Ammar Sabir Cheema

Bioinformatician, data helps understand biology

[dana_peer @dana_peer]

Excited to introduce scKINETICS, an algorithm for joint inference of gene regulatory mechanism and cellular velocity, co-developed by the amazing duo of @CassBurdziak and @juuuulianaar
https://t.co/6WfAtn6HT2

[Ammar Sabir Cheema @AmmarSCheema]

Happy to have contributed to this paper as a bioinformatician during my stay @CIML_Immunology as a PhD Student in lab of @DalodCiml, this paper was a collaboration between @LawrenceTobyLab and @DalodCiml

[Science Immunology @SciImmunology]

A 2021 study from @LawrenceTobyLab @CIML_Immunology @Inserm in mouse models of #melanoma pinpoints key signaling pathways involved in the development of dendritic cells capable of recruiting #cancer killing T cells to the tumor.

📄: https://t.co/kVC8QpQYJf #WeekendReads

[Ammar Sabir Cheema @AmmarSCheema]

@ColtEgelston @simocristea being a bioinformatician it is also confusing for me, I was also informed that for cDC1 (DC1) maturation and activation are same and for pDC (plasmacytoid DC) there are various stages of activation, 1st no IFN, then IFN+, then IFN+++, then again no IFN, then pDC become like cDC1

[Constantin Ahlmann-Eltze (@const-ae.bsky.social) @const_ae]

Incredibly proud to see our benchmark of single-cell preprocessing methods finally published 🥳🥳🎉

We show that despite its theoretical limitations, no other transformation consistently outperforms log(y/s+1).
All details at https://t.co/0caikoVmBa and https://t.co/X61ICd6sao

[bioRxiv Bioinfo @biorxiv_bioinfo]

scART: recognizing cell clusters and constructing trajectory from single-cell epigenomic data https://t.co/Sqwor34C4x #biorxiv_bioinfo

[Ming "Tommy" Tang @tangming2005]

Best practices for single-cell analysis across modalities https://t.co/3fsFvZjQwq

[Ammar Sabir Cheema @AmmarSCheema]

@arjunrajlab indeed it is always better to confirm your results of trajectory inference through another tool like the ones based on transcription dynamics.

[Ammar Sabir Cheema @AmmarSCheema]

You can reproduce the whole analysis using the docker image or you can use it to analyze your scRNA data as well. The whole code along with link to the docker image is provided here (https://t.co/YvNGIqefA9)

[Ammar Sabir Cheema @AmmarSCheema]

Published in this book (https://t.co/rSEgrtxSO2) is also my book chapter titled "Harnessing gene expression profiling to infer the activation states of dendritic cell types,

their dynamical relationships and their molecular regulation" for single cell RNA-seq data analysis.

[Ammar Sabir Cheema @AmmarSCheema]

After getting bonafide cDC1 we performed trajectories inference using monocle and velocyto. We found two distinct trajectories for cDC1 in normal vs tumor bearing lungs through monocle and velocyto.

[Ammar Sabir Cheema @AmmarSCheema]

Interestingly, velocyto (which relies on transcrinal dynamics of the genes within cell) was more robust for prediciting cDC1 fate within two different pathophysiological contexts whereas monocle required separating the analysis for the two different conditions.

[Ammar Sabir Cheema @AmmarSCheema]

During cell type identification, difficult part was to discriminate cDC1 from cDC2 upon maturation which is notoriously difficult to based only on gene expression. For cell type identification instead of relying on cluster-based approach we used a sc-based approach.

[Ammar Sabir Cheema @AmmarSCheema]

In this protocol we tried to find the activation trajectory of cDC1 in two different pathophysiological contexts (tumor vs normal conditions). Therefore, we used the data single cell RNA seq data from this paper as a reference (https://t.co/cfn8wxQ5AO).

[Ammar Sabir Cheema @AmmarSCheema]

The book chapter provides a detailed description for each step of the single cell RNA Seq data starting from the raw sequencing counts until the biological interpretation of results.

[Ammar Sabir Cheema @AmmarSCheema]

Interesting paper about the immune landscape of gastric adenocarcinoma

[Santosh Vardhana @SantoshVardhana]

I’m thrilled to share the @VardhanaLab’s first true pre-print led by @MisekerAbate, an equal collaboration with the brilliant @VStrongMD exploring the immune landscape of gastric adenocarcinoma (supported by @Cycle4Survival ). A brief tweetorial:
https://t.co/7N1Y1BqxJM

[Ammar Sabir Cheema @AmmarSCheema]

@tangming2005 Mygeneset (https://t.co/Zg5bEnGgag) from immgen also provides this functionality