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For GEO data, gene level normalization was performed by using Robust Multi-array Average (RMA) [ 22].
Copy number data (gene level) for cancer cell lines was obtained from CCLE (platform: Affymetrix SNP6) [ 7].
The TCGA CRC genomic data was obtained from the Broad Institute where the Tier 3 data (gene level calls that have been fully processed) are archived.
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The level 3 TCGA mRNA data provide gene level summary of mRNA expression, which is standardized by mean and standard deviation of entire dataset.
To explore our data at gene level, additional analysis and filtering was performed on the resulting file of 31,042 ProbeSets as mentioned earlier.
In our data gene expression levels for Ang were lower in SM/J animals fed a high-fat diet, while Pdgfa expression levels significantly increased.
For microarray data, gene expression levels are estimated using the Affymetrix GeneChip and associated manufacturer software.
For RNA-seq data, gene expression levels were approximated using the RPKM (reads per kilobase per million mapped reads) measure [ 21] and further normalized using the β-actin transcript (ENSEMBL identifier ENST00000331789).
To assess the validity of the microarray data, gene expression levels for selected chemokines (CXCL1, CXCL3 and CXCL13) were also measured by quantitative real-time polymerase chain reaction (PCR).
Low-level analysis which converts probe level data to a gene level expression data was done using robust multiarray average (RMA), implemented using the rma function of the Affymetrix package of the Bioconductor project in the R programming language [ 78- 80].
Finally, read counts of transcripts belonging to the same gene were summed to obtain count data at Ensembl gene level.
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