Sure. A genome-wide perturb-seq experiment is a huge (and expensive) technical accomplishment, but the authors did not "map every human gene to its function". (Nor did they claim to, it's press release hyperbole.)
One, there's ~20k protein-coding genes in the human genome, and they screened ~10k, analyzing about 2k (fig 2a).
Two, all the functional annotation is based off transcription profiles. They essentially looked for clusters of genes with correlated expression, and assigned function based on genes with previous annotations (fig 2d, S4).
It's a good resource, but there's a lot more molecular work to be done to validate the function of these genes.
> Two, all the functional annotation is based off transcription profiles. They essentially looked for clusters of genes with correlated expression, and assigned function based on genes with previous annotations
This is an important point, because if you've ever worked with single cell data you'll know that the transcriptional profile is extremely noisy and your transcriptional profile to cell type map has many researcher degrees of freedom. I heard a story about a paper early in the single cell work that started with 53 cell types and after review ended up with 37 cell types. Are those true cell types? Did the experimenters validate that those cell types all performed different functions? Well, of course not. That's way too much work.
Then add on technological biases, which make mapping between technologies difficult. I say this because they used a new sequencing technology that appears to have homopolymer bias (https://twitter.com/lpachter/status/1533875723995185153), which will bias the gene quantification.
I believe they used Illumina for the results presented in the main text and then re-sequenced with Ultima and replicated a subset of the analyses (fig s13). The Ultima proof-of-concept didn't appear to be relevant to the main study/conclusions
One, there's ~20k protein-coding genes in the human genome, and they screened ~10k, analyzing about 2k (fig 2a).
Two, all the functional annotation is based off transcription profiles. They essentially looked for clusters of genes with correlated expression, and assigned function based on genes with previous annotations (fig 2d, S4).
It's a good resource, but there's a lot more molecular work to be done to validate the function of these genes.