Search Results for: solution scattering

Back Services: Biophysics Facility offers MDS 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 standard molecular Read More...

The Crystallization Facility provides an automated environment for setting up crystallization experiments in a high-throughput format, storing the resulting plates under controlled conditions, and monitoring the status of prepared droplets remotely. The Facility is in 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 Biomedical Engineering and Physical Science (BEPS) shared resource supports NIH’s intramural basic and clinical scientists on applications of engineering, physics, imaging, measurement, and analysis. BEPS is centrally located on the main NIH campus Read More...

CREx Monthly Newsletter Learn about the NIH Collaborative Research Exchange (CREx), Core Facilities, Webinars, & More Technology Event Biophysical Methods for Protein Interactions Monday, May 15 – Friday, May 19, 2023 This workshop will review the strategies of Read More...

The NMR Facility for Biological Research operates six NMR spectrometers with proton resonance frequencies from 850 to 500 MHz. Three of the highest field spectrometers (850, 700, 600) are equipped with higher sensitivity cryoprobes optimized for high-resolution multi-dimensional and relaxation Read More...

Back Services: Biophysics Facility offers fluorometers as open-access instruments.  First-time users must complete a short training session before gaining access to the instrument reservation calendar. Location: Building 50, room 3226 Description: Some substances reemit light after 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...

Back Services: This instrument is not user accessible.  We provide both data collection and data analysis services. Location: Building 50, room 3331 Description: An analytical ultracentrifuge is equipped with absorption and interference optical systems that monitor Read More...

The Science and Technology Research Infrastructure for Discovery, Experimentation, and Sustainability (STRIDES) Initiative The STRIDES Initiative aims to help NIH and its institutes, centers, and offices (ICOs) accelerate biomedical research by reducing barriers in utilizing Read More...

The Spatial Imaging Technology Resource (formerly the Nanoscale Protein Analysis Section of the Collaborative Protein Technology Resource or CPTR) provides expertise and service in state-of-the-art protein analysis technologies to advance CCR research in basic discovery 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...

The facilities at AIM are available for use by the entire NIH intramural research community. While we welcome users with any size imaging project, AIM specializes in large, yearlong (or longer), collaborative research efforts with 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...

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...

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 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...

Lesson 2 Practice 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 Read More...

Can you print the first sequencing read (from header line to quality score line) in hcc1395_normal_rep1_r1.fastq.gz? {{Sdet}} Solution{{Esum}} zcat hcc1395_normal_rep1_r1.fastq.gz | head -4 {{Edet}} How Read More...

Here, we are going to align the Golden Snidget sequencing files to it's genome. Recall that we are working with RNA sequencing data. Given HISAT2 and Bowtie2 as the options for aligners, which is Read More...

Which reference genome are we using in this IGV session to view the alignment results for samples hcc1395_normal_rep1 and hcc1395_tumor_rep2? On what chromosome are the sequencing data mapping to? {{Sdet}} Solution{{ Read More...

The first step in using IGV is to load our reference genome. Take some time to see if you recall how to do this. {{Sdet}} Solution{{Esum}} {{Edet}} After loading the genome, let's view Read More...

Research Mission The goal of OMAC’s research is to understand molecular mechanisms driving carcinogenesis and the reversal of this process through treatment, by utilization and advancement of optical microscopy techniques. These techniques include Read More...

What geoms would you use to draw each of the following named plots? a. Scatterplot b. Line chart c. Histogram d. Bar chart e. Pie chart (Question taken from https://ggplot2-book.org/individual-geoms.html .) {{ Read More...

Lesson 5 Exercise Questions: ggplot2 What geoms would you use to draw each of the following named plots? a. Scatterplot b. Line chart c. Histogram d. Bar chart e. Pie chart (Question taken from https://ggplot2 Read More...

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...

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...

{{Sdet}} Solution{{Esum}} ggsave ( "iris.tiff" , width = 5.5 , height = 3.5 , units = "in" ) {{Edet}}

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

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

Given the following R code: numbers

{{Sdet}} Solution{{Esum}} library ( ggplot2 ) ggplot ( iris ) + geom_point ( aes ( Petal.Length , Petal.Width , color = Species )) {{Edet}}

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...

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 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...

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...

{{Sdet}} Solution{{Esum}} ggplot ( iris ) + geom_point ( aes ( Petal.Length , Petal.Width , color = Species )) + coord_fixed ( ratio = 1 , ylim = c ( 0 , 2.75 ), xlim = c ( 0 , 7 )) + scale_y_continuous ( breaks = c ( 0 , 0.5 , 1 , 1.5 , 2 , 2.5 )) + scale_x_continuous ( breaks = c ( 0 , 1 , 2 , 3 , 4 , 5 , 6 , 7 )) {{Edet}}

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...

According to the data availability statement, the data can be found in PRJNA803155 . Change to the Practice directory created above or make it now. Then make a new directory named raw_data . {{Sdet}} Solution{{Esum}} Read More...

This practice lesson is associated with Lesson 6 of the Microbiome Analysis with QIIME 2. In this practice lesson, we will view beta diversity results and determine whether our two conditions (old vs young) demonstrate significant differences Read More...

This practice lesson is associated with Lesson 3 of the Microbiome Analysis with QIIME 2. In this practice lesson, we will work on generating a feature table and representative sequences. We will continue working with the data Read More...

Practice Lesson 3 This practice lesson is associated with Lesson 3 of the Microbiome Analysis with QIIME 2. In this practice lesson, we will work on generating a feature table and representative sequences. We will continue working with Read More...

Practice Lesson 6 This practice lesson is associated with Lesson 6 of the Microbiome Analysis with QIIME 2. In this practice lesson, we will view beta diversity results and determine whether our two conditions (old vs young) demonstrate 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...

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 practice lesson is associated with Lesson 4 of the Microbiome Analysis with QIIME 2. In this practice lesson, we will work on filtering our feature table and representative sequences, classify our features, and generate a phylogenetic Read More...

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 cut of each diamond. --- R4 Read More...

Practice Lesson 4 This practice lesson is associated with Lesson 4 of the Microbiome Analysis with QIIME 2. In this practice lesson, we will work on filtering our feature table and representative sequences, classify our features, and generate 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...

{{Sdet}} Solution{{Esum}} qiime demux summarize \ --i-data 01_import/import.qza \ --o-visualization 01_import/import.qzv {{Edet}} Again, to view this file, you will need to move it to public . Note: It is easier to create the Read More...

Get the reads from http://data.biostarhandbook.com/books/rnaseq/data/golden.reads.tar.gz . You will also need to unpack the file. {{Sdet}} Solution{{Esum}} wget -nc http://data.biostarhandbook.com/books/rnaseq/data/ Read More...

Before getting started, let's create a folder called snidget within the ~/biostar_class directory to conduct our analysis. To do this, we need to first go to the ~/biostar_class folder, how do you Read More...

Start a new script, named fastqc.sh in the same directory in which you downloaded data from Lesson 5. The command you will include in the script is as follows: mkdir fastqc fastqc -o ./fastqc/ -t 4 *. Read More...

For today's practice, we are going to embark on a Unix treasure hunt created by the Sanders Lab at the University of California San Francisco. Note: the treasure hunt materials can be obtained directly Read More...

Why do we need a reference genome? {{Sdet}} Solution{{Esum}} The reference genome serves as a "known" that guides us in constructing the genome of the unknown from sequencing data. {{Edet}} What file Read More...

Lesson 4 Practice For today's practice, we are going to embark on a Unix treasure hunt created by the Sanders Lab at the University of California San Francisco. Note: the treasure hunt materials can be Read More...

Can we generate an expression heatmap? {{Sdet}} Solution{{Esum}} Rscript $CODE/create_heatmap.r {{Edet}} Next, let's generate the Principal Components Analysis plot. But first, we need to convert the counts.csv and design. Read More...

What is a FASTQ file? {{Sdet}} Solution{{Esum}} A fastq or fq file is the format for files that contain our sequencing data. Similar to a fasta file, which contains a header line that starts Read More...

04/14/2025 - Gene set analysis (GSA) is essential in genomic research, yet traditional methods often lack transparency and produce contextually irrelevant results, making interpretation challenging. While large language models (LLMs) offer a promising solution for result Read More...

11/21/2024 - Join this webinar to gain insights from Dr. Qi Long, who will explore how LLMs offer a promising solution to data issues, especially those stemming from incomplete information.   Dr. Long will share his team' Read More...

11/06/2024 - Join Our Training on Spatial Omics Data Analysis with MAWA (Multiplex Analysis Web Apps).   Want to learn how to process and analyze your spatial proteomics/transcriptomics data? Join us for four virtual hour-long sessions Read More...

10/29/2024 - Join Our Training on Spatial Omics Data Analysis with MAWA (Multiplex Analysis Web Apps).   Want to learn how to process and analyze your spatial proteomics/transcriptomics data? Join us for four virtual hour-long sessions Read More...

10/22/2024 - Join Our Training on Spatial Omics Data Analysis with MAWA (Multiplex Analysis Web Apps).   Want to learn how to process and analyze your spatial proteomics/transcriptomics data? Join us for four virtual hour-long sessions Read More...

10/15/2024 - Join Our Training on Spatial Omics Data Analysis with MAWA (Multiplex Analysis Web Apps).   Want to learn how to process and analyze your spatial proteomics/transcriptomics data? Join us for four virtual hour-long sessions Read More...

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 analyzing sequencing 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...

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 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...

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

{{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...

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...

{{Sdet}} Solution{{Esum}} ggplot ( iris ) + geom_point ( aes ( Petal.Length , Petal.Width , fill = Species ), size = 2 , shape = 21 ) + coord_fixed ( ratio = 1 , ylim = c ( 0 , 2.75 ), xlim = c ( 0 , 7 )) + scale_y_continuous ( breaks = c ( 0 , 0.5 , 1 , 1.5 , 2 , 2.5 )) + scale_x_continuous ( breaks = c ( 0 , 1 , 2 , 3 , 4 , 5 , 6 , 7 )) + scale_fill_ Read More...

Download the data using prefetch and fasterq-dump . {{Sdet}} Solution{{Esum}} cd raw_data cat ../sra_id.txt | while read sra_id; do prefetch $sra_id; fasterq-dump $sra_id; gzip ${sra_id}*.fastq;done {{Edet}} What 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...

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...

Using the boxplot you created above, reorder the x-axis so that color is organized from worst (J) to best (D). There are multiple possible solutions. Hint: Check out functions in the forcats package (a tidyverse 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...

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...

Practice Lesson 2 For the help sessions, we will work on processing sequences generated in Zhang Z, Feng Q, Li M, Li Z, Xu Q, Pan X, Chen W. Age-Related Cancer-Associated Microbiota Potentially Promotes Oral Squamous 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...

Separate the gene abbreviation from the Ensembl ID in column 1 using separate() . Save the output to a new object named dmat2 . {{Sdet}} Solution }. #separate gene abbreviation from ensembl id dmat2% select(1:2) dmat2% select(!gene_abb) {{ 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...

The data is available in the Sequence Read Archive (BioProject PRJNA803155 ), so the first step is to grab the data from the SRA. For your convenience, we have also created a compressed archive of the Read More...

Methods to reduce dimensionality in the data and visualize trends in the data. The following list includes commonly used methods and is not exhaustive. PCoA most common similar to PCA but works on distance metrics Read More...

To import we will need to keep in mind that our samples are paired-end with quality information and that we are using a manifest format. Note: Phred 64 quality scores are associated with older data, so 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...

We will need to use a manifest file to import. See the Import tutorial . Note: The manifest file can be comma separated depending on the format that you use at import , despite what is written Read More...

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...

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...

Begin by loading the data and saving to an object named dmat . {{Sdet}} Solution } library(tidyverse) dmat ## 1 ENSG00000001630.… 6877. 6614 7058. 11305. ## 2 ENSG00000002016.… 283. 287. 287. 265. ## 3 ENSG00000002330.… 1946 1662 2121 608 ## 4 ENSG00000002834.… 17636 19333 18917 4583 ## 5 ENSG00000003056.… 3874 4107 4005 5741 ## 6 ENSG00000003393.… 2041 2150 2141 1687 ## 7 ENSG00000003989.… 279 345 305 18586 ## 8 ENSG00000004534.… 2695. 3031 2871 1948 ## 9 ENSG00000004838.… 42 52 39 61 ## 10 ENSG00000004848.… 1 0 0 18 ## # ℹ 9,990 more rows ## # ℹ 2 more variables: `5_Cell2_Rep2` , `6_Cell2_Rep3`

Many of the packages that handle RNASeq count data do not work correctly with decimal numbers. We need to convert these numbers to integers using mutate() . Save your transformed data frame to an object named 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...

In addition to clasifying our organisms, we also want to reconstruct their phylogenetic relationships by generating a phylogenetic tree. We often assume that phylogenetic closeness can elucidate commonalities in phenotypic properties / functions, so it is Read More...

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2024 Date: Tuesday, October 15, 2024 Time and Location: 11 am EST, ZOOM (INVITATION BY LMIG LIST SERVER) Speaker: Dr. Diego Presman (U Buenos Aires) Title: “Insights on Glucocorticoid Receptor Activity Through Live Cell Imaging” Summary: Eucaryotic transcription factors ( Read More...

Cancer research is a complex and data-intensive field. Cloud computing offers a powerful solution for researchers to store, analyze, and share large datasets efficiently. In this month’s topic spotlight, we will explore cloud resources 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...

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...

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...

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...

Load in a tab delimited file (file_path= "./data/WebexSession_report.txt") using read_delim() . You will need to troubleshoot the error message and modify the function arguments as needed. {{Sdet}} Solution } library ( 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 4: Feature table filtering, taxonomic classification, and phylogeny Learning objectives learn how to apply different types of filtering to your ASV table and representative sequence data. classify your ASVs. Generate a phylogenetic tree. Now that Read More...

10/25/2023 - 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...

10/11/2023 - 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...

09/27/2023 - 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...

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...