) # for data import library(patchwork) # for plotting library(presto) # for differential expression library(glmGamPoi) # for sctransform Warning: package 'glmGamPoi' was built under R Read More...

April 3, 2024 - The CCR Single Cell Analysis Facility (SCAF): An Overview (Mike Kelly, SCAF) ( Recording ) April 10, 2024 - Introduction to single cell RNA-Seq (Charlie Seibert, Saeed Yadranji Aghdam, SCAF) ( Recording ) April 17, 2024 - SCAF: Overview of Cell 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...

Welcome to Getting Started with scRNA-Seq This is a mini seminar series designed to help attendees learn more about single cell RNA-Seq, from applicable technologies to data analysis. Seminar Schedule April 3, 2024 - The CCR Single 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...

05/08/2024 - Qlucore Omics Explorer is a point-and-click software that enables analysis of RNA sequencing (bulk and single cell), proteomics and metabolomics data. It’s machine learning capabilities also allow for classification of cell types. This Read More...

04/24/2024 - Please join us on Wednesday, April 24, 2024, when Dr. Abhishek Jha, co-founder and CEO of Elucidata, will present " Data Quality for LLMs: Building a Reliable Data Foundation." The presentation starts at 11:00 a.m. Read More...

03/20/2024 - Our series of talks continues with two 20-minute presentations.  There will be light refreshments (coffee and donuts) provided, so please consider attending in person! We encourage attendees to stay and chat with Read More...

03/13/2024 - Dear Colleagues,    UCSC Xena is a web-based visual integration and exploration tool for multi-omic data and associated clinical and phenotypic annotations.   Xena showcases seminal cancer genomics datasets from Read More...

After this class, participants will Become familiar analyzing single cell RNA sequencing data using Partek Flow including Importing of data into a Partek Flow project QA/QC, filtering, and normalizing of single cell RNA data 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...

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...

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...

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...

There are data sets available in R to practice with or showcase different packages. For today's lesson and the remainder of this course, we will use data from the Bioconductor package airway to showcase 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...

First, we will want to fix the three immediate problems discussed above. Once those have been fixed, we are going to create a bar plot, using ggplot2 to plot the total gene counts per sample. Read More...

To subset by column, we use the function select() . We can include and exclude columns, reorder columns, and rename columns using select() . Select a few columns from our differential expression results ( dexp ). We can select Read More...

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...

Let's grab some data. library ( tidyverse ) acount_smeta % dplyr :: rename ( "Feature" = "...1" ) acount #differential expression results dexp % filter ( ! Feature %in% dexp $ feature ) ## # A tibble: 48,176 × 9 ## Feature SRR1039508 SRR1039509 SRR1039512 SRR1039513 SRR1039516 SRR1039517 ## ## 1 Read More...

Help Session Lesson 6 Let's grab some data. library ( tidyverse ) acount_smeta % dplyr :: rename ( "Feature" = "...1" ) acount #differential expression results dexp % filter ( ! Feature %in% dexp $ feature ) ## # A tibble: 48,176 × 9 ## Feature SRR1039508 SRR1039509 SRR1039512 Read More...

Most BTEP courses include detailed course materials including lesson content, additional resources, and lesson associated data. These course materials are listed here so that learners can easily return to and review concepts taught in class Read More...

  Update: Please contact your IC representative to get access to the Anaconda Business license as it has transitioned from enterprise to seat-based licensing. For questions, email anaconda@nih.gov. Anaconda ( https://www.anaconda. 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...

12/05/2024 - This one and a half hour online training will provide a demonstration of how to identify cell types based on statistics, visualization, and canonical markers. One Peripheral blood mononuclear cells (PBMCs) sample will be Read More...

11/19/2024 - Partek Flow enables scientists to construct analysis workflows for multi-omics sequencing data including DNA, bulk and single cell RNA, spatial transcriptomics, ATAC and ChIP. It is a point-and-click software hosted on Biowulf, the NIH Read More...

06/12/2024 - Join us for an engaging training session where we will examine the similarities and differences between machine learning and statistical differential gene expression (DGE) analysis using Qlucore Omics Explorer. Qlucore Omics Explorer is a 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...

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...

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...

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...

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...

05/16/2024 - Qiagen CLC Genomics Workbench is a point-and-click bioinformatics software that runs on a personal computer and enables bulk RNA sequencing, ChIP sequencing, long reads, and variant analysis. NCI scientists can use CLC Genomics Workbench Read More...

Course Overview Partek Flow is a start-to-finish solution for analyzing high dimensional multi-omics sequencing data. It is a point-and-click software and is suitable for those who wish to avoid the steep learning curve associated with Read More...

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...

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...

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...

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...

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 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...

01/19/2024 - Trey Ideker, Ph.D., is a professor of medicine, bioengineering, and computer science, and former chief of genetics at the University of California San Diego (UCSD). Additionally, he is director or co-director of the Read More...

Work through DNAnexus? Find a nice cancer data set for 16S and shotgun Divide into two workshops or courses? Course structure or workshop structure? Amplicon processing and analysis (5 week course) (4 weeks) 1. Talk by Greg Caparaso? 1. Read More...

Microbiome Course Course Outline Work through DNAnexus? Find a nice cancer data set for 16S and shotgun Divide into two workshops or courses? Course structure or workshop structure? Amplicon processing and analysis (5 week course) (4 weeks) 1. 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...

For this lesson, we will use sample metadata and differential expression results from the airway RNA-Seq project. Let's begin by importing the data. #sample information smeta ## 1 GSM1275862 N61311 untrt untrt SRR10395… 126 SRX384345 SRS50… SAMN0242… ## 2 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...

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...

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...

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...

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 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...

NCI scientists have many choices available to them for bioinformatic analyses of Next Generation Sequencing (NGS) data. While some require expertise in programming, others provide a more user-friendly, point-and-click interface. These options include programs for Read More...

09/13/2023 - Partek Flow is your start-to-finish solution for analyzing high dimensional multi-omics sequencing data. It is a point-and-click software and is suitable for those who wish to avoid the steep learning curve associated with analyzing Read More...