> library(tidyverse) > library(maSigPro) > setwd("D:/ANorth/muilt/呼吸多组学-陈宵/Analy...
🚨 错误信息
> library(tidyverse)
> library(maSigPro)
> 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")
>
> ## 1. 读入推文格式数据 ---------------------------------------------------------
> counts <- read_csv("geneMatrix_Control_normalized_log2cpm.csv") %>%
+ column_to_rownames("symble") %>% as.matrix()
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.
>
> ## 2. 构建推文同款 design（time + replicate_factor）
> meta <- read_csv("meta_Control.csv") %>% arrange(sample)
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.
> design <- data.frame(
+ time = meta$time,
+ replicate = factor(meta$replicate)
+ )
> rownames(design) <- meta$sample
>
> ## 3. 严格对齐列顺序（推文没写，但必须）
> counts <- counts[, meta$sample]
>
> ## 4. 推文两步回归 -------------------------------------------------------------
> fit <- p.vector(counts, design, Q = 0.05, "gaussian")
[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"
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[1] "fitting gene 600 out of 13692"
[1] "fitting gene 700 out of 13692"
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[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"
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[1] "fitting gene 1400 out of 13692"
[1] "fitting gene 1500 out of 13692"
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[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"
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[1] "fitting gene 4100 out of 13692"
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[1] "fitting gene 4300 out of 13692"
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[1] "fitting gene 5000 out of 13692"
[1] "fitting gene 5100 out of 13692"
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[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"
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[1] "fitting gene 6800 out of 13692"
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[1] "fitting gene 7500 out of 13692"
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[1] "fitting gene 8000 out of 13692"
[1] "fitting gene 8100 out of 13692"
[1] "fitting gene 8200 out of 13692"
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[1] "fitting gene 9200 out of 13692"
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[1] "fitting gene 9800 out of 13692"
[1] "fitting gene 9900 out of 13692"
[1] "fitting gene 10000 out of 13692"
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[1] "fitting gene 10500 out of 13692"
[1] "fitting gene 10600 out of 13692"
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[1] "fitting gene 10800 out of 13692"
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[1] "fitting gene 12200 out of 13692"
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[1] "fitting gene 12600 out of 13692"
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[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”- 错误翻译:
Error in match.arg(method):
'arg' should be one of “holm”, “hochberg”, “hommel”, “bonferroni”, “BH”, “BY”, “fdr”, “none”
match.arg(method): 参数 arg 应该为下列值之一：“holm”, “hochberg”, “hommel”, “bonferroni”, “BH”, “BY”, “fdr”, “none”"
p.vector 函数的 Q 参数不应直接传递为 "gaussian"。Q 参数用于多重检验校正方法，而 "gaussian" 是分布类型，不属于允许的校正方法之一。
将
p.vector 函数的调用修改为： # 假设您想使用 BH 校正方法，Q 参数通常是 p 值阈值，这里根据上下文推测为 0.05
# 如果您没有设定 Q 的具体值，可以将其移除，函数会使用默认值
# maSigPro 默认使用 BH 校正，因此如果您的目的是 BH 校正，可以省略 Q 参数
fit <- p.vector(counts, design, Q = 0.05)
# 或者，如果您确实需要指定校正方法（尽管 p.vector 中 Q 是指 p-value threshold,
# maSigPro 的其他函数有 method 参数用于指定校正方法）
# 但在 p.vector 中，Q 是一个数值（例如 0.05），不是方法名。
# 如果您想直接指定方法，需要查看 maSigPro 的文档，但 p.vector 的参数是固定的。
# 鉴于错误信息，最可能的问题是 Q 参数被错误地设置为 "gaussian"。
请注意:
p.vector 函数的 Q 参数是用于设置 P-value threshold（通常是 0.05 或 0.1），而不是校正方法。您传入的 "gaussian" 极有可能是误将分布类型传给了 Q 参数。maSigPro 默认会进行多重检验校正（如 BH 法），您无需在 p.vector 中指定方法。
* 在调用函数前，仔细检查函数文档，确保所有参数都传递了正确的数据类型和值。
* 对于需要指定校正方法的函数，确认使用的校正方法名称是否在函数支持的列表中。