curate_to_df_by_pattern: Curate vector to data.frame by pattern matching

In jmw86069/platjam: Platform Jam, biological platform importers.

Curate vector to data.frame by pattern matching

Description

Curate vector to data.frame by pattern matching

Usage

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().

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.

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.

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(), nmat_summary(), nmatlist_summary(), rmd_tab_iterator(), rowNormScale(), summit_from_vector()

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);
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);

# note that output is in order defined by df
match(x, df_new$Filename)

# 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)

## 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);

jmw86069/platjam documentation built on Sept. 26, 2024, 3:31 p.m.