Curate vector to data.frame by pattern matching

Curate vector to data.frame by pattern matching

curate_to_df_by_pattern(
  x,
  df,
  pattern_colname = "pattern",
  group_colname = "group",
  id_colname = c("label", "sample"),
  input_colname = "filename",
  suffix = "_rep",
  renameOnes = TRUE,
  colname_hook = jamba::ucfirst,
  sep = "_",
  order_priority = c("df", "x"),
  verbose = FALSE,
  ...
)

Arguments

x

character vector of input data, often filenames used when importing data using one of the import_* functions.

df

data.frame whose first column contains character patterns, and subsequent columns contain annotations to be applied to entries in x that match a given pattern. The column that contains patterns can be specified with argument pattern_colname.

pattern_colname, group_colname, id_colname

character string indicating colname to use for patterns, group, and identifier, respectively. The group_colname and id_colname may be NULL in which case they are not used. When group_colname and id_colname are defined, then values in group_colname are used to make unique identifiers for each entry in x, and are stored in id_colname.

input_colname

character string indicating the colname to use for the input data supplied by x. For example when input_colname="filename" then values in x are stored in a column "filename".

suffix, renameOnes

arguments passed to jamba::makeNames(), used when group_colname and id_colname are defined, jamba::makeNames(df[[group_colname]], suffix, renameOnes) is used to make unique names for each row.

colname_hook

function called on colnames, for example jamba::ucfirst() applies upper-case to the first character in each colname. When colname_hook=NULL then no changes are made.

sep

character string passed to jamba::pasteByRow() when concatenating columns to create a unique identifier for each row.

order_priority

character string indicating how the output data.frame row order should be defined. Note that the output will only include entries in x that were found in the curation df.

• "df": output follows the order of matching rows in df

• "x": output follows the order of matching x values

...

additional arguments are passed to jamba::makeNames().

Value

data.frame with number of rows equal to the length of input, length(x). Columns are defined by the input colnames(df).

Note that the row order of the output will match the curation df input. The purpose of sorting by curation df is so this data can define the order of factors used in downstream statistical contrasts. The factor order is used to define the control group, as the first factor is preferentially the control group.

Details

This function takes a character vector, and converts it into a data.frame using pattern matching defined in the corresponding df argument data.frame. The first column of df contains character string patterns. Whenever a pattern matches the input vector x, the annotations for the corresponding row in df are applied to that entry in x.

See also

Other jam utility functions: cardinality(), color_complement(), convert_PD_df_to_SE(), convert_imputed_assays_to_na(), curate_se_colData(), design2layout(), get_numeric_transform(), handle_df_args(), merge_proteomics_se(), rowNormScale()

Examples

df <- data.frame(
   pattern=c("NOV14_p2w5_VEH",
      "NOV14_p4w4_VEH",
      "NOV14_UL3_VEH",
      "NS644_UL3VEH",
      "NS50644_UL3VEH",
      "NS644_p2w5VEH"),
   batch=c("NOV14",
      "NOV14",
      "NOV14",
      "NS644",
      "NS50644",
      "NS644"),
   group=c("p2w5_Veh",
      "p4w4_Veh",
      "UL3_Veh",
      "UL3_Veh",
      "UL3_Veh",
      "p2w5_Veh")
);
## review the input table format
print(df);
#>          pattern   batch    group
#> 1 NOV14_p2w5_VEH   NOV14 p2w5_Veh
#> 2 NOV14_p4w4_VEH   NOV14 p4w4_Veh
#> 3  NOV14_UL3_VEH   NOV14  UL3_Veh
#> 4   NS644_UL3VEH   NS644  UL3_Veh
#> 5 NS50644_UL3VEH NS50644  UL3_Veh
#> 6  NS644_p2w5VEH   NS644 p2w5_Veh
x <- c("NOV14_p2w5_VEH_25_v2_CoordSort_deduplicated_SingleFrag_38to100.bam",
   "NOV14_p4w4_VEHrep1_25_v2_CoordSort_deduplicated_SingleFrag_38to100.bam",
   "NOV14_UL3_VEH_25_v2_CoordSort_deduplicated_SingleFrag_38to100.bam",
   "NS644_UL3VEH_25_v3_CoordSort_deduplicated_SingleFrag_38to100.bam",
   "NOV14_p2w5_VEH_50_v2_CoordSort_dedup_singleFragment.bam",
   "NOV14_UL3_VEH_50_v2_CoordSort_dedup_singleFragment.bam",
   "NS50644_UL3VEH_25_v3_CoordSort_deduplicated_SingleFrag.bam",
   "NS644_p2w5VEH_12p5_v3_CoordSort_deduplicated_SingleFrag_38to100.bam")

df_new <- curate_to_df_by_pattern(x, df);
## Review the curated output
print(df_new);
#>                             Pattern   Batch    Group                Label
#> NOV14_p2w5_Veh_rep1  NOV14_p2w5_VEH   NOV14 p2w5_Veh  NOV14_p2w5_Veh_rep1
#> NOV14_p2w5_Veh_rep2  NOV14_p2w5_VEH   NOV14 p2w5_Veh  NOV14_p2w5_Veh_rep2
#> NOV14_p4w4_Veh_rep1  NOV14_p4w4_VEH   NOV14 p4w4_Veh  NOV14_p4w4_Veh_rep1
#> NOV14_UL3_Veh_rep1    NOV14_UL3_VEH   NOV14  UL3_Veh   NOV14_UL3_Veh_rep1
#> NOV14_UL3_Veh_rep2    NOV14_UL3_VEH   NOV14  UL3_Veh   NOV14_UL3_Veh_rep2
#> NS644_UL3_Veh_rep1     NS644_UL3VEH   NS644  UL3_Veh   NS644_UL3_Veh_rep1
#> NS50644_UL3_Veh_rep1 NS50644_UL3VEH NS50644  UL3_Veh NS50644_UL3_Veh_rep1
#> NS644_p2w5_Veh_rep1   NS644_p2w5VEH   NS644 p2w5_Veh  NS644_p2w5_Veh_rep1
#>                                                                                    Filename
#> NOV14_p2w5_Veh_rep1      NOV14_p2w5_VEH_25_v2_CoordSort_deduplicated_SingleFrag_38to100.bam
#> NOV14_p2w5_Veh_rep2                 NOV14_p2w5_VEH_50_v2_CoordSort_dedup_singleFragment.bam
#> NOV14_p4w4_Veh_rep1  NOV14_p4w4_VEHrep1_25_v2_CoordSort_deduplicated_SingleFrag_38to100.bam
#> NOV14_UL3_Veh_rep1        NOV14_UL3_VEH_25_v2_CoordSort_deduplicated_SingleFrag_38to100.bam
#> NOV14_UL3_Veh_rep2                   NOV14_UL3_VEH_50_v2_CoordSort_dedup_singleFragment.bam
#> NS644_UL3_Veh_rep1         NS644_UL3VEH_25_v3_CoordSort_deduplicated_SingleFrag_38to100.bam
#> NS50644_UL3_Veh_rep1             NS50644_UL3VEH_25_v3_CoordSort_deduplicated_SingleFrag.bam
#> NS644_p2w5_Veh_rep1     NS644_p2w5VEH_12p5_v3_CoordSort_deduplicated_SingleFrag_38to100.bam

# note that output is in order defined by df
match(x, df_new$Filename)
#> [1] 1 3 4 6 2 5 7 8

# output can be ordered by x
df_new_by_x <- curate_to_df_by_pattern(x, df, order_priority="x");
match(x, df_new_by_x$Filename)
#> [1] 1 2 3 4 5 6 7 8

## Print a colorized image
colorSub <- colorjam::group2colors(unique(unlist(df_new)));
colorSub <- jamba::makeColorDarker(colorSub, darkFactor=-1.6, sFactor=-1.6);
k <- c(1,2,3,4,5,5,5,5);
df_colors <- as.matrix(df_new[,k]);
df_colors[] <- colorSub[df_colors];
opar <- par("mar"=c(3,3,4,3));
jamba::imageByColors(df_colors,
   adjustMargins=FALSE,
   cellnote=df_new[,k],
   flip="y",
   cexCellnote=c(0.4,0.5)[c(1,2,2,2,1,1,1,1)],
   xaxt="n",
   yaxt="n",
   groupBy="row");
axis(3,
   at=c(1,2,3,4,6.5),
   labels=colnames(df_new));

par(opar);