Multiple testing correction
nfancy opened this issue · 0 comments
nfancy commented
Hi,
Thank you very much for this very useful package. We are using this for comparing the differential cell type proportion in single nuclei RNA sequencing data. A common example can be found here here. I was wondering do we need multiple testing correction for the p-values obtained from each of the cell types? For example see the summary below:
> summary(dirichlet_res)
Call:
DirichletReg::DirichReg(formula = dirichlet_dt ~ diagnosis + brain_region + sex + age + PMI + RIN,
data = sn_ct_prop)
Standardized Residuals:
Min 1Q Median 3Q Max
Astro -1.8915 -0.5861 -0.0432 0.4115 2.9265
Micro -1.9210 -0.6087 -0.1745 0.4216 2.1042
Oligo -3.8777 -1.2850 -0.0789 1.8296 5.8966
OPC -1.3329 -0.6777 -0.2168 0.1330 4.8122
Vasc -1.6562 -0.6634 -0.3561 0.2591 2.9518
------------------------------------------------------------------
Beta-Coefficients for variable no. 1: Astro
Estimate Std. Error z value Pr(>|z|)
(Intercept) 1.37217 0.30822 4.452 8.51e-06 ***
diagnosisAD -0.46457 0.18704 -2.484 0.013000 *
brain_regionmTemp 0.16425 0.18080 0.908 0.363626
brain_regionSSC 0.49240 0.17622 2.794 0.005202 **
sexM 0.81271 0.19663 4.133 3.58e-05 ***
age 0.45041 0.11205 4.020 5.83e-05 ***
PMI 0.04278 0.01148 3.727 0.000194 ***
RIN 0.29228 0.08503 3.437 0.000587 ***
------------------------------------------------------------------
Beta-Coefficients for variable no. 2: Micro
Estimate Std. Error z value Pr(>|z|)
(Intercept) 0.40135 0.37210 1.079 0.280766
diagnosisAD -0.37726 0.20383 -1.851 0.064187 .
brain_regionmTemp 0.06662 0.19641 0.339 0.734464
brain_regionSSC 0.00659 0.19295 0.034 0.972755
sexM 1.08002 0.23322 4.631 3.64e-06 ***
age 0.58778 0.12377 4.749 2.05e-06 ***
PMI 0.04783 0.01384 3.457 0.000547 ***
RIN 0.49261 0.09790 5.032 4.86e-07 ***
------------------------------------------------------------------
Beta-Coefficients for variable no. 3: Oligo
Estimate Std. Error z value Pr(>|z|)
(Intercept) 2.00718 0.33585 5.976 2.28e-09 ***
diagnosisAD -0.41222 0.19433 -2.121 0.033898 *
brain_regionmTemp 0.05895 0.17865 0.330 0.741409
brain_regionSSC 0.30981 0.17623 1.758 0.078757 .
sexM 0.94630 0.21737 4.353 1.34e-05 ***
age 0.42127 0.11598 3.632 0.000281 ***
PMI 0.02792 0.01170 2.387 0.017000 *
RIN 0.44467 0.07923 5.613 1.99e-08 ***
------------------------------------------------------------------
Beta-Coefficients for variable no. 4: OPC
Estimate Std. Error z value Pr(>|z|)
(Intercept) 0.93467 0.34043 2.746 0.006041 **
diagnosisAD -0.50381 0.19249 -2.617 0.008862 **
brain_regionmTemp 0.01828 0.19774 0.092 0.926360
brain_regionSSC 0.23676 0.19280 1.228 0.219448
sexM 0.77014 0.21564 3.571 0.000355 ***
age 0.33099 0.12007 2.757 0.005839 **
PMI 0.03451 0.01262 2.735 0.006246 **
RIN 0.12266 0.09404 1.304 0.192130
------------------------------------------------------------------
Beta-Coefficients for variable no. 5: Vasc
Estimate Std. Error z value Pr(>|z|)
(Intercept) -0.08012 0.41009 -0.195 0.84510
diagnosisAD -0.31873 0.22023 -1.447 0.14783
brain_regionmTemp -0.07216 0.24141 -0.299 0.76502
brain_regionSSC 0.38433 0.23053 1.667 0.09548 .
sexM 0.75324 0.24958 3.018 0.00254 **
age 0.38217 0.14067 2.717 0.00659 **
PMI 0.03057 0.01519 2.013 0.04412 *
RIN 0.27600 0.12116 2.278 0.02273 *
------------------------------------------------------------------
Significance codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Log-likelihood: 1564 on 104 df (462 BFGS + 2 NR Iterations)
AIC: -2921, BIC: -2718
Number of Observations: 52
Link: Log
Parametrization: common
So, if I extract the p-values for diagnosisAD for each cell type, do we need to do a multiple-testing correction. Thanks in advance.
Nurun