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* checking for file ‘tidybulk/DESCRIPTION’ ... OK
* preparing ‘tidybulk’:
* checking DESCRIPTION meta-information ... OK
* installing the package to process help pages
* building the PDF package manual
Hmm ... looks like a package
Converting Rd files to LaTeX ............
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[]\T1/ptm/m/n/10 aggregate_duplicates() 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 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 tidybulk::se_mini %>% tidybulk() %>% select(feature, count) %>
% head %>% as_matrix(rownames=feature)[] 
[9] [10] [11]
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[13] [14] [15] [16]
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 []\T1/zi4/m/n/9 tidybulk::se_mini %>% tidybulk() %>% filter(sample=="SRR174003
4") %>% deconvolve_cellularity(sample, feature, count, cores = 1)[] 
[17] [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]
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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,
[27] [28] [29]
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 []\T1/zi4/m/n/9 annotation = tidybulk::counts_SE %>% tidybulk() %>% as_tibble(
) %>% distinct(sample) %>% mutate(source = "AU")[] 

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 []\T1/zi4/m/n/9 annotation = tidybulk::counts_SE %>% tidybulk() %>% as_tibble(
) %>% distinct(sample) %>% mutate(source = "AU")[] 

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 []\T1/zi4/m/n/9 annotation = tidybulk::counts_SE %>% tidybulk() %>% as_tibble(
) %>% distinct(sample) %>% mutate(source = "AU")[] 
[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,
[32] [33] [34]
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 []\T1/zi4/m/n/9 annotation = tidybulk::counts_SE %>% tidybulk() %>% as_tibble(
) %>% distinct(sample) %>% mutate(source = "AU")[] 
[35] [36] [37] [38] [39] [40]
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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
[44]
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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)[] 
[50] [51] [52]
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[]\T1/ptm/m/n/10 Underlying method edgeR::calcNormFactors(.data, method = c("TM
M","TMMwsp","RLE","upperquartile")) 
[53] [54] [55]
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 []\T1/zi4/m/n/9 tidybulk::se_mini %>% tidybulk() %>% as_tibble() %>% symbol_to
_entrez(.transcript = feature, .sample = sample)[] 
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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/ptm/m/n/10 (i.e., LRT), "edger_robust_likelihood_ratio", "DE-Seq2", "limma_
voom", "limma_voom_sample_weights" 

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\T1/ptm/m/n/10 and limma-voom (i.e., edgeR::calcNormFactors; "TMM","TMMwsp","RL
E","upperquartile").
[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 , li
mma_voom_sample_weights

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[][]$\T1/zi4/m/n/10 https : / / doi . org / 10 . 1093 / nar / gkv412$[][] \T1/p
tm/m/n/10 or DE-Seq2 [][]$\T1/zi4/m/n/10 https : / / doi . org / 10 . 1186 / s1
3059-[]014-[]0550-[]8$[][]

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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 forms: mul-ti-vari-able (rec-om-mended) or uni-vari-able Re-spec
-tively: \"fac-tor_of_interest

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[]\T1/ptm/m/n/10 A string char-ac-ter. Ei-ther \"ciber-sort\", \"epic\" or \"ll
sr\". The re-gres-sion method

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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 This wrap-per ex-e-cutes en-sem-ble gene en-rich-ment anal-y-s
es 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/ptm/m/n/10 idx = buil-dIdx(entrezIDs = row-names(dge), species = species,
 msigdb.gsets = msigdb.gsets, kegg.exclude
[67]
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 []\T1/zi4/m/n/9 df_entrez = tidybulk::se_mini %>% tidybulk() %>% as_tibble() %
>% symbol_to_entrez( .transcript = feature, .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 gene_sets = c("h", "c1", "c2", "c3", "c4", "c5", "c6"
, "c7", "kegg_disease", "kegg_metabolism", "kegg_signaling"),[] 
[68] [69]
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 []\T1/zi4/m/n/9 df_entrez = tidybulk::se_mini %>% tidybulk() %>% as_tibble() %
>% symbol_to_entrez( .transcript = feature, .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 = feature %in% c("TNFRSF
4", "PLCH2", "PADI4", "PAX7"))[] 
[70] [71]
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[]\T1/ptm/m/n/10 # Ex-e-cute cal-cu-la-tion nest(data = -gs_cat) mu-tate(fit = 
map( data, ~ clus-ter-Pro-filer::GSEA( my_entrez_rank,
[72]
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 []\T1/zi4/m/n/9 df_entrez = tidybulk::se_mini %>% tidybulk() %>% as_tibble() %
>% symbol_to_entrez( .transcript = feature, .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 = feature %in% c("TNFRSF
4", "PLCH2", "PADI4", "PAX7"))[] 
[73]
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\T1/ptm/m/n/10 forms: mul-ti-vari-able (rec-om-mended) or uni-vari-able Re-spec
-tively: \"fac-tor_of_interest

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[]\T1/ptm/m/n/10 A string char-ac-ter. Ei-ther \"ciber-sort\", \"epic\" or \"ll
sr\". The re-gres-sion method
[75]
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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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[]\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 tidybulk::se_mini %>% tidybulk() %>% select(feature, count) %>
% head %>% as_matrix(rownames=feature)[] 
[10] [11] [12] [13] [14] [15] [16] [17]
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 []\T1/zi4/m/n/9 tidybulk::se_mini %>% tidybulk() %>% filter(sample=="SRR174003
4") %>% deconvolve_cellularity(sample, feature, count, cores = 1)[] 
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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>
[20] [21] [22] [23] [24] [25]
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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>
[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]
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 []\T1/zi4/m/n/9 annotation = tidybulk::counts_SE %>% tidybulk() %>% as_tibble(
) %>% distinct(sample) %>% mutate(source = "AU")[] 

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 []\T1/zi4/m/n/9 annotation = tidybulk::counts_SE %>% tidybulk() %>% as_tibble(
) %>% distinct(sample) %>% mutate(source = "AU")[] 

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 []\T1/zi4/m/n/9 annotation = tidybulk::counts_SE %>% tidybulk() %>% as_tibble(
) %>% distinct(sample) %>% mutate(source = "AU")[] 
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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]
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 []\T1/zi4/m/n/9 annotation = tidybulk::counts_SE %>% tidybulk() %>% as_tibble(
) %>% distinct(sample) %>% mutate(source = "AU")[] 
[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 | <
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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
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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)[] 
[51] [52] [53]
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[]\T1/ptm/m/n/10 Underlying method edgeR::calcNormFactors(.data, method = c("TM
M","TMMwsp","RLE","upperquartile")) 
[54] [55] [56]
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 []\T1/zi4/m/n/9 tidybulk::se_mini %>% tidybulk() %>% as_tibble() %>% symbol_to
_entrez(.transcript = feature, .sample = sample)[] 
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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/ptm/m/n/10 (i.e., LRT), "edger_robust_likelihood_ratio", "DE-Seq2", "limma_
voom", "limma_voom_sample_weights" 

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\T1/ptm/m/n/10 and limma-voom (i.e., edgeR::calcNormFactors; "TMM","TMMwsp","RL
E","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 , li
mma_voom_sample_weights

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[][]$\T1/zi4/m/n/10 https : / / doi . org / 10 . 1093 / nar / gkv412$[][] \T1/p
tm/m/n/10 or DE-Seq2 [][]$\T1/zi4/m/n/10 https : / / doi . org / 10 . 1186 / s1
3059-[]014-[]0550-[]8$[][]

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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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-tively: \"fac-tor_of_interest

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[]\T1/ptm/m/n/10 A string char-ac-ter. Ei-ther \"ciber-sort\", \"epic\" or \"ll
sr\". The re-gres-sion method

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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 This wrap-per ex-e-cutes en-sem-ble gene en-rich-ment anal-y-s
es 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/ptm/m/n/10 idx = buil-dIdx(entrezIDs = row-names(dge), species = species,
 msigdb.gsets = msigdb.gsets, kegg.exclude
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 []\T1/zi4/m/n/9 df_entrez = tidybulk::se_mini %>% tidybulk() %>% as_tibble() %
>% symbol_to_entrez( .transcript = feature, .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 gene_sets = c("h", "c1", "c2", "c3", "c4", "c5", "c6"
, "c7", "kegg_disease", "kegg_metabolism", "kegg_signaling"),[] 
[69] [70]
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 []\T1/zi4/m/n/9 df_entrez = tidybulk::se_mini %>% tidybulk() %>% as_tibble() %
>% symbol_to_entrez( .transcript = feature, .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 = feature %in% c("TNFRSF
4", "PLCH2", "PADI4", "PAX7"))[] 
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[]\T1/ptm/m/n/10 # Ex-e-cute cal-cu-la-tion nest(data = -gs_cat) mu-tate(fit = 
map( data, ~ clus-ter-Pro-filer::GSEA( my_entrez_rank,
[73]
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>% symbol_to_entrez( .transcript = feature, .sample = sample)[] 

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on = sum, .sample = sample, .transcript = entrez, .abundance = count)[] 

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 []\T1/zi4/m/n/9 df_entrez = mutate(df_entrez, do_test = feature %in% c("TNFRSF
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\T1/ptm/m/n/10 forms: mul-ti-vari-able (rec-om-mended) or uni-vari-able Re-spec
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sr\". The re-gres-sion method
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[]\T1/ptm/m/n/10 tidybulk() cre-ates a `tt` ob-ject from a `tbl` for-mat-ted as
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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()]
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 []\T1/zi4/m/n/9 tidybulk::se_mini %>% tidybulk() %>% select(feature, count) %>
% head %>% as_matrix(rownames=feature)[] 
[10] [11] [12] [13] [14] [15] [16] [17]
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 []\T1/zi4/m/n/9 tidybulk::se_mini %>% tidybulk() %>% filter(sample=="SRR174003
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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>
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rop_default()]
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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]
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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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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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_entrez(.transcript = feature, .sample = sample)[] 
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QLF), "edgeR_likelihood_ratio"

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E","upperquartile").
[60]
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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 , li
mma_voom_sample_weights

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[][]$\T1/zi4/m/n/10 https : / / doi . org / 10 . 1093 / nar / gkv412$[][] \T1/p
tm/m/n/10 or DE-Seq2 [][]$\T1/zi4/m/n/10 https : / / doi . org / 10 . 1186 / s1
3059-[]014-[]0550-[]8$[][]

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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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="get", ... ) [..] mu-tate(.proportion_0_corrected
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[]\T1/ptm/m/n/10 This wrap-per ex-e-cutes en-sem-ble gene en-rich-ment anal-y-s
es 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/ptm/m/n/10 idx = buil-dIdx(entrezIDs = row-names(dge), species = species,
 msigdb.gsets = msigdb.gsets, kegg.exclude
[68]
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 []\T1/zi4/m/n/9 df_entrez = tidybulk::se_mini %>% tidybulk() %>% as_tibble() %
>% symbol_to_entrez( .transcript = feature, .sample = sample)[] 

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on = sum, .sample = sample, .transcript = entrez, .abundance = count)[] 

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 []         \T1/zi4/m/n/9 gene_sets = c("h", "c1", "c2", "c3", "c4", "c5", "c6"
, "c7", "kegg_disease", "kegg_metabolism", "kegg_signaling"),[] 
[69] [70]
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 []\T1/zi4/m/n/9 df_entrez = tidybulk::se_mini %>% tidybulk() %>% as_tibble() %
>% symbol_to_entrez( .transcript = feature, .sample = sample)[] 

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on = sum, .sample = sample, .transcript = entrez, .abundance = count)[] 

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 []\T1/zi4/m/n/9 df_entrez = mutate(df_entrez, do_test = feature %in% c("TNFRSF
4", "PLCH2", "PADI4", "PAX7"))[] 
[71] [72]
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[]\T1/ptm/m/n/10 # Ex-e-cute cal-cu-la-tion nest(data = -gs_cat) mu-tate(fit = 
map( data, ~ clus-ter-Pro-filer::GSEA( my_entrez_rank,
[73]
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[]\T1/ptm/m/n/10 tidybulk() cre-ates a `tt` ob-ject from a `tbl` for-mat-ted as
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 : / / doi . org / 10 . 1186 / gb-[]2014-[]15-[]2-[]r29$[][]\T1/ptm/m/n/10 , li
mma_voom_sample_weights

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[][]$\T1/zi4/m/n/10 https : / / doi . org / 10 . 1093 / nar / gkv412$[][] \T1/p
tm/m/n/10 or DE-Seq2 [][]$\T1/zi4/m/n/10 https : / / doi . org / 10 . 1186 / s1
3059-[]014-[]0550-[]8$[][]

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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]]),
[61] [62]
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\T1/ptm/m/n/10 forms: mul-ti-vari-able (rec-om-mended) or uni-vari-able Re-spec
-tively: \"fac-tor_of_interest

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[]\T1/ptm/m/n/10 A string char-ac-ter. Ei-ther \"ciber-sort\", \"epic\" or \"ll
sr\". The re-gres-sion method

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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
[64] [65] [66]
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[]\T1/ptm/m/n/10 This wrap-per ex-e-cutes en-sem-ble gene en-rich-ment anal-y-s
es 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/ptm/m/n/10 idx = buil-dIdx(entrezIDs = row-names(dge), species = species,
 msigdb.gsets = msigdb.gsets, kegg.exclude
[68]
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 []\T1/zi4/m/n/9 df_entrez = tidybulk::se_mini %>% tidybulk() %>% as_tibble() %
>% symbol_to_entrez( .transcript = feature, .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 gene_sets = c("h", "c1", "c2", "c3", "c4", "c5", "c6"
, "c7", "kegg_disease", "kegg_metabolism", "kegg_signaling"),[] 
[69] [70]
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 []\T1/zi4/m/n/9 df_entrez = tidybulk::se_mini %>% tidybulk() %>% as_tibble() %
>% symbol_to_entrez( .transcript = feature, .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 = feature %in% c("TNFRSF
4", "PLCH2", "PADI4", "PAX7"))[] 
[71] [72]
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[]\T1/ptm/m/n/10 # Ex-e-cute cal-cu-la-tion nest(data = -gs_cat) mu-tate(fit = 
map( data, ~ clus-ter-Pro-filer::GSEA( my_entrez_rank,
[73]
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 []\T1/zi4/m/n/9 df_entrez = tidybulk::se_mini %>% tidybulk() %>% as_tibble() %
>% symbol_to_entrez( .transcript = feature, .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 = feature %in% c("TNFRSF
4", "PLCH2", "PADI4", "PAX7"))[] 
[74]
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\T1/ptm/m/n/10 forms: mul-ti-vari-able (rec-om-mended) or uni-vari-able Re-spec
-tively: \"fac-tor_of_interest

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[]\T1/ptm/m/n/10 A string char-ac-ter. Ei-ther \"ciber-sort\", \"epic\" or \"ll
sr\". The re-gres-sion method
[76]
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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>
[77] [78] [79] [80]
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