Classification in Partek Flow

Garnett

...

Classification in Partek Flow

Garnett[1] automated cell type classification has been wrapped as the Classification task in Partek Flow. Just like the original Garnett tool, Classification in Flow works on single-cell data, along with a cell type definition (marker) file, and trains a regression-based classifier. Once a classifier is obtained and published, it can be applied to classify future datasets from similar tissues. To improve the user experience, both maker file(.txt) and classifier file(.rds) have been implemented as library files in Flow. 

...

Flow hosts a few pre-trained classifiers as Managed classifiers. The list of available classifiers can be found here[2]. If a managed classifier exists for your data type, we recommend you try it. Besides the Managed classifiers, the Project classifiers trained on the same dataset prior to your classification can also be used to classify cell type. Project classifiers can be promoted to Managed classifiers if users publish them. 

...

Classify cell type task

...

in Flow

Task report is sample based. Users can use the dropdown list on the top left to switch samples.  Under the sample name, there are two tabs on each report - Summary report and Analysis report (Figure 7).  Important information on Estimated Number of Cells, Mean Reads per Cell, Median Genes per Cell, as well as information on Sequencing, Mapping, and Sample are summarized in different panels. The Barcode Rank Plot has also been included as an important piece in the Cells panel in the Summary report (Figure 7). 

Image Removed

Figure 7. The example report of Cell ranger task in Flow.

Another two plots -biplots of Sequencing Saturation and Median Genes per Cell to Mean Reads per Cell have been included in the Analysis report as they are important metrics to library complexity and sequencing depth (Figure 8).

Image Removed

Figure 8. Analysis report of Cell ranger task in Flow.

Details will be exhibited and the panel will be expanded correspondingly if the the Image Removed icon is clicked. In the example below, the plot of Median Genes per Cell has been expanded while the Sequencing Saturation plot hasn't (Figure 9).

Image Removed

Figure 9. Expanded panel of Cell ranger task report in Flow.

Users can click Configure to change the default settings In Advanced options (Figure 5).

Expected cells: Expected number of recovered cells. Default: 3,000 cells.

Force cells: Force pipeline to use this number of cells, bypassing the cell detection algorithm. Use this if the number of cells estimated by Cell Ranger is not consistent with the barcode rank plot.

References

1. https://support.10xgenomics.com/single-cell-gene-expression/software/overview/welcome

2. https://support.10xgenomics.com/single-cell-gene-expression/software/pipelines/6.0/release-notes

3. https://support.10xgenomics.com/single-cell-gene-expression/software/pipelines/4.0/release-notes

Additional Assistance

If you need additional assistance, please visit our support page to submit a help ticket or find phone numbers for regional support.

Classify cell type

To classify cell type using a pre-trained classifier:  

...

If users would like to classify cell type with the classifier obtained from the same project, ‘Project classifiers’ needs to be picked up from the dropdown list, then use ‘Select data node’ to choose the classifier before clicking the Finish button. 

No matter which type of classifier was applied, it will output a new datanode named Classify result (Figure 4). The outputs of cell type annotation are exactly the same as Garnett[1]. Downstream analysis like normalization, PCA, etc can be run on Classify result data node.

Train classifier in Flow

To train a classifier with a list of biomarkers with your own dataset:

Select any non-normalized single cell data node, Filter counts here 

Choose Train classifier task in Classification section (Figure 1)

Similarly, first time users will be asked to create the Marker file at the very first beginning. Since Flow currently doesn’t host any marker files although users can add them as library files. Users have to import the file they created outside of Flow. Marker file should be a .txt file with the marker information in correct format. The same example in the Garnett tutorial of a simple valid Marker file is provided here (Figure 5).  For more details on how to construct a Marker file, please refer to Garnett tutorial[3]. Click Create button, Flow will then save the file with the name that users provided for future use (Figure 6a).

After the proper Marker file has been chosen, click the Finish button to start running the task as default (Figure 6b).  

Train classifier task report in Flow

Once the task has been finished, click the Classifier datanode and find the Task report in the Task results section, or prompt it to be one of the  Managed classifiers by clicking the Publish classifier task in Classification section (Figure 7).

Other parameters in this task that you can adjust include:

Number of Unknown: it tells Garnett how many outgroup cells it should compare against. The default is 500. For a dataset with fewer cells, the number should be smaller.

References

1. https://www.nature.com/articles/s41592-019-0535-3

2. https://cole-trapnell-lab.github.io/garnett/classifiers/

3. https://cole-trapnell-lab.github.io/garnett/docs/#constructing-a-marker-file

Additional Assistance

If you need additional assistance, please visit our support page to submit a help ticket or find phone numbers for regional support.