library(MOSuite)
#> Warning: replacing previous import 'S4Arrays::makeNindexFromArrayViewport' by
#> 'DelayedArray::makeNindexFromArrayViewport' when loading 'SummarizedExperiment'
library(dplyr)
#>
#> Attaching package: 'dplyr'
#> The following objects are masked from 'package:stats':
#>
#> filter, lag
#> The following objects are masked from 'package:base':
#>
#> intersect, setdiff, setequal, union
RENEE dataset
# replace these lines with the actual paths to your files
gene_counts_tsv <- system.file("extdata",
"RSEM.genes.expected_count.all_samples.txt.gz",
package = "MOSuite"
)
metadata_tsv <- system.file("extdata", "sample_metadata.tsv.gz",
package = "MOSuite"
)
# create multi-omic object
moo <- create_multiOmicDataSet_from_files(
sample_meta_filepath = metadata_tsv,
feature_counts_filepath = gene_counts_tsv
)
#> Rows: 58929 Columns: 6
#> ── Column specification ────────────────────────────────────────────────────────
#> Delimiter: "\t"
#> chr (2): gene_id, GeneName
#> dbl (4): KO_S3, KO_S4, WT_S1, WT_S2
#>
#> ℹ Use `spec()` to retrieve the full column specification for this data.
#> ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.
#> Rows: 4 Columns: 2
#> ── Column specification ────────────────────────────────────────────────────────
#> Delimiter: "\t"
#> chr (2): sample_id, condition
#>
#> ℹ Use `spec()` to retrieve the full column specification for this data.
#> ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.
head(moo@counts$raw)
#> # A tibble: 6 × 5
#> gene_id KO_S3 KO_S4 WT_S1 WT_S2
#> <chr> <dbl> <dbl> <dbl> <dbl>
#> 1 ENSG00000121410.11 0 0 0 0
#> 2 ENSG00000268895.5 0 0 0 0
#> 3 ENSG00000148584.15 0 0 0 0
#> 4 ENSG00000175899.14 0 0 0 0
#> 5 ENSG00000245105.3 0 0 0 0
#> 6 ENSG00000166535.20 0 0 0 0
head(moo@sample_meta)
#> # A tibble: 4 × 2
#> sample_id condition
#> <chr> <chr>
#> 1 KO_S3 knockout
#> 2 KO_S4 knockout
#> 3 WT_S1 wildtype
#> 4 WT_S2 wildtype
head(moo@annotation)
#> # A tibble: 6 × 2
#> gene_id GeneName
#> <chr> <chr>
#> 1 ENSG00000121410.11 A1BG
#> 2 ENSG00000268895.5 A1BG-AS1
#> 3 ENSG00000148584.15 A1CF
#> 4 ENSG00000175899.14 A2M
#> 5 ENSG00000245105.3 A2M-AS1
#> 6 ENSG00000166535.20 A2ML1
moo <- moo |>
clean_raw_counts() |>
filter_counts(
group_colname = "condition",
label_colname = "sample_id",
minimum_count_value_to_be_considered_nonzero = 1,
minimum_number_of_samples_with_nonzero_counts_in_total = 1,
minimum_number_of_samples_with_nonzero_counts_in_a_group = 1,
) |>
normalize_counts(
group_colname = "condition",
label_colname = "sample_id"
) |>
diff_counts(
covariates_colnames = "condition",
contrast_colname = "condition",
contrasts = c("knockout-wildtype")
) |>
filter_diff(
significance_cutoff = 0.05,
significance_column = "adjpval",
change_column = "logFC",
change_cutoff = 1
)
#> Saving 7.29 x 4.51 in image
#> * cleaning raw counts
#>
#> Not able to identify multiple id's in gene_id
#>
#> Columns that can be used to aggregate gene information gene_id
#>
#> Aggregating the counts for the same ID in different chromosome locations.
#> Column used to Aggregate duplicate IDs: gene_id
#> Number of rows before Collapse: 58929
#>
#> no duplicated IDs in gene_id
#>
#> * filtering clean counts
#>
#> Number of features after filtering: 291
#>
#> colors_for_plots NULL
#>
#> colors_for_plots character
#>
#> Saving 7.29 x 4.51 in image
#> Saving 7.29 x 4.51 in image
#> * normalizing filt counts
#>
#> Total number of features included: 291
#>
#> Saving 7.29 x 4.51 in image
#> Saving 7.29 x 4.51 in image
#> Sample columns: KO_S3, Sample columns: KO_S4, Sample columns: WT_S1, Sample columns: WT_S2
#>
#> * differential counts
#>
#> Setting first column of `counts` as gene annotation.
#>
#> Total number of genes included: 291
#>
#> Saving 7.29 x 4.51 in image
#> `geom_smooth()` using method = 'loess' and formula = 'y ~ x'
#> * filtering differential features
#>
#> Total number of genes selected with adjpval < 0.05 and | logFC | ≥ 1 is sum(selgenes)
#>
#> Saving 7.29 x 4.51 in image
moo@counts$norm$voom |> head()
#> gene_id KO_S3 KO_S4 WT_S1 WT_S2
#> 1 ENSG00000215458.8 11.075196 12.348091 8.816153 10.004874
#> 2 ENSG00000160179.18 9.608634 12.770317 12.348091 12.236996
#> 3 ENSG00000258017.1 9.608634 8.816153 8.816153 8.816153
#> 4 ENSG00000282393.1 8.816153 9.608634 8.816153 8.816153
#> 5 ENSG00000286104.1 9.608634 8.816153 8.816153 8.816153
#> 6 ENSG00000274422.1 8.816153 9.608634 8.816153 8.816153
The multiOmicDataSet object structure
str(moo)
#> <MOSuite::multiOmicDataSet>
#> @ sample_meta: spc_tbl_ [4 × 2] (S3: spec_tbl_df/tbl_df/tbl/data.frame)
#> $ sample_id: chr [1:4] "KO_S3" "KO_S4" "WT_S1" "WT_S2"
#> $ condition: chr [1:4] "knockout" "knockout" "wildtype" "wildtype"
#> - attr(*, "spec")=
#> .. cols(
#> .. sample_id = col_character(),
#> .. condition = col_character()
#> .. )
#> - attr(*, "problems")=<externalptr>
#> @ annotation : tibble [58,929 × 2] (S3: tbl_df/tbl/data.frame)
#> $ gene_id : chr [1:58929] "ENSG00000121410.11" "ENSG00000268895.5" "ENSG00000148584.15" "ENSG00000175899.14" ...
#> $ GeneName: chr [1:58929] "A1BG" "A1BG-AS1" "A1CF" "A2M" ...
#> @ counts :List of 4
#> .. $ raw : tibble [58,929 × 5] (S3: tbl_df/tbl/data.frame)
#> .. ..$ gene_id: chr [1:58929] "ENSG00000121410.11" "ENSG00000268895.5" "ENSG00000148584.15" "ENSG00000175899.14" ...
#> .. ..$ KO_S3 : num [1:58929] 0 0 0 0 0 0 0 0 0 0 ...
#> .. ..$ KO_S4 : num [1:58929] 0 0 0 0 0 0 0 0 0 0 ...
#> .. ..$ WT_S1 : num [1:58929] 0 0 0 0 0 0 0 0 0 0 ...
#> .. ..$ WT_S2 : num [1:58929] 0 0 0 0 0 0 0 0 0 0 ...
#> .. $ clean:'data.frame': 58929 obs. of 5 variables:
#> .. ..$ gene_id: chr [1:58929] "ENSG00000121410.11" "ENSG00000268895.5" "ENSG00000148584.15" "ENSG00000175899.14" ...
#> .. ..$ KO_S3 : num [1:58929] 0 0 0 0 0 0 0 0 0 0 ...
#> .. ..$ KO_S4 : num [1:58929] 0 0 0 0 0 0 0 0 0 0 ...
#> .. ..$ WT_S1 : num [1:58929] 0 0 0 0 0 0 0 0 0 0 ...
#> .. ..$ WT_S2 : num [1:58929] 0 0 0 0 0 0 0 0 0 0 ...
#> .. $ filt :'data.frame': 291 obs. of 5 variables:
#> .. ..$ gene_id: chr [1:291] "ENSG00000215458.8" "ENSG00000160179.18" "ENSG00000258017.1" "ENSG00000282393.1" ...
#> .. ..$ KO_S3 : num [1:291] 2 1 1 0 1 0 0 0 3 0 ...
#> .. ..$ KO_S4 : num [1:291] 4 5 0 1 0 1 0 0 3 0 ...
#> .. ..$ WT_S1 : num [1:291] 0 6 0 0 0 0 46 33 9 0 ...
#> .. ..$ WT_S2 : num [1:291] 1 7 0 0 0 0 80 31 11 1 ...
#> .. $ norm :List of 1
#> .. ..$ voom:'data.frame': 291 obs. of 5 variables:
#> .. .. ..$ gene_id: chr [1:291] "ENSG00000215458.8" "ENSG00000160179.18" "ENSG00000258017.1" "ENSG00000282393.1" ...
#> .. .. ..$ KO_S3 : num [1:291] 11.08 9.61 9.61 8.82 9.61 ...
#> .. .. ..$ KO_S4 : num [1:291] 12.35 12.77 8.82 9.61 8.82 ...
#> .. .. ..$ WT_S1 : num [1:291] 8.82 12.35 8.82 8.82 8.82 ...
#> .. .. ..$ WT_S2 : num [1:291] 10 12.24 8.82 8.82 8.82 ...
#> @ analyses :List of 3
#> .. $ colors :List of 2
#> .. ..$ sample_id: Named chr [1:4] "#000000" "#E69F00" "#56B4E9" "#009E73"
#> .. .. ..- attr(*, "names")= chr [1:4] "KO_S3" "KO_S4" "WT_S1" "WT_S2"
#> .. ..$ condition: Named chr [1:2] "#000000" "#E69F00"
#> .. .. ..- attr(*, "names")= chr [1:2] "knockout" "wildtype"
#> .. $ diff :List of 1
#> .. ..$ knockout-wildtype:'data.frame': 291 obs. of 6 variables:
#> .. .. ..$ gene_id: chr [1:291] "ENSG00000215458.8" "ENSG00000160179.18" "ENSG00000258017.1" "ENSG00000282393.1" ...
#> .. .. ..$ FC : num [1:291] 5.02 -2.14 1.32 1.32 1.32 ...
#> .. .. ..$ logFC : num [1:291] 2.327 -1.094 0.396 0.396 0.396 ...
#> .. .. ..$ tstat : num [1:291] 3.098 -0.948 0.945 0.945 0.945 ...
#> .. .. ..$ pval : num [1:291] 0.0596 0.4186 0.4196 0.4196 0.4196 ...
#> .. .. ..$ adjpval: num [1:291] 0.175 0.49 0.49 0.49 0.49 ...
#> .. $ diff_filt:'data.frame': 58 obs. of 6 variables:
#> .. ..$ gene_id : chr [1:58] "ENSG00000154734.15" "ENSG00000154736.6" "ENSG00000232855.6" "ENSG00000231324.1" ...
#> .. ..$ knockout-wildtype_FC : num [1:58] -54.2 -35.6 -7.21 -2.76 6.7 -28.4 -6.43 -27.8 -5.6 6.25 ...
#> .. ..$ knockout-wildtype_logFC : num [1:58] -5.76 -5.15 -2.85 -1.46 2.74 -4.83 -2.69 -4.8 -2.49 2.64 ...
#> .. ..$ knockout-wildtype_tstat : num [1:58] -89.9 -26.5 -13.6 -6.39 10.5 -7.27 -12.4 -15.7 -10.4 8.19 ...
#> .. ..$ knockout-wildtype_pval : num [1:58] 7.21e-06 2.10e-04 1.30e-03 9.94e-03 2.67e-03 7.08e-03 1.68e-03 8.81e-04 2.72e-03 5.14e-03 ...
#> .. ..$ knockout-wildtype_adjpval: num [1:58] 0.00119 0.00595 0.0145 0.0402 0.0208 0.0326 0.0158 0.0117 0.0208 0.0245 ...
