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 0.1233256 1 0.06541479 0.7656442 0.135262370 -0.135262370 1.7271228 0.5789976 0.135262370 1.727123 0.5139389 0.4860611 0.5834637 0.4165363 0.4460479 0.5539521 -64.49956 -62.84965
## 2 CARHR000660_H 1.0000000 1 1.00000000 1.0000000 0.053684855 -0.053684855 1.3411438 0.7456322 0.053684855 1.341144 0.2533909 0.7466091 0.2769508 0.7230492 0.2212910 0.7787090 -31.81579 -32.06581
## 3 CARHR000770_H 1.0000000 1 1.00000000 1.0000000 -0.002602245 0.002602245 0.9721639 1.0286332 0.002602245 1.028633 0.8930171 0.1069829 0.8936688 0.1063312 0.8960575 0.1039425 -53.30633 -53.69221
## 4 CARHR000890_H 0.7696578 1 0.72625945 1.0000000 -0.084993555 0.084993555 0.6983480 1.4319519 0.084993555 1.431952 0.5863827 0.4136173 0.5442493 0.4557507 0.6292444 0.3707556 -41.19201 -41.26375
## 5 CARHR001940_H 1.0000000 1 1.00000000 1.0000000 -0.048105955 0.048105955 0.8195665 1.2201572 0.048105955 1.220157 0.5520396 0.4479604 0.5294441 0.4705559 0.5774278 0.4225722 -57.14303 -57.51374
## 6 CARHR003740_H 0.3542458 1 0.26803105 1.0000000 -0.089427170 0.089427170 0.6749970 1.4814881 0.089427170 1.481488 0.3601068 0.6398932 0.3143448 0.6856552 0.4038831 0.5961169 -75.39992 -74.78379The 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