Details
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Type:
Task
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Status: Closed (View Workflow)
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Priority:
Major
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Resolution: Done
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Affects Version/s: None
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Fix Version/s: None
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Labels:None
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Story Points:1
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Epic Link:
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Sprint:Summer 6, Summer 7
Description
For this task, locate a BAM format data files for the RNA-Seq data set described in this Seurat tutorial:
The above tutorial demonstrates features of the Seurat single-cell RNA-Seq data analysis library using data from peripheral blood mononuclear Cells (PBMC) originally from the 10X Genomics Web site.
Open the data file in IGB. Use the Seurat library (in R) to locate some genes with high counts and then check that IGB shows the same or similar numbers of read alignments.
Then, make it possible for other people to also open the file and view the contents in IGB.
Note: If you get stuck on figuring out how to use Seurat for this, ask for help from one of our many local R experts.
Attachments
Issue Links
Activity
| Field | Original Value | New Value |
|---|---|---|
| Epic Link | IGBF-3765 [ 22984 ] |
| Summary | Obtain a file containing sequence read alignments for Suerat vignette | Obtain and view sequence read alignments for PMBC dataset from Seurat vignette |
| Assignee | Ann Loraine [ aloraine ] |
| Description |
For this task, locate a BAM or FASTQ format data files for the RNA-Seq data set described in this Seurat tutorial:
* https://satijalab.org/seurat/articles/pbmc3k_tutorial The above tutorial demonstrates features of the Suerat single-cell RNA-Seq data analysis library using data from peripheral blood mononuclear Cells (PBMC) originally from the 10X Genomics Web site. Open the data file in IGB. Use R to locate some genes with high counts and then check that IGB shows the same or similar numbers of read alignments. |
For this task, locate a BAM format data files for the RNA-Seq data set described in this Seurat tutorial:
* https://satijalab.org/seurat/articles/pbmc3k_tutorial The above tutorial demonstrates features of the Seurat single-cell RNA-Seq data analysis library using data from peripheral blood mononuclear Cells (PBMC) originally from the 10X Genomics Web site. Open the data file in IGB. Use R to locate some genes with high counts and then check that IGB shows the same or similar numbers of read alignments. |
| Description |
For this task, locate a BAM format data files for the RNA-Seq data set described in this Seurat tutorial:
* https://satijalab.org/seurat/articles/pbmc3k_tutorial The above tutorial demonstrates features of the Seurat single-cell RNA-Seq data analysis library using data from peripheral blood mononuclear Cells (PBMC) originally from the 10X Genomics Web site. Open the data file in IGB. Use R to locate some genes with high counts and then check that IGB shows the same or similar numbers of read alignments. |
For this task, locate a BAM format data files for the RNA-Seq data set described in this Seurat tutorial:
* https://satijalab.org/seurat/articles/pbmc3k_tutorial The above tutorial demonstrates features of the Seurat single-cell RNA-Seq data analysis library using data from peripheral blood mononuclear Cells (PBMC) originally from the 10X Genomics Web site. Open the data file in IGB. Use the Seurat library (in R) to locate some genes with high counts and then check that IGB shows the same or similar numbers of read alignments. |
| Description |
For this task, locate a BAM format data files for the RNA-Seq data set described in this Seurat tutorial:
* https://satijalab.org/seurat/articles/pbmc3k_tutorial The above tutorial demonstrates features of the Seurat single-cell RNA-Seq data analysis library using data from peripheral blood mononuclear Cells (PBMC) originally from the 10X Genomics Web site. Open the data file in IGB. Use the Seurat library (in R) to locate some genes with high counts and then check that IGB shows the same or similar numbers of read alignments. |
For this task, locate a BAM format data files for the RNA-Seq data set described in this Seurat tutorial:
* https://satijalab.org/seurat/articles/pbmc3k_tutorial The above tutorial demonstrates features of the Seurat single-cell RNA-Seq data analysis library using data from peripheral blood mononuclear Cells (PBMC) originally from the 10X Genomics Web site. Open the data file in IGB. Use the Seurat library (in R) to locate some genes with high counts and then check that IGB shows the same or similar numbers of read alignments. Note: If you get stuck on figuring out how to use Seurat for this, ask for help from one of our many local R experts. |
| Status | To-Do [ 10305 ] | In Progress [ 3 ] |
| Assignee | Karthik Raveendran [ karthik ] |
| Description |
For this task, locate a BAM format data files for the RNA-Seq data set described in this Seurat tutorial:
* https://satijalab.org/seurat/articles/pbmc3k_tutorial The above tutorial demonstrates features of the Seurat single-cell RNA-Seq data analysis library using data from peripheral blood mononuclear Cells (PBMC) originally from the 10X Genomics Web site. Open the data file in IGB. Use the Seurat library (in R) to locate some genes with high counts and then check that IGB shows the same or similar numbers of read alignments. Note: If you get stuck on figuring out how to use Seurat for this, ask for help from one of our many local R experts. |
For this task, locate a BAM format data files for the RNA-Seq data set described in this Seurat tutorial:
* https://satijalab.org/seurat/articles/pbmc3k_tutorial The above tutorial demonstrates features of the Seurat single-cell RNA-Seq data analysis library using data from peripheral blood mononuclear Cells (PBMC) originally from the 10X Genomics Web site. Open the data file in IGB. Use the Seurat library (in R) to locate some genes with high counts and then check that IGB shows the same or similar numbers of read alignments. Then, make it possible for other people to also open the file and view the contents in IGB. Note: If you get stuck on figuring out how to use Seurat for this, ask for help from one of our many local R experts. |
| Attachment | quickload.zip [ 18464 ] |
Karthik Raveendran - I attached the quickload pointing at the data 10X_PBMC3k files online. Try downloading and adding the quickload to IGB and make sure the data load and look the same as the bam file you downloaded.
| Summary | Obtain and view sequence read alignments for PMBC dataset from Seurat vignette | Obtain and view sequence read alignments for PBMC dataset from Seurat vignette |
The following genes (with high counts in count matrix file) was observed:
IQSEC1
TCEANC
DNASE1L1
MLLT3
GDPD5
SNHG9
CPNE7
FTL
RPL13A
No match found for the following:
TCEB3
AP003733.1
AP000769.7
TRIM69
No reads for the following:
B3GALT4
| Status | In Progress [ 3 ] | Needs 1st Level Review [ 10005 ] |
| Assignee | Karthik Raveendran [ karthik ] |
| Sprint | Summer 6 [ 200 ] | Summer 6, Summer 7 [ 200, 201 ] |
| Rank | Ranked higher |
| Attachment | 2024-08-12-IQSEC1-PBMC-scRNA-Seq.png [ 18465 ] |
| Attachment | 2024-08-12-IQSEC1-PBMC-scRNA-Seq.png [ 18465 ] |
| Attachment | 2024-08-12-IQSEC1-PBMC-scRNA-Seq.png [ 18466 ] |
Review from Ann Loraine:
I looked IQSEC1. The alignments are really weird looking! Please see attached image. I see what appears to be a pretty significant problem in that it really seems like lots and lots of the alignments are not matching the exon/intron boundaries of the gene models.
Request for Karthik Raveendran:
Is there any on-line documentation for this alignment file? Can you provide some links so that we can investigate further? If this is truly the source of the big matrix of counts, then there is a very serious problem here that might blow up the entire field. Unlikely! But we need to figure out why the alignments do not appear match up with the gene models very well, regardless.
To complete this task, please add relevant links and pass the ticket back to "Needs first level review." We'll then make a new task to investigate why the alignments do not match the gene models very well.
| Assignee | Karthik Raveendran [ karthik ] |
| Status | Needs 1st Level Review [ 10005 ] | First Level Review in Progress [ 10301 ] |
| Status | First Level Review in Progress [ 10301 ] | To-Do [ 10305 ] |
| Description |
For this task, locate a BAM format data files for the RNA-Seq data set described in this Seurat tutorial:
* https://satijalab.org/seurat/articles/pbmc3k_tutorial The above tutorial demonstrates features of the Seurat single-cell RNA-Seq data analysis library using data from peripheral blood mononuclear Cells (PBMC) originally from the 10X Genomics Web site. Open the data file in IGB. Use the Seurat library (in R) to locate some genes with high counts and then check that IGB shows the same or similar numbers of read alignments. Then, make it possible for other people to also open the file and view the contents in IGB. Note: If you get stuck on figuring out how to use Seurat for this, ask for help from one of our many local R experts. |
For this task, locate a BAM format data files for the RNA-Seq data set described in this Seurat tutorial:
* https://satijalab.org/seurat/articles/pbmc3k_tutorial The above tutorial demonstrates features of the Seurat single-cell RNA-Seq data analysis library using data from peripheral blood mononuclear Cells (PBMC) originally from the 10X Genomics Web site. Open the data file in IGB. Use the Seurat library (in R) to locate some genes with high counts and then check that IGB shows the same or similar numbers of read alignments. Then, make it possible for other people to also open the file and view the contents in IGB. Note: If you get stuck on figuring out how to use Seurat for this, ask for help from one of our many local R experts. 10X data: https://www.10xgenomics.com/datasets/3-k-pbm-cs-from-a-healthy-donor-1-standard-1-1-0 |
The 10X data is currently located at: https://www.10xgenomics.com/datasets/3-k-pbm-cs-from-a-healthy-donor-1-standard-1-1-0
Note that the website will ask for some personal information.
The bam file is aligned against the hg19 genome (H_sapiens_Feb_2009)
Documentation for 10X Genomics Cell Ranger Barcoded BAM tags: https://www.10xgenomics.com/support/software/cell-ranger/latest/analysis/outputs/cr-outputs-bam
Part of the SAM header:
STAR ID:STAR VN:STAR_2.5.1b CL:STAR --runThreadN 4 --genomeDir /mnt/opt/refdata_cellranger/hg19-1.1.0/star --readFilesIn /mnt/yard/rudy/testing/cellranger_110/public_datasets/pbmc3k/pbmc3k/CELLRANGER_CS/CELLRANGER/EXTRACT_READS/fork0/chnk0/files/reads.fastq/1.fastq --outStd BAM_Unsorted --outSAMtype BAM Unsorted --outSAMunmapped Within --chimOutType WithinBAM --quantMode TranscriptomeSAM --quantTranscriptomeBan Singleend
| Attachment | quickload.zip [ 18464 ] |
| Attachment | quickload.zip [ 18467 ] |
| Description |
For this task, locate a BAM format data files for the RNA-Seq data set described in this Seurat tutorial:
* https://satijalab.org/seurat/articles/pbmc3k_tutorial The above tutorial demonstrates features of the Seurat single-cell RNA-Seq data analysis library using data from peripheral blood mononuclear Cells (PBMC) originally from the 10X Genomics Web site. Open the data file in IGB. Use the Seurat library (in R) to locate some genes with high counts and then check that IGB shows the same or similar numbers of read alignments. Then, make it possible for other people to also open the file and view the contents in IGB. Note: If you get stuck on figuring out how to use Seurat for this, ask for help from one of our many local R experts. 10X data: https://www.10xgenomics.com/datasets/3-k-pbm-cs-from-a-healthy-donor-1-standard-1-1-0 |
For this task, locate a BAM format data files for the RNA-Seq data set described in this Seurat tutorial:
* https://satijalab.org/seurat/articles/pbmc3k_tutorial The above tutorial demonstrates features of the Seurat single-cell RNA-Seq data analysis library using data from peripheral blood mononuclear Cells (PBMC) originally from the 10X Genomics Web site. Open the data file in IGB. Use the Seurat library (in R) to locate some genes with high counts and then check that IGB shows the same or similar numbers of read alignments. Then, make it possible for other people to also open the file and view the contents in IGB. Note: If you get stuck on figuring out how to use Seurat for this, ask for help from one of our many local R experts. |
| Status | To-Do [ 10305 ] | In Progress [ 3 ] |
| Assignee | Karthik Raveendran [ karthik ] |
| Status | In Progress [ 3 ] | Needs 1st Level Review [ 10005 ] |
| Status | Needs 1st Level Review [ 10005 ] | First Level Review in Progress [ 10301 ] |
| Status | First Level Review in Progress [ 10301 ] | Ready for Pull Request [ 10304 ] |
| Status | Ready for Pull Request [ 10304 ] | Pull Request Submitted [ 10101 ] |
| Status | Pull Request Submitted [ 10101 ] | Reviewing Pull Request [ 10303 ] |
| Status | Reviewing Pull Request [ 10303 ] | Merged Needs Testing [ 10002 ] |
| Status | Merged Needs Testing [ 10002 ] | Post-merge Testing In Progress [ 10003 ] |
| Resolution | Done [ 10000 ] | |
| Status | Post-merge Testing In Progress [ 10003 ] | Closed [ 6 ] |
| Assignee | Karthik Raveendran [ karthik ] |
Finding some genes with high UMI counts for a cell was done but to check that IGB shows the same number of reads alignments of a gene from a particular cell is a challenge. After discussing with Nowlan Freese on Wednesday, working on filter by or color by function where a cell can be filtered or colored would make it easy to finish the task