dukekuang/pass-on-codes

PASS-ON-codes

1. System requirements:

• R version 4.0.3 & Rstudio Version 1.3.1093 has to be installed.

2. R Dependencies and version:

• here 1.0.1
• openxlsx 4.2.3
• org.Hs.eg.db 3.12.0
• annotate 1.68.0
• BiocParallel 1.24.1
• DESeq2 1.30.1
• fgsea 1.16.0
• GSVA 1.38.2
• glmnet 4.1-1
• pROC 1.17.0.1
• ROCR 1.0-11
• survival 3.2-7
• survminer 0.4.9
• ggplot2 3.3.3
• ggplot2 3.3.3
• ggrepel 0.9.1
• plotmo 3.6.0
• ggpubr 0.4.0
• RColorBrewer 1.1-2

3. Installation Guide:

Packages install from R CRAN (https://cran.r-project.org/):

• install.packages(c("here", "openxlsx", "glmnet", "pROC","ROCR","survival" "survminer", "ggplot2", "plotmo", "ggpubr", "RColorBrewer"))

Packages install from Bioconductor (https://www.bioconductor.org/):

• if (!requireNamespace("BiocManager", quietly = TRUE)) install.packages("BiocManager")

• BiocManager::install(c("org.Hs.eg.db","annotate","BiocParallel","DESeq2", "fgsea" "GSVA"))

4. Other Public Signatures:

   IMPRES: https://github.com/noamaus/IMPRES-codes

   CIBERSORT: https://cibersort.stanford.edu/index.php

5. Licenses:

GNU General Public License (GPL)

pass-on-codes's Issues

data processing & normalization -Lee

Hi, thank you so much for a really nice publication and for providing the code and data for your analyses.

I have a question regarding the Lee et al. data provided in the repository (the Lee_data.RData in the /data folder), particularly the TPM values for this dataset. Has there been any kind of other processing of the data, since the TPM matrix for the Lee dataset looks a bit weird, just by looking at colSums(); in contrast with the other TPM matrices of the other datasets, which are at least colSums --> 1e6. I would really appreciate your help and response, so I can understand how the data were processed.

Thank you!

Best,
Aimilia