The wranglr package contains general functions necessary to manipulate
and wrangle internal R representations of proteomic data into
convenient forms for analysis.
# current dev version
remotes::install_github("stufield/wranglr")
# or a specific version
remotes::install_github("stufield/[email protected]")To load wranglr simply make a call to library() as usual:
library(wranglr)library(help = wranglr)center_scale()create_recipe()
scaled <- center_scale(mtcars)
apply(feature_matrix(scaled), 2, mean) |> sum() # mean = 0
#> [1] 3.410697e-16
apply(feature_matrix(scaled), 2, sd) # sd = 1
#> mpg cyl disp hp drat wt qsec vs am gear carb
#> 1 1 1 1 1 1 1 1 1 1 1
# `create_recipe()`
rcp <- create_recipe(mtcars)
rcp
#> ══ Pre-processing recipe ═══════════════════════════════════════════════════════════════════════════
#>
#> ─── Training data:
#> ✓ Data containing 32 samples used in recipe
#> ✓ RFU features ( n = 11 ) will be processed by:
#>
#> ─── Steps:
#> • log10-transformed ✓
#> • Centered (mean = 0) ✓
#> • Scaled (sd = 1) ✓
#>
#> ════════════════════════════════════════════════════════════════════════════════════════════════════impute_outliers(),get_outliers()(helpr)imputeNAs()impute_predictors()
# Outliers
x <- withr::with_seed(1, rnorm(10)) # normal
x <- c(x, 100) # add outlier
get_outliers(x) # index of the outlier
#> [1] 11
# parameters stored in attributes (parametric only)
attributes(get_outliers(x, type = "para"))
#> $mu
#> [1] 0.1157106
#>
#> $sigma
#> [1] 0.9440787
#>
#> $crit
#> [1] -2.716526 2.947947
# Impute 11th value
impute_outliers(x)
#> [1] -0.6264538 0.1836433 -0.8356286 1.5952808 0.3295078 -0.8204684 0.4874291 0.7383247
#> [9] 0.5757814 -0.3053884 2.9479467
# Impute NAs
x <- withr::with_seed(1, rnorm(6))
x[ c(3, 5) ] <- NA
median(x, na.rm = TRUE)
#> [1] -0.2214052
imputeNAs(x)
#> [1] -0.6264538 0.1836433 -0.2214052 1.5952808 -0.2214052 -0.8204684
table(imputeNAs(x))
#>
#> -0.820468384118015 -0.626453810742332 -0.221405243260125 0.183643324222082 1.59528080213779
#> 1 1 2 1 1
# Predictors
x <- data.frame(a = 1:3, b = 4:6, c = 7:9, d = c(1.23, 4.56, 7.89))
tbl <- tibble::tribble(
~ Feature, ~ xtrm_max, ~ impute_max, ~ xtrm_min, ~ impute_min,
"a", NA, NA, NA, NA,
"b", 5, 5, 0, 1,
"c", 9, 7, 7.1, 7.1
)
impute_predictors(x, tbl)
#> a b c d
#> 1 1 4 7.1 1.23
#> 2 2 5 8.0 4.56
#> 3 3 5 9.0 7.89bind_intersect()bind_union()
df1 <- data.frame(a = 1, b = 2, c = 3, row.names = "A")
df2 <- data.frame(a = 4, b = 5, d = 6, row.names = "B")
df3 <- data.frame(a = 7, b = 8, e = 9, row.names = "C")
list_df <- list(a = df1, b = df2, c = df3)
list_df
#> $a
#> a b c
#> A 1 2 3
#>
#> $b
#> a b d
#> B 4 5 6
#>
#> $c
#> a b e
#> C 7 8 9
bind_intersect(list_df)
#> data a b
#> A a 1 2
#> B b 4 5
#> C c 7 8
bind_union(list_df)
#> data a b c d e
#> A a 1 2 3 NA NA
#> B b 4 5 NA 6 NA
#> C c 7 8 NA NA 9refactor_data()feature_matrix()
df <- data.frame(a = factor(c("a", "b")), b = 1:2L)
foo <- df[df$a == "a", ]
foo
#> a b
#> 1 a 1
levels(foo$a) # 2 levels! "b" is a ghost level
#> [1] "a" "b"
bar <- refactor_data(foo)
levels(bar$a) # 1 level now
#> [1] "a"lookup_anno()seq_lookup()seqify()
seqs <- withr::with_seed(101, sample(names(sample_df), 10L))
seqs
#> [1] "seq.4500.50" "seq.2654.19" "seq.4993.16" "seq.3074.6" "seq.4721.54" "seq.3516.60"
#> [7] "seq.3194.36" "seq.3197.70" "seq.3448.13" "seq.4719.58"
# NAs for those analytes dropped from menu
seq_lookup(seqs)
#> # A tibble: 10 × 9
#> seq SeqId EntrezGeneSymbol Target TargetFullName Dilution UniProt List Reason
#> <chr> <chr> <chr> <chr> <chr> <chr> <chr> <chr> <chr>
#> 1 seq.4500.50 4500-50 CLEC11A SCGF-alpha Stem cell gro… 0.5% Q9Y240 "" ""
#> 2 seq.2654.19 2654-19 TNFRSF1A TNF sR-I Tumor necrosi… 20% P19438 "" ""
#> 3 seq.4993.16 4993-16 GSTA3 GSTA3 Glutathione S… 20% Q16772 "" ""
#> 4 seq.3074.6 3074-6 LBP LBP Lipopolysacch… 0.005% P18428 "" ""
#> 5 seq.4721.54 4721-54 TFF3 TFF3 Trefoil facto… 0.5% Q07654 "" ""
#> 6 seq.3516.60 3516-60 CXCL12 SDF-1 Stromal cell-… 20% P48061 "" ""
#> 7 seq.3194.36 3194-36 GP6 GPVI Platelet glyc… 0.5% Q9HCN6 "" ""
#> 8 seq.3197.70 3197-70 IDE IDE Insulin-degra… 20% P14735 "" ""
#> 9 seq.3448.13 3448-13 INSR IR Insulin recep… 20% P06213 "" ""
#> 10 seq.4719.58 4719-58 PDIA3 Protein disulf… Protein disul… 0.5% P30101 "" ""
seqify(seqs)
#> ══ SeqId Lookup ════════════════════════════════════════════════════════════════════════════════════
#> SeqId-Feature GeneID Target List Reason
#> ────────────────────────────────────────────────────────────────────────────────────────────────────
#> ▶ seq.4500.50 ❯ CLEC11A ❯ Stem cell growth factor-alpha ❯ ❯
#> ▶ seq.2654.19 ❯ TNFRSF1A ❯ Tumor necrosis factor receptor superfamily member 1A ❯ ❯
#> ▶ seq.4993.16 ❯ GSTA3 ❯ Glutathione S-transferase A3 ❯ ❯
#> ▶ seq.3074.6 ❯ LBP ❯ Lipopolysaccharide-binding protein ❯ ❯
#> ▶ seq.4721.54 ❯ TFF3 ❯ Trefoil factor 3 ❯ ❯
#> ▶ seq.3516.60 ❯ CXCL12 ❯ Stromal cell-derived factor 1 ❯ ❯
#> ▶ seq.3194.36 ❯ GP6 ❯ Platelet glycoprotein VI ❯ ❯
#> ▶ seq.3197.70 ❯ IDE ❯ Insulin-degrading enzyme ❯ ❯
#> ▶ seq.3448.13 ❯ INSR ❯ Insulin receptor ❯ ❯
#> ▶ seq.4719.58 ❯ PDIA3 ❯ Protein disulfide-isomerase A3 ❯ ❯
# Pass `tbl` containing annotations
# to reconstitute those missing ones
anno <- attr(sample_df, "anno")
seq_lookup(seqs, tbl = anno)
#> # A tibble: 10 × 5
#> seq SeqId EntrezGeneSymbol Target TargetFullName
#> <chr> <chr> <chr> <chr> <chr>
#> 1 seq.4500.50 4500-50 CLEC11A SCGF-alpha Stem Cell Growth Factor-alpha
#> 2 seq.2654.19 2654-19 TNFRSF1A TNF sR-I Tumor necrosis factor recept…
#> 3 seq.4993.16 4993-16 GSTA3 GSTA3 Glutathione S-transferase A3
#> 4 seq.3074.6 3074-6 LBP LBP Lipopolysaccharide-binding p…
#> 5 seq.4721.54 4721-54 TFF3 TFF3 Trefoil factor 3
#> 6 seq.3516.60 3516-60 CXCL12 SDF-1 Stromal cell-derived factor 1
#> 7 seq.3194.36 3194-36 GP6 GPVI Platelet glycoprotein VI
#> 8 seq.3197.70 3197-70 IDE IDE Insulin-degrading enzyme
#> 9 seq.3448.13 3448-13 INSR IR Insulin receptor
#> 10 seq.4719.58 4719-58 PDIA3 Protein disulfide isomerase A3 Protein disulfide-isomerase …