# Read and analyse an MTZ file

## Introduction

Main aim of this tutorial is to load the full content of an MTZ file in the workspace, to analyse and modify data, and to write the modified content into a new MTZ file.

## Sample MTZ file

Some sample files are stored as external data in this package. These are the MTZ files available with the current release. To access the file, first load the `cry` package.

    library(cry)
    library(ggplot2)

Next, have a look at what is included in the external-data directory of `cry`.

    datadir <- system.file("extdata",package="cry")
    all_files <- list.files(datadir)
    print(all_files)
    #>  [1] "1dei-sf.cif"               "1dei_phases.mtz"
    #>  [3] "2ol9_phases.mtz"           "3syu.cif"
    #>  [5] "6vww_xds_ascii_merged.hkl" "AMS_DATA.cif"
    #>  [7] "e-65-00i60-Isup2.rtv"      "shelxc.log"
    #>  [9] "shelxd.log"                "shelxe_i.log"
    #> [11] "shelxe_o.log"              "xds00_ascii.hkl"

We are interested in the MTZ file “1dei_phases.mtz”. This file can be loaded using function `readMTZ`.

## The structure of an MTZ file in R

Let’s load “1dei_phases.mtz” in memory. The created object is a named list.

    filename <- file.path(datadir,"1dei_phases.mtz")
    objMTZ <- readMTZ(filename)
    #> Warning in readChar(f, 4): truncating string with embedded nuls
    class(objMTZ)
    #> [1] "list"
    names(objMTZ)
    #> [1] "reflections"  "header"       "batch_header"

Each component of is either a named list, or a data frame. More specifically:

1. `reflections`

   It’s a data frame. It contains the actual x-ray diffraction data from a given crystal.

2. `header`

   It’s a named list. It contains information on the crystal used to obtain the x-ray diffraction data and on the diffraction experiment, in general.

3. `batch_header` It’s a named list. It gets filled only when the MTZ file
   includes so-called “unmerged” data.

For the merged MTZ file we are exploring in this tutorial, the `batch_header` is `NULL`.

## The MTZ `header`

This is a named list. The names hint at the content of the specific components.

    hdr <- objMTZ$header
    class(hdr)
    #> [1] "list"
    names(hdr)
    #>  [1] "TITLE"   "NCOL"    "CELL"    "SORT"    "SYMINF"  "RESO"    "NDIF"
    #>  [8] "SYMM"    "PROJECT" "CRYSTAL" "DATASET" "DCELL"   "DWAVEL"  "COLUMN"
    #> [15] "COLSRC"

For example, `NCOL` is the number of columns of the `reflections` data frame, `CELL` contains the crystal unit cell parameters and `SYMM` includes information on the crystal symmetry.

A merged MTZ file stores reflections x-ray data in a hierarchical way. The main purpose of collecting x-ray data is to determine the 3D structure of the molecule arranged in an ordered crystallographic lattice. Reflections related to a same molecule can come from different crystals. Different datasets, corresponding to different rotation sweeps from the same crystal, can be related to a crystal. Each MTZ file can thus include data from several crystals, each crystal giving origin to several datasets. Normally, a single MTZ file will be part of a same project, but this does not necessarily have to be the case. In summary, the hierarchy defining each dataset is:
Project \(\quad\Rightarrow\quad\) Crystal \(\quad\Rightarrow\quad\) Dataset

For the file loaded we find:

    print(objMTZ$header$PROJECT)
    #>   id      pname
    #> 1  0   HKL_base
    #> 2  1 sf_convert
    print(objMTZ$header$CRYSTAL)
    #>   id    cname
    #> 1  0 HKL_base
    #> 2  1  cryst_1
    print(objMTZ$header$DATASET)
    #>   id    dname
    #> 1  0 HKL_base
    #> 2  1   data_1

The only dataset in this MTZ file is called data_1. It was obtained from x-ray diffraction of a crystal called cryst_1. This crystal is one of the samples belonging to a project called sf_convert. All MTZ files contain, by default, a dataset HKL_base which comes from the crystal HKL_base, which is part of the project HKL_base; this peculiar project is always present to make sure that data observations made only of the Miller indices, are always included. More information on the header’s content can be read in the documentation for `readMTZ` or `readMTZHeader`.

## The MTZ `reflections`

Data are actually contained in this component. It is a data frame whose columns have the names found in the header component `COLUMN[,1]`:

    objMTZ$header$COLUMN[,1]
    #>  [1] "H"           "K"           "L"           "FP"          "SIGFP"
    #>  [6] "FC"          "PHIC"        "FC_ALL"      "PHIC_ALL"    "FWT"
    #> [11] "PHWT"        "DELFWT"      "PHDELWT"     "FOM"         "FC_ALL_LS"
    #> [16] "PHIC_ALL_LS"
    objMTZ$reflections[1:5,]
    #>   H K  L        FP     SIGFP        FC PHIC    FC_ALL PHIC_ALL       FWT PHWT
    #> 1 0 0  4 420.23508 48.025589 411.46274    0 441.54556        0 398.92459    0
    #> 2 0 0  6 784.55963 53.490089 719.30823  180 780.32288      180 788.79639  180
    #> 3 0 0  8 266.88480 18.889168 156.35133    0 145.58990      360 388.17963  360
    #> 4 0 0 10  60.62275 21.459837  79.47448    0  82.45022        0  30.81676    0
    #> 5 0 0 14  94.31614  5.330165  42.39147    0  46.47320      360 142.01843  360
    #>       DELFWT PHDELWT       FOM FC_ALL_LS PHIC_ALL_LS
    #> 1  42.620972     180 1.0000000 460.96564           0
    #> 2   8.473511     180 1.0000000 743.42645         180
    #> 3 242.589722     360 0.9999999 145.39508         360
    #> 4  51.633461     180 0.9341952  81.84018           0
    #> 5  95.545235     360 0.9992544  42.30125         360

A data frame is the appropriate class for observations like the crystallographic x-ray data. One reason for this is, for instance, that grouping or selection according to a specific criterion can be carried out very easily with a data frame. Let us, as an example, select part of the data using a condition on the Miller indices. We can be interested to select reflections for which \(h\) has a specific value; this is shown in the following chunk of code.

    # List the different values of H
    unique(objMTZ$reflections$H)
    #>  [1]  0  1  2  3  4  5  6  7  8  9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24
    #> [26] 25 26 27 28 29 30 31 32 33 34 35

    # Select all reflections with H=1
    idx <- which(objMTZ$reflections$H == 1)
    length(idx)  # 373 reflections have H=0
    #> [1] 373

    # Save these reflections in a different object
    refs <- objMTZ$reflections[idx,]

    # Show the first 10 selected reflections
    refs[1:10,]
    #>     H K  L        FP     SIGFP        FC         PHIC    FC_ALL     PHIC_ALL
    #> 338 1 0  4  69.46197  7.137606  67.48492 1.800000e+02  38.35452 1.800000e+02
    #> 339 1 0  5  55.82940  1.932214 137.18262 9.000000e+01 120.74961 8.999999e+01
    #> 340 1 0  6 296.25644 22.226982 283.05109 3.617508e-06 302.89392 2.897944e-06
    #> 341 1 0  7 294.59216 23.033159 276.94324 2.700000e+02 294.35089 2.700000e+02
    #> 342 1 0  8  77.35909  3.981911  69.41461 2.083899e-06  64.56831 2.083780e-06
    #> 343 1 0  9  46.07975  4.606244  32.10196 2.700001e+02  30.71046 2.700001e+02
    #> 344 1 0 12 118.85085  2.718317  90.03316 0.000000e+00  94.01305 0.000000e+00
    #> 345 1 0 13  56.22640  4.300251  56.44312 8.999997e+01  53.37141 8.999997e+01
    #> 346 1 0 14  52.02279  4.389729  70.51323 1.800000e+02  77.82328 1.800000e+02
    #> 347 1 1  3 292.26788 26.617142 374.87027 1.705684e+02 279.07269 1.674537e+02
    #>           FWT         PHWT      DELFWT      PHDELWT       FOM FC_ALL_LS
    #> 338  39.99242 1.800000e+02   1.6379013 1.800000e+02 0.5639557  50.18671
    #> 339  12.13563 2.700000e+02 132.8852386 2.700000e+02 0.9727311 127.82761
    #> 340 289.61896 2.897945e-06  13.2749634 1.800000e+02 1.0000000 290.93329
    #> 341 294.83344 2.700000e+02   0.4825439 2.700000e+02 1.0000000 268.81738
    #> 342  87.53786 2.083780e-06  22.9695587 2.083780e-06 0.9831176  73.09277
    #> 343  32.66603 2.700001e+02   1.9555779 2.700001e+02 0.6876827  31.74592
    #> 344 143.68866 0.000000e+00  49.6756134 0.000000e+00 1.0000000  90.22993
    #> 345  59.07624 8.999997e+01   5.7048264 8.999997e+01 0.9999542  56.48866
    #> 346  26.22218 1.800000e+02  51.6010971 8.662659e-06 0.9999989  70.44583
    #> 347 294.21536 1.674537e+02  15.1426640 1.674536e+02 0.9807579 297.09183
    #>      PHIC_ALL_LS
    #> 338 1.800000e+02
    #> 339 8.999999e+01
    #> 340 3.346423e-06
    #> 341 2.700000e+02
    #> 342 1.979033e-06
    #> 343 2.700001e+02
    #> 344 0.000000e+00
    #> 345 8.999997e+01
    #> 346 1.800000e+02
    #> 347 1.674240e+02

    # Find out the range of FP/sigFP for the selected reflections
    range(refs$FP/refs$SIGFP)
    #> [1]  2.374856 56.249997

Clearly, a lot of different operations and investigations can be carried out on the selected data. A second example involves data for which the signal-to-noise ratio (FP/sigFP) is greater than 1, as shown in the following snippet.

    idx <- which(objMTZ$reflections$FP/objMTZ$reflections$SIGFP >= 1)
    length(idx) # 8377 reflections have FP/sigFP >= 1
    #> [1] 8377

    # The reflections can be extracted
    refs <- objMTZ$reflections[idx,]

    # Histogram of I/sigI with ggplot2
    ggplot(data = refs, aes(FP/SIGFP)) +
      geom_histogram(fill = "#5494AB", colour = "#125CD2", binwidth = 3) +
      xlab("FP/sigFP") + ylab("Number of Reflections") +
      labs(title = "Signal-to-noise ratio FP/sigFP greater than 1") +
      theme_cry()

A third example is related to the reflections’ resolution. Once it has been calculated (cell parameters are needed to do that), data can be selected within a given resolution range (a so-called resolution shell). There is a function in `cry`, called `hkl_to_reso`, which calculates the resolution in angstroms for a reflection of Miller indices \(h, k, l\).

    # Extract cell parameters from header
    cpars <- objMTZ$header$CELL
    print(cpars)
    #> [1] 57.4 56.3 23.0 90.0 90.0 90.0
    a <- cpars[1]; b <- cpars[2]; c <- cpars[3]
    aa <- cpars[4]; bb <- cpars[5]; cc <- cpars[6]

    # Resolution of reflection (1,0,0) (0,1,0) (0,0,1)
    hkl_to_reso(1,0,0,a,b,c,aa,bb,cc)
    #> [1] 57.4
    hkl_to_reso(0,1,0,a,b,c,aa,bb,cc)
    #> [1] 56.3
    hkl_to_reso(0,0,1,a,b,c,aa,bb,cc)
    #> [1] 23

    # Reflections with higher Miller indices have higher resolutions
    hkl_to_reso(10,0,0,a,b,c,aa,bb,cc)
    #> [1] 5.74
    hkl_to_reso(10,0,-20,a,b,c,aa,bb,cc)
    #> [1] 1.127592

Once resolutions are calculated (and the calculation is done on data having the same order as the original reflections), data within a given resolution shell can be selected.

    resos <- hkl_to_reso(objMTZ$reflections$H,
                         objMTZ$reflections$K,
                         objMTZ$reflections$L,
                         a,b,c,aa,bb,cc)

    # Resolution range for all data
    range(resos)
    #> [1] 1.600050 9.981333

    # Select data with resolution between 5 and 9 angstroms
    idx <- which(resos >= 5 & resos <= 9)
    length(idx)  # Only 314 reflections
    #> [1] 314

## Output to a new MTZ file

In order to investigate specific ideas it might be worth to modify the observations in a specific way and to write them out to a new MTZ file to be later handled by the tools proper of the CCP4 family of programs for crystallography. The `cry` function to write reflections content to an MTZ file is called `writeMTZ`. In order to make this a flexible function, it has been deemed appropriate to take as input the three named lists returned by `readMTZ`. These will have been changed by the user, prior to use with `writeMTZ`. By default, the MTZ file title is left unchanged and the `batch_header` list is set to `NULL`; thus by default the MTZ file is assumed to be a merged-reflections file. Special attention will have to be devoted to parts of the `header` list, which store information on the observations. These are, for instance, `NCOL`, `SORT`, `RESO`, `COLUMN`, etc. An example can help to clarify this concept.

A typical modifications of the observed data is when high-resolution reflections are eliminated. This means that the number of reflections and other quantities in the `header` will have to be changed.

    # Copy original data to 2 separate lists
    refs <- objMTZ$reflections
    hdr <- objMTZ$header
    length(refs[,1])  # Number of reflections before cut
    #> [1] 8377
    print(hdr$NCOL[2])
    #> [1] 8377

    # Cut data to 5 angstroms resolution
    # (see previous chunk of code for resos)
    idx <- which(resos >= 5)
    refs <- refs[idx,]
    length(refs[,1])  # Now there are only 333 observations
    #> [1] 333

    # Modify specific parts of header
    hdr$NCOL[2] <- length(refs[,1])            # Number of reflections
    hdr$RESO <- sort(1/range(resos[idx])^2)    # Resolution range
    hdr$DATASET[2,2] <- "Data cut to 5A"  # Dataset
    obsmin <- apply(refs,2,min,na.rm=TRUE)
    obsmax <- apply(refs,2,max,na.rm=TRUE)
    hdr$COLUMN[,3] <- obsmin                   # COLUMN
    hdr$COLUMN[,4] <- obsmax

The data frame `COLSRC` contains the date and time at which the specific data columns have been generated. This can be changed with the `cry` function `change_COLSRC`; the new date and time is the current one.

    # Original COLSRC
    print(hdr$COLSRC)
    #>         labels                     created id
    #> 1            H CREATED_11/10/2017_01:55:14  0
    #> 2            K CREATED_11/10/2017_01:55:14  0
    #> 3            L CREATED_11/10/2017_01:55:14  0
    #> 4           FP CREATED_11/10/2017_01:55:14  1
    #> 5        SIGFP CREATED_11/10/2017_01:55:14  1
    #> 6           FC CREATED_11/10/2017_01:55:14  1
    #> 7         PHIC CREATED_11/10/2017_01:55:14  1
    #> 8       FC_ALL CREATED_11/10/2017_01:55:14  1
    #> 9     PHIC_ALL CREATED_11/10/2017_01:55:14  1
    #> 10         FWT CREATED_11/10/2017_01:55:14  1
    #> 11        PHWT CREATED_11/10/2017_01:55:14  1
    #> 12      DELFWT CREATED_11/10/2017_01:55:14  1
    #> 13     PHDELWT CREATED_11/10/2017_01:55:14  1
    #> 14         FOM CREATED_11/10/2017_01:55:14  1
    #> 15   FC_ALL_LS CREATED_11/10/2017_01:55:14  1
    #> 16 PHIC_ALL_LS CREATED_11/10/2017_01:55:14  1

    # Change date and time
    hdr <- change_COLSRC(hdr)

    # New COLSRC
    print(hdr$COLSRC)
    #>         labels                     created id
    #> 1            H CREATED_05/10/2022_15:03:42  0
    #> 2            K CREATED_05/10/2022_15:03:42  0
    #> 3            L CREATED_05/10/2022_15:03:42  0
    #> 4           FP CREATED_05/10/2022_15:03:42  1
    #> 5        SIGFP CREATED_05/10/2022_15:03:42  1
    #> 6           FC CREATED_05/10/2022_15:03:42  1
    #> 7         PHIC CREATED_05/10/2022_15:03:42  1
    #> 8       FC_ALL CREATED_05/10/2022_15:03:42  1
    #> 9     PHIC_ALL CREATED_05/10/2022_15:03:42  1
    #> 10         FWT CREATED_05/10/2022_15:03:42  1
    #> 11        PHWT CREATED_05/10/2022_15:03:42  1
    #> 12      DELFWT CREATED_05/10/2022_15:03:42  1
    #> 13     PHDELWT CREATED_05/10/2022_15:03:42  1
    #> 14         FOM CREATED_05/10/2022_15:03:42  1
    #> 15   FC_ALL_LS CREATED_05/10/2022_15:03:42  1
    #> 16 PHIC_ALL_LS CREATED_05/10/2022_15:03:42  1

Once all changes have been done, the list components are ready to be saved out to a new MTZ file called, in this specific instance, “new.mtz”.

    # Temporary directory for output
    tdir <- tempdir()
    fname <- file.path(tdir,"new.mtz")

    # Write changed data to the new MTZ file
    writeMTZ(refs,hdr,fname,title="New truncated dataset")

Data from the new MTZ file can either be explored using CCP4 programs, or read back into R using again `readMTZ`.

    # Read data from "new.mtz"
    newMTZ <- readMTZ(fname,TRUE)
    #> Warning in readChar(f, 4): truncating string with embedded nuls
    #> [1] "COLUMN  H                             H                 0                11    0"
    #> [1] "COLUMN  K                             H                 0                11    0"
    #> [1] "COLUMN  L                             H                 0                 4    0"
    #> [1] "COLUMN  FP                            F            12.105          1095.345    1"
    #> [1] "COLUMN  SIGFP                         Q             1.365            96.585    1"
    #> [1] "COLUMN  FC                            F             5.035          1055.738    1"
    #> [1] "COLUMN  PHIC                          P                 0               360    1"
    #> [1] "COLUMN  FC_ALL                        F             1.210          1136.601    1"
    #> [1] "COLUMN  PHIC_ALL                      P                 0               360    1"
    #> [1] "COLUMN  FWT                           F             0.133          1054.090    1"
    #> [1] "COLUMN  PHWT                          P                 0               360    1"
    #> [1] "COLUMN  DELFWT                        F             0.070           393.415    1"
    #> [1] "COLUMN  PHDELWT                       P                 0               360    1"
    #> [1] "COLUMN  FOM                           W             0.023             1.000    1"
    #> [1] "COLUMN  FC_ALL_LS                     F             1.537          1171.841    1"
    #> [1] "COLUMN  PHIC_ALL_LS                   P                 0               360    1"