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Prikvačeni tweet
“There are five important things for living a successful and fulfilling life: never stop dreaming, never stop believing, never give up, never stop trying, and never stop learning.” ― Roy Bennett -------- My Infographic educational background (Mini-CV) Design by:
@cancerevolutionpic.twitter.com/jiX83bS5hQ
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Single-Cell Transcriptomics of Regulatory T Cells Reveals Trajectories of Tissue Adaptation |
@jeremygoettel Fig. Steady-State scRNA-Seq Datasets of CD4+ T Cells from LT and NLT Ref: http://bit.ly/392R84t#singlecell#scRNAseq#Transcriptomics#Tcellpic.twitter.com/2eqtupT0mW
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Single-cell genomic approaches for developing the next generation of immunotherapies. |
@NatureMedicine by @IdoAmitLab Ref: https://go.nature.com/36VaSpo#singlecell#scRNAseq#immunotherapy#genomicspic.twitter.com/bn2kLReaKT
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Mapping human cell phenotypes to genotypes with single-cell genomics. Fig. Human organ maps can resolve disease phenotypes Ref: http://bit.ly/2vEcsiF
#singlecell#scRNAseq#phenotype#genotype#genomics@randall_platt@GrayCampLab@TreutleinLabpic.twitter.com/dr28oHeQYS
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Analyses of single‐cell MAPK/ERK signaling dynamics in response to temporally controlled EGF, NGF, FGF2 stimulations show that FGF2 evokes distinct signaling dynamics compared to EGF/NGF. A mathematical model ... Ref: http://bit.ly/2RTG0kR
#singlecell#ERK#MAPK#EGFpic.twitter.com/SxLfFHoLe0
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Mapping human cell phenotypes to genotypes with single-cell genomics. Fig. Human organoids to recapitulate human phenotypes in vitro Ref: http://bit.ly/2vEcsiF
#singlecell#scRNAseq#phenotype#genotype#genomics@randall_platt@GrayCampLab@treutleinlabpic.twitter.com/pU9SYAysN7
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Mapping human cell phenotypes to genotypes with single-cell genomics. Fig. Genetic manipulation toolkit to link phenotype to genotype by using stem cells Ref: http://bit.ly/2vEcsiF
#singlecell#scRNAseq#phenotype#genotype#genomicspic.twitter.com/E4GdJ8Qd1N
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Quantitative Proteomics of the Cancer Cell Line Encyclopedia Quantified the proteomes of 375 cell lines from diverse lineages in the CCLE Ref: http://bit.ly/37DDJQa
#Proteomics#CCLE#cancercellline#cancerbiologypic.twitter.com/uVi8RpTZ89
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Metabolic landscape of the tumor microenvironment at single cell resolution. Fig. Landscape of metabolic gene expression at single-cell level. Ref: https://go.nature.com/31eiQsi
#microenvironment#singlecell#geneexpression#scRNAseqpic.twitter.com/ajsSmYv9QX
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Single-Cell Transcriptomic Atlas of Primate Ovarian Aging Ref: http://bit.ly/31dgk5O
#singlecell#scRNAseq#ovary#aging#Transcriptomicpic.twitter.com/LpcLnwDRgo
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A Systematic Evaluation of Single-cell RNA-sequencing Imputation Methods. Fig. Motivation and overview of benchmark evaluation of scRNA-seq imputation methods Ref: http://bit.ly/3aVJCub
#singlecell#scrnaseq#RNAseq@jhubiostat@HWenpin@stephaniehickspic.twitter.com/FzY3rrJEAK
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In Situ RNA Sequencing (ISS) technology will be applied to spatially map cell types for international Human Cell Atlas initiative. Fig. CARTANA ISS technology for high throughput spatial transcriptomic analysis. Ref: https://go.nature.com/2SkLBQn
#RNAseq#HCA#ISS#CARTANApic.twitter.com/al8tPpw2Rj
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Metabolic landscape of the tumor microenvironment at single cell resolution. Fig. Landscape of metabolic gene expression at single-cell level. Ref: https://go.nature.com/31eiQsi
#singlecell#scRNAseq#tumormicroenvironment#metabolicpic.twitter.com/UclRdY9nhi
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A generic roadmap for RNA-seq computational analyses. The major analysis steps are listed above the lines for pre-analysis, core analysis and advanced analysis. Ref: A survey of best practices for RNA-seq data analysis (http://bit.ly/38W4SxX )
#singlecell#rnaseq#scRNAseqpic.twitter.com/Fp3meqFzJM
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Arash Davari Serej proslijedio/la je Tweet
An excellent course for anyone wanting to learn about brain tumors: Great speakers, great students, great venue. https://meetings.cshl.edu/courses.aspx?course=C-BRAIN&year=18 ….
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Retina Development in Vertebrates: Systems Biology Approaches to Understanding Genetic Programs Ref: http://bit.ly/2uM7FLr
#singlecell#scRNAseq#genetics#retina@CABD_UPO_CSICpic.twitter.com/yioMJyrsFm
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Arash Davari Serej proslijedio/la je Tweet
This led us to evaluating many downstream analyses across diff experimental platforms & datasets, incl the wonderful CellBench
#scrnaseq benchmarking dataset from@Luyi_T@trashystats@mritchieau et al (https://www.nature.com/articles/s41592-019-0425-8 …) (https://github.com/LuyiTian/sc_mixology/blob/master/cellbench.md …) e.g. for unsup clusteringpic.twitter.com/PQI3YQSiZA
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Single-cell RNA expression profiling of ACE2, the putative receptor of Wuhan 2019-nCov "Coronavirus specifically likes to infect Asian male" Fig. Single-cell analysis of normal human lung. Ref: http://bit.ly/2S64nKZ
#singlecell#scRNAseq#RNAexpressionprofilingpic.twitter.com/52SwWUMEt5
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A single-cell transcriptomic and anatomic atlas of mouse dorsal raphe Pet1 neurons. Ref: http://bit.ly/37GIQz4
#singlecell#scRNAseq#transcriptomic@giac_maddpic.twitter.com/EZnpnHinc4
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A mouse tissue atlas of small non-coding RNA. 473 new small RNAs ! Tissue specific and sexually dimorphic! Ref: http://bit.ly/2tlqdlu
#tissueatlas#singlecell#scRNAseq#rna#noncodingrna@alinaisk0va@FehlmannTobias@StanfordBioE@czbiohub@czisciencepic.twitter.com/6NfjJJj76g
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Modeling metabolic variation with single-cell expression data Fig. Tissue and cell type context-specific metabolic models constructed using Tabula Muris scRNA-seq dataset (Schaum et al., 2018) Ref: http://bit.ly/2OeVa1W
#singlecell#scRNAseq#metabolicmodelpic.twitter.com/NdhkB7F6BU
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Microenvironment & Heterogeneity of Tumor
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