Online Tables for the thesis "Deconvoluting Bulk RNA-seq Data to Uncover Cell-Type Specific Signatures in Depression" by Roie Dvir

This repository contains the online tables that accompany my thesis, "Deconvoluting Bulk RNA-seq Datato Uncover Cell-Type Specific Signatures in Depression." In the following, I list the description of all tables: The data is divided into seven files. The first file (sig genes DEA thesis Roie Dvir) includes all the tables of significant genes (FDR < 0.1) of the DE analyses conducted by DESeq2. 1. Significant genes for status (228 individuals with depression vs. 33 controls) of the bulk RNA-seq data. 2. Significant genes for age as the main variable of the bulk RNA-seq data. 3. Significant genes for CRP level as the main variable of the bulk RNA-seq data 4. Significant genes for status (individuals with depression vs. controls) of the downsampled bulk RNA-seq data (66 total samples). 5. Significant genes for age as the main variable of the downsampled bulk RNA-seq data (66 total samples). 6. Significant genes for CRP as the main variable of the downsampled bulk RNA-seq data (66 total samples). 7. Significant genes for age as the main variable of the bulk RNA-seq data, including the first three PCs of the data's cell type estimates. 8. Significant genes for CRP as the main variable of the bulk RNA-seq data, including the first three PCs of the data's cell type estimates. 9. Significant genes for status (individuals with depression vs. controls) of the downsampled bulk RNA-seq data (66 total samples), including the first three PCs of the data's cell type estimates. 10. Significant genes for age as the main variable of the downsampled bulk RNA-seq data (66 total samples), including the first three PCs of the data's cell type estimates. 11. Significant genes for CRP as the main variable of the downsampled bulk RNA-seq data (66 total samples), including the first three PCs of the data's cell type estimates. 12. Significant transcripts for age as the main variable of the bulk transcript-level RNA-seq data. 13. Significant transcripts for CRP as the main variable of the bulk transcript level RNA-seq data. 14. Significant transcripts for status (individuals with depression vs. controls) of the downsampled bulk transcript level RNA-seq data (66 total samples). 15. Significant transcripts for age as the main variable of the downsampled bulk transcript-level RNA-seq data (66 total samples). 16. Significant transcripts for CRP as the main variable of the downsampled bulk transcript-level RNA-seq data (66 total samples). The rest of the files (6) are divided by cell types. Each file contains the tables of the significant genes identified by the cell-type-specific DE methods. For each cell type, there are the results for status, age, CRP, and BMI. If there were no significant genes identified for any of the tests, they are not included.本仓库收录了伴随我的学位论文《解析批量RNA测序数据以揭示抑郁症中的细胞类型特异性特征》（"Deconvoluting Bulk RNA-seq Data to Uncover Cell-Type Specific Signatures in Depression"）的配套在线附表。 下文将逐一说明所有附表的内容： 本数据集共分为7个文件。 第一个文件（标注为sig genes DEA thesis Roie Dvir）收录了通过DESeq2进行差异表达（Differential Expression, DE）分析得到的全部显著基因表（错误发现率False Discovery Rate, FDR < 0.1），具体包含以下16项分析结果： 1. 针对疾病状态（228名抑郁症患者与33名健康对照）的批量RNA测序（Bulk RNA-seq）数据的显著基因分析结果； 2. 以年龄为核心变量的批量RNA测序数据的显著基因分析结果； 3. 以C反应蛋白（C-reactive protein, CRP）水平为核心变量的批量RNA测序数据的显著基因分析结果； 4. 针对疾病状态（抑郁症患者与健康对照）的降采样批量RNA测序数据（共66个样本）的显著基因分析结果； 5. 以年龄为核心变量的降采样批量RNA测序数据（共66个样本）的显著基因分析结果； 6. 以CRP水平为核心变量的降采样批量RNA测序数据（共66个样本）的显著基因分析结果； 7. 纳入数据细胞类型估计值前3个主成分（Principal Components, PCs）、以年龄为核心变量的批量RNA测序数据的显著基因分析结果； 8. 纳入数据细胞类型估计值前3个主成分、以CRP水平为核心变量的批量RNA测序数据的显著基因分析结果； 9. 纳入数据细胞类型估计值前3个主成分、针对疾病状态（抑郁症患者与健康对照）的降采样批量RNA测序数据（共66个样本）的显著基因分析结果； 10. 纳入数据细胞类型估计值前3个主成分、以年龄为核心变量的降采样批量RNA测序数据（共66个样本）的显著基因分析结果； 11. 纳入数据细胞类型估计值前3个主成分、以CRP水平为核心变量的降采样批量RNA测序数据（共66个样本）的显著基因分析结果； 12. 以年龄为核心变量的转录本层面批量RNA测序数据的显著转录本分析结果； 13. 以CRP水平为核心变量的转录本层面批量RNA测序数据的显著转录本分析结果； 14. 针对疾病状态（抑郁症患者与健康对照）的降采样转录本层面批量RNA测序数据（共66个样本）的显著转录本分析结果； 15. 以年龄为核心变量的降采样转录本层面批量RNA测序数据（共66个样本）的显著转录本分析结果； 16. 以CRP水平为核心变量的降采样转录本层面批量RNA测序数据（共66个样本）的显著转录本分析结果。 其余6个文件均按细胞类型进行划分。每个文件包含通过细胞类型特异性差异表达分析方法鉴定得到的显著基因表。针对每种细胞类型，均包含疾病状态、年龄、CRP水平以及身体质量指数（Body Mass Index, BMI）的分析结果。若某类测试未鉴定到任何显著基因，则不包含对应表格。