Hi ShuhuaGao,

thanks for your input! Monocle 2 has many more options for preprocessing, that's right. I believe though that you should get along with the limited options of Scanpy for a robust pseudotime and branching inference using DPT; simply because DPT is very robust. Nonetheless I have to admit that I've not worked with an extensive number of data types. From this experience, my understanding is the following

• for RNA-Seq data, you should normalize and extract highly-variable genes. this is most simply done by using the procedure of cell ranger sc.pp.recipe_zheng17 (example here) or, if you want more control, the Seurat workflow (example here)
• for qPCR, a simple log-normalization (sc.pp.log1p) should suffice (see example here); you might though consider "normalizing per cell / UMI correction", one of the steps done in RNA-seq part (sc.pp.normalize_per_cell)

Ask if you have further questions. 😄

Hi, Alex,

Many thanks for your quick reply. I just saw your reply as it is almost 10PM in Singapore now. It is understandable to perform quality control, in-cell normalization and to extract the highly variable genes for ordering. I got your point.

For your reply about qPCR, do we need a log normalization? I think a log transform is only required for RNA-Seq data to get a non-skewed normal distribution. As for qPCR data, the delta_Ct value is actually already in a log scale. In the example you have mentioned, there is no call of sc.pp.log1p, either. Instead, we just read the data by
adata = sc.read(filename, sheet='dCt_values.txt', backup_url=backup_url)
and no more processing is applied. As can be found from the original paper, the so-called dCt_value is just defined as HK_Ct - Ct, where HK_Ct is the mean Ct of 4 housing keeping genes on a cell-wise basis.

Besides, in many cases, there may be no UMI data available. In such a case, the normalization per cell for RNA-Seq is actually to compute the FPKM/TPM to compensate for the sequencing depth, right? Usually, the RNA-Seq data in FPKM form is already provided in publications. And then we work on this data to find the highly variable genes. (Just personal understanding. I am new to this field from mechatronics engineering.)

Anyway, thanks again for your help. I noticed that there are no examples for pseudo-time ordering with RNA-Seq data. Maybe I can provide one in the near future, as I am working on gene network modeling based on the pseudo-time information.

Hi!

Everything that you write makes sense: if the qPCR values are already on a log scale, you shouldn't log-transform them anymore / if the RNA-seq data is already in FPKM form, you do not need to do account for UMI correction ...

Regarding the pseudotime example for RNA-seq data: here is a public one. But it would be nice to have more!

Thanks for your input!

Thanks for your reply. I will try that and may given more feedback. Cheers!