Hi, I use fishpond for differential genes and transcripts analysis. But I didn't find the expanation about results in manual, for example, log10mean. Does log10mean stand for log10(InfRV) (inferential relative variance )? What's more, I caculated the mean of readcount or TMP, which is quantified by salmon, but the mean of readcount or TMP is not equal to log10mean.

> se
class: RangedSummarizedExperiment
dim: 137934 10
metadata(6): tximetaInfo quantInfo ... txomeInfo txdbInfo
assays(23): counts abundance ... infRep19 infRep20
rownames(137934): ENSMUST00000177564 ENSMUST00000196221 ...
  ENSMUST00000158369 ENSMUST00000183553
rowData names(9): tx_id tx_biotype ... gc_content tx_name
colnames(10): SRR6868519 SRR6868520 ... SRR6868527 SRR6868528
colData names(8): names BioSample ... surgery X
> y
class: RangedSummarizedExperiment
dim: 47187 10
metadata(8): tximetaInfo quantInfo ... infRepsScaled preprocessed
assays(23): counts abundance ... infRep19 infRep20
rownames(47187): ENSMUST00000174625 ENSMUST00000174382 ...
  ENSMUST00000175508 ENSMUST00000177296
rowData names(16): tx_id tx_biotype ... locfdr qvalue
colnames(10): SRR6868519 SRR6868520 ... SRR6868527 SRR6868528
colData names(8): names BioSample ... surgery X

> tmp<-assays(se)$count
> tmp<-data.frame(tmp)
> tmp$mean<-log10(rowMeans(tmp))
> tmp["ENSMUST00000129980",]
                   SRR6868519 SRR6868520 SRR6868521 SRR6868522 SRR6868523
ENSMUST00000129980          0          0          0      7.725          0
                   SRR6868524 SRR6868525 SRR6868526 SRR6868527 SRR6868528
ENSMUST00000129980     12.051     13.597      5.245     14.685      5.025
                        mean
ENSMUST00000129980 **0.7658771**
> mcols(y)["ENSMUST00000129980",]
DataFrame with 1 row and 16 columns
                                tx_id  tx_biotype tx_cds_seq_start
                          <character> <character>        <integer>
ENSMUST00000129980 ENSMUST00000129980      lncRNA               NA
                   tx_cds_seq_end            gene_id tx_support_level
                        <integer>        <character>        <integer>
ENSMUST00000129980             NA ENSMUSG00000087396                5
                          tx_id_version gc_content            tx_name log10mean
                            <character>  <numeric>        <character> <numeric>
ENSMUST00000129980 ENSMUST00000129980.7    46.1786 ENSMUST00000129980  **0.927711**
                        keep      stat    log2FC     pvalue    locfdr    qvalue
                   <logical> <numeric> <numeric>  <numeric> <numeric> <numeric>
ENSMUST00000129980      TRUE     10.15   1.11901 0.00464725  0.135291 0.0572111

> sessionInfo()
R version 4.0.0 (2020-04-24)
Platform: x86_64-conda_cos6-linux-gnu (64-bit)
Running under: CentOS Linux 7 (Core)

Matrix products: default
BLAS/LAPACK: /home2/ymwang/miniconda3/envs/R40/lib/libopenblasp-r0.3.9.so

locale:
 [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C
 [3] LC_TIME=en_US.UTF-8        LC_COLLATE=en_US.UTF-8
 [5] LC_MONETARY=en_US.UTF-8    LC_MESSAGES=en_US.UTF-8
 [7] LC_PAPER=en_US.UTF-8       LC_NAME=C
 [9] LC_ADDRESS=C               LC_TELEPHONE=C
[11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C

attached base packages:
[1] parallel  stats4    stats     graphics  grDevices utils     datasets
[8] methods   base

other attached packages:
 [1] SummarizedExperiment_1.18.1 DelayedArray_0.14.0
 [3] matrixStats_0.56.0          Biobase_2.48.0
 [5] GenomicRanges_1.40.0        GenomeInfoDb_1.24.0
 [7] IRanges_2.22.2              S4Vectors_0.26.1
 [9] BiocGenerics_0.34.0         fishpond_1.4.1
[11] tximeta_1.6.2

loaded via a namespace (and not attached):
 [1] httr_1.4.1                    splines_4.0.0
 [3] bit64_0.9-7                   jsonlite_1.6.1
 [5] AnnotationHub_2.20.0          gtools_3.8.2
 [7] shiny_1.4.0.2                 assertthat_0.2.1
 [9] interactiveDisplayBase_1.26.3 askpass_1.1
[11] BiocManager_1.30.10           BiocFileCache_1.12.0
[13] blob_1.2.1                    GenomeInfoDbData_1.2.3
[15] Rsamtools_2.4.0               yaml_2.2.1
[17] progress_1.2.2                BiocVersion_3.11.1
[19] pillar_1.4.4                  RSQLite_2.2.0
[21] lattice_0.20-41               glue_1.4.1
[23] digest_0.6.25                 promises_1.1.1
[25] XVector_0.28.0                qvalue_2.20.0
[27] colorspace_1.4-1              plyr_1.8.6
[29] htmltools_0.4.0               httpuv_1.5.4
[31] Matrix_1.2-18                 XML_3.99-0.3
[33] pkgconfig_2.0.3               biomaRt_2.44.0
[35] zlibbioc_1.34.0               purrr_0.3.4
[37] xtable_1.8-4                  scales_1.1.1
[39] later_1.1.0.1                 BiocParallel_1.22.0
[41] tibble_3.0.1                  openssl_1.4.1
[43] ggplot2_3.3.1                 generics_0.0.2
[45] AnnotationFilter_1.12.0       ellipsis_0.3.1
[47] GenomicFeatures_1.40.0        lazyeval_0.2.2
[49] magrittr_1.5                  crayon_1.3.4
[51] mime_0.9                      memoise_1.1.0
[53] tools_4.0.0                   prettyunits_1.1.1
[55] hms_0.5.3                     lifecycle_0.2.0
[57] stringr_1.4.0                 munsell_0.5.0
[59] AnnotationDbi_1.50.0          ensembldb_2.12.1
[61] Biostrings_2.56.0             svMisc_1.1.0
[63] compiler_4.0.0                rlang_0.4.6
[65] grid_4.0.0                    RCurl_1.98-1.2
[67] tximport_1.16.1               rappdirs_0.3.1
[69] bitops_1.0-6                  gtable_0.3.0
[71] abind_1.4-5                   DBI_1.1.0
[73] curl_4.3                      reshape2_1.4.4
[75] R6_2.4.1                      GenomicAlignments_1.24.0
[77] dplyr_1.0.0                   rtracklayer_1.48.0
[79] fastmap_1.0.1                 bit_1.1-15.2
[81] ProtGenerics_1.20.0           stringi_1.4.6
[83] Rcpp_1.0.4.6                  vctrs_0.3.1
[85] dbplyr_1.4.4                  tidyselect_1.1.0
>

Best, Ci

Hi Ci,

log10mean is the log10 of the mean of scaled counts across all inferential replicates and all samples. So you could manually calculate this after scaleInfReps by calculating the rowMean of each infRep assay, and then taking the global mean. I'll add this to the ?scaleInfReps man page.