如何更改 S4 对象中的对象类型？
How do I change object types within an S4 object?
我正在尝试使用 R 中的包 anamiR（通过 Rstudio）执行 miRNA 相关。我使用的脚本是：
library(anamiR)
mrna1 = read.csv("D:\file1.csv", row.names = 1, header= TRUE)
mrna <- as.matrix(mrna1)
rm(mrna1)
mirna1 = read.csv("D:\file2.csv", row.names = 1, header= TRUE)
mirna <- as.matrix(mirna1)
rm(mirna1)
pheno.mirna1 = read.csv("D:\file3.csv", row.names = 1, header= TRUE)
pheno.mirna <- as.matrix(pheno.mirna1)
rm(pheno.mirna1)
pheno.mrna1 = read.csv("D:\file4.csv", row.names = 1, header= TRUE)
pheno.mrna <- as.matrix(pheno.mrna1)
rm(pheno.mrna1)
mrna_se <- SummarizedExperiment::SummarizedExperiment(
assays = S4Vectors::SimpleList(counts=mrna),
colData = pheno.mrna)
mirna_se <- SummarizedExperiment::SummarizedExperiment(
assays = S4Vectors::SimpleList(counts=mirna),
colData = pheno.mirna)
mrna_d <- differExp_discrete(se = mrna_se,
class = "ER", method = "DESeq",
t_test.var = FALSE, log2 = FALSE,
p_value.cutoff = 0.05, logratio = 0.5
)
mirna_d <- differExp_discrete(se = mirna_se,
class = "ER", method = "DESeq",
t_test.var = FALSE, log2 = FALSE,
p_value.cutoff = 0.05, logratio = 0.5
)
当我到达时（这是产生错误的代码）。
mrna_d <- differExp_discrete(se = mrna_se,
class = "ER", method = "DESeq",
t_test.var = FALSE, log2 = FALSE,
p_value.cutoff = 0.05, logratio = 0.5
)
mirna_d <- differExp_discrete(se = mirna_se,
class = "ER", method = "DESeq",
t_test.var = FALSE, log2 = FALSE,
p_value.cutoff = 0.05, logratio = 0.5
)
我明白了
Error in model.matrix.formula(design(object), colData(object)) :
data must be a data.frame
In addition: Warning message:
In DESeq2::DESeqDataSet(se, design = tmp) :
some variables in design formula are characters, converting to factors
我的会话信息是：
> sessionInfo()
R version 3.6.0 (2019-04-26)
Platform: x86_64-w64-mingw32/x64 (64-bit)
Running under: Windows 10 x64 (build 18363)
Matrix products: default
locale:
[1] LC_COLLATE=English_Australia.1252 LC_CTYPE=English_Australia.1252 LC_MONETARY=English_Australia.1252
[4] LC_NUMERIC=C LC_TIME=English_Australia.1252
attached base packages:
[1] stats graphics grDevices utils datasets methods base
other attached packages:
[1] anamiR_1.13.0
loaded via a namespace (and not attached):
[1] backports_1.2.1 Hmisc_4.5-0 BiocFileCache_1.10.2 plyr_1.8.6
[5] splines_3.6.0 BiocParallel_1.20.1 AlgDesign_1.2.0 GenomeInfoDb_1.22.1
[9] ggplot2_3.3.3 digest_0.6.27 foreach_1.5.1 htmltools_0.5.1.1
[13] fansi_0.4.2 magrittr_2.0.1 checkmate_2.0.0 memoise_2.0.0
[17] cluster_2.1.1 limma_3.42.2 readr_1.4.0 Biostrings_2.54.0
[21] annotate_1.64.0 matrixStats_0.58.0 askpass_1.1 siggenes_1.60.0
[25] prettyunits_1.1.1 jpeg_0.1-8.1 colorspace_2.0-0 rappdirs_0.3.3
[29] blob_1.2.1 haven_2.3.1 xfun_0.22 dplyr_1.0.5
[33] crayon_1.4.1 RCurl_1.98-1.3 graph_1.64.0 genefilter_1.68.0
[37] GEOquery_2.54.1 survival_3.2-10 iterators_1.0.13 glue_1.4.2
[41] gtable_0.3.0 lumi_2.38.0 zlibbioc_1.32.0 XVector_0.26.0
[45] DelayedArray_0.12.3 questionr_0.7.4 Rhdf5lib_1.8.0 BiocGenerics_0.32.0
[49] HDF5Array_1.14.4 scales_1.1.1 rngtools_1.5 DBI_1.1.1
[53] miniUI_0.1.1.1 Rcpp_1.0.6 progress_1.2.2 xtable_1.8-4
[57] htmlTable_2.1.0 gage_2.36.0 bumphunter_1.28.0 foreign_0.8-71
[61] bit_4.0.4 mclust_5.4.7 preprocessCore_1.48.0 Formula_1.2-4
[65] stats4_3.6.0 htmlwidgets_1.5.3 httr_1.4.2 gplots_3.1.1
[69] RColorBrewer_1.1-2 ellipsis_0.3.1 pkgconfig_2.0.3 reshape_0.8.8
[73] XML_3.99-0.3 dbplyr_2.1.0 nnet_7.3-15 locfit_1.5-9.4
[77] utf8_1.2.1 tidyselect_1.1.0 rlang_0.4.10 later_1.1.0.1
[81] AnnotationDbi_1.48.0 munsell_0.5.0 tools_3.6.0 cachem_1.0.4
[85] generics_0.1.0 RSQLite_2.2.5 stringr_1.4.0 fastmap_1.1.0
[89] knitr_1.31 bit64_4.0.5 beanplot_1.2 caTools_1.18.2
[93] methylumi_2.32.0 scrime_1.3.5 purrr_0.3.4 KEGGREST_1.26.1
[97] doRNG_1.8.2 nlme_3.1-152 mime_0.10 nor1mix_1.3-0
[101] xml2_1.3.2 biomaRt_2.42.1 compiler_3.6.0 rstudioapi_0.13
[105] curl_4.3 png_0.1-7 affyio_1.56.0 klaR_0.6-15
[109] tibble_3.1.0 geneplotter_1.64.0 stringi_1.5.3 highr_0.8
[113] GenomicFeatures_1.38.2 minfi_1.32.0 forcats_0.5.1 lattice_0.20-41
[117] Matrix_1.3-2 multtest_2.42.0 vctrs_0.3.7 pillar_1.5.1
[121] lifecycle_1.0.0 BiocManager_1.30.12 combinat_0.0-8 data.table_1.14.0
[125] bitops_1.0-6 rtracklayer_1.46.0 httpuv_1.5.5 agricolae_1.3-3
[129] GenomicRanges_1.38.0 affy_1.64.0 R6_2.5.0 latticeExtra_0.6-29
[133] RMySQL_0.10.21 promises_1.2.0.1 KernSmooth_2.23-18 gridExtra_2.3
[137] nleqslv_3.3.2 IRanges_2.20.2 codetools_0.2-18 MASS_7.3-53.1
[141] gtools_3.8.2 assertthat_0.2.1 rhdf5_2.30.1 Sum
[145] openssl_1.4.3 DESeq2_1.26.0 GenomicAlignments_1.22.1 Rsamtools_2.2.3
[149] S4Vectors_0.24.4 GenomeInfoDbData_1.2.2 mgcv_1.8-34 parallel_3.6.0
[153] hms_1.0.0 quadprog_1.5-8 grid_3.6.0 rpart_4.1-15
[157] labelled_2.8.0 tidyr_1.1.3 base64_2.0 DelayedMatrixStats_1.8.0
[161] illuminaio_0.28.0 Biobase_2.46.0 shiny_1.6.0 base64enc_0.1-3
我可以更改 R 版本，但这确实无济于事。我已将问题确定为 mrna_se@colData 和 miRNA@colData 都不是数据帧：
> is.data.frame(mirna_se@colData)
[1] FALSE
> is.data.frame(mrna_se@colData)
[1] FALSE
那么如何将整个 s4 对象中的这些对象转换为数据帧，以便 DESEQ2 可以使用它们来生成差异表达数据？这让我发疯。
另外在有人问之前：
> packageVersion("DESeq2")
[1] ‘1.26.0’
作为对评论的回应，我更改了如下代码，但出现了以下错误。
mrna_se <- SummarizedExperiment::SummarizedExperiment(
assays = S4Vectors::SimpleList(counts=mrna),
colData = as.data.frame(pheno.mrna))
it appears that the last variable in the design formula, 'ER',
has a factor level, 'control', which is not the reference level. we recommend
to use factor(...,levels=...) or relevel() to set this as the reference level
before proceeding. for more information, please see the 'Note on factor levels'
in vignette('DESeq2').
Error in model.matrix.formula(design(object), colData(object)) :
data must be a data.frame
进一步编辑：
如果您尝试只读取 csv 文件而不将其作为矩阵读取，您会得到：
> library(anamiR)
>
> mrna = read.csv("D:\file1.csv", row.names = 1, header= TRUE)
> mirna = read.csv("D:\file2.csv", row.names = 1, header= TRUE)
> pheno.mirna = read.csv("D:\file3.csv", row.names = 1, header= TRUE)
> pheno.mrna = read.csv("D:\file4.csv", row.names = 1, header= TRUE)
>
> mrna_se <- SummarizedExperiment::SummarizedExperiment(
+ assays = S4Vectors::SimpleList(counts=mrna),
+ colData = as.data.frame(pheno.mrna))
Error in all_dims[, 1L] : incorrect number of dimensions
>
> mirna_se <- SummarizedExperiment::SummarizedExperiment(
+ assays = S4Vectors::SimpleList(counts=mirna),
+ colData = pheno.mirna)
Error in all_dims[, 1L] : incorrect number of dimensions
>
> mrna_d <- differExp_discrete(se = mrna_se,
+ class = "ER", method = "DESeq",
+ t_test.var = FALSE, log2 = FALSE,
+ p_value.cutoff = 0.05, logratio = 0.5
+ )
Error in SummarizedExperiment::assays(se) : object 'mrna_se' not found
>
> mirna_d <- differExp_discrete(se = mirna_se,
+ class = "ER", method = "DESeq",
+ t_test.var = FALSE, log2 = FALSE,
+ p_value.cutoff = 0.05, logratio = 0.5
+ )
Error in SummarizedExperiment::assays(se) : object 'mirna_se' not found
我最初错误的回溯是（显示一个文件使用建议的 as.data.frame 解决方案，另一个文件使用我的原始矩阵加载）：
> library(anamiR)
>
> mrna1 = read.csv("D:\file1.csv.csv", row.names = 1, header= TRUE)
> mrna <- as.matrix(mrna1)
> rm(mrna1)
> mirna1 = read.csv("D:\file2.csv.csv", row.names = 1, header= TRUE)
> mirna <- as.matrix(mirna1)
> rm(mirna1)
> pheno.mirna1 = read.csv("D:\file3.csv.csv", row.names = 1, header= TRUE)
> pheno.mirna <- as.matrix(pheno.mirna1)
> rm(pheno.mirna1)
> pheno.mrna1 = read.csv("D:\file4.csv.csv", row.names = 1, header= TRUE)
> pheno.mrna <- as.matrix(pheno.mrna1)
> rm(pheno.mrna1)
>
> mrna_se <- SummarizedExperiment::SummarizedExperiment(
+ assays = S4Vectors::SimpleList(counts=mrna),
+ colData = as.data.frame(pheno.mrna))
>
> mirna_se <- SummarizedExperiment::SummarizedExperiment(
+ assays = S4Vectors::SimpleList(counts=mirna),
+ colData = pheno.mirna)
>
> mrna_d <- differExp_discrete(se = mrna_se,
+ class = "ER", method = "DESeq",
+ t_test.var = FALSE, log2 = FALSE,
+ p_value.cutoff = 0.05, logratio = 0.5
+ )
it appears that the last variable in the design formula, 'ER',
has a factor level, 'control', which is not the reference level. we recommend
to use factor(...,levels=...) or relevel() to set this as the reference level
before proceeding. for more information, please see the 'Note on factor levels'
in vignette('DESeq2').
Error in model.matrix.formula(design(object), colData(object)) :
data must be a data.frame
>
> traceback()
6: stop("data must be a data.frame")
5: model.matrix.formula(design(object), colData(object))
4: stats::model.matrix(design(object), colData(object))
3: designAndArgChecker(object, betaPrior)
2: DESeq2::DESeq(dds)
1: differExp_discrete(se = mrna_se, class = "ER", method = "DESeq",
t_test.var = FALSE, log2 = FALSE, p_value.cutoff = 0.05,
logratio = 0.5)
>
> mirna_d <- differExp_discrete(se = mirna_se,
+ class = "ER", method = "DESeq",
+ t_test.var = FALSE, log2 = FALSE,
+ p_value.cutoff = 0.05, logratio = 0.5
+ )
Error in model.matrix.formula(design(object), colData(object)) :
data must be a data.frame
In addition: Warning message:
In DESeq2::DESeqDataSet(se, design = tmp) :
some variables in design formula are characters, converting to factors
>
> traceback()
6: stop("data must be a data.frame")
5: model.matrix.formula(design(object), colData(object))
4: stats::model.matrix(design(object), colData(object))
3: designAndArgChecker(object, betaPrior)
2: DESeq2::DESeq(dds)
1: differExp_discrete(se = mirna_se, class = "ER", method = "DESeq",
t_test.var = FALSE, log2 = FALSE, p_value.cutoff = 0.05,
logratio = 0.5)
新的回溯是：
> library(anamiR)
>
> mrna = read.csv("D:\file1.csv", row.names = 1, header= TRUE)
> mirna = read.csv("D:\file2.csv", row.names = 1, header= TRUE)
> pheno.mirna = read.csv("D:\file3.csv", row.names = 1, header= TRUE)
> pheno.mrna = read.csv("D:\file4.csv", row.names = 1, header= TRUE)
>
> mrna_se <- SummarizedExperiment::SummarizedExperiment(
+ assays = S4Vectors::SimpleList(counts=mrna),
+ colData = as.data.frame(pheno.mrna))
Error in all_dims[, 1L] : incorrect number of dimensions
> traceback()
7: method(object)
6: validityMethod(as(object, superClass))
5: isTRUE(x)
4: anyStrings(validityMethod(as(object, superClass)))
3: validObject(ans)
2: Assays(assays)
1: SummarizedExperiment::SummarizedExperiment(assays = S4Vectors::SimpleList(counts = mrna),
colData = as.data.frame(pheno.mrna))
>
> mirna_se <- SummarizedExperiment::SummarizedExperiment(
+ assays = S4Vectors::SimpleList(counts=mirna),
+ colData = pheno.mirna)
Error in all_dims[, 1L] : incorrect number of dimensions
> traceback()
7: method(object)
6: validityMethod(as(object, superClass))
5: isTRUE(x)
4: anyStrings(validityMethod(as(object, superClass)))
3: validObject(ans)
2: Assays(assays)
1: SummarizedExperiment::SummarizedExperiment(assays = S4Vectors::SimpleList(counts = mirna),
colData = pheno.mirna)
最新编辑 (12/4/21)
因此，为了回应评论，我现在加载我的数据文件如下：
mrna <- as.matrix(read.csv("D:\CorrelationDataProcessing\TRAMP30w\mrnaTRAMP_Mut30w_v_WT30w_normcounts.csv", row.names = 1, header= TRUE))
mirna <- as.matrix(read.csv("D:\CorrelationDataProcessing\TRAMP30w\mirnaTRAMP_Mut30w_vs_WT30w_normcounts.csv", row.names = 1, header= TRUE))
pheno.mirna = read.csv("D:\CorrelationDataProcessing\TRAMP30w\mirnapheno.csv", row.names = 1, header= TRUE)
pheno.mrna = read.csv("D:\CorrelationDataProcessing\TRAMP30w\mrnapheno.csv", row.names = 1, header= TRUE)
这导致：
> mrna_d <- differExp_discrete(se = mrna_se,
+ class = "ER", method = "DESeq",
+ t_test.var = FALSE, log2 = FALSE,
+ p_value.cutoff = 0.05, logratio = 0.5
+ )
it appears that the last variable in the design formula, 'ER',
has a factor level, 'control', which is not the reference level. we recommend
to use factor(...,levels=...) or relevel() to set this as the reference level
before proceeding. for more information, please see the 'Note on factor levels'
in vignette('DESeq2').
Error in model.matrix.formula(design(object), colData(object)) :
data must be a data.frame
>
> mirna_d <- differExp_discrete(se = mirna_se,
+ class = "ER", method = "DESeq",
+ t_test.var = FALSE, log2 = FALSE,
+ p_value.cutoff = 0.05, logratio = 0.5
+ )
it appears that the last variable in the design formula, 'ER',
has a factor level, 'control', which is not the reference level. we recommend
to use factor(...,levels=...) or relevel() to set this as the reference level
before proceeding. for more information, please see the 'Note on factor levels'
in vignette('DESeq2').
Error in model.matrix.formula(design(object), colData(object)) :
data must be a data.frame
不幸的是，这个包似乎有问题，无论如何都被弃用了。在这一点上，我建议任何想要进行 miRNA 关联的人都忽略 anaMIR 和 mirCOMB。
我正在尝试使用 R 中的包 anamiR（通过 Rstudio）执行 miRNA 相关。我使用的脚本是：
library(anamiR)
mrna1 = read.csv("D:\file1.csv", row.names = 1, header= TRUE)
mrna <- as.matrix(mrna1)
rm(mrna1)
mirna1 = read.csv("D:\file2.csv", row.names = 1, header= TRUE)
mirna <- as.matrix(mirna1)
rm(mirna1)
pheno.mirna1 = read.csv("D:\file3.csv", row.names = 1, header= TRUE)
pheno.mirna <- as.matrix(pheno.mirna1)
rm(pheno.mirna1)
pheno.mrna1 = read.csv("D:\file4.csv", row.names = 1, header= TRUE)
pheno.mrna <- as.matrix(pheno.mrna1)
rm(pheno.mrna1)
mrna_se <- SummarizedExperiment::SummarizedExperiment(
assays = S4Vectors::SimpleList(counts=mrna),
colData = pheno.mrna)
mirna_se <- SummarizedExperiment::SummarizedExperiment(
assays = S4Vectors::SimpleList(counts=mirna),
colData = pheno.mirna)
mrna_d <- differExp_discrete(se = mrna_se,
class = "ER", method = "DESeq",
t_test.var = FALSE, log2 = FALSE,
p_value.cutoff = 0.05, logratio = 0.5
)
mirna_d <- differExp_discrete(se = mirna_se,
class = "ER", method = "DESeq",
t_test.var = FALSE, log2 = FALSE,
p_value.cutoff = 0.05, logratio = 0.5
)
当我到达时（这是产生错误的代码）。
mrna_d <- differExp_discrete(se = mrna_se,
class = "ER", method = "DESeq",
t_test.var = FALSE, log2 = FALSE,
p_value.cutoff = 0.05, logratio = 0.5
)
mirna_d <- differExp_discrete(se = mirna_se,
class = "ER", method = "DESeq",
t_test.var = FALSE, log2 = FALSE,
p_value.cutoff = 0.05, logratio = 0.5
)
我明白了
Error in model.matrix.formula(design(object), colData(object)) :
data must be a data.frame
In addition: Warning message:
In DESeq2::DESeqDataSet(se, design = tmp) :
some variables in design formula are characters, converting to factors
我的会话信息是：
> sessionInfo()
R version 3.6.0 (2019-04-26)
Platform: x86_64-w64-mingw32/x64 (64-bit)
Running under: Windows 10 x64 (build 18363)
Matrix products: default
locale:
[1] LC_COLLATE=English_Australia.1252 LC_CTYPE=English_Australia.1252 LC_MONETARY=English_Australia.1252
[4] LC_NUMERIC=C LC_TIME=English_Australia.1252
attached base packages:
[1] stats graphics grDevices utils datasets methods base
other attached packages:
[1] anamiR_1.13.0
loaded via a namespace (and not attached):
[1] backports_1.2.1 Hmisc_4.5-0 BiocFileCache_1.10.2 plyr_1.8.6
[5] splines_3.6.0 BiocParallel_1.20.1 AlgDesign_1.2.0 GenomeInfoDb_1.22.1
[9] ggplot2_3.3.3 digest_0.6.27 foreach_1.5.1 htmltools_0.5.1.1
[13] fansi_0.4.2 magrittr_2.0.1 checkmate_2.0.0 memoise_2.0.0
[17] cluster_2.1.1 limma_3.42.2 readr_1.4.0 Biostrings_2.54.0
[21] annotate_1.64.0 matrixStats_0.58.0 askpass_1.1 siggenes_1.60.0
[25] prettyunits_1.1.1 jpeg_0.1-8.1 colorspace_2.0-0 rappdirs_0.3.3
[29] blob_1.2.1 haven_2.3.1 xfun_0.22 dplyr_1.0.5
[33] crayon_1.4.1 RCurl_1.98-1.3 graph_1.64.0 genefilter_1.68.0
[37] GEOquery_2.54.1 survival_3.2-10 iterators_1.0.13 glue_1.4.2
[41] gtable_0.3.0 lumi_2.38.0 zlibbioc_1.32.0 XVector_0.26.0
[45] DelayedArray_0.12.3 questionr_0.7.4 Rhdf5lib_1.8.0 BiocGenerics_0.32.0
[49] HDF5Array_1.14.4 scales_1.1.1 rngtools_1.5 DBI_1.1.1
[53] miniUI_0.1.1.1 Rcpp_1.0.6 progress_1.2.2 xtable_1.8-4
[57] htmlTable_2.1.0 gage_2.36.0 bumphunter_1.28.0 foreign_0.8-71
[61] bit_4.0.4 mclust_5.4.7 preprocessCore_1.48.0 Formula_1.2-4
[65] stats4_3.6.0 htmlwidgets_1.5.3 httr_1.4.2 gplots_3.1.1
[69] RColorBrewer_1.1-2 ellipsis_0.3.1 pkgconfig_2.0.3 reshape_0.8.8
[73] XML_3.99-0.3 dbplyr_2.1.0 nnet_7.3-15 locfit_1.5-9.4
[77] utf8_1.2.1 tidyselect_1.1.0 rlang_0.4.10 later_1.1.0.1
[81] AnnotationDbi_1.48.0 munsell_0.5.0 tools_3.6.0 cachem_1.0.4
[85] generics_0.1.0 RSQLite_2.2.5 stringr_1.4.0 fastmap_1.1.0
[89] knitr_1.31 bit64_4.0.5 beanplot_1.2 caTools_1.18.2
[93] methylumi_2.32.0 scrime_1.3.5 purrr_0.3.4 KEGGREST_1.26.1
[97] doRNG_1.8.2 nlme_3.1-152 mime_0.10 nor1mix_1.3-0
[101] xml2_1.3.2 biomaRt_2.42.1 compiler_3.6.0 rstudioapi_0.13
[105] curl_4.3 png_0.1-7 affyio_1.56.0 klaR_0.6-15
[109] tibble_3.1.0 geneplotter_1.64.0 stringi_1.5.3 highr_0.8
[113] GenomicFeatures_1.38.2 minfi_1.32.0 forcats_0.5.1 lattice_0.20-41
[117] Matrix_1.3-2 multtest_2.42.0 vctrs_0.3.7 pillar_1.5.1
[121] lifecycle_1.0.0 BiocManager_1.30.12 combinat_0.0-8 data.table_1.14.0
[125] bitops_1.0-6 rtracklayer_1.46.0 httpuv_1.5.5 agricolae_1.3-3
[129] GenomicRanges_1.38.0 affy_1.64.0 R6_2.5.0 latticeExtra_0.6-29
[133] RMySQL_0.10.21 promises_1.2.0.1 KernSmooth_2.23-18 gridExtra_2.3
[137] nleqslv_3.3.2 IRanges_2.20.2 codetools_0.2-18 MASS_7.3-53.1
[141] gtools_3.8.2 assertthat_0.2.1 rhdf5_2.30.1 Sum
[145] openssl_1.4.3 DESeq2_1.26.0 GenomicAlignments_1.22.1 Rsamtools_2.2.3
[149] S4Vectors_0.24.4 GenomeInfoDbData_1.2.2 mgcv_1.8-34 parallel_3.6.0
[153] hms_1.0.0 quadprog_1.5-8 grid_3.6.0 rpart_4.1-15
[157] labelled_2.8.0 tidyr_1.1.3 base64_2.0 DelayedMatrixStats_1.8.0
[161] illuminaio_0.28.0 Biobase_2.46.0 shiny_1.6.0 base64enc_0.1-3
我可以更改 R 版本，但这确实无济于事。我已将问题确定为 mrna_se@colData 和 miRNA@colData 都不是数据帧：
> is.data.frame(mirna_se@colData)
[1] FALSE
> is.data.frame(mrna_se@colData)
[1] FALSE
那么如何将整个 s4 对象中的这些对象转换为数据帧，以便 DESEQ2 可以使用它们来生成差异表达数据？这让我发疯。
另外在有人问之前：
> packageVersion("DESeq2")
[1] ‘1.26.0’
作为对评论的回应，我更改了如下代码，但出现了以下错误。
mrna_se <- SummarizedExperiment::SummarizedExperiment(
assays = S4Vectors::SimpleList(counts=mrna),
colData = as.data.frame(pheno.mrna))
it appears that the last variable in the design formula, 'ER',
has a factor level, 'control', which is not the reference level. we recommend
to use factor(...,levels=...) or relevel() to set this as the reference level
before proceeding. for more information, please see the 'Note on factor levels'
in vignette('DESeq2').
Error in model.matrix.formula(design(object), colData(object)) :
data must be a data.frame
进一步编辑：
如果您尝试只读取 csv 文件而不将其作为矩阵读取，您会得到：
> library(anamiR)
>
> mrna = read.csv("D:\file1.csv", row.names = 1, header= TRUE)
> mirna = read.csv("D:\file2.csv", row.names = 1, header= TRUE)
> pheno.mirna = read.csv("D:\file3.csv", row.names = 1, header= TRUE)
> pheno.mrna = read.csv("D:\file4.csv", row.names = 1, header= TRUE)
>
> mrna_se <- SummarizedExperiment::SummarizedExperiment(
+ assays = S4Vectors::SimpleList(counts=mrna),
+ colData = as.data.frame(pheno.mrna))
Error in all_dims[, 1L] : incorrect number of dimensions
>
> mirna_se <- SummarizedExperiment::SummarizedExperiment(
+ assays = S4Vectors::SimpleList(counts=mirna),
+ colData = pheno.mirna)
Error in all_dims[, 1L] : incorrect number of dimensions
>
> mrna_d <- differExp_discrete(se = mrna_se,
+ class = "ER", method = "DESeq",
+ t_test.var = FALSE, log2 = FALSE,
+ p_value.cutoff = 0.05, logratio = 0.5
+ )
Error in SummarizedExperiment::assays(se) : object 'mrna_se' not found
>
> mirna_d <- differExp_discrete(se = mirna_se,
+ class = "ER", method = "DESeq",
+ t_test.var = FALSE, log2 = FALSE,
+ p_value.cutoff = 0.05, logratio = 0.5
+ )
Error in SummarizedExperiment::assays(se) : object 'mirna_se' not found
我最初错误的回溯是（显示一个文件使用建议的 as.data.frame 解决方案，另一个文件使用我的原始矩阵加载）：
> library(anamiR)
>
> mrna1 = read.csv("D:\file1.csv.csv", row.names = 1, header= TRUE)
> mrna <- as.matrix(mrna1)
> rm(mrna1)
> mirna1 = read.csv("D:\file2.csv.csv", row.names = 1, header= TRUE)
> mirna <- as.matrix(mirna1)
> rm(mirna1)
> pheno.mirna1 = read.csv("D:\file3.csv.csv", row.names = 1, header= TRUE)
> pheno.mirna <- as.matrix(pheno.mirna1)
> rm(pheno.mirna1)
> pheno.mrna1 = read.csv("D:\file4.csv.csv", row.names = 1, header= TRUE)
> pheno.mrna <- as.matrix(pheno.mrna1)
> rm(pheno.mrna1)
>
> mrna_se <- SummarizedExperiment::SummarizedExperiment(
+ assays = S4Vectors::SimpleList(counts=mrna),
+ colData = as.data.frame(pheno.mrna))
>
> mirna_se <- SummarizedExperiment::SummarizedExperiment(
+ assays = S4Vectors::SimpleList(counts=mirna),
+ colData = pheno.mirna)
>
> mrna_d <- differExp_discrete(se = mrna_se,
+ class = "ER", method = "DESeq",
+ t_test.var = FALSE, log2 = FALSE,
+ p_value.cutoff = 0.05, logratio = 0.5
+ )
it appears that the last variable in the design formula, 'ER',
has a factor level, 'control', which is not the reference level. we recommend
to use factor(...,levels=...) or relevel() to set this as the reference level
before proceeding. for more information, please see the 'Note on factor levels'
in vignette('DESeq2').
Error in model.matrix.formula(design(object), colData(object)) :
data must be a data.frame
>
> traceback()
6: stop("data must be a data.frame")
5: model.matrix.formula(design(object), colData(object))
4: stats::model.matrix(design(object), colData(object))
3: designAndArgChecker(object, betaPrior)
2: DESeq2::DESeq(dds)
1: differExp_discrete(se = mrna_se, class = "ER", method = "DESeq",
t_test.var = FALSE, log2 = FALSE, p_value.cutoff = 0.05,
logratio = 0.5)
>
> mirna_d <- differExp_discrete(se = mirna_se,
+ class = "ER", method = "DESeq",
+ t_test.var = FALSE, log2 = FALSE,
+ p_value.cutoff = 0.05, logratio = 0.5
+ )
Error in model.matrix.formula(design(object), colData(object)) :
data must be a data.frame
In addition: Warning message:
In DESeq2::DESeqDataSet(se, design = tmp) :
some variables in design formula are characters, converting to factors
>
> traceback()
6: stop("data must be a data.frame")
5: model.matrix.formula(design(object), colData(object))
4: stats::model.matrix(design(object), colData(object))
3: designAndArgChecker(object, betaPrior)
2: DESeq2::DESeq(dds)
1: differExp_discrete(se = mirna_se, class = "ER", method = "DESeq",
t_test.var = FALSE, log2 = FALSE, p_value.cutoff = 0.05,
logratio = 0.5)
新的回溯是：
> library(anamiR)
>
> mrna = read.csv("D:\file1.csv", row.names = 1, header= TRUE)
> mirna = read.csv("D:\file2.csv", row.names = 1, header= TRUE)
> pheno.mirna = read.csv("D:\file3.csv", row.names = 1, header= TRUE)
> pheno.mrna = read.csv("D:\file4.csv", row.names = 1, header= TRUE)
>
> mrna_se <- SummarizedExperiment::SummarizedExperiment(
+ assays = S4Vectors::SimpleList(counts=mrna),
+ colData = as.data.frame(pheno.mrna))
Error in all_dims[, 1L] : incorrect number of dimensions
> traceback()
7: method(object)
6: validityMethod(as(object, superClass))
5: isTRUE(x)
4: anyStrings(validityMethod(as(object, superClass)))
3: validObject(ans)
2: Assays(assays)
1: SummarizedExperiment::SummarizedExperiment(assays = S4Vectors::SimpleList(counts = mrna),
colData = as.data.frame(pheno.mrna))
>
> mirna_se <- SummarizedExperiment::SummarizedExperiment(
+ assays = S4Vectors::SimpleList(counts=mirna),
+ colData = pheno.mirna)
Error in all_dims[, 1L] : incorrect number of dimensions
> traceback()
7: method(object)
6: validityMethod(as(object, superClass))
5: isTRUE(x)
4: anyStrings(validityMethod(as(object, superClass)))
3: validObject(ans)
2: Assays(assays)
1: SummarizedExperiment::SummarizedExperiment(assays = S4Vectors::SimpleList(counts = mirna),
colData = pheno.mirna)
最新编辑 (12/4/21)
因此，为了回应评论，我现在加载我的数据文件如下：
mrna <- as.matrix(read.csv("D:\CorrelationDataProcessing\TRAMP30w\mrnaTRAMP_Mut30w_v_WT30w_normcounts.csv", row.names = 1, header= TRUE))
mirna <- as.matrix(read.csv("D:\CorrelationDataProcessing\TRAMP30w\mirnaTRAMP_Mut30w_vs_WT30w_normcounts.csv", row.names = 1, header= TRUE))
pheno.mirna = read.csv("D:\CorrelationDataProcessing\TRAMP30w\mirnapheno.csv", row.names = 1, header= TRUE)
pheno.mrna = read.csv("D:\CorrelationDataProcessing\TRAMP30w\mrnapheno.csv", row.names = 1, header= TRUE)
这导致：
> mrna_d <- differExp_discrete(se = mrna_se,
+ class = "ER", method = "DESeq",
+ t_test.var = FALSE, log2 = FALSE,
+ p_value.cutoff = 0.05, logratio = 0.5
+ )
it appears that the last variable in the design formula, 'ER',
has a factor level, 'control', which is not the reference level. we recommend
to use factor(...,levels=...) or relevel() to set this as the reference level
before proceeding. for more information, please see the 'Note on factor levels'
in vignette('DESeq2').
Error in model.matrix.formula(design(object), colData(object)) :
data must be a data.frame
>
> mirna_d <- differExp_discrete(se = mirna_se,
+ class = "ER", method = "DESeq",
+ t_test.var = FALSE, log2 = FALSE,
+ p_value.cutoff = 0.05, logratio = 0.5
+ )
it appears that the last variable in the design formula, 'ER',
has a factor level, 'control', which is not the reference level. we recommend
to use factor(...,levels=...) or relevel() to set this as the reference level
before proceeding. for more information, please see the 'Note on factor levels'
in vignette('DESeq2').
Error in model.matrix.formula(design(object), colData(object)) :
data must be a data.frame
不幸的是，这个包似乎有问题，无论如何都被弃用了。在这一点上，我建议任何想要进行 miRNA 关联的人都忽略 anaMIR 和 mirCOMB。