Simon Cockell @[email protected]

Our Perspective "Mapping the Transcriptome - realising the full potential of spatial data analysis" is out today in @CellCellPress cell.com/cell/fulltext/…. A short thread on this work follows...

Me too :) #UKGS24

So, Conservatives pledge to increase apprenticeships by 100k by cutting 1 in 8 degrees. I feel a little thread is in order.

How much more money do we need to dump into automating analysis for non experts until we realize that we need to better train scientists to interpret results!

@TrostLab @NCBI @NLM_NIH @StudentsNCL @NCBI @NLM_NIH we have been in contact with NLM Support (case number #CAS-1234575-J7P7W3) as traffic from our network is being rejected; we’ve chased for updates but had no response since 15th Dec. Can you provide an update on our case please?

This is a massive problem @NCBI. As @TrostLab mentions, @NU_ITservice maintain this is a server-side problem, some clarity (and a fix!) would be very welcome:

In the latest issue! Mapping the transcriptome: Realizing the full potential of spatial data analysis dlvr.it/T0VhqM

A PhD studentship available with me and collaborators @LabPeffers and @IGM_YoungLab from October 2024. findaphd.com/phds/project/b… Combining omics data to identify regulators of chondrogenesis. Funded via @NLD_BBSRCDTP Deadline to apply 15th January #bioinformatics #PhD

@ajskelton73 Thanks man :)

@sjcockell Excellent work, congrats!

Things you never thought you’d see in your career… featured content in Cell from “Cockell and colleagues”

Paper now officially published: cell.com/cell/fulltext/…

Bringing 2023 to a close, the new issue features a 3D atlas of the developing human head, how the ineffective control of EBV-induced autoimmunity increases the risk for multiple sclerosis and a machine learning architecture that integrates cell types across single-cell datasets! To read more of our new issue click👉 cell.com/cell/current

Now online! Mapping the transcriptome: Realizing the full potential of spatial data analysis dlvr.it/SzrT2M

To quote ourselves, these features mean that the analysis of these types of data should be "spatial by default", and encourage inter-disciplinary collaboration with those in the geographical sciences to help transfer decades of knowledge from that domain

3) spatial heterogeneity - the relationships between variables is not static across space, and local relationships rarely look the same as the global picture

We demonstrate that key features of spatially-resolved data can be shown in biological data (using Visium data by way of example). 1) The modifiable areal unit problem - that the scale you chose for your analysis can strongly influence the results of that analysis

This paper is essentially the manifesto for @LZormpas's PhD work - that the features of spatial data, so well studied in the geographic sciences, manifest themselves in micro-scale spatial molecular biology data.

So happy, particularly for my student (@LZormpas), that the editors saw the value in this commentary. Thanks also to the helpful reviewers. And of course coauthors @lexcomber and @rachelq_ncl!

Our Perspective "Mapping the Transcriptome - realising the full potential of spatial data analysis" is out today in @CellCellPress cell.com/cell/fulltext/…. A short thread on this work follows...