Frequently Asked Questions


1.Why scMCA returns with error?

The format of the data matrix is wrong (colomn should be splited by comma or tab). The file name is wrong (No space or other special character).

2. What is the meaning of each column title in the data table?

p_val :   the probability value is the probability for a given statistical model. The smaller the p-value, the higher the significance
avg_diff :   log fold-chage of the average expression between the two groups. Positive values indicate that the gene is more highly expressed in the first group.
pct.1 :   The percentage of cells where the gene is detected in current group.
pct.2 :   The percentage of cells where the gene is detected in other groups.
Annotation :   Defined cell type.
gene:   gene symbol

3. How did the MCA do the cell type annotation?

We read as many papers as possible to correspond cluster specific genes to known cell types. If a cell type is not described before we call it XXX_high cells based on the most specific marker XXX. However the annotation might not be always accurate. We really appreciate your help on correcting annotations on the MCA.

4. How accurate is the cell number ratio in different tissue?

The ratio might be affected by cell digestion method as well as gene expression profiling method. The cell number ratio identified by Microwell-seq might be different from the ratio identified by other methods such as FACS.

5. How to find the corresponding dataset and code in this work?

For MCA 3.0:
Raw Data: GSE153562; DGE Data: https://figshare.com/s/1ab3c6d7648d12247eb2; Analysis Code: https://github.com/ggjlab/cell_landscape/.

For MCA 2.0:
Raw Data: GSE176063; DGE Data: https://figshare.com/s/340e8e7f349559f61ef6; Analysis Code: https://github.com/ggjlab/MCDA/.

For MCA 1.0:
DGE Data: https://figshare.com/articles/MCA_DGE_Data/7235471; Analysis Code: https://github.com/ggjlab/MCA/; MCA BAM data: GSE134355 or CNP0000325.

6. How to cite this work?

Please cite:
Han, X. et al. Mapping the Mouse Cell Atlas by Microwell-Seq. Cell, 2018. DOI: 10.1016/j.cell.2018.02.001.
Fei, L. et al. Systematic identification of cell-fate regulatory programs using a single-cell atlas of mouse development. Nature Genetics, 2022. DOI: 10.1038/s41588-022-01118-8.
Wang, R. et al. Construction of a cross-species cell landscape at single-cell level. Nucleic Acids Research, 2022. DOI: 10.1093/nar/gkac633.