> > library(tidyverse) > library(maSigPro) > library(readr) > > setwd("D:/ANorth...

2025年09月06日 21:29 响应: 3.77秒状态: success

🚨 错误信息

> > library(tidyverse) > library(maSigPro) > library(readr) > > setwd("D:/ANorth/muilt/呼吸多组学-陈宵/Analysis-lym/1.单组学分析/转录组/4时序分析") > dir.create("maSigPro_results", showWarnings = FALSE) > > groups <- c("Control", "Low", "High") > > for (g in groups) { + cat("\n====== running", g, "======\n") + + ## 1 读入并对齐 ------------------------------------------------------------ + mat <- read_csv(str_c("geneMatrix_", g, "_normalized_log2cpm.csv")) %>% + column_to_rownames("symble") %>% as.matrix() + meta <- read_csv(str_c("meta_", g, ".csv")) %>% arrange(sample) + mat <- mat[, meta$sample] # 严格对齐列顺序 + + ## 2 design ≥2 列，replicate 为因子 + design <- data.frame( + time = meta$time, + replicate = factor(meta$replicate) + ) + rownames(design) <- meta$sample + + ## 3 maSigPro 两步回归 ----------------------------------------------------- + fit <- p.vector(mat, design, Q = 0.05, "gaussian") # 修正：直接传递 "gaussian" + tfit <- T.fit(fit, + step.method = "two.ways.backward", + alfa = 0.05, + adjust = "fdr") # 使用 fdr 校正 + sig <- get.siggenes(tfit, rsq = 0.6) + + ## 4 写出结果 -------------------------------------------------------------- + prefix <- str_c("maSigPro_results/", g) + + write_csv(sig$sig.genes %>% as_tibble(rownames = "gene"), + str_c(prefix, "_sig_genes.csv")) + write_csv(tfit$sig.models %>% as_tibble(rownames = "gene"), + str_c(prefix, "_coef.csv")) + + ## 5 趋势图 + pdf(str_c(prefix, "_trends.pdf"), width = 8, height = 5) + plot(tfit, name = g, clustering = TRUE) + dev.off() + + ## 6 可选：持续上调/下调基因 + coef <- tfit$sig.models + up <- rownames(coef)[coef$time > 0 & coef$time < 0.05 & is.na(coef$time2)] + down <- rownames(coef)[coef$time < 0 & coef$time < 0.05 & is.na(coef$time2)] + write_csv(tibble(gene = up), str_c(prefix, "_up_genes.csv")) + write_csv(tibble(gene = down), str_c(prefix, "_down_genes.csv")) + } ====== running Control ====== Rows: 13692 Columns: 13 ── Column specification ────────────────────────────────────────────────────────────────────────────── Delimiter: "," chr (1): symble dbl (12): 6hC1, 6hC2, 6hC3, 24hC1, 24hC2, 24hC3, 72hC1, 72hC2, 72hC3, 120hC1, 120hC2, 120hC3 ℹ Use `spec()` to retrieve the full column specification for this data. ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message. Rows: 12 Columns: 4 ── Column specification ────────────────────────────────────────────────────────────────────────────── Delimiter: "," chr (2): sample, group dbl (2): time, replicate ℹ Use `spec()` to retrieve the full column specification for this data. ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message. [1] "fitting gene 100 out of 13692" [1] "fitting gene 200 out of 13692" [1] "fitting gene 300 out of 13692" [1] "fitting gene 400 out of 13692" [1] "fitting gene 500 out of 13692" [1] "fitting gene 600 out of 13692" [1] "fitting gene 700 out of 13692" [1] "fitting gene 800 out of 13692" [1] "fitting gene 900 out of 13692" [1] "fitting gene 1000 out of 13692" [1] "fitting gene 1100 out of 13692" [1] "fitting gene 1200 out of 13692" [1] "fitting gene 1300 out of 13692" [1] "fitting gene 1400 out of 13692" [1] "fitting gene 1500 out of 13692" [1] "fitting gene 1600 out of 13692" [1] "fitting gene 1700 out of 13692" [1] "fitting gene 1800 out of 13692" [1] "fitting gene 1900 out of 13692" [1] "fitting gene 2000 out of 13692" [1] "fitting gene 2100 out of 13692" [1] "fitting gene 2200 out of 13692" [1] "fitting gene 2300 out of 13692" [1] "fitting gene 2400 out of 13692" [1] "fitting gene 2500 out of 13692" [1] "fitting gene 2600 out of 13692" [1] "fitting gene 2700 out of 13692" [1] "fitting gene 2800 out of 13692" [1] "fitting gene 2900 out of 13692" [1] "fitting gene 3000 out of 13692" [1] "fitting gene 3100 out of 13692" [1] "fitting gene 3200 out of 13692" [1] "fitting gene 3300 out of 13692" [1] "fitting gene 3400 out of 13692" [1] "fitting gene 3500 out of 13692" [1] "fitting gene 3600 out of 13692" [1] "fitting gene 3700 out of 13692" [1] "fitting gene 3800 out of 13692" [1] "fitting gene 3900 out of 13692" [1] "fitting gene 4000 out of 13692" [1] "fitting gene 4100 out of 13692" [1] "fitting gene 4200 out of 13692" [1] "fitting gene 4300 out of 13692" [1] "fitting gene 4400 out of 13692" [1] "fitting gene 4500 out of 13692" [1] "fitting gene 4600 out of 13692" [1] "fitting gene 4700 out of 13692" [1] "fitting gene 4800 out of 13692" [1] "fitting gene 4900 out of 13692" [1] "fitting gene 5000 out of 13692" [1] "fitting gene 5100 out of 13692" [1] "fitting gene 5200 out of 13692" [1] "fitting gene 5300 out of 13692" [1] "fitting gene 5400 out of 13692" [1] "fitting gene 5500 out of 13692" [1] "fitting gene 5600 out of 13692" [1] "fitting gene 5700 out of 13692" [1] "fitting gene 5800 out of 13692" [1] "fitting gene 5900 out of 13692" [1] "fitting gene 6000 out of 13692" [1] "fitting gene 6100 out of 13692" [1] "fitting gene 6200 out of 13692" [1] "fitting gene 6300 out of 13692" [1] "fitting gene 6400 out of 13692" [1] "fitting gene 6500 out of 13692" [1] "fitting gene 6600 out of 13692" [1] "fitting gene 6700 out of 13692" [1] "fitting gene 6800 out of 13692" [1] "fitting gene 6900 out of 13692" [1] "fitting gene 7000 out of 13692" [1] "fitting gene 7100 out of 13692" [1] "fitting gene 7200 out of 13692" [1] "fitting gene 7300 out of 13692" [1] "fitting gene 7400 out of 13692" [1] "fitting gene 7500 out of 13692" [1] "fitting gene 7600 out of 13692" [1] "fitting gene 7700 out of 13692" [1] "fitting gene 7800 out of 13692" [1] "fitting gene 7900 out of 13692" [1] "fitting gene 8000 out of 13692" [1] "fitting gene 8100 out of 13692" [1] "fitting gene 8200 out of 13692" [1] "fitting gene 8300 out of 13692" [1] "fitting gene 8400 out of 13692" [1] "fitting gene 8500 out of 13692" [1] "fitting gene 8600 out of 13692" [1] "fitting gene 8700 out of 13692" [1] "fitting gene 8800 out of 13692" [1] "fitting gene 8900 out of 13692" [1] "fitting gene 9000 out of 13692" [1] "fitting gene 9100 out of 13692" [1] "fitting gene 9200 out of 13692" [1] "fitting gene 9300 out of 13692" [1] "fitting gene 9400 out of 13692" [1] "fitting gene 9500 out of 13692" [1] "fitting gene 9600 out of 13692" [1] "fitting gene 9700 out of 13692" [1] "fitting gene 9800 out of 13692" [1] "fitting gene 9900 out of 13692" [1] "fitting gene 10000 out of 13692" [1] "fitting gene 10100 out of 13692" [1] "fitting gene 10200 out of 13692" [1] "fitting gene 10300 out of 13692" [1] "fitting gene 10400 out of 13692" [1] "fitting gene 10500 out of 13692" [1] "fitting gene 10600 out of 13692" [1] "fitting gene 10700 out of 13692" [1] "fitting gene 10800 out of 13692" [1] "fitting gene 10900 out of 13692" [1] "fitting gene 11000 out of 13692" [1] "fitting gene 11100 out of 13692" [1] "fitting gene 11200 out of 13692" [1] "fitting gene 11300 out of 13692" [1] "fitting gene 11400 out of 13692" [1] "fitting gene 11500 out of 13692" [1] "fitting gene 11600 out of 13692" [1] "fitting gene 11700 out of 13692" [1] "fitting gene 11800 out of 13692" [1] "fitting gene 11900 out of 13692" [1] "fitting gene 12000 out of 13692" [1] "fitting gene 12100 out of 13692" [1] "fitting gene 12200 out of 13692" [1] "fitting gene 12300 out of 13692" [1] "fitting gene 12400 out of 13692" [1] "fitting gene 12500 out of 13692" [1] "fitting gene 12600 out of 13692" [1] "fitting gene 12700 out of 13692" [1] "fitting gene 12800 out of 13692" [1] "fitting gene 12900 out of 13692" [1] "fitting gene 13000 out of 13692" [1] "fitting gene 13100 out of 13692" [1] "fitting gene 13200 out of 13692" [1] "fitting gene 13300 out of 13692" [1] "fitting gene 13400 out of 13692" [1] "fitting gene 13500 out of 13692" [1] "fitting gene 13600 out of 13692" 错误于match.arg(method): 'arg' should be one of “holm”, “hochberg”, “hommel”, “bonferroni”, “BH”, “BY”, “fdr”, “none”

🤖 AI解决方案

错误于match.arg(method): 
  'arg' should be one of “holm”, “hochberg”, “hommel”, “bonferroni”, “BH”, “BY”, “fdr”, “none”
Error in match.arg(method) : 
  'arg' should be one of “holm”, “hochberg”, “hommel”, “bonferroni”, “BH”, “BY”, “fdr”, “none”

1. 错误翻译

错误于match.arg(method): 'arg' 应该是 "holm", "hochberg", "hommel", "bonferroni", "BH", "BY", "fdr", "none" 中的一个。

2. 原因分析

T.fit 函数中的 adjust 参数接受的校正方法字符串不正确。您传递的是 "fdr"，而函数期望的是 "BH"（Benjamini & Hochberg），这是 fdr 的常见实现。

3. 解决方案

将 T.fit 函数中 adjust 参数的值从 "fdr" 修改为 "BH"。

library(tidyverse)
library(maSigPro)
library(readr)

setwd("D:/ANorth/muilt/呼吸多组学-陈宵/Analysis-lym/1.单组学分析/转录组/4时序分析")
dir.create("maSigPro_results", showWarnings = FALSE)

groups <- c("Control", "Low", "High")

for (g in groups) {
    cat("\n======  running", g, "======\n")
    
    ## 1 读入并对齐 ------------------------------------------------------------
    mat  <- read_csv(str_c("geneMatrix_", g, "_normalized_log2cpm.csv")) %>%
        column_to_rownames("symble") %>% as.matrix()
    meta <- read_csv(str_c("meta_", g, ".csv")) %>% arrange(sample)
    mat  <- mat[, meta$sample]               # 严格对齐列顺序
    
    ## 2 design ≥2 列，replicate 为因子
    design <- data.frame(
        time      = meta$time,
        replicate = factor(meta$replicate)
    )
    rownames(design) <- meta$sample
    
    ## 3 maSigPro 两步回归 -----------------------------------------------------
    fit  <- p.vector(mat, design, Q = 0.05, "gaussian")
    tfit <- T.fit(fit,
                  step.method = "two.ways.backward",
                  alfa        = 0.05,
                  adjust      = "BH")   # 修正：使用 "BH" 代替 "fdr"
    sig  <- get.siggenes(tfit, rsq = 0.6)
    
    ## 4 写出结果 --------------------------------------------------------------
    prefix <- str_c("maSigPro_results/", g)
    
    write_csv(sig$sig.genes  %>% as_tibble(rownames = "gene"),
              str_c(prefix, "_sig_genes.csv"))
    write_csv(tfit$sig.models %>% as_tibble(rownames = "gene"),
              str_c(prefix, "_coef.csv"))
    
    ## 5 趋势图
    pdf(str_c(prefix, "_trends.pdf"), width = 8, height = 5)
    plot(tfit, name = g, clustering = TRUE)
    dev.off()
    
    ## 6 可选：持续上调/下调基因
    coef <- tfit$sig.models
    up   <- rownames(coef)[coef$time > 0 & coef$time < 0.05 & is.na(coef$time2)]
    down <- rownames(coef)[coef$time < 0 & coef$time < 0.05 & is.na(coef$time2)]
    write_csv(tibble(gene = up),   str_c(prefix, "_up_genes.csv"))
    write_csv(tibble(gene = down), str_c(prefix, "_down_genes.csv"))
}

4. 预防措施

查阅函数文档：在使用 R 包中的函数时，仔细阅读其帮助文档（通过 ?function_name 命令）以了解参数的有效值和预期格式。
使用已知的校正方法：maSigPro 包是基于 limma 等包的，了解常用的多重检验校正方法（如 "BH"）并使用它们可以避免此类问题。

🔍 分析新问题 📝 查看更多问题