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* checking for file ‘tidybulk/DESCRIPTION’ ... OK
* preparing ‘tidybulk’:
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* installing the package to process help pages
* building the PDF package manual
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[]\T1/ptm/m/n/10 Underlying cus-tom method: data fil-ter(n_aggr > 1) group_by(!
!.sample,!!.transcript) dplyr::mutate(!!.abundance
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[]\T1/ptm/m/n/10 <[`tidy-eval`][dplyr_tidy_eval]> Vari-ables, or func-tions or
vari-ables. Use [desc()]
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[]\T1/zi4/m/n/9 as_matrix(head(dplyr::select(tidybulk::counts_mini, transcript
, count)), rownames=transcript)[]
[9] [10] [11] [12]
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[] \T1/zi4/m/n/9 cluster_elements(tidybulk::counts_mini, sample, transcript
, count,centers = 2, method="kmeans")[]
[13] [14] [15]
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[]\T1/zi4/m/n/9 deconvolve_cellularity(filter(tidybulk::counts, sample=="SRR17
40034"), sample, transcript, \TS1/cmtt/m/n/9 `\T1/zi4/m/n/9 count\TS1/cmtt/m/n/
9 `\T1/zi4/m/n/9 , cores = 1)[]
[16] [17]
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[]\T1/ptm/m/n/10 ensembl_to_symbol() takes as in-put a `tbl` for-mat-ted as | <
SAM-PLE> | <EN-SEMBL_ID> | <COUNT>
[18] [19] [20] [21] [22] [23]
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[]\T1/ptm/m/n/10 When `.drop = TRUE`, empty groups are dropped. See [group_by_d
rop_default()]
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[]\T1/ptm/m/n/10 identify_abundant() takes as in-put a `tbl` for-mat-ted as | <
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[]\T1/ptm/m/n/10 Underlying method: edgeR::filterByExpr( data, min.count = min-
i-mum_counts, group = string_factor_of_interest,
[26] [27] [28] [29] [30]
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[]\T1/ptm/m/n/10 Underlying method: edgeR::filterByExpr( data, min.count = min-
i-mum_counts, group = string_factor_of_interest,
[31] [32] [33] [34] [35] [36] [37] [38] [39]
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[]\T1/ptm/m/n/10 reduce_dimensions() takes as in-put a `tbl` for-mat-ted as | <
SAM-PLE> | <TRAN-SCRIPT> | <COUNT>
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[]\T1/ptm/m/n/10 remove_redundancy() takes as in-put a `tbl` for-mat-ted as | <
SAM-PLE> | <TRAN-SCRIPT> | <COUNT>
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\T1/ptm/m/n/10 | <...> | for cor-re-la-tion method or | <DI-MEN-SION 1> | <DI-M
EN-SION 2> | <...> | for re-duced_dimensions
[43] [44] [45] [46] [47] [48] [49] [50]
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[]\T1/zi4/m/n/9 counts.MDS.rotated = rotate_dimensions(counts.MDS, \TS1/cmtt/
m/n/9 `\T1/zi4/m/n/9 Dim1\TS1/cmtt/m/n/9 `\T1/zi4/m/n/9 , \TS1/cmtt/m/n/9 `\T1/
zi4/m/n/9 Dim2\TS1/cmtt/m/n/9 `\T1/zi4/m/n/9 , rotation_degrees = 45, .element
= sample)[]
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[]\T1/ptm/m/n/10 Underlying method edgeR::calcNormFactors(.data, method = c("TM
M","TMMwsp","RLE","upperquartile"))
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[]\T1/ptm/m/n/10 A string char-ac-ter. Ei-ther "edgeR_quasi_likelihood" (i.e.,
QLF), "edgeR_likelihood_ratio"
[59]
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\T1/zi4/m/n/10 btp616$[][]\T1/ptm/m/n/10 , limma-voom [][]$\T1/zi4/m/n/10 https
: / / doi . org / 10 . 1186 / gb-[]2014-[]15-[]2-[]r29$[][]\T1/ptm/m/n/10 , or
DE-Seq2 [][]$\T1/zi4/m/n/10 https : / / doi .
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[]\T1/ptm/m/n/10 # Fil-ter keep_abundant( fac-tor_of_interest = !!(as.symbol(pa
rse_formula(.formula)[1])), min-i-mum_counts
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[]\T1/ptm/m/n/10 # For-mat se-lect(!!.transcript,!!.sample,!!.abundance) spread
(!!.sample,!!.abundance) as_matrix(rownames
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[]\T1/ptm/m/n/10 # edgeR edgeR::DGEList(counts = .) edgeR::calcNormFactors(meth
od = scal-ing_method) edgeR::estimateDisp(design)
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[]\T1/ptm/m/n/10 # Fit edgeR::glmQLFit(design) edgeR::glmQLFTest(coef = 2, con-
trast = my_contrasts) // or glmLRT
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[]\T1/ptm/m/n/10 Underlying method for DE-Seq2 frame-work: keep_abundant( fac-t
or_of_interest = !!as.symbol(parse_formula(.formula)[[1]]),
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\T1/ptm/m/n/10 script, !!.abun-dance, method=method, ref-er-ence = ref-er-ence,
ac-tion="get", ... ) [..] betareg::betareg(.my_formula,
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\T1/ptm/m/n/10 !!.abun-dance, method=method, ref-er-ence = ref-er-ence, ac-tion
="get", ... ) [..] mu-tate(.proportion_0_corrected
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[]\T1/ptm/m/n/10 A char-ac-ter vec-tor. The meth-ods to be in-cluded in the en-
sembl. Type EGSEA::egsea.base()
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[]\T1/ptm/m/n/10 This wrap-per ex-e-cute en-sem-ble gene en-rich-ment anal-y-se
s of the dataset us-ing EGSEA (DOI:0.12688/f1000research.12544.1)
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[]\T1/ptm/m/n/10 # Make sure tran-script names are ad-ja-cent [...] as_matrix(r
ownames = !!.en-trez) edgeR::DGEList(counts
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[]\T1/zi4/m/n/9 df_entrez = symbol_to_entrez(tidybulk::counts_mini, .transcrip
t = transcript, .sample = sample)[]
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[]\T1/zi4/m/n/9 df_entrez = aggregate_duplicates(df_entrez, aggregation_functi
on = sum, .sample = sample, .transcript = entrez, .abundance = count)[]
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[]\T1/zi4/m/n/9 df_entrez = symbol_to_entrez(tidybulk::counts_mini, .transcrip
t = transcript, .sample = sample)[]
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[]\T1/zi4/m/n/9 df_entrez = aggregate_duplicates(df_entrez, aggregation_functi
on = sum, .sample = sample, .transcript = entrez, .abundance = count)[]
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[]\T1/zi4/m/n/9 df_entrez = mutate(df_entrez, do_test = transcript %in% c("TNF
RSF4", "PLCH2", "PADI4", "PAX7"))[]
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[]\T1/ptm/m/n/10 tidybulk() cre-ates a `tt` ob-ject from a `tbl` for-mat-ted as
| <SAM-PLE> | <TRAN-SCRIPT> | <COUNT>
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[]\T1/ptm/m/n/10 Underlying cus-tom method: data fil-ter(n_aggr > 1) group_by(!
!.sample,!!.transcript) dplyr::mutate(!!.abundance
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vari-ables. Use [desc()]
[9]
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[]\T1/zi4/m/n/9 as_matrix(head(dplyr::select(tidybulk::counts_mini, transcript
, count)), rownames=transcript)[]
[10] [11] [12] [13]
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[] \T1/zi4/m/n/9 cluster_elements(tidybulk::counts_mini, sample, transcript
, count,centers = 2, method="kmeans")[]
[14] [15] [16] [17]
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[]\T1/zi4/m/n/9 deconvolve_cellularity(filter(tidybulk::counts, sample=="SRR17
40034"), sample, transcript, \TS1/cmtt/m/n/9 `\T1/zi4/m/n/9 count\TS1/cmtt/m/n/
9 `\T1/zi4/m/n/9 , cores = 1)[]
[18]
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[]\T1/ptm/m/n/10 ensembl_to_symbol() takes as in-put a `tbl` for-mat-ted as | <
SAM-PLE> | <EN-SEMBL_ID> | <COUNT>
[19] [20] [21] [22] [23] [24]
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[]\T1/ptm/m/n/10 When `.drop = TRUE`, empty groups are dropped. See [group_by_d
rop_default()]
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[]\T1/ptm/m/n/10 identify_abundant() takes as in-put a `tbl` for-mat-ted as | <
SAM-PLE> | <TRAN-SCRIPT> | <COUNT>
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[]\T1/ptm/m/n/10 Underlying method: edgeR::filterByExpr( data, min.count = min-
i-mum_counts, group = string_factor_of_interest,
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[]\T1/ptm/m/n/10 Underlying method: edgeR::filterByExpr( data, min.count = min-
i-mum_counts, group = string_factor_of_interest,
[33] [34] [35] [36] [37] [38] [39] [40] [41]
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\T1/ptm/m/n/10 | <...> | for cor-re-la-tion method or | <DI-MEN-SION 1> | <DI-M
EN-SION 2> | <...> | for re-duced_dimensions
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[]\T1/zi4/m/n/9 counts.MDS.rotated = rotate_dimensions(counts.MDS, \TS1/cmtt/
m/n/9 `\T1/zi4/m/n/9 Dim1\TS1/cmtt/m/n/9 `\T1/zi4/m/n/9 , \TS1/cmtt/m/n/9 `\T1/
zi4/m/n/9 Dim2\TS1/cmtt/m/n/9 `\T1/zi4/m/n/9 , rotation_degrees = 45, .element
= sample)[]
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[]\T1/ptm/m/n/10 Underlying method edgeR::calcNormFactors(.data, method = c("TM
M","TMMwsp","RLE","upperquartile"))
[55] [56] [57] [58] [59]
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[]\T1/ptm/m/n/10 A string char-ac-ter. Ei-ther "edgeR_quasi_likelihood" (i.e.,
QLF), "edgeR_likelihood_ratio"
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\T1/zi4/m/n/10 btp616$[][]\T1/ptm/m/n/10 , limma-voom [][]$\T1/zi4/m/n/10 https
: / / doi . org / 10 . 1186 / gb-[]2014-[]15-[]2-[]r29$[][]\T1/ptm/m/n/10 , or
DE-Seq2 [][]$\T1/zi4/m/n/10 https : / / doi .
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[]\T1/ptm/m/n/10 # Fil-ter keep_abundant( fac-tor_of_interest = !!(as.symbol(pa
rse_formula(.formula)[1])), min-i-mum_counts
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[]\T1/ptm/m/n/10 # For-mat se-lect(!!.transcript,!!.sample,!!.abundance) spread
(!!.sample,!!.abundance) as_matrix(rownames
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[]\T1/ptm/m/n/10 # edgeR edgeR::DGEList(counts = .) edgeR::calcNormFactors(meth
od = scal-ing_method) edgeR::estimateDisp(design)
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[]\T1/ptm/m/n/10 # Fit edgeR::glmQLFit(design) edgeR::glmQLFTest(coef = 2, con-
trast = my_contrasts) // or glmLRT
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[]\T1/ptm/m/n/10 Underlying method for DE-Seq2 frame-work: keep_abundant( fac-t
or_of_interest = !!as.symbol(parse_formula(.formula)[[1]]),
[62] [63] [64]
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\T1/ptm/m/n/10 script, !!.abun-dance, method=method, ref-er-ence = ref-er-ence,
ac-tion="get", ... ) [..] betareg::betareg(.my_formula,
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\T1/ptm/m/n/10 !!.abun-dance, method=method, ref-er-ence = ref-er-ence, ac-tion
="get", ... ) [..] mu-tate(.proportion_0_corrected
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[]\T1/ptm/m/n/10 A char-ac-ter vec-tor. The meth-ods to be in-cluded in the en-
sembl. Type EGSEA::egsea.base()
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[]\T1/ptm/m/n/10 This wrap-per ex-e-cute en-sem-ble gene en-rich-ment anal-y-se
s of the dataset us-ing EGSEA (DOI:0.12688/f1000research.12544.1)
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[]\T1/ptm/m/n/10 # Make sure tran-script names are ad-ja-cent [...] as_matrix(r
ownames = !!.en-trez) edgeR::DGEList(counts
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[]\T1/zi4/m/n/9 df_entrez = symbol_to_entrez(tidybulk::counts_mini, .transcrip
t = transcript, .sample = sample)[]
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[]\T1/zi4/m/n/9 df_entrez = aggregate_duplicates(df_entrez, aggregation_functi
on = sum, .sample = sample, .transcript = entrez, .abundance = count)[]
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[]\T1/zi4/m/n/9 df_entrez = symbol_to_entrez(tidybulk::counts_mini, .transcrip
t = transcript, .sample = sample)[]
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[]\T1/zi4/m/n/9 df_entrez = aggregate_duplicates(df_entrez, aggregation_functi
on = sum, .sample = sample, .transcript = entrez, .abundance = count)[]
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[]\T1/zi4/m/n/9 df_entrez = mutate(df_entrez, do_test = transcript %in% c("TNF
RSF4", "PLCH2", "PADI4", "PAX7"))[]
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[]\T1/ptm/m/n/10 tidybulk() cre-ates a `tt` ob-ject from a `tbl` for-mat-ted as
| <SAM-PLE> | <TRAN-SCRIPT> | <COUNT>
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[19] [20] [21] [22] [23] [24]
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[]\T1/ptm/m/n/10 Underlying method: edgeR::filterByExpr( data, min.count = min-
i-mum_counts, group = string_factor_of_interest,
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[]\T1/ptm/m/n/10 Underlying method: edgeR::filterByExpr( data, min.count = min-
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zi4/m/n/9 Dim2\TS1/cmtt/m/n/9 `\T1/zi4/m/n/9 , rotation_degrees = 45, .element
= sample)[]
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[]\T1/ptm/m/n/10 Underlying method edgeR::calcNormFactors(.data, method = c("TM
M","TMMwsp","RLE","upperquartile"))
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\T1/zi4/m/n/10 btp616$[][]\T1/ptm/m/n/10 , limma-voom [][]$\T1/zi4/m/n/10 https
: / / doi . org / 10 . 1186 / gb-[]2014-[]15-[]2-[]r29$[][]\T1/ptm/m/n/10 , or
DE-Seq2 [][]$\T1/zi4/m/n/10 https : / / doi .
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od = scal-ing_method) edgeR::estimateDisp(design)
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trast = my_contrasts) // or glmLRT
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[]\T1/ptm/m/n/10 Underlying method for DE-Seq2 frame-work: keep_abundant( fac-t
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[]\T1/ptm/m/n/10 This wrap-per ex-e-cute en-sem-ble gene en-rich-ment anal-y-se
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[]\T1/zi4/m/n/9 df_entrez = aggregate_duplicates(df_entrez, aggregation_functi
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[]\T1/zi4/m/n/9 df_entrez = mutate(df_entrez, do_test = transcript %in% c("TNF
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[]\T1/ptm/m/n/10 Underlying cus-tom method: data fil-ter(n_aggr > 1) group_by(!
!.sample,!!.transcript) dplyr::mutate(!!.abundance
(/usr/local/texlive/2020/texmf-dist/tex/latex/base/t1cmtt.fd)
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[]\T1/ptm/m/n/10 <[`tidy-eval`][dplyr_tidy_eval]> Vari-ables, or func-tions or
vari-ables. Use [desc()]
[9]
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[]\T1/zi4/m/n/9 as_matrix(head(dplyr::select(tidybulk::counts_mini, transcript
, count)), rownames=transcript)[]
[10] [11] [12] [13]
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[] \T1/zi4/m/n/9 cluster_elements(tidybulk::counts_mini, sample, transcript
, count,centers = 2, method="kmeans")[]
[14] [15] [16] [17]
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[]\T1/zi4/m/n/9 deconvolve_cellularity(filter(tidybulk::counts, sample=="SRR17
40034"), sample, transcript, \TS1/cmtt/m/n/9 `\T1/zi4/m/n/9 count\TS1/cmtt/m/n/
9 `\T1/zi4/m/n/9 , cores = 1)[]
[18]
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[]\T1/ptm/m/n/10 ensembl_to_symbol() takes as in-put a `tbl` for-mat-ted as | <
SAM-PLE> | <EN-SEMBL_ID> | <COUNT>
[19] [20] [21] [22] [23] [24]
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[]\T1/ptm/m/n/10 When `.drop = TRUE`, empty groups are dropped. See [group_by_d
rop_default()]
[25]
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[]\T1/ptm/m/n/10 identify_abundant() takes as in-put a `tbl` for-mat-ted as | <
SAM-PLE> | <TRAN-SCRIPT> | <COUNT>
[26] [27]
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[]\T1/ptm/m/n/10 Underlying method: edgeR::filterByExpr( data, min.count = min-
i-mum_counts, group = string_factor_of_interest,
[28] [29] [30] [31]
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[]\T1/ptm/m/n/10 Underlying method: edgeR::filterByExpr( data, min.count = min-
i-mum_counts, group = string_factor_of_interest,
[33] [34] [35] [36] [37] [38] [39] [40] [41]
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[]\T1/ptm/m/n/10 reduce_dimensions() takes as in-put a `tbl` for-mat-ted as | <
SAM-PLE> | <TRAN-SCRIPT> | <COUNT>
[42]
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[44]
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[]\T1/ptm/m/n/10 remove_redundancy() takes as in-put a `tbl` for-mat-ted as | <
SAM-PLE> | <TRAN-SCRIPT> | <COUNT>
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\T1/ptm/m/n/10 | <...> | for cor-re-la-tion method or | <DI-MEN-SION 1> | <DI-M
EN-SION 2> | <...> | for re-duced_dimensions
[45]
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[47] [48] [49] [50] [51]
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[]\T1/zi4/m/n/9 counts.MDS.rotated = rotate_dimensions(counts.MDS, \TS1/cmtt/
m/n/9 `\T1/zi4/m/n/9 Dim1\TS1/cmtt/m/n/9 `\T1/zi4/m/n/9 , \TS1/cmtt/m/n/9 `\T1/
zi4/m/n/9 Dim2\TS1/cmtt/m/n/9 `\T1/zi4/m/n/9 , rotation_degrees = 45, .element
= sample)[]
[52] [53] [54]
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[]\T1/ptm/m/n/10 Underlying method edgeR::calcNormFactors(.data, method = c("TM
M","TMMwsp","RLE","upperquartile"))
[55] [56] [57] [58] [59]
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[]\T1/ptm/m/n/10 A string char-ac-ter. Ei-ther "edgeR_quasi_likelihood" (i.e.,
QLF), "edgeR_likelihood_ratio"
[61]
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\T1/zi4/m/n/10 btp616$[][]\T1/ptm/m/n/10 , limma-voom [][]$\T1/zi4/m/n/10 https
: / / doi . org / 10 . 1186 / gb-[]2014-[]15-[]2-[]r29$[][]\T1/ptm/m/n/10 , or
DE-Seq2 [][]$\T1/zi4/m/n/10 https : / / doi .
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[]\T1/ptm/m/n/10 # Fil-ter keep_abundant( fac-tor_of_interest = !!(as.symbol(pa
rse_formula(.formula)[1])), min-i-mum_counts
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[]\T1/ptm/m/n/10 # For-mat se-lect(!!.transcript,!!.sample,!!.abundance) spread
(!!.sample,!!.abundance) as_matrix(rownames
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[]\T1/ptm/m/n/10 # edgeR edgeR::DGEList(counts = .) edgeR::calcNormFactors(meth
od = scal-ing_method) edgeR::estimateDisp(design)
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[]\T1/ptm/m/n/10 # Fit edgeR::glmQLFit(design) edgeR::glmQLFTest(coef = 2, con-
trast = my_contrasts) // or glmLRT
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[]\T1/ptm/m/n/10 Underlying method for DE-Seq2 frame-work: keep_abundant( fac-t
or_of_interest = !!as.symbol(parse_formula(.formula)[[1]]),
[62] [63] [64]
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\T1/ptm/m/n/10 script, !!.abun-dance, method=method, ref-er-ence = ref-er-ence,
ac-tion="get", ... ) [..] betareg::betareg(.my_formula,
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\T1/ptm/m/n/10 !!.abun-dance, method=method, ref-er-ence = ref-er-ence, ac-tion
="get", ... ) [..] mu-tate(.proportion_0_corrected
[65] [66]
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[]\T1/ptm/m/n/10 A char-ac-ter vec-tor. The meth-ods to be in-cluded in the en-
sembl. Type EGSEA::egsea.base()
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[]\T1/ptm/m/n/10 This wrap-per ex-e-cute en-sem-ble gene en-rich-ment anal-y-se
s of the dataset us-ing EGSEA (DOI:0.12688/f1000research.12544.1)
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[]\T1/ptm/m/n/10 # Make sure tran-script names are ad-ja-cent [...] as_matrix(r
ownames = !!.en-trez) edgeR::DGEList(counts
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[]\T1/zi4/m/n/9 df_entrez = symbol_to_entrez(tidybulk::counts_mini, .transcrip
t = transcript, .sample = sample)[]
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[]\T1/zi4/m/n/9 df_entrez = aggregate_duplicates(df_entrez, aggregation_functi
on = sum, .sample = sample, .transcript = entrez, .abundance = count)[]
[67] [68]
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[]\T1/zi4/m/n/9 df_entrez = symbol_to_entrez(tidybulk::counts_mini, .transcrip
t = transcript, .sample = sample)[]
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[]\T1/zi4/m/n/9 df_entrez = aggregate_duplicates(df_entrez, aggregation_functi
on = sum, .sample = sample, .transcript = entrez, .abundance = count)[]
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[]\T1/zi4/m/n/9 df_entrez = mutate(df_entrez, do_test = transcript %in% c("TNF
RSF4", "PLCH2", "PADI4", "PAX7"))[]
[69]
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[]\T1/ptm/m/n/10 tidybulk() cre-ates a `tt` ob-ject from a `tbl` for-mat-ted as
| <SAM-PLE> | <TRAN-SCRIPT> | <COUNT>
[70] [71] [72] [73] [74]
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[]| \T1/zi4/m/n/10 vignette_manuscript_signature_boxplot\T1/ptm/m/n/10 ,
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[]| \T1/zi4/m/n/10 vignette_manuscript_signature_tsne\T1/ptm/m/n/10 ,
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[]| \T1/zi4/m/n/10 vignette_manuscript_signature_tsne2\T1/ptm/m/n/10 ,
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[]\T1/zi4/m/n/10 adjust_abundance,RangedSummarizedExperiment-method
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[]\T1/zi4/m/it/10 adjust_abundance,SummarizedExperiment-method
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[]\T1/zi4/m/it/10 aggregate_duplicates,SummarizedExperiment-method
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[76]
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[]\T1/zi4/m/it/10 identify_abundant,RangedSummarizedExperiment-method
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[]\T1/zi4/m/it/10 identify_abundant,SummarizedExperiment-method
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[]\T1/zi4/m/it/10 reduce_dimensions,SummarizedExperiment-method
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[77]
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[]\T1/zi4/m/it/10 rotate_dimensions,RangedSummarizedExperiment-method
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[]\T1/zi4/m/it/10 rotate_dimensions,SummarizedExperiment-method
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[]\T1/zi4/m/it/10 scale_abundance,RangedSummarizedExperiment-method
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[]\T1/zi4/m/it/10 test_differential_abundance,RangedSummarizedExperiment-method
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od
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\T1/ptm/m/it/10 (\T1/zi4/m/it/10 test_differential_cellularity\T1/ptm/m/it/10 )
\T1/ptm/m/n/10 ,
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\T1/ptm/m/n/10 ,
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\T1/ptm/m/n/10 ,
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[78] [79])
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Output written on Rd2.pdf (79 pages, 232437 bytes).
Transcript written on Rd2.log.
Saving output to ‘/private/tmp/RtmpdR9Jxr/Rbuilda02d467aca01/tidybulk/build/tidybulk.pdf’ ...
Done
* creating vignettes ... ERROR
--- re-building ‘comparison_with_base_R.Rmd’ using knitr
Warning in aggregate_duplicated_transcripts_bulk(.data, .sample = !!.sample, :
tidybulk says: for aggregation, factors and logical columns were converted to character
Converted to characters
logical
Getting the 500 most variable genes
tidybulk says: to access the raw results do `attr(..., "internals")$MDS`
Getting the 393 most variable genes
Fraction of variance explained by the selected principal components
# A tibble: 2 x 2
`Fraction of variance` PC
<dbl> <int>
1 0.581 1
2 0.321 2
tidybulk says: to access the raw results do `attr(..., "internals")$PCA`
No group or design set. Assuming all samples belong to one group.
Warning in eliminate_sparse_transcripts(., !!.feature) :
tidybulk says: Some transcripts have been omitted from the analysis because not present in every sample.
Getting the 488 most variable genes
=====================================
tidybulk says: All testing methods use raw counts, irrespective of if scale_abundance
or adjust_abundance have been calculated. Therefore, it is essential to add covariates
such as batch effects (if applicable) in the formula.
=====================================
Warning in eval(dots[[i]][[action]], env, env) :
tidybulk says: highly abundant transcripts were not identified (i.e. identify_abundant()) or filtered (i.e., keep_abundant), therefore this operation will be performed on unfiltered data. In rare occasions this could be wanted. In standard whole-transcriptome workflows is generally unwanted.
tidybulk says: The design column names are "(Intercept), conditionTRUE"
tidybulk says: to access the raw results (fitted GLM) do `attr(..., "internals")$edgeR`
Found2batches
Adjusting for1covariate(s) or covariate level(s)
Standardizing Data across genes
Fitting L/S model and finding priors
Finding parametric adjustments
Adjusting the Data
Registered S3 method overwritten by 'spatstat.geom':
method from
print.boxx cli
Warning: The following arguments are not used: display.progress, num.cores, do.par
Suggested parameter: verbose instead of display.progress
Centering and scaling data matrix
|
| | 0%
|
|======================================================================| 100%
Calculating gene variances
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Calculating feature variances of standardized and clipped values
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
PC_ 1
Positive: ENSG00000129514, ENSG00000109436, ENSG00000124942, ENSG00000115648, ENSG00000167978, ENSG00000117335, ENSG00000074410, ENSG00000091831, ENSG00000134107, ENSG00000164125
ENSG00000113494, ENSG00000107485, ENSG00000139644, ENSG00000168743, ENSG00000166147, ENSG00000196405, ENSG00000075275, ENSG00000133110, ENSG00000135821, ENSG00000168461
ENSG00000123384, ENSG00000104763, ENSG00000111799, ENSG00000163359, ENSG00000245532, ENSG00000185630, ENSG00000065361, ENSG00000260032, ENSG00000123124, ENSG00000148154
Negative: ENSG00000171863, ENSG00000143947, ENSG00000231500, ENSG00000182899, ENSG00000166441, ENSG00000105372, ENSG00000177600, ENSG00000142937, ENSG00000140988, ENSG00000105640
ENSG00000197756, ENSG00000167526, ENSG00000114391, ENSG00000122406, ENSG00000174444, ENSG00000142676, ENSG00000142534, ENSG00000008988, ENSG00000221983, ENSG00000145592
ENSG00000138326, ENSG00000130255, ENSG00000065978, ENSG00000171858, ENSG00000136942, ENSG00000118181, ENSG00000089009, ENSG00000182774, ENSG00000063177, ENSG00000137154
PC_ 2
Positive: ENSG00000091831, ENSG00000129514, ENSG00000004478, ENSG00000107485, ENSG00000104447, ENSG00000151892, ENSG00000106537, ENSG00000136068, ENSG00000109436, ENSG00000183779
ENSG00000134759, ENSG00000115648, ENSG00000096384, ENSG00000102316, ENSG00000138696, ENSG00000113739, ENSG00000139644, ENSG00000065361, ENSG00000123124, ENSG00000167978
ENSG00000081479, ENSG00000251562, ENSG00000074410, ENSG00000160862, ENSG00000124145, ENSG00000108953, ENSG00000185630, ENSG00000110092, ENSG00000076554, ENSG00000075275
Negative: ENSG00000163430, ENSG00000087245, ENSG00000204262, ENSG00000035862, ENSG00000164692, ENSG00000163359, ENSG00000130635, ENSG00000113140, ENSG00000168542, ENSG00000108821
ENSG00000101825, ENSG00000011465, ENSG00000142173, ENSG00000186340, ENSG00000038427, ENSG00000139329, ENSG00000166033, ENSG00000106333, ENSG00000166147, ENSG00000157227
ENSG00000142156, ENSG00000123384, ENSG00000106624, ENSG00000182492, ENSG00000169604, ENSG00000133110, ENSG00000111799, ENSG00000060718, ENSG00000123500, ENSG00000145423
PC_ 3
Positive: ENSG00000145741, ENSG00000186468, ENSG00000164587, ENSG00000141753, ENSG00000162244, ENSG00000143878, ENSG00000115648, ENSG00000107485, ENSG00000197958, ENSG00000100316
ENSG00000196531, ENSG00000148303, ENSG00000174748, ENSG00000109475, ENSG00000170889, ENSG00000198034, ENSG00000129514, ENSG00000204628, ENSG00000188846, ENSG00000063177
ENSG00000163682, ENSG00000137818, ENSG00000071082, ENSG00000012660, ENSG00000142541, ENSG00000065361, ENSG00000139644, ENSG00000111057, ENSG00000133112, ENSG00000075275
Negative: ENSG00000103257, ENSG00000152558, ENSG00000147065, ENSG00000115415, ENSG00000146731, ENSG00000112096, ENSG00000253729, ENSG00000065978, ENSG00000094755, ENSG00000143321
ENSG00000138755, ENSG00000196230, ENSG00000104419, ENSG00000074800, ENSG00000166598, ENSG00000140545, ENSG00000102144, ENSG00000105220, ENSG00000117632, ENSG00000084207
ENSG00000142949, ENSG00000111716, ENSG00000083444, ENSG00000136235, ENSG00000090382, ENSG00000173898, ENSG00000130066, ENSG00000164754, ENSG00000146648, ENSG00000115053
PC_ 4
Positive: ENSG00000152583, ENSG00000159388, ENSG00000156508, ENSG00000120738, ENSG00000071967, ENSG00000170345, ENSG00000104332, ENSG00000150593, ENSG00000175061, ENSG00000265972
ENSG00000122406, ENSG00000109475, ENSG00000091986, ENSG00000132465, ENSG00000175899, ENSG00000137154, ENSG00000185650, ENSG00000198755, ENSG00000167978, ENSG00000111716
ENSG00000198034, ENSG00000071082, ENSG00000049540, ENSG00000143947, ENSG00000118523, ENSG00000133112, ENSG00000125730, ENSG00000118181, ENSG00000163682, ENSG00000112306
Negative: ENSG00000109062, ENSG00000178719, ENSG00000172757, ENSG00000185624, ENSG00000106211, ENSG00000117450, ENSG00000039068, ENSG00000111057, ENSG00000149925, ENSG00000170421
ENSG00000165949, ENSG00000092841, ENSG00000113719, ENSG00000143761, ENSG00000096384, ENSG00000167004, ENSG00000108679, ENSG00000108829, ENSG00000126709, ENSG00000080824
ENSG00000125534, ENSG00000067225, ENSG00000004478, ENSG00000171345, ENSG00000141367, ENSG00000164924, ENSG00000184009, ENSG00000167642, ENSG00000102144, ENSG00000135404
PC_ 5
Positive: ENSG00000253729, ENSG00000080824, ENSG00000110321, ENSG00000113494, ENSG00000111371, ENSG00000102144, ENSG00000164754, ENSG00000164924, ENSG00000142949, ENSG00000109971
ENSG00000070756, ENSG00000104408, ENSG00000096696, ENSG00000146731, ENSG00000096384, ENSG00000144381, ENSG00000009307, ENSG00000147676, ENSG00000198363, ENSG00000272398
ENSG00000260032, ENSG00000147604, ENSG00000112378, ENSG00000104341, ENSG00000076554, ENSG00000141367, ENSG00000169604, ENSG00000123500, ENSG00000064651, ENSG00000115221
Negative: ENSG00000204592, ENSG00000166710, ENSG00000204525, ENSG00000234745, ENSG00000019582, ENSG00000206503, ENSG00000231389, ENSG00000196126, ENSG00000204287, ENSG00000142089
ENSG00000185885, ENSG00000126709, ENSG00000157601, ENSG00000165949, ENSG00000130203, ENSG00000160932, ENSG00000102265, ENSG00000138755, ENSG00000101439, ENSG00000115415
ENSG00000030582, ENSG00000108679, ENSG00000135404, ENSG00000125730, ENSG00000205542, ENSG00000185499, ENSG00000182326, ENSG00000140264, ENSG00000160182, ENSG00000167004
Computing nearest neighbor graph
Computing SNN
Quitting from lines 434-437 (comparison_with_base_R.Rmd)
Error: processing vignette 'comparison_with_base_R.Rmd' failed with diagnostics:
invalid class "Graph" object: superclass "Mnumeric" not defined in the environment of the object's class
--- failed re-building ‘comparison_with_base_R.Rmd’
--- re-building ‘introduction.Rmd’ using knitr
Warning in aggregate_duplicated_transcripts_bulk(.data, .sample = !!.sample, :
tidybulk says: for aggregation, factors and logical columns were converted to character
Converted to characters
logical
Warning in aggregate_duplicated_transcripts_bulk(.data, .sample = !!.sample, :
tidybulk says: for aggregation, factors and logical columns were converted to character
Converted to characters
factorfactorfactorfactorlogical
tidybulk says: to access the raw results do `attr(..., "internals")$MDS`
tidybulk says: to access the raw results do `attr(..., "internals")$MDS`
Getting the 393 most variable genes
Fraction of variance explained by the selected principal components
# A tibble: 3 x 2
`Fraction of variance` PC
<dbl> <int>
1 0.581 1
2 0.321 2
3 0.0896 3
tidybulk says: to access the raw results do `attr(..., "internals")$PCA`
Getting the 393 most variable genes
Fraction of variance explained by the selected principal components
# A tibble: 3 x 2
`Fraction of variance` PC
<dbl> <int>
1 0.581 1
2 0.321 2
3 0.0896 3
tidybulk says: to access the raw results do `attr(..., "internals")$PCA`
No group or design set. Assuming all samples belong to one group.
Warning in eliminate_sparse_transcripts(., !!.feature) :
tidybulk says: Some transcripts have been omitted from the analysis because not present in every sample.
Getting the 488 most variable genes
No group or design set. Assuming all samples belong to one group.
Warning in eliminate_sparse_transcripts(., !!.feature) :
tidybulk says: Some transcripts have been omitted from the analysis because not present in every sample.
Getting the 488 most variable genes
=====================================
tidybulk says: All testing methods use raw counts, irrespective of if scale_abundance
or adjust_abundance have been calculated. Therefore, it is essential to add covariates
such as batch effects (if applicable) in the formula.
=====================================
tidybulk says: The design column names are "(Intercept), conditionTRUE"
tidybulk says: to access the raw results (fitted GLM) do `attr(..., "internals")$edgeR`
=====================================
tidybulk says: All testing methods use raw counts, irrespective of if scale_abundance
or adjust_abundance have been calculated. Therefore, it is essential to add covariates
such as batch effects (if applicable) in the formula.
=====================================
Warning in eval(dots[[i]][[action]], env, env) :
tidybulk says: highly abundant transcripts were not identified (i.e. identify_abundant()) or filtered (i.e., keep_abundant), therefore this operation will be performed on unfiltered data. In rare occasions this could be wanted. In standard whole-transcriptome workflows is generally unwanted.
tidybulk says: The design column names are "(Intercept), conditionTRUE"
tidybulk says: to access the raw results (fitted GLM) do `attr(..., "internals")$edgeR`
Joining, by = "sample"
Found2batches
Adjusting for1covariate(s) or covariate level(s)
Standardizing Data across genes
Fitting L/S model and finding priors
Finding parametric adjustments
Adjusting the Data
Warning in eval(dots[[i]][[action]], env, env) :
tidybulk says: highly abundant transcripts were not identified (i.e. identify_abundant()) or filtered (i.e., keep_abundant), therefore this operation will be performed on unfiltered data. In rare occasions this could be wanted. In standard whole-transcriptome workflows is generally unwanted.
Found2batches
Adjusting for1covariate(s) or covariate level(s)
Standardizing Data across genes
Fitting L/S model and finding priors
Finding parametric adjustments
Adjusting the Data
Warning: The following arguments are not used: display.progress, num.cores, do.par
Suggested parameter: verbose instead of display.progress
Centering and scaling data matrix
|
| | 0%
|
|======================================================================| 100%
Calculating gene variances
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Calculating feature variances of standardized and clipped values
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
PC_ 1
Positive: ENSG00000129514, ENSG00000109436, ENSG00000124942, ENSG00000115648, ENSG00000167978, ENSG00000117335, ENSG00000074410, ENSG00000091831, ENSG00000134107, ENSG00000164125
ENSG00000113494, ENSG00000107485, ENSG00000139644, ENSG00000168743, ENSG00000166147, ENSG00000196405, ENSG00000075275, ENSG00000133110, ENSG00000135821, ENSG00000168461
ENSG00000123384, ENSG00000104763, ENSG00000111799, ENSG00000163359, ENSG00000245532, ENSG00000185630, ENSG00000065361, ENSG00000260032, ENSG00000123124, ENSG00000148154
Negative: ENSG00000171863, ENSG00000143947, ENSG00000231500, ENSG00000182899, ENSG00000166441, ENSG00000105372, ENSG00000177600, ENSG00000142937, ENSG00000140988, ENSG00000105640
ENSG00000197756, ENSG00000167526, ENSG00000114391, ENSG00000122406, ENSG00000174444, ENSG00000142676, ENSG00000142534, ENSG00000008988, ENSG00000221983, ENSG00000145592
ENSG00000138326, ENSG00000130255, ENSG00000065978, ENSG00000171858, ENSG00000136942, ENSG00000118181, ENSG00000089009, ENSG00000182774, ENSG00000063177, ENSG00000137154
PC_ 2
Positive: ENSG00000091831, ENSG00000129514, ENSG00000004478, ENSG00000107485, ENSG00000104447, ENSG00000151892, ENSG00000106537, ENSG00000136068, ENSG00000109436, ENSG00000183779
ENSG00000134759, ENSG00000115648, ENSG00000096384, ENSG00000102316, ENSG00000138696, ENSG00000113739, ENSG00000139644, ENSG00000065361, ENSG00000123124, ENSG00000167978
ENSG00000081479, ENSG00000251562, ENSG00000074410, ENSG00000160862, ENSG00000124145, ENSG00000108953, ENSG00000185630, ENSG00000110092, ENSG00000076554, ENSG00000075275
Negative: ENSG00000163430, ENSG00000087245, ENSG00000204262, ENSG00000035862, ENSG00000164692, ENSG00000163359, ENSG00000130635, ENSG00000113140, ENSG00000168542, ENSG00000108821
ENSG00000101825, ENSG00000011465, ENSG00000142173, ENSG00000186340, ENSG00000038427, ENSG00000139329, ENSG00000166033, ENSG00000106333, ENSG00000166147, ENSG00000157227
ENSG00000142156, ENSG00000123384, ENSG00000106624, ENSG00000182492, ENSG00000169604, ENSG00000133110, ENSG00000111799, ENSG00000060718, ENSG00000123500, ENSG00000145423
PC_ 3
Positive: ENSG00000145741, ENSG00000186468, ENSG00000164587, ENSG00000141753, ENSG00000162244, ENSG00000143878, ENSG00000115648, ENSG00000107485, ENSG00000197958, ENSG00000100316
ENSG00000196531, ENSG00000148303, ENSG00000174748, ENSG00000109475, ENSG00000170889, ENSG00000198034, ENSG00000129514, ENSG00000204628, ENSG00000188846, ENSG00000063177
ENSG00000163682, ENSG00000137818, ENSG00000071082, ENSG00000012660, ENSG00000142541, ENSG00000065361, ENSG00000139644, ENSG00000111057, ENSG00000133112, ENSG00000075275
Negative: ENSG00000103257, ENSG00000152558, ENSG00000147065, ENSG00000115415, ENSG00000146731, ENSG00000112096, ENSG00000253729, ENSG00000065978, ENSG00000094755, ENSG00000143321
ENSG00000138755, ENSG00000196230, ENSG00000104419, ENSG00000074800, ENSG00000166598, ENSG00000140545, ENSG00000102144, ENSG00000105220, ENSG00000117632, ENSG00000084207
ENSG00000142949, ENSG00000111716, ENSG00000083444, ENSG00000136235, ENSG00000090382, ENSG00000173898, ENSG00000130066, ENSG00000164754, ENSG00000146648, ENSG00000115053
PC_ 4
Positive: ENSG00000152583, ENSG00000159388, ENSG00000156508, ENSG00000120738, ENSG00000071967, ENSG00000170345, ENSG00000104332, ENSG00000150593, ENSG00000175061, ENSG00000265972
ENSG00000122406, ENSG00000109475, ENSG00000091986, ENSG00000132465, ENSG00000175899, ENSG00000137154, ENSG00000185650, ENSG00000198755, ENSG00000167978, ENSG00000111716
ENSG00000198034, ENSG00000071082, ENSG00000049540, ENSG00000143947, ENSG00000118523, ENSG00000133112, ENSG00000125730, ENSG00000118181, ENSG00000163682, ENSG00000112306
Negative: ENSG00000109062, ENSG00000178719, ENSG00000172757, ENSG00000185624, ENSG00000106211, ENSG00000117450, ENSG00000039068, ENSG00000111057, ENSG00000149925, ENSG00000170421
ENSG00000165949, ENSG00000092841, ENSG00000113719, ENSG00000143761, ENSG00000096384, ENSG00000167004, ENSG00000108679, ENSG00000108829, ENSG00000126709, ENSG00000080824
ENSG00000125534, ENSG00000067225, ENSG00000004478, ENSG00000171345, ENSG00000141367, ENSG00000164924, ENSG00000184009, ENSG00000167642, ENSG00000102144, ENSG00000135404
PC_ 5
Positive: ENSG00000253729, ENSG00000080824, ENSG00000110321, ENSG00000113494, ENSG00000111371, ENSG00000102144, ENSG00000164754, ENSG00000164924, ENSG00000142949, ENSG00000109971
ENSG00000070756, ENSG00000104408, ENSG00000096696, ENSG00000146731, ENSG00000096384, ENSG00000144381, ENSG00000009307, ENSG00000147676, ENSG00000198363, ENSG00000272398
ENSG00000260032, ENSG00000147604, ENSG00000112378, ENSG00000104341, ENSG00000076554, ENSG00000141367, ENSG00000169604, ENSG00000123500, ENSG00000064651, ENSG00000115221
Negative: ENSG00000204592, ENSG00000166710, ENSG00000204525, ENSG00000234745, ENSG00000019582, ENSG00000206503, ENSG00000231389, ENSG00000196126, ENSG00000204287, ENSG00000142089
ENSG00000185885, ENSG00000126709, ENSG00000157601, ENSG00000165949, ENSG00000130203, ENSG00000160932, ENSG00000102265, ENSG00000138755, ENSG00000101439, ENSG00000115415
ENSG00000030582, ENSG00000108679, ENSG00000135404, ENSG00000125730, ENSG00000205542, ENSG00000185499, ENSG00000182326, ENSG00000140264, ENSG00000160182, ENSG00000167004
Computing nearest neighbor graph
Computing SNN
Quitting from lines 470-489 (introduction.Rmd)
Error: processing vignette 'introduction.Rmd' failed with diagnostics:
invalid class "Graph" object: superclass "Mnumeric" not defined in the environment of the object's class
--- failed re-building ‘introduction.Rmd’
--- re-building ‘manuscript_differential_transcript_abundance.Rmd’ using knitr
Joining, by = "sample"
Warning in aggregate_duplicated_transcripts_bulk(.data, .sample = !!.sample, :
tidybulk says: for aggregation, factors and logical columns were converted to character
Converted to characters
factorfactorfactorfactor
No group or design set. Assuming all samples belong to one group.
tidybulk says: to access the raw results do `attr(..., "internals")$MDS`
Registered S3 method overwritten by 'GGally':
method from
+.gg ggplot2
Found2batches
Adjusting for1covariate(s) or covariate level(s)
Standardizing Data across genes
Fitting L/S model and finding priors
Finding parametric adjustments
Adjusting the Data
tidybulk says: to access the raw results do `attr(..., "internals")$MDS`
=====================================
tidybulk says: All testing methods use raw counts, irrespective of if scale_abundance
or adjust_abundance have been calculated. Therefore, it is essential to add covariates
such as batch effects (if applicable) in the formula.
=====================================
tidybulk says: The design column names are "(Intercept), conditionuntreated, typesingle-read"
tidybulk says: to access the raw results (fitted GLM) do `attr(..., "internals")$edgeR`
Warning: Using size for a discrete variable is not advised.
Warning: Using alpha for a discrete variable is not advised.
Warning: Removed 7390 rows containing missing values (geom_text_repel).
Joining, by = "transcript"
--- finished re-building ‘manuscript_differential_transcript_abundance.Rmd’
--- re-building ‘manuscript_transcriptional_signatures.Rmd’ using knitr
--- finished re-building ‘manuscript_transcriptional_signatures.Rmd’
SUMMARY: processing the following files failed:
‘comparison_with_base_R.Rmd’ ‘introduction.Rmd’
Error: Vignette re-building failed.
Execution halted