Jarning Gau

PhD candidate major in bioinformatics, good at #singlecell transcriptome analysis.

[bioRxiv Bioinfo @biorxiv_bioinfo]

ProjectSVR: Mapping single-cell RNA-seq data to reference atlases by supported vector regression https://t.co/xJBDNUeRWi #biorxiv_bioinfo

[Molecular Cell @MolecularCell]

Models that matter https://t.co/hqFHsnQ0OD

[antisense. @razoralign]

CellOracle: Dissecting cell identity via network inference and in silico gene perturbation https://t.co/7QcJTxYErv

[evo-devo @Xiaojie_Qiu]

Building Transformers from Neurons and Astrocytes https://t.co/zmZDNpzgzu

[Molecular Cell @MolecularCell]

Right on target: Chromatin jets arise from targeted cohesin loading in wild-type cells https://t.co/e8kXv138Yj

[Nature Methods @naturemethods]

Introducing STELLAR: a geometric deep learning method for cell type discovery and identification in spatially resolved single-cell datasets. @jure @GarryPNolan @SnyderShot https://t.co/Lj2TO3NCQt

[antisense. @razoralign]

Census: accurate, automated, deep, fast, and hierarchical scRNA-seq cell-type annotation https://t.co/79GN0W4nu1

[Jarning Gau @GauJarning]

@Stella__Guo 从专业性的角度，科学家能对病毒做的真的有限。导致全人类的灭亡的病毒是不可能造出来的（从生物技术的角度），另外人类的基因库，从能筛出来免疫病毒的人。从群体角度来说，人类基因池的多样性保证了他们面对自然选择的韧性。

[Jarning Gau @GauJarning]

@Stella__Guo 犬儒主义也许是一味良药。

[Cancer Cell @Cancer_Cell]

Tumor microbiome links cellular programs and immunity in pancreatic cancer https://t.co/fNxcnMg28k

[Nature Methods @naturemethods]

Out today and freely available, a tool many in the image analysis community have been waiting for!

PyImageJ: A library for integrating ImageJ and Python

@EliceiriK @UWMadisonLOCI @ctrueden @CiminiLab @DrAnneCarpenter

https://t.co/2sjWSfJJ8n

[Jarning Gau @GauJarning]

@gali_bai @tangming2005 @constantAmateur decontX solved my problem: https://t.co/nz2rDDLHpf

[Mo Lotfollahi @mo_lotfollahi]

(1/n) This thread will describe recent advances in life-long learning (LL) as an essential player in the current single-cell atlases. LL in this context means building a map, and iteratively updating it with new data.blob,

[Jimmie Ye @yimmieg]

In 2018, we developed mux-seq and demuxlet to enable population-scale single-cell sequencing. Today, excited to share our paper in @ScienceMagazine where we deployed mux-seq to profile 1.2M immune cells from 162 lupus patients and 99 healthy controls: https://t.co/ITKGnuEsBl. 1/n

[Slava Bobrov @slava__bobrov]

Single-celled Lacrymaria olor attacks another cell:
#biology

[Cell Reports @CellReports]

ZFP541 maintains the repression of pre-pachytene transcriptional programs and promotes male meiosis progression https://t.co/LAYGY8TD8r

[Oded Rechavi @OdedRechavi]

If Darwin went on his voyage of the Beagle today his research would've ended with a t-SNE of the bird specimens that he collected instead of a theory of evolution

[Genome Biology @GenomeBiology]

Review on zero-inflation of single-cell RNA-seq data, from @Ruochen_Jiang, @jsb_ucla & co. They discuss the sources of biological and non-biological zeroes, methods to address them, and how they influence analysis. https://t.co/JgJw5Js47X

[Cell Press @CellPressNews]

Our fingerprint patterns—whether they are circular, wavy, or winding —are influenced by genes controlling limb development and not skin patterning, finds a comprehensive analysis. Learn more in @CellCellPress: https://t.co/fBLuiXxMjh

[Jarning Gau @GauJarning]

Gene expression units explained: RPM, RPKM, FPKM, TPM, <i>DESeq</i>, TMM, SCnorm, GeTMM, and ComBat-Seq https://t.co/FyTnMaqGy7 via @renbedre