What is he best known for?
The fields of study he is best known for:
Rahul Satija mainly investigates Computational biology, Genetics, Gene expression profiling, Single-cell analysis and Transcriptome.
His study on Systems biology is often connected to Function as part of broader study in Computational biology.
His research investigates the connection between Gene expression profiling and topics such as Cell biology that intersect with problems in Immunology, Monocyte, Major histocompatibility complex and Antigen presentation.
His Single-cell analysis research is multidisciplinary, incorporating perspectives in Cancer cell, Cancer stem cell, Epigenetics and Copy-number variation.
The concepts of his Transcriptome study are interwoven with issues in Phenotype and Regulation of gene expression.
Rahul Satija works mostly in the field of Sequence analysis, limiting it down to concerns involving Single cell sequencing and, occasionally, Genomics and Cell.
His most cited work include:
- Highly Parallel Genome-wide Expression Profiling of Individual Cells Using Nanoliter Droplets (3597 citations)
- Highly Parallel Genome-wide Expression Profiling of Individual Cells Using Nanoliter Droplets (3597 citations)
- Integrating single-cell transcriptomic data across different conditions, technologies, and species. (3497 citations)
What are the main themes of his work throughout his whole career to date?
His primary scientific interests are in Computational biology, Genetics, Transcriptome, Cell biology and Cell.
His Computational biology study combines topics in areas such as RNA, Genomics, Single cell sequencing, Cell type and Profiling.
Transcriptome is a subfield of Gene expression that Rahul Satija explores.
His Cell biology study incorporates themes from Regulation of gene expression, Major histocompatibility complex, Antigen presentation and Monocyte.
The study incorporates disciplines such as Epigenomics, RNA-Seq, Messenger RNA and Immune system in addition to Cell.
His research integrates issues of Neuroscience, Immunology and Gene expression profiling in his study of Single-cell analysis.
He most often published in these fields:
- Computational biology (39.83%)
- Genetics (30.51%)
- Transcriptome (31.36%)
What were the highlights of his more recent work (between 2018-2021)?
- Cell biology (28.81%)
- Computational biology (39.83%)
- Cell (27.12%)
In recent papers he was focusing on the following fields of study:
Rahul Satija focuses on Cell biology, Computational biology, Cell, Stem cell and Haematopoiesis.
His work in Cell biology covers topics such as Glucose phosphate which are related to areas like Toxicity and Inflammation.
Rahul Satija has researched Computational biology in several fields, including Single cell transcriptome and Protein expression.
Rahul Satija interconnects Nuclear medicine, RNA and CRISPR in the investigation of issues within Cell.
His study in Stem cell is interdisciplinary in nature, drawing from both Blood cell and Epigenetics.
His work deals with themes such as Cellular differentiation, Myeloid, Transcriptome, Single-cell analysis and Bone marrow, which intersect with Haematopoiesis.
Between 2018 and 2021, his most popular works were:
- Comprehensive Integration of Single-Cell Data. (2444 citations)
- Normalization and variance stabilization of single-cell RNA-seq data using regularized negative binomial regression (488 citations)
- Integrative single-cell analysis. (364 citations)
In his most recent research, the most cited papers focused on:
Computational biology, Modalities, Shared vision, Technology development and Common fund are his primary areas of study.
His Computational biology research includes themes of Single cell transcriptome and Protein expression.
Modalities is connected with Data type, Datasets as Topic, Epigenesis and Cell state in his research.
Single-cell analysis and Pooling are fields of study that intersect with his Data type research.
The various areas that Rahul Satija examines in his Single-cell analysis study include Myeloid, Haematopoiesis, Cellular differentiation and Transcriptome.
Technology development combines with fields such as Human body and Atlas in his investigation.
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