geoffschieb
commented
3 years ago Hi James,
We used the log-normalized matrix for the OT computations. The truncated expression matrix was used in the regulatory regressions.
We didn't do batch correction. This allowed us to use the distance between batches as a base-line for performance in our geodesic interpolation computations. We tested the batch effect for each time-point and identified one or two time-points with large batch effects and removed those corrupted samples.
As for your last question, we downsample to a maximum of 15,000 UMIs. We still need to normalize by UMI count because most of the cells have less than 15,000 UMIs. This expression matrix is similar to the log-normalized expression matrix (the final difference is the log + 1).
Something like SCTransform also works well, as we describe in our newer work: https://www.biorxiv.org/content/10.1101/2020.11.12.380675v1
Best, Geoff
On Mon, Dec 14, 2020 at 1:51 PM James Ashmore notifications@github.com wrote:
I am a bit confused about the "preparation of expression matrices" section in the STAR methods of the optimal transport paper. You define three different expression matrices: the UMI matrix, the log-normalized expression matrix, and the truncated expression matrix. Which of these should be used as the input expression matrix? Additionally, if there are multiple batches of cells, should the expression matrix be batch-corrected beforehand? Either by regression or some other method (e.g. fastMNN). I couldn't find any mention of batch correction in the STAR methods, apart from:
"The expression matrix was downsampled to 15,000 UMIs per cell. Cells with less than 2000 UMIs per cell in total and all genes that were expressed in less than 50 cells were discarded, leaving 251,203 cells and G = 19,089 genes for further analysis. The elements of expression matrix were normalized by dividing UMI count by the total UMI counts per cell and multiplied by 10,000 i.e., expression level is reported as transcripts per 10,000 counts."
I'm not sure why the data was downsampled and then normalized by UMI count? Doesn't the first correction make the second redundant? Also, is this downsampled / normalized matrix different from the three defined above? If so, should I be using this as the input expression matrix instead?
Thanks, James
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jma1991
I am a bit confused about the "preparation of expression matrices" section in the STAR methods of the optimal transport paper. You define three different expression matrices: the UMI matrix, the log-normalized expression matrix, and the truncated expression matrix. Which of these should be used as the input expression matrix? Additionally, if there are multiple batches of cells, should the expression matrix be batch-corrected beforehand? Either by regression or some other method (e.g. fastMNN). I couldn't find any mention of batch correction in the STAR methods, apart from:
"The expression matrix was downsampled to 15,000 UMIs per cell. Cells with less than 2000 UMIs per cell in total and all genes that were expressed in less than 50 cells were discarded, leaving 251,203 cells and G = 19,089 genes for further analysis. The elements of expression matrix were normalized by dividing UMI count by the total UMI counts per cell and multiplied by 10,000 i.e., expression level is reported as transcripts per 10,000 counts."
I'm not sure why the data was downsampled and then normalized by UMI count? Doesn't the first correction make the second redundant? Also, is this downsampled / normalized matrix different from the three defined above? If so, should I be using this as the input expression matrix instead? Finally, you use library size as a scaling factor to correct for differences in sequencing depth, but is there a requirement to normalize for compositional biases as well? (e.g. using pool-based size factors)
Thanks, James