Workflow For Analysis plants scRNA Data

Customize your own workflow for the analysis of plant's scRNA by customizing Cell Ranger Count Information , Quality Control threshold , Normalization and ScaleData Method , Dimensionality Reduction Algorithm , Clustering Resolutions and Marker Genes Identification threshold .

Loading Cell Ranger count output

Output directory from Cell Ranger count and metadata for this scRNA-seq dataset. It's must conform to fixed file format.

Project Name:

Samples Metadata:

Genes Annotation File:

Mitochondria Genes:

Chloroplast Genes:

Marker Genes Of Known Cell-Type:

filtered_feature_bc_matrix:

Quality Control

Set follows parameters to removing low-quality cells based on the median absolute deviation ( MADs ) from the median value of each metric across all cells, number of UMIs per cell ( raw_nCount_RNA ), number of genes detected per cell ( raw_nFeature_RNA ), number of genes per UMI for each cell ( log10GenesPerUMI ), mitochondrial counts ratio ( mitoRatio ) and genes which are expressed in cells ( Gene Expressiom At Least Cell ).

MADs

raw_nCount_RNA

raw_nFeature_RNA

log10GenesPerUMI

mitoRatio

Gene Expressiom At Least Cell

Remove Genes That Expression Valus Equal to Zero In All Cell:

Yes No

Normalization and ScaleData

Read literature (Schneider et al., 2021) to assess different clustering solutions of different normalization parameters.

Normalization Method

Number Of Most Variant Genes

Clustering

The resolution is an important argument that sets the granularity of the downstream clustering and will need to be optimized for every individual experiment.

For datasets of 3,000 - 5,000 cells, the resolution set between 0.4-1.4 generally yields good clustering.

Increased resolution values lead to a greater number of clusters, which is often required for larger datasets.

Resolutions For Cluster

Dimensionality Reduction

Selected uses PCA, tSNE and UMAP to plot of cells in each cluster.

Clustering Algorithm:

PCA

tSNE

UMAP

PCA maxdims

tSNE perplexitys

UMAP mindists

tSNE perplexitys: how to balance attention between local and global aspects of your data. See more in How to Use t-SNE Effectively .

UMAP mindists: this controls how tightly the embedding is allowed compress points together. Larger values ensure embedded points are moreevenly distributed, while smaller values allow the algorithm to optimise more accurately with regard to local structure.

Marker Genes Identification

Identification of all markers for each cluster and annotation the cell types of the different clusters via our MarkerGeneDB (Reviewed) .

Expected Number Of Cell Clusters:

Top Gene To Plot DoHeatmap, FeaturePlot, VlnPlot and DotPlot:

only.pos

Yes No

min.pct

thresh.use

logfc.threshold

Trajectory inference

We are suggesting using Monocle and dyno package to performed this trajectory inference for whole cluster or subcluster.

Download

Celebrate! Your custom workflow is almost finished. Just click the button below to download it and used to analysis your scRNA-seq data.