While TCGA is a good resource for tumor data from patients, finding expression data for normal tissue for comparison can be challenging. There are two main sources of normal expression data in Xena. The first is normal tissue samples from TCGA patients themselves. These patient's samples are called "solid tissue normals" and are taken from normal tissues near the tumor. Solid tissue normal samples from TCGA patients are typically limited in number but some cancer types may have enough for a robust statistical comparison. It is important to note that their proximity to tumor may introduce signals of tumor microenvironment in its transcriptome profile. The second way is to compare patient's tumor samples to samples from GTEx, which has expression data from normal tissue of individuals who do not have cancer. There are typically many more samples in GTEx then in TCGA solid tissue normals, however, experimental sample processing are different from TCGA, which may lead to batch effects.
You can use the TCGA TARGET GTEx study for both types of 'normal' samples. Data from the study is from the UCSC RNA-seq Compendium, where TCGA, TARGET, and GTEx samples are re-analyzed (re-aligned to hg38 genome and expressions are called using RSEM and Kallisto methods) by the same RNA-seq pipeline. Because all samples are processed using a uniform bioinformatic pipeline, batch effect due to different computational processing is eliminated.
To compare tumor vs normal, you will need to filter down to just the samples you want to compare and then compare gene expression between your groups of samples.
There are four gene expression datasets in this study. Two are normalized using with-in sample methods. The 'RSEM norm__count' dataset is normalized by the upper quartile method, the 'RSEM expected__count (DESeq2 standardized)' dataset is by DESeq2 normalization. Therefore, these two gene expression datasets should be used.
If you are looking to compare just a few genes, you can use our chart view to run your analysis. If you are looking to run a genome-wide differential gene expression analysis, you can use our DEA feature. Note that we only allow users to run our Differential Gene Expression Analysis on less than 2,000 samples total. Thus, you will need to filter to run this analysis on this dataset.