model hic
Yunqi Yang
8/5/2020
Last updated: 2020-08-05
Checks: 6 1
Knit directory: gene_level_fine_mapping/
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load("/Users/nicholeyang/Desktop/gene_level_fine_mapping/data/train_add_hic.RData")data summary
head(dat_add_hic) gene_name snp_loc38 variant_id UTR5 UTR3 exon intron
1 A1BG chr19:57866502 chr19_57866502_T_C_b38 0 0 0 0
2 A1BG chr19:58059544 chr19_58059544_T_G_b38 0 0 0 0
3 A1BG chr19:58170494 chr19_58170494_G_T_b38 0 0 0 0
4 A1BG chr19:58228973 chr19_58228973_T_G_b38 0 0 0 0
5 A1BG chr19:58330182 chr19_58330182_C_T_b38 0 0 0 0
6 A1BG chr19:58359927 chr19_58359927_G_A_b38 0 0 0 0
upstream tss_dist_to_snp y Mon Mac0 Mac1 Mac2 Neu MK EP Ery FoeT nCD4 tCD4
1 0 481132 0 0 0 0 0 0 0 0 0 0 0 0
2 0 288090 0 0 0 0 0 0 0 0 0 0 0 0
3 0 177140 0 0 0 0 0 0 0 0 0 0 0 0
4 0 118661 0 0 0 0 0 0 0 0 0 0 0 0
5 0 17452 1 0 0 0 0 0 0 0 0 0 0 0
6 0 6428 1 0 0 0 0 0 0 0 0 0 0 0
aCD4 naCD4 nCD8 tCD8 nB tB
1 0 0 0 0 0 0
2 0 0 0 0 0 0
3 0 0 0 0 0 0
4 0 0 0 0 0 0
5 0 0 0 0 0 0
6 0 0 0 0 0 0hist(dat_add_hic$tss_dist_to_snp)
range(dat_add_hic$tss_dist_to_snp)[1] 0 2176373plot the proportion of features in the positive/negative set
dist_range = c(0, 1e5, 2e5, 3e5, 4e5, 5e5, 3e6)
for (i in 1:6){
par(mfrow = c(1,2))
sub_dat = dat_add_hic[dat_add_hic$tss_dist_to_snp %in% seq(dist_range[i], dist_range[i+1], by = 1), ]
# proportion of features in the positive set
tot_count = apply(sub_dat[sub_dat$y==1, c(11:27)], 2, function(x) sum(x == 1))
barplot(tot_count/dim(sub_dat[sub_dat$y==1, ])[1], las = 2, main = paste('positive set', dist_range[i], dist_range[i+1], sep = ':'))
# proportion of features in the negative set
tot_count = apply(sub_dat[sub_dat$y==0, c(11:27)], 2, function(x) sum(x == 1))
barplot(tot_count/dim(sub_dat[sub_dat$y==0, ])[1], las = 2,
main = paste('negative set', dist_range[i], dist_range[i+1], sep = ':'))
}
by varaible logistic regression
only Mac1 and Mac2 features are not significant
for (i in 11: 27){
fit = glm( dat_add_hic$y ~ dat_add_hic[, i], family = "binomial")
print(summary(fit))
}
Call:
glm(formula = dat_add_hic$y ~ dat_add_hic[, i], family = "binomial")
Deviance Residuals:
Min 1Q Median 3Q Max
-0.3524 -0.3524 -0.3524 -0.3524 2.5238
Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) -2.74794 0.01663 -165.252 < 2e-16 ***
dat_add_hic[, i] -0.39445 0.09042 -4.362 1.29e-05 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
(Dispersion parameter for binomial family taken to be 1)
Null deviance: 30206 on 67105 degrees of freedom
Residual deviance: 30185 on 67104 degrees of freedom
AIC: 30189
Number of Fisher Scoring iterations: 5
Call:
glm(formula = dat_add_hic$y ~ dat_add_hic[, i], family = "binomial")
Deviance Residuals:
Min 1Q Median 3Q Max
-0.3518 -0.3518 -0.3518 -0.3518 2.5079
Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) -2.75131 0.01660 -165.729 < 2e-16 ***
dat_add_hic[, i] -0.34953 0.09477 -3.688 0.000226 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
(Dispersion parameter for binomial family taken to be 1)
Null deviance: 30206 on 67105 degrees of freedom
Residual deviance: 30191 on 67104 degrees of freedom
AIC: 30195
Number of Fisher Scoring iterations: 5
Call:
glm(formula = dat_add_hic$y ~ dat_add_hic[, i], family = "binomial")
Deviance Residuals:
Min 1Q Median 3Q Max
-0.3499 -0.3499 -0.3499 -0.3499 2.3864
Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) -2.76281 0.01669 -165.549 <2e-16 ***
dat_add_hic[, i] -0.02499 0.08241 -0.303 0.762
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
(Dispersion parameter for binomial family taken to be 1)
Null deviance: 30206 on 67105 degrees of freedom
Residual deviance: 30206 on 67104 degrees of freedom
AIC: 30210
Number of Fisher Scoring iterations: 5
Call:
glm(formula = dat_add_hic$y ~ dat_add_hic[, i], family = "binomial")
Deviance Residuals:
Min 1Q Median 3Q Max
-0.350 -0.350 -0.350 -0.350 2.392
Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) -2.76224 0.01669 -165.534 <2e-16 ***
dat_add_hic[, i] -0.03887 0.08262 -0.471 0.638
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
(Dispersion parameter for binomial family taken to be 1)
Null deviance: 30206 on 67105 degrees of freedom
Residual deviance: 30206 on 67104 degrees of freedom
AIC: 30210
Number of Fisher Scoring iterations: 5
Call:
glm(formula = dat_add_hic$y ~ dat_add_hic[, i], family = "binomial")
Deviance Residuals:
Min 1Q Median 3Q Max
-0.3533 -0.3533 -0.3533 -0.3533 2.5568
Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) -2.74263 0.01664 -164.820 < 2e-16 ***
dat_add_hic[, i] -0.48706 0.08897 -5.475 4.38e-08 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
(Dispersion parameter for binomial family taken to be 1)
Null deviance: 30206 on 67105 degrees of freedom
Residual deviance: 30172 on 67104 degrees of freedom
AIC: 30176
Number of Fisher Scoring iterations: 5
Call:
glm(formula = dat_add_hic$y ~ dat_add_hic[, i], family = "binomial")
Deviance Residuals:
Min 1Q Median 3Q Max
-0.351 -0.351 -0.351 -0.351 2.448
Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) -2.75638 0.01665 -165.542 <2e-16 ***
dat_add_hic[, i] -0.18843 0.08710 -2.163 0.0305 *
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
(Dispersion parameter for binomial family taken to be 1)
Null deviance: 30206 on 67105 degrees of freedom
Residual deviance: 30201 on 67104 degrees of freedom
AIC: 30205
Number of Fisher Scoring iterations: 5
Call:
glm(formula = dat_add_hic$y ~ dat_add_hic[, i], family = "binomial")
Deviance Residuals:
Min 1Q Median 3Q Max
-0.3518 -0.3518 -0.3518 -0.3518 2.5059
Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) -2.75165 0.01660 -165.773 < 2e-16 ***
dat_add_hic[, i] -0.34396 0.09517 -3.614 0.000301 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
(Dispersion parameter for binomial family taken to be 1)
Null deviance: 30206 on 67105 degrees of freedom
Residual deviance: 30192 on 67104 degrees of freedom
AIC: 30196
Number of Fisher Scoring iterations: 5
Call:
glm(formula = dat_add_hic$y ~ dat_add_hic[, i], family = "binomial")
Deviance Residuals:
Min 1Q Median 3Q Max
-0.3533 -0.3533 -0.3533 -0.3533 2.5757
Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) -2.74261 0.01661 -165.162 < 2e-16 ***
dat_add_hic[, i] -0.53767 0.09441 -5.695 1.24e-08 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
(Dispersion parameter for binomial family taken to be 1)
Null deviance: 30206 on 67105 degrees of freedom
Residual deviance: 30168 on 67104 degrees of freedom
AIC: 30172
Number of Fisher Scoring iterations: 5
Call:
glm(formula = dat_add_hic$y ~ dat_add_hic[, i], family = "binomial")
Deviance Residuals:
Min 1Q Median 3Q Max
-0.355 -0.355 -0.355 -0.355 2.718
Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) -2.73287 0.01653 -165.354 <2e-16 ***
dat_add_hic[, i] -0.93448 0.11373 -8.217 <2e-16 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
(Dispersion parameter for binomial family taken to be 1)
Null deviance: 30206 on 67105 degrees of freedom
Residual deviance: 30115 on 67104 degrees of freedom
AIC: 30119
Number of Fisher Scoring iterations: 6
Call:
glm(formula = dat_add_hic$y ~ dat_add_hic[, i], family = "binomial")
Deviance Residuals:
Min 1Q Median 3Q Max
-0.356 -0.356 -0.356 -0.356 2.669
Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) -2.72682 0.01662 -164.109 <2e-16 ***
dat_add_hic[, i] -0.80680 0.09371 -8.609 <2e-16 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
(Dispersion parameter for binomial family taken to be 1)
Null deviance: 30206 on 67105 degrees of freedom
Residual deviance: 30111 on 67104 degrees of freedom
AIC: 30115
Number of Fisher Scoring iterations: 6
Call:
glm(formula = dat_add_hic$y ~ dat_add_hic[, i], family = "binomial")
Deviance Residuals:
Min 1Q Median 3Q Max
-0.3536 -0.3536 -0.3536 -0.3536 2.6059
Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) -2.74079 0.01658 -165.299 <2e-16 ***
dat_add_hic[, i] -0.62042 0.09920 -6.254 4e-10 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
(Dispersion parameter for binomial family taken to be 1)
Null deviance: 30206 on 67105 degrees of freedom
Residual deviance: 30159 on 67104 degrees of freedom
AIC: 30163
Number of Fisher Scoring iterations: 5
Call:
glm(formula = dat_add_hic$y ~ dat_add_hic[, i], family = "binomial")
Deviance Residuals:
Min 1Q Median 3Q Max
-0.3548 -0.3548 -0.3548 -0.3548 2.6459
Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) -2.73371 0.01659 -164.750 < 2e-16 ***
dat_add_hic[, i] -0.73593 0.09737 -7.558 4.1e-14 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
(Dispersion parameter for binomial family taken to be 1)
Null deviance: 30206 on 67105 degrees of freedom
Residual deviance: 30135 on 67104 degrees of freedom
AIC: 30139
Number of Fisher Scoring iterations: 6
Call:
glm(formula = dat_add_hic$y ~ dat_add_hic[, i], family = "binomial")
Deviance Residuals:
Min 1Q Median 3Q Max
-0.3538 -0.3538 -0.3538 -0.3538 2.5884
Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) -2.73957 0.01662 -164.835 < 2e-16 ***
dat_add_hic[, i] -0.57462 0.09215 -6.236 4.5e-10 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
(Dispersion parameter for binomial family taken to be 1)
Null deviance: 30206 on 67105 degrees of freedom
Residual deviance: 30160 on 67104 degrees of freedom
AIC: 30164
Number of Fisher Scoring iterations: 5
Call:
glm(formula = dat_add_hic$y ~ dat_add_hic[, i], family = "binomial")
Deviance Residuals:
Min 1Q Median 3Q Max
-0.3537 -0.3537 -0.3537 -0.3537 2.5699
Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) -2.74059 0.01664 -164.687 < 2e-16 ***
dat_add_hic[, i] -0.52399 0.08890 -5.894 3.77e-09 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
(Dispersion parameter for binomial family taken to be 1)
Null deviance: 30206 on 67105 degrees of freedom
Residual deviance: 30166 on 67104 degrees of freedom
AIC: 30170
Number of Fisher Scoring iterations: 5
Call:
glm(formula = dat_add_hic$y ~ dat_add_hic[, i], family = "binomial")
Deviance Residuals:
Min 1Q Median 3Q Max
-0.3543 -0.3543 -0.3543 -0.3543 2.5827
Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) -2.73713 0.01666 -164.312 < 2e-16 ***
dat_add_hic[, i] -0.56181 0.08672 -6.478 9.28e-11 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
(Dispersion parameter for binomial family taken to be 1)
Null deviance: 30206 on 67105 degrees of freedom
Residual deviance: 30157 on 67104 degrees of freedom
AIC: 30161
Number of Fisher Scoring iterations: 5
Call:
glm(formula = dat_add_hic$y ~ dat_add_hic[, i], family = "binomial")
Deviance Residuals:
Min 1Q Median 3Q Max
-0.3545 -0.3545 -0.3545 -0.3545 2.6712
Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) -2.73575 0.01655 -165.309 < 2e-16 ***
dat_add_hic[, i] -0.80332 0.10705 -7.504 6.17e-14 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
(Dispersion parameter for binomial family taken to be 1)
Null deviance: 30206 on 67105 degrees of freedom
Residual deviance: 30134 on 67104 degrees of freedom
AIC: 30138
Number of Fisher Scoring iterations: 6
Call:
glm(formula = dat_add_hic$y ~ dat_add_hic[, i], family = "binomial")
Deviance Residuals:
Min 1Q Median 3Q Max
-0.3545 -0.3545 -0.3545 -0.3545 2.6708
Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) -2.73582 0.01655 -165.313 < 2e-16 ***
dat_add_hic[, i] -0.80199 0.10705 -7.492 6.79e-14 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
(Dispersion parameter for binomial family taken to be 1)
Null deviance: 30206 on 67105 degrees of freedom
Residual deviance: 30134 on 67104 degrees of freedom
AIC: 30138
Number of Fisher Scoring iterations: 6
sessionInfo()R version 3.6.3 (2020-02-29)
Platform: x86_64-apple-darwin15.6.0 (64-bit)
Running under: macOS Catalina 10.15.5
Matrix products: default
BLAS: /Library/Frameworks/R.framework/Versions/3.6/Resources/lib/libRblas.0.dylib
LAPACK: /Library/Frameworks/R.framework/Versions/3.6/Resources/lib/libRlapack.dylib
locale:
[1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
attached base packages:
[1] stats graphics grDevices utils datasets methods base
other attached packages:
[1] workflowr_1.6.1
loaded via a namespace (and not attached):
[1] Rcpp_1.0.4 rprojroot_1.3-2 digest_0.6.25 later_1.0.0
[5] R6_2.4.1 backports_1.1.5 git2r_0.26.1 magrittr_1.5
[9] evaluate_0.14 highr_0.8 stringi_1.4.6 rlang_0.4.5
[13] fs_1.3.2 promises_1.1.0 whisker_0.4 rmarkdown_2.1
[17] tools_3.6.3 stringr_1.4.0 glue_1.3.2 httpuv_1.5.2
[21] xfun_0.12 yaml_2.2.1 compiler_3.6.3 htmltools_0.4.0
[25] knitr_1.28