2 Quick Start
The following example illustrates the complete workflow for HOBIT, from loading the data to performing the statistical test.
# gene expression
gexp <- read.table("data/c_flexuosa.0516.mini.txt.gz",
header = TRUE, sep = "\t", row.names = 1)
# experimental groups
group <- c("wet", "wet", "wet", "dry", "dry", "dry")
# mapping table to organize gene expression into homeolog expression
mapping_table <- read.table("data/c_flexuosa.homeolog.txt.gz",
header = TRUE, sep = "\t")
# data preparation
x <- newExpMX(gexp, group, mapping_table)
# normalization
x <- norm_counts(x)
# statistical test
x_output <- hobit(x)## gene pvalue qvalue raw_pvalue raw_qvalue D__C_hirsuta__(dry-wet) D__C_amara__(dry-wet) OR__C_hirsuta__(dry/wet) OR__C_amara__(dry/wet) Dmax ORmax theta0__C_hirsuta theta0__C_amara theta1__C_hirsuta__dry theta1__C_amara__dry theta1__C_hirsuta__wet theta1__C_amara__wet logLik_H0 logLik_H1
## 1 CARHR000190_H 0.1257453 1 0.0671578 0.7994976 0.13851574 -0.13851574 1.7524086 0.5706432 0.13851574 1.752409 0.5145677 0.4854323 0.5851042 0.4148958 0.4456696 0.5543304 -64.62743 -63.01814
## 2 CARHR000660_H 1.0000000 1 1.0000000 1.0000000 0.05785510 -0.05785509 1.3678354 0.7310821 0.05785510 1.367835 0.2535746 0.7464254 0.2815327 0.7184673 0.2193108 0.7806892 -31.63977 -31.90169
## 3 CARHR000770_H 1.0000000 1 1.0000000 1.0000000 0.00042667 -0.00042667 1.0041632 0.9958542 0.00042667 1.004163 0.8931450 0.1068550 0.8953420 0.1046580 0.8948587 0.1051413 -53.31894 -53.65784
## 4 CARHR000890_H 0.7523488 1 0.7059717 1.0000000 -0.07999806 0.07999806 0.7076250 1.4131778 0.07999806 1.413178 0.5882221 0.4117779 0.5494622 0.4505378 0.6321275 0.3678725 -41.38102 -41.42952
## 5 CARHR001940_H 1.0000000 1 1.0000000 1.0000000 -0.04958549 0.04958549 0.8155551 1.2261587 0.04958549 1.226159 0.5514201 0.4485799 0.5273460 0.4726540 0.5761417 0.4238583 -57.12549 -57.46951
## 6 CARHR003740_H 0.3571554 1 0.2709275 1.0000000 -0.08852804 0.08852804 0.6771142 1.4768559 0.08852804 1.476856 0.3603599 0.6396401 0.3148542 0.6851458 0.4043972 0.5956028 -75.30201 -74.68230The following example demonstrates the workflow for HomeoRoq.
Compared to HOBIT, the only difference is that the statistical test is performed
using the homeoroq() function instead of hobit().
gexp <- read.table("data/c_flexuosa.0516.mini.txt.gz",
header = TRUE, sep = "\t", row.names = 1)
group <- c("wet", "wet", "wet", "dry", "dry", "dry")
mapping_table <- read.table("data/c_flexuosa.homeolog.txt.gz",
header = TRUE, sep = "\t")
x <- newExpMX(gexp, group, mapping_table)
x <- norm_counts(x)
x_output <- homeoroq(x)## gene pvalue qvalue sumexp__wet__C_hirsuta sumexp__wet__C_amara sumexp__dry__C_hirsuta sumexp__dry__C_amara ratio__wet__C_hirsuta ratio__dry__C_hirsuta ratio_sd
## 1 CARHR000190_H 0.238925 1 383.26142 475.04089 419.43533 285.21986 0.4465343 0.4689173 0.11000959
## 2 CARHR000660_H 0.939075 1 14.57256 46.19000 15.95469 40.22082 0.2398279 0.2567345 0.04073867
## 3 CARHR000770_H 0.989575 1 385.98379 44.96460 850.62370 95.35341 0.8956613 0.9497932 0.01737532
## 4 CARHR000890_H 0.523750 1 181.89090 107.32037 12.38467 10.08555 0.6289205 0.1034599 0.12263357
## 5 CARHR001940_H 0.979025 1 110.98129 81.00309 318.05281 285.09804 0.5780746 0.7970132 0.05006071
## 6 CARHR003740_H 0.197725 1 1549.17077 2270.28481 1179.77571 2596.99673 0.4055999 0.3419580 0.05277229