在这一步中，我们使用基于杏仁核基因的分析结果来测试共表达网络中的哪些模块与MDD发病风险更相关。该试验是利用MAGMA中的基因集函数分析进行的。
共表达网络文件在文件network_files.zip中：
https://github.com/AngelaMinaVargas/eMAGMA-tutorial/blob/master/network_files.zip
共表达网络的组装细节在Gerring et al., 2019a中提供。每个共表达网络被细分为高度相关的基因的模块。每个共表达网络文件有2列：模块在第一列中表示，并被标记为颜色；这只是在分析期间标识模块或组的标签。第二列对应于基因ID。
第一步，解压缩networks.zip文件并使用以下代码运行基因集分析：
./magma --gene-results Amygdala_emagma.genes.raw
--set-annot network_files/Brain_Amygdala_entrez_gtex_v7_normalised.txt col=2,1
--out Amygdala_emagma
日志文件[Amygdala_emagma.log]显示从输入文件中读取了24个基因集(模块)，其中23个基因集包括基因型数据中定义的基因(总共包含1188个独特基因)。
基因组分析的结果显示在gsa.out文件[Amygdala_emagma.gsa.out ]中。这个文件显示，最小的P值=0.03703是由16个基因组成的模块：白色。下面是输出文件格式的示例。
# MEAN_SAMPLE_SIZE = 64134
# TOTAL_GENES = 1258
# TEST_DIRECTION = one-sided, positive (set), two-sided (covar)
# CONDITIONED_INTERNAL = gene size, gene density, sample size, inverse mac, log(gene size), log(gene density), log(sample size),
VARIABLE TYPE NGENES BETA BETA_STD SE P
black SET 55 0.0073145 0.0014962 0.13874 0.47898
skyblue3 SET 3 -0.64108 -0.031281 0.57506 0.86742
turquoise SET 280 0.071266 0.029657 0.067712 0.14639
white SET 16 0.45136 0.050599 0.25247 0.03703
通过多重检验后 MAGMA 会生成一个genes.sets.out文件导出显著的基因信息。在这里Amygdala network网络化模块不显著，Gerring et al.,2009a等人使用了一个更大的数据集(即包括23andMe的结果)并提供一个例子显著的模块共表达网络可以呈现出来了。
根据教程运行结果如下所示：
(base) bogon:eMAGMA chelsea$ unzip network_files.zip
Archive: network_files.zip
inflating: network_files/Adipose_Subcutaneous_entrez_gtex_v7_normalised.txt
inflating: network_files/Adipose_Visceral_Omentum_entrez_gtex_v7_normalised.txt
inflating: network_files/Adrenal_Gland_entrez_gtex_v7_normalised.txt
inflating: network_files/Artery_Aorta_entrez_gtex_v7_normalised.txt
inflating: network_files/Artery_Coronary_entrez_gtex_v7_normalised.txt
inflating: network_files/Artery_Tibial_entrez_gtex_v7_normalised.txt
inflating: network_files/Brain_Amygdala_entrez_gtex_v7_normalised.txt
inflating: network_files/Brain_Anterior_cingulate_cortex_BA24_entrez_gtex_v7_normalised.txt
inflating: network_files/Brain_Caudate_basal_ganglia_entrez_gtex_v7_normalised.txt
inflating: network_files/Brain_Cerebellar_Hemisphere_entrez_gtex_v7_normalised.txt
inflating: network_files/Brain_Cerebellum_entrez_gtex_v7_normalised.txt
inflating: network_files/Brain_Cortex_entrez_gtex_v7_normalised.txt
inflating: network_files/Brain_Frontal_Cortex_BA9_entrez_gtex_v7_normalised.txt
inflating: network_files/Brain_Hippocampus_entrez_gtex_v7_normalised.txt
inflating: network_files/Brain_Hypothalamus_entrez_gtex_v7_normalised.txt
inflating: network_files/Brain_Nucleus_accumbens_basal_ganglia_entrez_gtex_v7_normalised.txt
inflating: network_files/Brain_Putamen_basal_ganglia_entrez_gtex_v7_normalised.txt
inflating: network_files/Brain_Spinal_cord_cervical_c-1_entrez_gtex_v7_normalised.txt
inflating: network_files/Brain_Substantia_nigra_entrez_gtex_v7_normalised.txt
inflating: network_files/Breast_Mammary_Tissue_entrez_gtex_v7_normalised.txt
inflating: network_files/Cells_EBV-transformed_lymphocytes_entrez_gtex_v7_normalised.txt
inflating: network_files/Cells_Transformed_fibroblasts_entrez_gtex_v7_normalised.txt
inflating: network_files/Colon_Sigmoid_entrez_gtex_v7_normalised.txt
inflating: network_files/Colon_Transverse_entrez_gtex_v7_normalised.txt
inflating: network_files/Esophagus_Gastroesophageal_Junction_entrez_gtex_v7_normalised.txt
inflating: network_files/Esophagus_Mucosa_entrez_gtex_v7_normalised.txt
inflating: network_files/Esophagus_Muscularis_entrez_gtex_v7_normalised.txt
inflating: network_files/Heart_Atrial_Appendage_entrez_gtex_v7_normalised.txt
inflating: network_files/Heart_Left_Ventricle_entrez_gtex_v7_normalised.txt
inflating: network_files/Liver_entrez_gtex_v7_normalised.txt
inflating: network_files/Lung_entrez_gtex_v7_normalised.txt
inflating: network_files/Minor_Salivary_Gland_entrez_gtex_v7_normalised.txt
inflating: network_files/Muscle_Skeletal_entrez_gtex_v7_normalised.txt
inflating: network_files/Nerve_Tibial_entrez_gtex_v7_normalised.txt
extracting: network_files/network_files.zip
inflating: network_files/Ovary_entrez_gtex_v7_normalised.txt
inflating: network_files/Pancreas_entrez_gtex_v7_normalised.txt
inflating: network_files/Pituitary_entrez_gtex_v7_normalised.txt
inflating: network_files/Prostate_entrez_gtex_v7_normalised.txt
inflating: network_files/Skin_Not_Sun_Exposed_Suprapubic_entrez_gtex_v7_normalised.txt
inflating: network_files/Skin_Sun_Exposed_Lower_leg_entrez_gtex_v7_normalised.txt
inflating: network_files/Small_Intestine_Terminal_Ileum_entrez_gtex_v7_normalised.txt
inflating: network_files/Spleen_entrez_gtex_v7_normalised.txt
inflating: network_files/Stomach_entrez_gtex_v7_normalised.txt
inflating: network_files/Testis_entrez_gtex_v7_normalised.txt
inflating: network_files/Thyroid_entrez_gtex_v7_normalised.txt
inflating: network_files/Uterus_entrez_gtex_v7_normalised.txt
inflating: network_files/Vagina_entrez_gtex_v7_normalised.txt
inflating: network_files/Whole_Blood_entrez_gtex_v7_normalised.txt
(base) bogon:eMAGMA chelsea$ ls
Amygdala_emagma.genes.out CHANGELOG g1000_eur.synonyms
Amygdala_emagma.genes.raw MDD2018_ex23andMe g1000_eur.zip
Amygdala_emagma.log MDD2018_ex23andMe_emagma.txt g1000_eurreadme
Amygdala_emagma.log.suppl MDD2018_ex23andMe_emagma2.txt id.txt
Amygdala_outputs.zip NCBI37.3.gene.loc id2.txt
Amygdala_signif_genes.txt NCBI37.3.zip magma
Batch2_annot.zip README magma_v1.07b_mac.zip
Batch3_annot.zip REPORT manual_v1.07b.pdf
Batch4_annot.zip emagma_Amygdala_script ncbi_readme
Batch5_annot.zip g1000_eur.bed network_files
Batch6_annot.zip g1000_eur.bim network_files.zip
(base) bogon:eMAGMA chelsea$ ./magma --gene-results Amygdala_emagma.genes.raw --set-annot network_files/Brain_Amygdala_entrez_gtex_v7_normalised.txt col=2,1 --out Amygdala_emagma
Welcome to MAGMA v1.07b (mac)
Using flags:
--gene-results Amygdala_emagma.genes.raw
--set-annot network_files/Brain_Amygdala_entrez_gtex_v7_normalised.txt col=2,1
--out Amygdala_emagma
Start time is 11:13:41, Wednesday 29 Apr 2020
Reading file Amygdala_emagma.genes.raw...
1258 genes read from file
Loading gene-set annotation...
Reading file network_files/Brain_Amygdala_entrez_gtex_v7_normalised.txt...
detected 2 variables in file
using variable: variable 2 (gene ID)
using variable: variable 1 (set ID)
WARNING: gene set violet contains no genes defined in genotype data
24 gene-set definitions read from file
found 23 gene sets containing genes defined in genotype data (containing a total of 1188 unique genes)
Preparing variables for analysis...
truncating Z-scores 3 points below zero or 6 standard deviations above the mean
truncating covariate values more than 5 standard deviations from the mean
total variables available for analysis: 23 gene sets
Parsing model specifications...
Inverting gene-gene correlation matrix...
Performing regression analysis...
testing direction: one-sided, positive (sets), two-sided (covar)
conditioning on internal variables:
gene size, log(gene size)
gene density, log(gene density)
sample size, log(sample size)
inverse mac, log(inverse mac)
analysing individual variables
analysing single-variable models (number of models: 23)
writing results to file Amygdala_emagma.gsa.out
End time is 11:13:41, Wednesday 29 Apr 2020 (elapsed: 00:00:00)
(base) bogon:eMAGMA chelsea$ head Amygdala_emagma.gsa.out
# MEAN_SAMPLE_SIZE = 64134
# TOTAL_GENES = 1258
# TEST_DIRECTION = one-sided, positive (set), two-sided (covar)
# CONDITIONED_INTERNAL = gene size, gene density, sample size, inverse mac, log(gene size), log(gene density), log(sample size), log(inverse mac)
VARIABLE TYPE NGENES BETA BETA_STD SE P
black SET 55 0.0073148 0.0014963 0.13874 0.47898
blue SET 141 -0.058082 -0.01833 0.086617 0.74869
brown SET 78 0.039986 0.0096469 0.11469 0.3637
cyan SET 38 0.074409 0.012741 0.14678 0.30614
darkgreen SET 33 -0.28213 -0.04511 0.17484 0.94657
(base) bogon:eMAGMA chelsea$ tail Amygdala_emagma.gsa.out
lightcyan SET 27 0.053718 0.0077879 0.17959 0.38245
magenta SET 162 0.016619 0.0055687 0.080991 0.41873
paleturquoise SET 11 -0.15683 -0.014607 0.26683 0.7216
purple SET 36 0.10167 0.016958 0.16141 0.26445
red SET 41 -0.27599 -0.049026 0.17285 0.94471
royalblue SET 36 -0.07355 -0.012268 0.15804 0.67913
skyblue SET 17 -0.40256 -0.046498 0.24071 0.95265
skyblue3 SET 3 -0.64108 -0.031281 0.57506 0.86742
turquoise SET 280 0.071266 0.029657 0.067712 0.14639
white SET 16 0.45136 0.050599 0.25247 0.03703
鼓励用户用该教程来分析自己的数据，并且最大限度的利用已有的注释文件和网络分析文件，或者与作者的其它数据文件的关联分析也是被鼓励的，尽管去尝试吧，希望这些数据碰撞出新的火花。
网友评论