SINGLE CELL_PBMC8k : (10x chromium(cellrangerMatrix) – PROJECT

Login to Partek flow

partek.crc.pitt.edu

Pitt user name and pw

After login – click on new project – Name of the project

Follow the Partek documentation for Single cell analysis

Partek singlecell pipeline documentation:

Partek flow tutorials:

https://documentation.partek.com/display/FLOWDOC/Tutorials

Import 10X Genomics matrix files :

https://documentation.partek.com/display/FLOWDOC/Importing+10X+Genomics+Matrix+Files

Analyze 10X Genomics matrix files in partek flow :

https://documentation.partek.com/display/FLOWDOC/Analyzing+Single+Cell+RNA-Seq+Data

1. Import cellranger matrix data:

Import – import single cell data

If it is one sample,

barcodes.tsv genes.tsv matrix.mtx

Select all 3 files for importing into Partek Flow and click Next

2. QC and Filtering cells in single cell RNA-Seq data

Single cell data node – QA/QC - Single-cell QA/QC

Single cell QA/QC – Task report

Cell barcode QA/QC:

Cell barcode report:

3. Filtering cells:

There are three plots: number of read counts per cell, number of detected genes per cell, and the percentage of mitochondrial reads per cell.

Here we set the filters to a maximum of 12000 Read counts, 2500 Detected genes, and 8% Mitochondrial reads

4. Normalization:

Because different cells will have a different number of total counts, it is important to normalize the data prior to downstream analysis.

5. Filtering genes in single cell RNA-Seq data

6. PCAFiltered counts – Exploratory analysis - PCAYou can choose Features contribute equally to standardize the genes prior to PCA or allow more variable genes to have a larger effect on the PCA by choosing by variance. By default, we take variance into account and focus on the most variable genes.

7. Graph based clustering:

Graph-based clustering identifies groups of similar cells using PC values as the input. By including only the most informative PCs, noise in the data set is excluded, improving the results of clustering.

Filtered counts – Exploratory analysis – Graph-based clustering

Clustering can be performed on each sample individually or on all samples together. Here, we are working with a single sample.

8. t-SNE plots:

t-SNE (t-distributed stochastic neighbor embedding) is a visualization method commonly used analyze single-cell RNA-Seq data. Using the t-SNE plot, cells can be classified based on clustering results or differences in gene and pathway expression.

Filtered counts – Exploratory analysis – t-SNE

t-SNE result:

9. Classifying cells in tSNE:

We can classify cell types using known biomarkers. Another option is using Cell marker database or using the list of biomarkers hosted by Partek flow

a. Classify cell types using known biomarkers:

b. Classify cell types by list provided by Partek:

c. Classify cells using cell marker database:

http://biocc.hrbmu.edu.cn/CellMarker/

Download the list and copy the gene names into txt file. Only gene names.

Partek

Settings – Partek Flow components - List management

After saving all the classifications, we can apply classifications

10. Differential analysis:

Anova results:

Generate filtered node:

The table lists all of genes in the data set; using the filter control panel on the left, we can filter to just the genes that are significantly different for the comparison.

Click FDR step up and click the arrow next to it Set to 0.05

Click Fold change and click the arrow next to it Set to -2 to 2

The number of genes at the top of the filter control panel updates to indicate how many genes are left after the filters are applied.

Click to generate a filtered version of the table for downstream analysis