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5 changes: 5 additions & 0 deletions components/dictionary.txt
Original file line number Diff line number Diff line change
Expand Up @@ -84,6 +84,7 @@ curation
customizable
customization
customizations
CytAssist
cytometry
cytotoxic
cytotoxicity
Expand Down Expand Up @@ -148,9 +149,11 @@ FASTQ
FastQC
FCS
FDR
FFPE
fgsea
fibroblast
fibrotic
fiducial
fiducials
Figshare
FLI
Expand Down Expand Up @@ -431,6 +434,8 @@ SNE
snRNA
Soneson
SOX
SpatialExperiment
SPE
SRA
Srivastava
SRP
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4 changes: 2 additions & 2 deletions scRNA-seq/04-dimension_reduction_scRNA.Rmd
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Expand Up @@ -25,7 +25,7 @@ Visualizing highly dimensional data is a common challenge in genomics, and espec
The expression of every gene we look at is another dimension describing a sample.
When we also have hundreds or thousands of individual samples, as in the case of single-cell analysis, figuring out how to clearly display all of the data in a meaningful way is difficult.

A common practice is to common to use dimension reduction techniques so all of the data is in a more manageable form for plotting, clustering, and other downstream analyses.
A common practice is to use dimension reduction techniques so all of the data is in a more manageable form for plotting, clustering, and other downstream analyses.

## Set Up

Expand All @@ -45,7 +45,7 @@ The data we will be using for this module comes from a a 10x Genomics data set o
The data was generated with the 10Xv3.1 chemistry, and processed with Cell Ranger and 10x Genomics standard pipeline.


There are a variety of files that you will often see as part of the standard output from Cell Ranger, which are described in detail in [10x Genomics documentation](https://support.10xgenomics.com/single-cell-gene-expression/software/pipelines/latest/output/overview).
There are a variety of files that you will often see as part of the standard output from Cell Ranger, which are described in detail in [10x Genomics documentation](https://www.10xgenomics.com/support/software/cell-ranger/latest/analysis/outputs/cr-outputs-overview).
We have included some of these in the `data/hodkins/cellranger` directory, including the `web_summary.html` file that includes some similar QC statistics to those we generated with `alevinQC`.
The main file we will be working with are the feature by barcode matrices.
Cell Ranger does some filtering on its own, but we will start with the raw data.
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