What can we do?

How to prepare input data for workflow?

The gene id should be have same format in follows files. Especially for Cell Ranger Count Matrix.

1. Cell Ranger Count Data

The filtered_feature_bc_matrix need input into analysis workflow can be count follows cellranger mkref and cellranger count. More detail can be find in Running cellranger count.

2. Samples Metadata

A comma separated csv format file that used to describe the information of samples. It's should been have the header: library_id, molecule_h5, batch, AMLStatus and condition.

A example file can be found here: Sample_MetaData.csv

library_id	molecule_h5	batch	AMLStatus	condition
T0_1	Cell_Ranger/T0_1/outs/molecule_info.h5	v3_lib	T0	treatment
T0_2	Cell_Ranger/T0_2/outs/molecule_info.h5	v3_lib	T0	treatment
T1_1	Cell_Ranger/T1_1/outs/molecule_info.h5	v3_lib	T1	control
T1_2	Cell_Ranger/T1_2/outs/molecule_info.h5	v3_lib	T1	control

3. Genes Annotation File

A tab separated txt format file that includes the annotation of gene function. It's should be no header.

A example file can be found here: Genome_Genes_Function_Annotation.txt

Ghir-A01G000050	AT4G21192.1	3.00E-44	Symbols: | Cytochrome c oxidase biogenesis protein Cmc1-like | chr4:11294563-11295192 FORWARD LENGTH=80
Ghir-A01G000040	AT1G64260.1	2.00E-39	Symbols: | MuDR family transposase | chr1:23847756-23849915 FORWARD LENGTH=719

4. Mitochondria Genes

A csv format file that includes the mitochondria genes. It's should have the header: gene_id.

A example file can be found here: MitochondriaGenes.csv

gene_id
Ghir-D13G022910
Ghir-D09G012400

5. Chloroplast Genes

A csv format file that includes the chloroplast genes. It's should have the header: gene_id.

A example file can be found here: ChloroplastGenes.csv

gene_id
Ghir-D08G003430
Ghir-A05G030920

6. Marker Genes Of Known Cell-Type

A comma separated csv format file that includes the marker genes and cell annotation. It's should have the header: name, parent and genes.

Multiple marker genes for a cell cluster should be list in different row.

A example file can be found here: Maker_SC.csv

name	parent	genes
cortex	cortex	Ghir-A03G023470
cortex	cortex	Ghir-A03G023570
metaphloem sieve element	phloem	Ghir-A05G017430
metaxylem	xylem	Ghir-A02G012540

How to run workflow?

We are provides a Single Cell Conda Environment and R Session Infomation: SingleCellCondaEnvironment.yml and RsessionInfo.txt.

You can easily configure an environment used to run this workflow.👇

conda env update --file SingleCellCondaEnvironment.yml --prune

Firstly, you should have a conda environment. If not, please install it.

Why not provide online scRNA analysis services?

We are based on the following two considerations: plants Single cell RNA data is really big data and extreme complexity.

We need to combine species information, single cell sample status and the corresponding biological background to adjust the parameters repeatedly.

Fixed bioinformatics calculation process can not get ideal results.

Therefore, we customize the calculation process and provide download by specifying key parameters. Users can perform multiple analyses on their own servers.

How to submit data or facrback question with us?

Don't be shy.😳 xzp@mail.hzau.edu.cn

Updated History/Release Information

2025-01-18

Updated 2021-12-10

Add single-cell RNA sequencing data from the poplar and peanut.

Add the menu of singleCellPapers to update and track the latest literature of plant single cell sequencing.

Updated 2021-11-3

Add single-cell RNA sequencing data from the Arabidopsis vegetative shoot apex.

Updated 2021-05-23

We currently track 9 tissues in over 51 cell types of 5 species that total 98 marker genes that have evidenced via RNA in situ hybridization or GFP reporter.

15 raw sequencing data of 4 species were curated in Plant Single Cell Database (SingleCellDB) page.

We provide a plant scRNA-seq analysis workflow that can result from fastq to marker genes identification.

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